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diff --git a/docs/build/html/_modules/index.html b/docs/build/html/_modules/index.html
index e3edf0f..2e5ac84 100644
--- a/docs/build/html/_modules/index.html
+++ b/docs/build/html/_modules/index.html
@@ -54,12 +54,24 @@
           <div class="body" role="main">
             
   <h1>All modules for which code is available</h1>
-<ul><li><a href="pyFTS/common/Composite.html">pyFTS.common.Composite</a></li>
+<ul><li><a href="pyFTS/benchmarks/BSTS.html">pyFTS.benchmarks.BSTS</a></li>
+<li><a href="pyFTS/benchmarks/Measures.html">pyFTS.benchmarks.Measures</a></li>
+<li><a href="pyFTS/benchmarks/ResidualAnalysis.html">pyFTS.benchmarks.ResidualAnalysis</a></li>
+<li><a href="pyFTS/benchmarks/Tests.html">pyFTS.benchmarks.Tests</a></li>
+<li><a href="pyFTS/benchmarks/Util.html">pyFTS.benchmarks.Util</a></li>
+<li><a href="pyFTS/benchmarks/arima.html">pyFTS.benchmarks.arima</a></li>
+<li><a href="pyFTS/benchmarks/benchmarks.html">pyFTS.benchmarks.benchmarks</a></li>
+<li><a href="pyFTS/benchmarks/knn.html">pyFTS.benchmarks.knn</a></li>
+<li><a href="pyFTS/benchmarks/naive.html">pyFTS.benchmarks.naive</a></li>
+<li><a href="pyFTS/benchmarks/quantreg.html">pyFTS.benchmarks.quantreg</a></li>
+<li><a href="pyFTS/common/Composite.html">pyFTS.common.Composite</a></li>
 <li><a href="pyFTS/common/FLR.html">pyFTS.common.FLR</a></li>
 <li><a href="pyFTS/common/FuzzySet.html">pyFTS.common.FuzzySet</a></li>
 <li><a href="pyFTS/common/Membership.html">pyFTS.common.Membership</a></li>
 <li><a href="pyFTS/common/SortedCollection.html">pyFTS.common.SortedCollection</a></li>
+<li><a href="pyFTS/common/Util.html">pyFTS.common.Util</a></li>
 <li><a href="pyFTS/common/flrg.html">pyFTS.common.flrg</a></li>
+<li><a href="pyFTS/common/fts.html">pyFTS.common.fts</a></li>
 <li><a href="pyFTS/common/tree.html">pyFTS.common.tree</a></li>
 <li><a href="pyFTS/data/AirPassengers.html">pyFTS.data.AirPassengers</a></li>
 <li><a href="pyFTS/data/Bitcoin.html">pyFTS.data.Bitcoin</a></li>
@@ -83,27 +95,61 @@
 <li><a href="pyFTS/data/mackey_glass.html">pyFTS.data.mackey_glass</a></li>
 <li><a href="pyFTS/data/rossler.html">pyFTS.data.rossler</a></li>
 <li><a href="pyFTS/data/sunspots.html">pyFTS.data.sunspots</a></li>
+<li><a href="pyFTS/distributed/dispy.html">pyFTS.distributed.dispy</a></li>
+<li><a href="pyFTS/hyperparam/Evolutionary.html">pyFTS.hyperparam.Evolutionary</a></li>
+<li><a href="pyFTS/hyperparam/GridSearch.html">pyFTS.hyperparam.GridSearch</a></li>
 <li><a href="pyFTS/hyperparam/Util.html">pyFTS.hyperparam.Util</a></li>
+<li><a href="pyFTS/models/chen.html">pyFTS.models.chen</a></li>
+<li><a href="pyFTS/models/cheng.html">pyFTS.models.cheng</a></li>
+<li><a href="pyFTS/models/ensemble/ensemble.html">pyFTS.models.ensemble.ensemble</a></li>
+<li><a href="pyFTS/models/ensemble/multiseasonal.html">pyFTS.models.ensemble.multiseasonal</a></li>
+<li><a href="pyFTS/models/hofts.html">pyFTS.models.hofts</a></li>
+<li><a href="pyFTS/models/hwang.html">pyFTS.models.hwang</a></li>
+<li><a href="pyFTS/models/ifts.html">pyFTS.models.ifts</a></li>
+<li><a href="pyFTS/models/incremental/IncrementalEnsemble.html">pyFTS.models.incremental.IncrementalEnsemble</a></li>
+<li><a href="pyFTS/models/incremental/TimeVariant.html">pyFTS.models.incremental.TimeVariant</a></li>
+<li><a href="pyFTS/models/ismailefendi.html">pyFTS.models.ismailefendi</a></li>
 <li><a href="pyFTS/models/multivariate/FLR.html">pyFTS.models.multivariate.FLR</a></li>
+<li><a href="pyFTS/models/multivariate/cmvfts.html">pyFTS.models.multivariate.cmvfts</a></li>
 <li><a href="pyFTS/models/multivariate/common.html">pyFTS.models.multivariate.common</a></li>
 <li><a href="pyFTS/models/multivariate/flrg.html">pyFTS.models.multivariate.flrg</a></li>
+<li><a href="pyFTS/models/multivariate/granular.html">pyFTS.models.multivariate.granular</a></li>
 <li><a href="pyFTS/models/multivariate/grid.html">pyFTS.models.multivariate.grid</a></li>
+<li><a href="pyFTS/models/multivariate/mvfts.html">pyFTS.models.multivariate.mvfts</a></li>
 <li><a href="pyFTS/models/multivariate/partitioner.html">pyFTS.models.multivariate.partitioner</a></li>
+<li><a href="pyFTS/models/multivariate/variable.html">pyFTS.models.multivariate.variable</a></li>
+<li><a href="pyFTS/models/multivariate/wmvfts.html">pyFTS.models.multivariate.wmvfts</a></li>
 <li><a href="pyFTS/models/nonstationary/common.html">pyFTS.models.nonstationary.common</a></li>
+<li><a href="pyFTS/models/nonstationary/cvfts.html">pyFTS.models.nonstationary.cvfts</a></li>
 <li><a href="pyFTS/models/nonstationary/flrg.html">pyFTS.models.nonstationary.flrg</a></li>
+<li><a href="pyFTS/models/nonstationary/honsfts.html">pyFTS.models.nonstationary.honsfts</a></li>
+<li><a href="pyFTS/models/nonstationary/nsfts.html">pyFTS.models.nonstationary.nsfts</a></li>
 <li><a href="pyFTS/models/nonstationary/partitioners.html">pyFTS.models.nonstationary.partitioners</a></li>
 <li><a href="pyFTS/models/nonstationary/perturbation.html">pyFTS.models.nonstationary.perturbation</a></li>
+<li><a href="pyFTS/models/nonstationary/util.html">pyFTS.models.nonstationary.util</a></li>
+<li><a href="pyFTS/models/pwfts.html">pyFTS.models.pwfts</a></li>
+<li><a href="pyFTS/models/sadaei.html">pyFTS.models.sadaei</a></li>
 <li><a href="pyFTS/models/seasonal/SeasonalIndexer.html">pyFTS.models.seasonal.SeasonalIndexer</a></li>
+<li><a href="pyFTS/models/seasonal/cmsfts.html">pyFTS.models.seasonal.cmsfts</a></li>
 <li><a href="pyFTS/models/seasonal/common.html">pyFTS.models.seasonal.common</a></li>
+<li><a href="pyFTS/models/seasonal/msfts.html">pyFTS.models.seasonal.msfts</a></li>
 <li><a href="pyFTS/models/seasonal/partitioner.html">pyFTS.models.seasonal.partitioner</a></li>
+<li><a href="pyFTS/models/seasonal/sfts.html">pyFTS.models.seasonal.sfts</a></li>
+<li><a href="pyFTS/models/song.html">pyFTS.models.song</a></li>
+<li><a href="pyFTS/models/yu.html">pyFTS.models.yu</a></li>
 <li><a href="pyFTS/partitioners/CMeans.html">pyFTS.partitioners.CMeans</a></li>
 <li><a href="pyFTS/partitioners/Entropy.html">pyFTS.partitioners.Entropy</a></li>
 <li><a href="pyFTS/partitioners/FCM.html">pyFTS.partitioners.FCM</a></li>
 <li><a href="pyFTS/partitioners/Grid.html">pyFTS.partitioners.Grid</a></li>
+<li><a href="pyFTS/partitioners/Huarng.html">pyFTS.partitioners.Huarng</a></li>
 <li><a href="pyFTS/partitioners/Simple.html">pyFTS.partitioners.Simple</a></li>
 <li><a href="pyFTS/partitioners/Singleton.html">pyFTS.partitioners.Singleton</a></li>
 <li><a href="pyFTS/partitioners/SubClust.html">pyFTS.partitioners.SubClust</a></li>
+<li><a href="pyFTS/partitioners/Util.html">pyFTS.partitioners.Util</a></li>
+<li><a href="pyFTS/partitioners/parallel_util.html">pyFTS.partitioners.parallel_util</a></li>
 <li><a href="pyFTS/partitioners/partitioner.html">pyFTS.partitioners.partitioner</a></li>
+<li><a href="pyFTS/probabilistic/ProbabilityDistribution.html">pyFTS.probabilistic.ProbabilityDistribution</a></li>
+<li><a href="pyFTS/probabilistic/kde.html">pyFTS.probabilistic.kde</a></li>
 </ul>
 
             <div class="clearer"></div>
diff --git a/docs/build/html/_modules/pyFTS/benchmarks/BSTS.html b/docs/build/html/_modules/pyFTS/benchmarks/BSTS.html
new file mode 100644
index 0000000..ea190d2
--- /dev/null
+++ b/docs/build/html/_modules/pyFTS/benchmarks/BSTS.html
@@ -0,0 +1,220 @@
+
+
+<!doctype html>
+
+<html>
+  <head>
+    <meta charset="utf-8" />
+    <meta name="viewport" content="width=device-width, initial-scale=1.0"><script type="text/javascript">
+
+      var _gaq = _gaq || [];
+      _gaq.push(['_setAccount', 'UA-55120145-3']);
+      _gaq.push(['_trackPageview']);
+
+      (function() {
+        var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true;
+        ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js';
+        var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s);
+      })();
+    </script>
+    <title>pyFTS.benchmarks.BSTS &#8212; pyFTS 1.6 documentation</title>
+    <link rel="stylesheet" href="../../../_static/bizstyle.css" type="text/css" />
+    <link rel="stylesheet" href="../../../_static/pygments.css" type="text/css" />
+    
+    <script id="documentation_options" data-url_root="../../../" src="../../../_static/documentation_options.js"></script>
+    <script src="../../../_static/jquery.js"></script>
+    <script src="../../../_static/underscore.js"></script>
+    <script src="../../../_static/doctools.js"></script>
+    <script src="../../../_static/language_data.js"></script>
+    <script src="../../../_static/bizstyle.js"></script>
+    <link rel="index" title="Index" href="../../../genindex.html" />
+    <link rel="search" title="Search" href="../../../search.html" />
+    <meta name="viewport" content="width=device-width,initial-scale=1.0">
+    <!--[if lt IE 9]>
+    <script src="_static/css3-mediaqueries.js"></script>
+    <![endif]-->
+  </head><body>
+    <div class="related" role="navigation" aria-label="related navigation">
+      <h3>Navigation</h3>
+      <ul>
+        <li class="right" style="margin-right: 10px">
+          <a href="../../../genindex.html" title="General Index"
+             accesskey="I">index</a></li>
+        <li class="right" >
+          <a href="../../../py-modindex.html" title="Python Module Index"
+             >modules</a> |</li>
+        <li class="nav-item nav-item-0"><a href="../../../index.html">pyFTS 1.6 documentation</a> &#187;</li>
+          <li class="nav-item nav-item-1"><a href="../../index.html" accesskey="U">Module code</a> &#187;</li>
+        <li class="nav-item nav-item-this"><a href="">pyFTS.benchmarks.BSTS</a></li> 
+      </ul>
+    </div>  
+
+    <div class="document">
+      <div class="documentwrapper">
+        <div class="bodywrapper">
+          <div class="body" role="main">
+            
+  <h1>Source code for pyFTS.benchmarks.BSTS</h1><div class="highlight"><pre>
+<span></span><span class="ch">#!/usr/bin/python</span>
+<span class="c1"># -*- coding: utf8 -*-</span>
+
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+<span class="kn">import</span> <span class="nn">scipy.stats</span> <span class="k">as</span> <span class="nn">st</span>
+<span class="kn">from</span> <span class="nn">pyFTS.common</span> <span class="kn">import</span> <span class="n">SortedCollection</span><span class="p">,</span> <span class="n">fts</span>
+<span class="kn">from</span> <span class="nn">pyFTS.probabilistic</span> <span class="kn">import</span> <span class="n">ProbabilityDistribution</span>
+
+
+<div class="viewcode-block" id="ARIMA"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.BSTS.ARIMA">[docs]</a><span class="k">class</span> <span class="nc">ARIMA</span><span class="p">(</span><span class="n">fts</span><span class="o">.</span><span class="n">FTS</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Façade for statsmodels.tsa.arima_model</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">(</span><span class="n">ARIMA</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="s2">&quot;BSTS&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">detail</span> <span class="o">=</span> <span class="s2">&quot;Bayesian Structural Time Series&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">is_high_order</span> <span class="o">=</span> <span class="kc">True</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">has_point_forecasting</span> <span class="o">=</span> <span class="kc">True</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">has_interval_forecasting</span> <span class="o">=</span> <span class="kc">True</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">has_probability_forecasting</span> <span class="o">=</span> <span class="kc">True</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">uod_clip</span> <span class="o">=</span> <span class="kc">False</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">model</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">model_fit</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">trained_data</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">p</span> <span class="o">=</span> <span class="mi">1</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">d</span> <span class="o">=</span> <span class="mi">0</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">q</span> <span class="o">=</span> <span class="mi">0</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">benchmark_only</span> <span class="o">=</span> <span class="kc">True</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">min_order</span> <span class="o">=</span> <span class="mi">1</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">alpha</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;alpha&quot;</span><span class="p">,</span> <span class="mf">0.05</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">order</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;order&quot;</span><span class="p">,</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">))</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_decompose_order</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">model</span> <span class="o">=</span> <span class="kc">None</span>
+
+    <span class="k">def</span> <span class="nf">_decompose_order</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">order</span><span class="p">):</span>
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">order</span><span class="p">,</span> <span class="p">(</span><span class="nb">tuple</span><span class="p">,</span> <span class="nb">set</span><span class="p">,</span> <span class="nb">list</span><span class="p">)):</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">p</span> <span class="o">=</span> <span class="n">order</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">d</span> <span class="o">=</span> <span class="n">order</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">q</span> <span class="o">=</span> <span class="n">order</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">order</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">p</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">q</span> <span class="o">+</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">q</span> <span class="o">-</span> <span class="mi">1</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">q</span> <span class="o">&gt;</span> <span class="mi">0</span> <span class="k">else</span> <span class="mi">0</span><span class="p">)</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">d</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">transformations</span><span class="p">)</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">shortname</span> <span class="o">=</span> <span class="s2">&quot;BSTS(</span><span class="si">{}</span><span class="s2">,</span><span class="si">{}</span><span class="s2">,</span><span class="si">{}</span><span class="s2">)-</span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">p</span><span class="p">,</span><span class="bp">self</span><span class="o">.</span><span class="n">d</span><span class="p">,</span><span class="bp">self</span><span class="o">.</span><span class="n">q</span><span class="p">,</span><span class="bp">self</span><span class="o">.</span><span class="n">alpha</span><span class="p">)</span>
+
+<div class="viewcode-block" id="ARIMA.train"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.BSTS.ARIMA.train">[docs]</a>    <span class="k">def</span> <span class="nf">train</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="kn">import</span> <span class="nn">pyflux</span> <span class="k">as</span> <span class="nn">pf</span>
+
+        <span class="k">if</span> <span class="s1">&#39;order&#39;</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="p">:</span>
+            <span class="n">order</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s1">&#39;order&#39;</span><span class="p">)</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_decompose_order</span><span class="p">(</span><span class="n">order</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">indexer</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">data</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">indexer</span><span class="o">.</span><span class="n">get_data</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+
+        <span class="k">try</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">model</span> <span class="o">=</span>  <span class="n">pf</span><span class="o">.</span><span class="n">ARIMA</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">data</span><span class="p">,</span> <span class="n">ar</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">p</span><span class="p">,</span> <span class="n">ma</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">q</span><span class="p">,</span> <span class="n">integ</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">d</span><span class="p">,</span> <span class="n">family</span><span class="o">=</span><span class="n">pf</span><span class="o">.</span><span class="n">Normal</span><span class="p">())</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">model_fit</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="s1">&#39;M-H&#39;</span><span class="p">,</span> <span class="n">nsims</span><span class="o">=</span><span class="mi">20000</span><span class="p">)</span>
+        <span class="k">except</span> <span class="ne">Exception</span> <span class="k">as</span> <span class="n">ex</span><span class="p">:</span>
+            <span class="nb">print</span><span class="p">(</span><span class="n">ex</span><span class="p">)</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">model_fit</span> <span class="o">=</span> <span class="kc">None</span></div>
+
+<div class="viewcode-block" id="ARIMA.inference"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.BSTS.ARIMA.inference">[docs]</a>    <span class="k">def</span> <span class="nf">inference</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">steps</span><span class="p">):</span>
+        <span class="n">t_z</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">transform_z</span><span class="p">()</span>
+        <span class="n">mu</span><span class="p">,</span> <span class="n">Y</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">_model</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">latent_variables</span><span class="o">.</span><span class="n">get_z_values</span><span class="p">())</span>
+        <span class="n">date_index</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">shift_dates</span><span class="p">(</span><span class="n">steps</span><span class="p">)</span>
+        <span class="n">sim_vector</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">_sim_prediction</span><span class="p">(</span><span class="n">mu</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">steps</span><span class="p">,</span> <span class="n">t_z</span><span class="p">,</span> <span class="mi">1000</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">sim_vector</span></div>
+
+<div class="viewcode-block" id="ARIMA.forecast"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.BSTS.ARIMA.forecast">[docs]</a>    <span class="k">def</span> <span class="nf">forecast</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ndata</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">()</span></div>
+
+<div class="viewcode-block" id="ARIMA.forecast_ahead"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.BSTS.ARIMA.forecast_ahead">[docs]</a>    <span class="k">def</span> <span class="nf">forecast_ahead</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">steps</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">steps</span><span class="p">,</span> <span class="n">intervals</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span><span class="o">.</span><span class="n">values</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span></div>
+
+<div class="viewcode-block" id="ARIMA.forecast_interval"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.BSTS.ARIMA.forecast_interval">[docs]</a>    <span class="k">def</span> <span class="nf">forecast_interval</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">()</span></div>
+
+<div class="viewcode-block" id="ARIMA.forecast_ahead_interval"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.BSTS.ARIMA.forecast_ahead_interval">[docs]</a>    <span class="k">def</span> <span class="nf">forecast_ahead_interval</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ndata</span><span class="p">,</span> <span class="n">steps</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="n">sim_vector</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">inference</span><span class="p">(</span><span class="n">steps</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="s1">&#39;alpha&#39;</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="p">:</span>
+            <span class="n">alpha</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;alpha&#39;</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">alpha</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">alpha</span>
+
+        <span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
+
+        <span class="k">for</span> <span class="n">ct</span><span class="p">,</span> <span class="n">sample</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">sim_vector</span><span class="p">):</span>
+            <span class="n">i</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">percentile</span><span class="p">(</span><span class="n">sample</span><span class="p">,</span> <span class="p">[</span><span class="n">alpha</span><span class="o">*</span><span class="mi">100</span><span class="p">,</span> <span class="p">(</span><span class="mi">1</span><span class="o">-</span><span class="n">alpha</span><span class="p">)</span><span class="o">*</span><span class="mi">100</span><span class="p">])</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
+            <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">i</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">ret</span></div>
+
+<div class="viewcode-block" id="ARIMA.forecast_distribution"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.BSTS.ARIMA.forecast_distribution">[docs]</a>    <span class="k">def</span> <span class="nf">forecast_distribution</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="kn">import</span> <span class="nn">pyflux</span> <span class="k">as</span> <span class="nn">pf</span>
+
+        <span class="n">sim_vector</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">inference</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
+
+        <span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
+
+        <span class="k">for</span> <span class="n">ct</span><span class="p">,</span> <span class="n">sample</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">sim_vector</span><span class="p">):</span>
+            <span class="n">pd</span> <span class="o">=</span> <span class="n">ProbabilityDistribution</span><span class="o">.</span><span class="n">ProbabilityDistribution</span><span class="p">(</span><span class="nb">type</span><span class="o">=</span><span class="s1">&#39;histogram&#39;</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="n">sample</span><span class="p">,</span> <span class="n">nbins</span><span class="o">=</span><span class="mi">500</span><span class="p">)</span>
+            <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">pd</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">ret</span></div>
+
+
+<div class="viewcode-block" id="ARIMA.forecast_ahead_distribution"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.BSTS.ARIMA.forecast_ahead_distribution">[docs]</a>    <span class="k">def</span> <span class="nf">forecast_ahead_distribution</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">steps</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+
+        <span class="n">sim_vector</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">inference</span><span class="p">(</span><span class="n">steps</span><span class="p">)</span>
+
+        <span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
+
+        <span class="k">for</span> <span class="n">ct</span><span class="p">,</span> <span class="n">sample</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">sim_vector</span><span class="p">):</span>
+            <span class="n">pd</span> <span class="o">=</span> <span class="n">ProbabilityDistribution</span><span class="o">.</span><span class="n">ProbabilityDistribution</span><span class="p">(</span><span class="nb">type</span><span class="o">=</span><span class="s1">&#39;histogram&#39;</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="n">sample</span><span class="p">,</span> <span class="n">nbins</span><span class="o">=</span><span class="mi">500</span><span class="p">)</span>
+            <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">pd</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">ret</span></div></div>
+</pre></div>
+
+            <div class="clearer"></div>
+          </div>
+        </div>
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+      <div class="sphinxsidebar" role="navigation" aria-label="main navigation">
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+      <h3>Navigation</h3>
+      <ul>
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+             >index</a></li>
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+             >modules</a> |</li>
+        <li class="nav-item nav-item-0"><a href="../../../index.html">pyFTS 1.6 documentation</a> &#187;</li>
+          <li class="nav-item nav-item-1"><a href="../../index.html" >Module code</a> &#187;</li>
+        <li class="nav-item nav-item-this"><a href="">pyFTS.benchmarks.BSTS</a></li> 
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+        &#169; Copyright 2018, Machine Intelligence and Data Science Laboratory - UFMG - Brazil.
+      Created using <a href="https://www.sphinx-doc.org/">Sphinx</a> 3.1.2.
+    </div>
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diff --git a/docs/build/html/_modules/pyFTS/benchmarks/Measures.html b/docs/build/html/_modules/pyFTS/benchmarks/Measures.html
new file mode 100644
index 0000000..4266854
--- /dev/null
+++ b/docs/build/html/_modules/pyFTS/benchmarks/Measures.html
@@ -0,0 +1,663 @@
+
+
+<!doctype html>
+
+<html>
+  <head>
+    <meta charset="utf-8" />
+    <meta name="viewport" content="width=device-width, initial-scale=1.0"><script type="text/javascript">
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+    <title>pyFTS.benchmarks.Measures &#8212; pyFTS 1.6 documentation</title>
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+             >modules</a> |</li>
+        <li class="nav-item nav-item-0"><a href="../../../index.html">pyFTS 1.6 documentation</a> &#187;</li>
+          <li class="nav-item nav-item-1"><a href="../../index.html" accesskey="U">Module code</a> &#187;</li>
+        <li class="nav-item nav-item-this"><a href="">pyFTS.benchmarks.Measures</a></li> 
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+          <div class="body" role="main">
+            
+  <h1>Source code for pyFTS.benchmarks.Measures</h1><div class="highlight"><pre>
+<span></span><span class="c1"># -*- coding: utf8 -*-</span>
+
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">pyFTS module for common benchmark metrics</span>
+<span class="sd">&quot;&quot;&quot;</span>
+
+<span class="kn">import</span> <span class="nn">time</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+<span class="kn">from</span> <span class="nn">pyFTS.common</span> <span class="kn">import</span> <span class="n">FuzzySet</span><span class="p">,</span> <span class="n">SortedCollection</span>
+<span class="kn">from</span> <span class="nn">pyFTS.probabilistic</span> <span class="kn">import</span> <span class="n">ProbabilityDistribution</span>
+
+
+<div class="viewcode-block" id="acf"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.Measures.acf">[docs]</a><span class="k">def</span> <span class="nf">acf</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">k</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Autocorrelation function estimative</span>
+
+<span class="sd">    :param data: </span>
+<span class="sd">    :param k: </span>
+<span class="sd">    :return: </span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">mu</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nanmean</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+    <span class="n">sigma</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">var</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+    <span class="n">n</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+    <span class="n">s</span> <span class="o">=</span> <span class="mi">0</span>
+    <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">n</span> <span class="o">-</span> <span class="n">k</span><span class="p">):</span>
+        <span class="n">s</span> <span class="o">+=</span> <span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="n">t</span><span class="p">]</span> <span class="o">-</span> <span class="n">mu</span><span class="p">)</span> <span class="o">*</span> <span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="n">t</span> <span class="o">+</span> <span class="n">k</span><span class="p">]</span> <span class="o">-</span> <span class="n">mu</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="mi">1</span> <span class="o">/</span> <span class="p">((</span><span class="n">n</span> <span class="o">-</span> <span class="n">k</span><span class="p">)</span> <span class="o">*</span> <span class="n">sigma</span><span class="p">)</span> <span class="o">*</span> <span class="n">s</span></div>
+
+
+<div class="viewcode-block" id="rmse"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.Measures.rmse">[docs]</a><span class="k">def</span> <span class="nf">rmse</span><span class="p">(</span><span class="n">targets</span><span class="p">,</span> <span class="n">forecasts</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Root Mean Squared Error</span>
+
+<span class="sd">    :param targets: </span>
+<span class="sd">    :param forecasts: </span>
+<span class="sd">    :return: </span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">targets</span><span class="p">,</span> <span class="nb">list</span><span class="p">):</span>
+        <span class="n">targets</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">targets</span><span class="p">)</span>
+    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">forecasts</span><span class="p">,</span> <span class="nb">list</span><span class="p">):</span>
+        <span class="n">forecasts</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">forecasts</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">nanmean</span><span class="p">((</span><span class="n">targets</span> <span class="o">-</span> <span class="n">forecasts</span><span class="p">)</span> <span class="o">**</span> <span class="mi">2</span><span class="p">))</span></div>
+
+
+<div class="viewcode-block" id="rmse_interval"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.Measures.rmse_interval">[docs]</a><span class="k">def</span> <span class="nf">rmse_interval</span><span class="p">(</span><span class="n">targets</span><span class="p">,</span> <span class="n">forecasts</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Root Mean Squared Error</span>
+
+<span class="sd">    :param targets: </span>
+<span class="sd">    :param forecasts: </span>
+<span class="sd">    :return: </span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">fmean</span> <span class="o">=</span> <span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">i</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">forecasts</span><span class="p">]</span>
+    <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">nanmean</span><span class="p">((</span><span class="n">fmean</span> <span class="o">-</span> <span class="n">targets</span><span class="p">)</span> <span class="o">**</span> <span class="mi">2</span><span class="p">))</span></div>
+
+
+<div class="viewcode-block" id="mape"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.Measures.mape">[docs]</a><span class="k">def</span> <span class="nf">mape</span><span class="p">(</span><span class="n">targets</span><span class="p">,</span> <span class="n">forecasts</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Mean Average Percentual Error</span>
+
+<span class="sd">    :param targets: </span>
+<span class="sd">    :param forecasts: </span>
+<span class="sd">    :return: </span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">targets</span><span class="p">,</span> <span class="nb">list</span><span class="p">):</span>
+        <span class="n">targets</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">targets</span><span class="p">)</span>
+    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">forecasts</span><span class="p">,</span> <span class="nb">list</span><span class="p">):</span>
+        <span class="n">forecasts</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">forecasts</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">nanmean</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">divide</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">subtract</span><span class="p">(</span><span class="n">targets</span><span class="p">,</span> <span class="n">forecasts</span><span class="p">),</span> <span class="n">targets</span><span class="p">)))</span> <span class="o">*</span> <span class="mi">100</span></div>
+
+
+<div class="viewcode-block" id="smape"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.Measures.smape">[docs]</a><span class="k">def</span> <span class="nf">smape</span><span class="p">(</span><span class="n">targets</span><span class="p">,</span> <span class="n">forecasts</span><span class="p">,</span> <span class="nb">type</span><span class="o">=</span><span class="mi">2</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Symmetric Mean Average Percentual Error</span>
+
+<span class="sd">    :param targets: </span>
+<span class="sd">    :param forecasts: </span>
+<span class="sd">    :param type: </span>
+<span class="sd">    :return: </span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">targets</span><span class="p">,</span> <span class="nb">list</span><span class="p">):</span>
+        <span class="n">targets</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">targets</span><span class="p">)</span>
+    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">forecasts</span><span class="p">,</span> <span class="nb">list</span><span class="p">):</span>
+        <span class="n">forecasts</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">forecasts</span><span class="p">)</span>
+    <span class="k">if</span> <span class="nb">type</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
+        <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">nanmean</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">forecasts</span> <span class="o">-</span> <span class="n">targets</span><span class="p">)</span> <span class="o">/</span> <span class="p">((</span><span class="n">forecasts</span> <span class="o">+</span> <span class="n">targets</span><span class="p">)</span> <span class="o">/</span> <span class="mi">2</span><span class="p">))</span>
+    <span class="k">elif</span> <span class="nb">type</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
+        <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">nanmean</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">forecasts</span> <span class="o">-</span> <span class="n">targets</span><span class="p">)</span> <span class="o">/</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">forecasts</span><span class="p">)</span> <span class="o">+</span> <span class="nb">abs</span><span class="p">(</span><span class="n">targets</span><span class="p">)))</span> <span class="o">*</span> <span class="mi">100</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">forecasts</span> <span class="o">-</span> <span class="n">targets</span><span class="p">))</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">(</span><span class="n">forecasts</span> <span class="o">+</span> <span class="n">targets</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="mape_interval"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.Measures.mape_interval">[docs]</a><span class="k">def</span> <span class="nf">mape_interval</span><span class="p">(</span><span class="n">targets</span><span class="p">,</span> <span class="n">forecasts</span><span class="p">):</span>
+    <span class="n">fmean</span> <span class="o">=</span> <span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">i</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">forecasts</span><span class="p">]</span>
+    <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="nb">abs</span><span class="p">(</span><span class="n">fmean</span> <span class="o">-</span> <span class="n">targets</span><span class="p">)</span> <span class="o">/</span> <span class="n">fmean</span><span class="p">)</span> <span class="o">*</span> <span class="mi">100</span></div>
+
+
+<div class="viewcode-block" id="UStatistic"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.Measures.UStatistic">[docs]</a><span class="k">def</span> <span class="nf">UStatistic</span><span class="p">(</span><span class="n">targets</span><span class="p">,</span> <span class="n">forecasts</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Theil&#39;s U Statistic</span>
+
+<span class="sd">    :param targets: </span>
+<span class="sd">    :param forecasts: </span>
+<span class="sd">    :return: </span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">forecasts</span><span class="p">,</span> <span class="p">(</span><span class="nb">list</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">)):</span>
+        <span class="n">forecasts</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">forecasts</span><span class="p">])</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">forecasts</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">forecasts</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">targets</span><span class="p">,</span> <span class="p">(</span><span class="nb">list</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">)):</span>
+        <span class="n">targets</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">targets</span><span class="p">])</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">targets</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">targets</span><span class="p">)</span>
+
+    <span class="n">l</span> <span class="o">=</span> <span class="n">forecasts</span><span class="o">.</span><span class="n">size</span>
+    <span class="n">l</span> <span class="o">=</span> <span class="mi">2</span> <span class="k">if</span> <span class="n">l</span> <span class="o">==</span> <span class="mi">1</span> <span class="k">else</span> <span class="n">l</span>
+
+    <span class="n">naive</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="n">y</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">l</span> <span class="o">-</span> <span class="mi">1</span><span class="p">):</span>
+        <span class="n">y</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">subtract</span><span class="p">(</span><span class="n">forecasts</span><span class="p">[</span><span class="n">k</span><span class="p">],</span> <span class="n">targets</span><span class="p">[</span><span class="n">k</span><span class="p">])</span> <span class="o">**</span> <span class="mi">2</span><span class="p">)</span>
+        <span class="n">naive</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">subtract</span><span class="p">(</span><span class="n">targets</span><span class="p">[</span><span class="n">k</span> <span class="o">+</span> <span class="mi">1</span><span class="p">],</span> <span class="n">targets</span><span class="p">[</span><span class="n">k</span><span class="p">])</span> <span class="o">**</span> <span class="mi">2</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">divide</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">(</span><span class="n">y</span><span class="p">),</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">(</span><span class="n">naive</span><span class="p">)))</span></div>
+
+
+<div class="viewcode-block" id="TheilsInequality"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.Measures.TheilsInequality">[docs]</a><span class="k">def</span> <span class="nf">TheilsInequality</span><span class="p">(</span><span class="n">targets</span><span class="p">,</span> <span class="n">forecasts</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Theil’s Inequality Coefficient</span>
+
+<span class="sd">    :param targets: </span>
+<span class="sd">    :param forecasts: </span>
+<span class="sd">    :return: </span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">res</span> <span class="o">=</span> <span class="n">targets</span> <span class="o">-</span> <span class="n">forecasts</span>
+    <span class="n">t</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">res</span><span class="p">)</span>
+    <span class="n">us</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">([</span><span class="n">u</span> <span class="o">**</span> <span class="mi">2</span> <span class="k">for</span> <span class="n">u</span> <span class="ow">in</span> <span class="n">res</span><span class="p">]))</span>
+    <span class="n">ys</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">([</span><span class="n">y</span> <span class="o">**</span> <span class="mi">2</span> <span class="k">for</span> <span class="n">y</span> <span class="ow">in</span> <span class="n">targets</span><span class="p">]))</span>
+    <span class="n">fs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">([</span><span class="n">f</span> <span class="o">**</span> <span class="mi">2</span> <span class="k">for</span> <span class="n">f</span> <span class="ow">in</span> <span class="n">forecasts</span><span class="p">]))</span>
+    <span class="k">return</span> <span class="n">us</span> <span class="o">/</span> <span class="p">(</span><span class="n">ys</span> <span class="o">+</span> <span class="n">fs</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="sharpness"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.Measures.sharpness">[docs]</a><span class="k">def</span> <span class="nf">sharpness</span><span class="p">(</span><span class="n">forecasts</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;Sharpness - Mean size of the intervals&quot;&quot;&quot;</span>
+    <span class="n">tmp</span> <span class="o">=</span> <span class="p">[</span><span class="n">i</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">i</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">forecasts</span><span class="p">]</span>
+    <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">tmp</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="resolution"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.Measures.resolution">[docs]</a><span class="k">def</span> <span class="nf">resolution</span><span class="p">(</span><span class="n">forecasts</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;Resolution - Standard deviation of the intervals&quot;&quot;&quot;</span>
+    <span class="n">shp</span> <span class="o">=</span> <span class="n">sharpness</span><span class="p">(</span><span class="n">forecasts</span><span class="p">)</span>
+    <span class="n">tmp</span> <span class="o">=</span> <span class="p">[</span><span class="nb">abs</span><span class="p">((</span><span class="n">i</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">i</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> <span class="o">-</span> <span class="n">shp</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">forecasts</span><span class="p">]</span>
+    <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">tmp</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="coverage"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.Measures.coverage">[docs]</a><span class="k">def</span> <span class="nf">coverage</span><span class="p">(</span><span class="n">targets</span><span class="p">,</span> <span class="n">forecasts</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;Percent of target values that fall inside forecasted interval&quot;&quot;&quot;</span>
+
+    <span class="n">preds</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">forecasts</span><span class="p">)):</span>
+        <span class="k">if</span> <span class="n">targets</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">&gt;=</span> <span class="n">forecasts</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="ow">and</span> <span class="n">targets</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">&lt;=</span> <span class="n">forecasts</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">1</span><span class="p">]:</span>
+            <span class="n">preds</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">preds</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">nanmean</span><span class="p">(</span><span class="n">preds</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="pinball"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.Measures.pinball">[docs]</a><span class="k">def</span> <span class="nf">pinball</span><span class="p">(</span><span class="n">tau</span><span class="p">,</span> <span class="n">target</span><span class="p">,</span> <span class="n">forecast</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Pinball loss function. Measure the distance of forecast to the tau-quantile of the target</span>
+
+<span class="sd">    :param tau: quantile value in the range (0,1)</span>
+<span class="sd">    :param target: </span>
+<span class="sd">    :param forecast: </span>
+<span class="sd">    :return: float, distance of forecast to the tau-quantile of the target</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="k">if</span> <span class="n">target</span> <span class="o">&gt;=</span> <span class="n">forecast</span><span class="p">:</span>
+        <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">subtract</span><span class="p">(</span><span class="n">target</span><span class="p">,</span> <span class="n">forecast</span><span class="p">)</span> <span class="o">*</span> <span class="n">tau</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">subtract</span><span class="p">(</span><span class="n">forecast</span><span class="p">,</span> <span class="n">target</span><span class="p">)</span> <span class="o">*</span> <span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">tau</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="pinball_mean"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.Measures.pinball_mean">[docs]</a><span class="k">def</span> <span class="nf">pinball_mean</span><span class="p">(</span><span class="n">tau</span><span class="p">,</span> <span class="n">targets</span><span class="p">,</span> <span class="n">forecasts</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Mean pinball loss value of the forecast for a given tau-quantile of the targets</span>
+
+<span class="sd">    :param tau: quantile value in the range (0,1)</span>
+<span class="sd">    :param targets: list of target values</span>
+<span class="sd">    :param forecasts: list of prediction intervals</span>
+<span class="sd">    :return: float, the pinball loss mean for tau quantile</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="k">if</span> <span class="n">tau</span> <span class="o">&lt;=</span> <span class="mf">0.5</span><span class="p">:</span>
+        <span class="n">preds</span> <span class="o">=</span> <span class="p">[</span><span class="n">pinball</span><span class="p">(</span><span class="n">tau</span><span class="p">,</span> <span class="n">targets</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">forecasts</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">0</span><span class="p">])</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">forecasts</span><span class="p">))]</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">preds</span> <span class="o">=</span> <span class="p">[</span><span class="n">pinball</span><span class="p">(</span><span class="n">tau</span><span class="p">,</span> <span class="n">targets</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">forecasts</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">1</span><span class="p">])</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">forecasts</span><span class="p">))]</span>
+    <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">nanmean</span><span class="p">(</span><span class="n">preds</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="winkler_score"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.Measures.winkler_score">[docs]</a><span class="k">def</span> <span class="nf">winkler_score</span><span class="p">(</span><span class="n">tau</span><span class="p">,</span> <span class="n">target</span><span class="p">,</span> <span class="n">forecast</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    R. L. Winkler, A Decision-Theoretic Approach to Interval Estimation, J. Am. Stat. Assoc. 67 (337) (1972) 187–191. doi:10.2307/2284720.</span>
+
+<span class="sd">    :param tau:</span>
+<span class="sd">    :param target:</span>
+<span class="sd">    :param forecast:</span>
+<span class="sd">    :return:</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">delta</span> <span class="o">=</span> <span class="n">forecast</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">forecast</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+    <span class="k">if</span> <span class="n">forecast</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">&lt;=</span> <span class="n">target</span> <span class="o">&lt;=</span> <span class="n">forecast</span><span class="p">[</span><span class="mi">1</span><span class="p">]:</span>
+        <span class="k">return</span> <span class="n">delta</span>
+    <span class="k">elif</span> <span class="n">forecast</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">&gt;</span> <span class="n">target</span><span class="p">:</span>
+        <span class="k">return</span> <span class="n">delta</span> <span class="o">+</span> <span class="p">(</span><span class="mi">2</span> <span class="o">*</span> <span class="p">(</span><span class="n">forecast</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="n">target</span><span class="p">))</span> <span class="o">/</span> <span class="n">tau</span>
+    <span class="k">elif</span> <span class="n">forecast</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">&lt;</span> <span class="n">target</span><span class="p">:</span>
+        <span class="k">return</span> <span class="n">delta</span> <span class="o">+</span> <span class="p">(</span><span class="mi">2</span> <span class="o">*</span> <span class="p">(</span><span class="n">target</span> <span class="o">-</span> <span class="n">forecast</span><span class="p">[</span><span class="mi">1</span><span class="p">]))</span> <span class="o">/</span> <span class="n">tau</span></div>
+
+
+<div class="viewcode-block" id="winkler_mean"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.Measures.winkler_mean">[docs]</a><span class="k">def</span> <span class="nf">winkler_mean</span><span class="p">(</span><span class="n">tau</span><span class="p">,</span> <span class="n">targets</span><span class="p">,</span> <span class="n">forecasts</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Mean Winkler score value of the forecast for a given tau-quantile of the targets</span>
+
+<span class="sd">    :param tau: quantile value in the range (0,1)</span>
+<span class="sd">    :param targets: list of target values</span>
+<span class="sd">    :param forecasts: list of prediction intervals</span>
+<span class="sd">    :return: float, the Winkler score mean for tau quantile</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">preds</span> <span class="o">=</span> <span class="p">[</span><span class="n">winkler_score</span><span class="p">(</span><span class="n">tau</span><span class="p">,</span> <span class="n">targets</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">forecasts</span><span class="p">[</span><span class="n">i</span><span class="p">])</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">forecasts</span><span class="p">))]</span>
+
+    <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">nanmean</span><span class="p">(</span><span class="n">preds</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="brier_score"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.Measures.brier_score">[docs]</a><span class="k">def</span> <span class="nf">brier_score</span><span class="p">(</span><span class="n">targets</span><span class="p">,</span> <span class="n">densities</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Brier Score for probabilistic forecasts.</span>
+<span class="sd">    Brier (1950). &quot;Verification of Forecasts Expressed in Terms of Probability&quot;. Monthly Weather Review. 78: 1–3.</span>
+
+<span class="sd">    :param targets: a list with the target values</span>
+<span class="sd">    :param densities: a list with pyFTS.probabil objectsistic.ProbabilityDistribution</span>
+<span class="sd">    :return: float</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">densities</span><span class="p">,</span> <span class="nb">list</span><span class="p">):</span>
+        <span class="n">densities</span> <span class="o">=</span> <span class="p">[</span><span class="n">densities</span><span class="p">]</span>
+        <span class="n">targets</span> <span class="o">=</span> <span class="p">[</span><span class="n">targets</span><span class="p">]</span>
+
+    <span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
+
+    <span class="k">for</span> <span class="n">ct</span><span class="p">,</span> <span class="n">d</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">densities</span><span class="p">):</span>
+        <span class="k">try</span><span class="p">:</span>
+            <span class="n">v</span> <span class="o">=</span> <span class="n">d</span><span class="o">.</span><span class="n">bin_index</span><span class="o">.</span><span class="n">find_le</span><span class="p">(</span><span class="n">targets</span><span class="p">[</span><span class="n">ct</span><span class="p">])</span>
+
+            <span class="n">score</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">([</span><span class="n">d</span><span class="o">.</span><span class="n">density</span><span class="p">(</span><span class="n">k</span><span class="p">)</span> <span class="o">**</span> <span class="mi">2</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">d</span><span class="o">.</span><span class="n">bins</span> <span class="k">if</span> <span class="n">k</span> <span class="o">!=</span> <span class="n">v</span><span class="p">])</span>
+            <span class="n">score</span> <span class="o">+=</span> <span class="p">(</span><span class="n">d</span><span class="o">.</span><span class="n">density</span><span class="p">(</span><span class="n">v</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span> <span class="o">**</span> <span class="mi">2</span>
+            <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">score</span><span class="p">)</span>
+        <span class="k">except</span> <span class="ne">ValueError</span> <span class="k">as</span> <span class="n">ex</span><span class="p">:</span>
+            <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">([</span><span class="n">d</span><span class="o">.</span><span class="n">density</span><span class="p">(</span><span class="n">k</span><span class="p">)</span> <span class="o">**</span> <span class="mi">2</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">d</span><span class="o">.</span><span class="n">bins</span><span class="p">]))</span>
+    <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">(</span><span class="n">ret</span><span class="p">)</span> <span class="o">/</span> <span class="nb">len</span><span class="p">(</span><span class="n">ret</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="logarithm_score"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.Measures.logarithm_score">[docs]</a><span class="k">def</span> <span class="nf">logarithm_score</span><span class="p">(</span><span class="n">targets</span><span class="p">,</span> <span class="n">densities</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Logarithm Score for probabilistic forecasts.</span>
+<span class="sd">    Good IJ (1952).  “Rational Decisions.”Journal of the Royal Statistical Society B,14(1),107–114. URLhttps://www.jstor.org/stable/2984087.</span>
+
+<span class="sd">    :param targets: a list with the target values</span>
+<span class="sd">    :param densities: a list with pyFTS.probabil objectsistic.ProbabilityDistribution</span>
+<span class="sd">    :return: float</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">_ls</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="mf">0.0</span><span class="p">)</span>
+    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">densities</span><span class="p">,</span> <span class="n">ProbabilityDistribution</span><span class="o">.</span><span class="n">ProbabilityDistribution</span><span class="p">):</span>
+        <span class="n">densities</span> <span class="o">=</span> <span class="p">[</span><span class="n">densities</span><span class="p">]</span>
+        <span class="n">targets</span> <span class="o">=</span> <span class="p">[</span><span class="n">targets</span><span class="p">]</span>
+
+    <span class="n">n</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">densities</span><span class="p">)</span>
+    <span class="k">for</span> <span class="n">ct</span><span class="p">,</span> <span class="n">df</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">densities</span><span class="p">):</span>
+        <span class="n">_ls</span> <span class="o">+=</span> <span class="o">-</span><span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">df</span><span class="o">.</span><span class="n">density</span><span class="p">(</span><span class="n">targets</span><span class="p">[</span><span class="n">ct</span><span class="p">]))</span>
+
+    <span class="k">return</span> <span class="n">_ls</span> <span class="o">/</span> <span class="n">n</span></div>
+
+
+<div class="viewcode-block" id="crps"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.Measures.crps">[docs]</a><span class="k">def</span> <span class="nf">crps</span><span class="p">(</span><span class="n">targets</span><span class="p">,</span> <span class="n">densities</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Continuous Ranked Probability Score</span>
+
+<span class="sd">    :param targets: a list with the target values</span>
+<span class="sd">    :param densities: a list with pyFTS.probabil objectsistic.ProbabilityDistribution</span>
+<span class="sd">    :return: float</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">_crps</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="mf">0.0</span><span class="p">)</span>
+    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">densities</span><span class="p">,</span> <span class="n">ProbabilityDistribution</span><span class="o">.</span><span class="n">ProbabilityDistribution</span><span class="p">):</span>
+        <span class="n">densities</span> <span class="o">=</span> <span class="p">[</span><span class="n">densities</span><span class="p">]</span>
+        <span class="n">targets</span> <span class="o">=</span> <span class="p">[</span><span class="n">targets</span><span class="p">]</span>
+
+    <span class="n">n</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">densities</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">n</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+        <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
+
+    <span class="k">for</span> <span class="n">ct</span><span class="p">,</span> <span class="n">df</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">densities</span><span class="p">):</span>
+        <span class="n">_crps</span> <span class="o">+=</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">([(</span><span class="n">df</span><span class="o">.</span><span class="n">cumulative</span><span class="p">(</span><span class="nb">bin</span><span class="p">)</span> <span class="o">-</span> <span class="p">(</span><span class="mi">1</span> <span class="k">if</span> <span class="nb">bin</span> <span class="o">&gt;=</span> <span class="n">targets</span><span class="p">[</span><span class="n">ct</span><span class="p">]</span> <span class="k">else</span> <span class="mi">0</span><span class="p">))</span> <span class="o">**</span> <span class="mi">2</span> <span class="k">for</span> <span class="nb">bin</span> <span class="ow">in</span> <span class="n">df</span><span class="o">.</span><span class="n">bins</span><span class="p">])</span>
+
+    <span class="k">return</span> <span class="n">_crps</span> <span class="o">/</span> <span class="n">n</span></div>
+
+
+<div class="viewcode-block" id="get_point_statistics"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.Measures.get_point_statistics">[docs]</a><span class="k">def</span> <span class="nf">get_point_statistics</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">model</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Condensate all measures for point forecasters</span>
+
+<span class="sd">    :param data: test data</span>
+<span class="sd">    :param model: FTS model with point forecasting capability</span>
+<span class="sd">    :param kwargs:</span>
+<span class="sd">    :return: a list with the RMSE, SMAPE and U Statistic</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">steps_ahead</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;steps_ahead&#39;</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+    <span class="n">kwargs</span><span class="p">[</span><span class="s1">&#39;type&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="s1">&#39;point&#39;</span>
+
+    <span class="n">indexer</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;indexer&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="n">indexer</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="n">ndata</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">indexer</span><span class="o">.</span><span class="n">get_data</span><span class="p">(</span><span class="n">data</span><span class="p">))</span>
+    <span class="k">elif</span> <span class="n">model</span><span class="o">.</span><span class="n">is_multivariate</span><span class="p">:</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">):</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Multivariate data must be a Pandas DataFrame!&quot;</span><span class="p">)</span>
+        <span class="n">ndata</span> <span class="o">=</span> <span class="n">data</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">ndata</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+
+    <span class="n">ret</span> <span class="o">=</span> <span class="nb">list</span><span class="p">()</span>
+
+    <span class="k">if</span> <span class="n">steps_ahead</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
+        <span class="n">forecasts</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">ndata</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">model</span><span class="o">.</span><span class="n">is_multivariate</span> <span class="ow">and</span> <span class="n">model</span><span class="o">.</span><span class="n">has_seasonality</span><span class="p">:</span>
+            <span class="n">ndata</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">indexer</span><span class="o">.</span><span class="n">get_data</span><span class="p">(</span><span class="n">ndata</span><span class="p">)</span>
+        <span class="k">elif</span> <span class="n">model</span><span class="o">.</span><span class="n">is_multivariate</span><span class="p">:</span>
+            <span class="n">ndata</span> <span class="o">=</span> <span class="n">ndata</span><span class="p">[</span><span class="n">model</span><span class="o">.</span><span class="n">target_variable</span><span class="o">.</span><span class="n">data_label</span><span class="p">]</span><span class="o">.</span><span class="n">values</span>
+
+        <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">forecasts</span><span class="p">,</span> <span class="p">(</span><span class="nb">list</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">)):</span>
+            <span class="n">forecasts</span> <span class="o">=</span> <span class="p">[</span><span class="n">forecasts</span><span class="p">]</span>
+
+        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">forecasts</span><span class="p">)</span> <span class="o">!=</span> <span class="nb">len</span><span class="p">(</span><span class="n">ndata</span><span class="p">)</span> <span class="o">-</span> <span class="n">model</span><span class="o">.</span><span class="n">max_lag</span><span class="p">:</span>
+            <span class="n">forecasts</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">forecasts</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">forecasts</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">forecasts</span><span class="p">)</span>
+
+        <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">rmse</span><span class="p">(</span><span class="n">ndata</span><span class="p">[</span><span class="n">model</span><span class="o">.</span><span class="n">max_lag</span><span class="p">:],</span> <span class="n">forecasts</span><span class="p">),</span> <span class="mi">2</span><span class="p">))</span>
+        <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">mape</span><span class="p">(</span><span class="n">ndata</span><span class="p">[</span><span class="n">model</span><span class="o">.</span><span class="n">max_lag</span><span class="p">:],</span> <span class="n">forecasts</span><span class="p">),</span> <span class="mi">2</span><span class="p">))</span>
+        <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">UStatistic</span><span class="p">(</span><span class="n">ndata</span><span class="p">[</span><span class="n">model</span><span class="o">.</span><span class="n">max_lag</span><span class="p">:],</span> <span class="n">forecasts</span><span class="p">),</span> <span class="mi">2</span><span class="p">))</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">steps_ahead_sampler</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;steps_ahead_sampler&#39;</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+        <span class="n">nforecasts</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">order</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">ndata</span><span class="p">)</span> <span class="o">-</span> <span class="n">steps_ahead</span><span class="p">,</span> <span class="n">steps_ahead_sampler</span><span class="p">):</span>
+            <span class="n">sample</span> <span class="o">=</span> <span class="n">ndata</span><span class="p">[</span><span class="n">k</span> <span class="o">-</span> <span class="n">model</span><span class="o">.</span><span class="n">order</span><span class="p">:</span> <span class="n">k</span><span class="p">]</span>
+            <span class="n">tmp</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">sample</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+            <span class="n">nforecasts</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">tmp</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
+
+        <span class="n">start</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">max_lag</span> <span class="o">+</span> <span class="n">steps_ahead</span> <span class="o">-</span> <span class="mi">1</span>
+        <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">rmse</span><span class="p">(</span><span class="n">ndata</span><span class="p">[</span><span class="n">start</span><span class="p">:</span><span class="o">-</span><span class="mi">1</span><span class="p">:</span><span class="n">steps_ahead_sampler</span><span class="p">],</span> <span class="n">nforecasts</span><span class="p">),</span> <span class="mi">2</span><span class="p">))</span>
+        <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">mape</span><span class="p">(</span><span class="n">ndata</span><span class="p">[</span><span class="n">start</span><span class="p">:</span><span class="o">-</span><span class="mi">1</span><span class="p">:</span><span class="n">steps_ahead_sampler</span><span class="p">],</span> <span class="n">nforecasts</span><span class="p">),</span> <span class="mi">2</span><span class="p">))</span>
+        <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">UStatistic</span><span class="p">(</span><span class="n">ndata</span><span class="p">[</span><span class="n">start</span><span class="p">:</span><span class="o">-</span><span class="mi">1</span><span class="p">:</span><span class="n">steps_ahead_sampler</span><span class="p">],</span> <span class="n">nforecasts</span><span class="p">),</span> <span class="mi">2</span><span class="p">))</span>
+
+    <span class="k">return</span> <span class="n">ret</span></div>
+
+
+<div class="viewcode-block" id="get_point_ahead_statistics"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.Measures.get_point_ahead_statistics">[docs]</a><span class="k">def</span> <span class="nf">get_point_ahead_statistics</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">forecasts</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Condensate all measures for point forecasters</span>
+
+<span class="sd">    :param data: test data</span>
+<span class="sd">    :param model: FTS model with point forecasting capability</span>
+<span class="sd">    :param kwargs:</span>
+<span class="sd">    :return: a list with the RMSE, SMAPE and U Statistic</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">l</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">forecasts</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">)</span> <span class="o">!=</span> <span class="n">l</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span><span class="s2">&quot;Data and intervals have different lenghts!&quot;</span><span class="p">)</span>
+
+    <span class="n">lags</span> <span class="o">=</span> <span class="p">{}</span>
+
+    <span class="k">for</span> <span class="n">lag</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">l</span><span class="p">):</span>
+        <span class="n">ret</span> <span class="o">=</span> <span class="p">{}</span>
+        <span class="n">datum</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="n">lag</span><span class="p">]</span>
+        <span class="n">forecast</span> <span class="o">=</span> <span class="n">forecasts</span><span class="p">[</span><span class="n">lag</span><span class="p">]</span>
+        <span class="n">ret</span><span class="p">[</span><span class="s1">&#39;steps&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">lag</span>
+        <span class="n">ret</span><span class="p">[</span><span class="s1">&#39;method&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="s1">&#39;&#39;</span>
+        <span class="n">ret</span><span class="p">[</span><span class="s1">&#39;rmse&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">rmse</span><span class="p">(</span><span class="n">datum</span><span class="p">,</span> <span class="n">forecast</span><span class="p">)</span>
+        <span class="n">ret</span><span class="p">[</span><span class="s1">&#39;mape&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">mape</span><span class="p">(</span><span class="n">datum</span><span class="p">,</span> <span class="n">forecast</span><span class="p">)</span>
+        <span class="n">sample</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="n">lag</span><span class="o">-</span><span class="mi">1</span><span class="p">:</span><span class="n">lag</span><span class="o">+</span><span class="mi">1</span><span class="p">]</span> <span class="k">if</span> <span class="n">lag</span> <span class="o">&gt;</span> <span class="mi">0</span> <span class="k">else</span> <span class="p">[</span><span class="n">datum</span><span class="p">,</span> <span class="n">datum</span><span class="p">]</span>
+        <span class="n">ret</span><span class="p">[</span><span class="s1">&#39;u&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">UStatistic</span><span class="p">(</span><span class="n">sample</span><span class="p">,</span> <span class="n">forecast</span><span class="p">)</span>
+        <span class="n">lags</span><span class="p">[</span><span class="n">lag</span><span class="p">]</span> <span class="o">=</span> <span class="n">ret</span>
+
+    <span class="k">return</span> <span class="n">lags</span></div>
+
+
+<div class="viewcode-block" id="get_interval_statistics"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.Measures.get_interval_statistics">[docs]</a><span class="k">def</span> <span class="nf">get_interval_statistics</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">model</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Condensate all measures for point interval forecasters</span>
+
+<span class="sd">    :param data: test data</span>
+<span class="sd">    :param model: FTS model with interval forecasting capability</span>
+<span class="sd">    :param kwargs:</span>
+<span class="sd">    :return: a list with the sharpness, resolution, coverage, .05 pinball mean,</span>
+<span class="sd">            .25 pinball mean, .75 pinball mean and .95 pinball mean.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">steps_ahead</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;steps_ahead&#39;</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+    <span class="n">kwargs</span><span class="p">[</span><span class="s1">&#39;type&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="s1">&#39;interval&#39;</span>
+
+    <span class="n">ret</span> <span class="o">=</span> <span class="nb">list</span><span class="p">()</span>
+
+    <span class="k">if</span> <span class="n">steps_ahead</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
+        <span class="n">forecasts</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+        <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">sharpness</span><span class="p">(</span><span class="n">forecasts</span><span class="p">),</span> <span class="mi">2</span><span class="p">))</span>
+        <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">resolution</span><span class="p">(</span><span class="n">forecasts</span><span class="p">),</span> <span class="mi">2</span><span class="p">))</span>
+        <span class="k">if</span> <span class="n">model</span><span class="o">.</span><span class="n">is_multivariate</span><span class="p">:</span>
+            <span class="n">data</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="n">model</span><span class="o">.</span><span class="n">target_variable</span><span class="o">.</span><span class="n">data_label</span><span class="p">]</span><span class="o">.</span><span class="n">values</span>
+        <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">coverage</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="n">model</span><span class="o">.</span><span class="n">max_lag</span><span class="p">:],</span> <span class="n">forecasts</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">]),</span> <span class="mi">2</span><span class="p">))</span>
+        <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">pinball_mean</span><span class="p">(</span><span class="mf">0.05</span><span class="p">,</span> <span class="n">data</span><span class="p">[</span><span class="n">model</span><span class="o">.</span><span class="n">max_lag</span><span class="p">:],</span> <span class="n">forecasts</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">]),</span> <span class="mi">2</span><span class="p">))</span>
+        <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">pinball_mean</span><span class="p">(</span><span class="mf">0.25</span><span class="p">,</span> <span class="n">data</span><span class="p">[</span><span class="n">model</span><span class="o">.</span><span class="n">max_lag</span><span class="p">:],</span> <span class="n">forecasts</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">]),</span> <span class="mi">2</span><span class="p">))</span>
+        <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">pinball_mean</span><span class="p">(</span><span class="mf">0.75</span><span class="p">,</span> <span class="n">data</span><span class="p">[</span><span class="n">model</span><span class="o">.</span><span class="n">max_lag</span><span class="p">:],</span> <span class="n">forecasts</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">]),</span> <span class="mi">2</span><span class="p">))</span>
+        <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">pinball_mean</span><span class="p">(</span><span class="mf">0.95</span><span class="p">,</span> <span class="n">data</span><span class="p">[</span><span class="n">model</span><span class="o">.</span><span class="n">max_lag</span><span class="p">:],</span> <span class="n">forecasts</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">]),</span> <span class="mi">2</span><span class="p">))</span>
+        <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">winkler_mean</span><span class="p">(</span><span class="mf">0.05</span><span class="p">,</span> <span class="n">data</span><span class="p">[</span><span class="n">model</span><span class="o">.</span><span class="n">max_lag</span><span class="p">:],</span> <span class="n">forecasts</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">]),</span> <span class="mi">2</span><span class="p">))</span>
+        <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">winkler_mean</span><span class="p">(</span><span class="mf">0.25</span><span class="p">,</span> <span class="n">data</span><span class="p">[</span><span class="n">model</span><span class="o">.</span><span class="n">max_lag</span><span class="p">:],</span> <span class="n">forecasts</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">]),</span> <span class="mi">2</span><span class="p">))</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">forecasts</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">order</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">)</span> <span class="o">-</span> <span class="n">steps_ahead</span><span class="p">):</span>
+            <span class="n">sample</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="n">k</span> <span class="o">-</span> <span class="n">model</span><span class="o">.</span><span class="n">order</span><span class="p">:</span> <span class="n">k</span><span class="p">]</span>
+            <span class="n">tmp</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">sample</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+            <span class="n">forecasts</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">tmp</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
+
+        <span class="n">start</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">max_lag</span> <span class="o">+</span> <span class="n">steps_ahead</span> <span class="o">-</span> <span class="mi">1</span>
+        <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">sharpness</span><span class="p">(</span><span class="n">forecasts</span><span class="p">),</span> <span class="mi">2</span><span class="p">))</span>
+        <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">resolution</span><span class="p">(</span><span class="n">forecasts</span><span class="p">),</span> <span class="mi">2</span><span class="p">))</span>
+        <span class="k">if</span> <span class="n">model</span><span class="o">.</span><span class="n">is_multivariate</span><span class="p">:</span>
+            <span class="n">data</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="n">model</span><span class="o">.</span><span class="n">target_variable</span><span class="o">.</span><span class="n">data_label</span><span class="p">]</span><span class="o">.</span><span class="n">values</span>
+        <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">coverage</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="n">model</span><span class="o">.</span><span class="n">max_lag</span><span class="p">:],</span> <span class="n">forecasts</span><span class="p">),</span> <span class="mi">2</span><span class="p">))</span>
+        <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">pinball_mean</span><span class="p">(</span><span class="mf">0.05</span><span class="p">,</span> <span class="n">data</span><span class="p">[</span><span class="n">start</span><span class="p">:],</span> <span class="n">forecasts</span><span class="p">),</span> <span class="mi">2</span><span class="p">))</span>
+        <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">pinball_mean</span><span class="p">(</span><span class="mf">0.25</span><span class="p">,</span> <span class="n">data</span><span class="p">[</span><span class="n">start</span><span class="p">:],</span> <span class="n">forecasts</span><span class="p">),</span> <span class="mi">2</span><span class="p">))</span>
+        <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">pinball_mean</span><span class="p">(</span><span class="mf">0.75</span><span class="p">,</span> <span class="n">data</span><span class="p">[</span><span class="n">start</span><span class="p">:],</span> <span class="n">forecasts</span><span class="p">),</span> <span class="mi">2</span><span class="p">))</span>
+        <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">pinball_mean</span><span class="p">(</span><span class="mf">0.95</span><span class="p">,</span> <span class="n">data</span><span class="p">[</span><span class="n">start</span><span class="p">:],</span> <span class="n">forecasts</span><span class="p">),</span> <span class="mi">2</span><span class="p">))</span>
+        <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">winkler_mean</span><span class="p">(</span><span class="mf">0.05</span><span class="p">,</span> <span class="n">data</span><span class="p">[</span><span class="n">start</span><span class="p">:],</span> <span class="n">forecasts</span><span class="p">),</span> <span class="mi">2</span><span class="p">))</span>
+        <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">winkler_mean</span><span class="p">(</span><span class="mf">0.25</span><span class="p">,</span> <span class="n">data</span><span class="p">[</span><span class="n">start</span><span class="p">:],</span> <span class="n">forecasts</span><span class="p">),</span> <span class="mi">2</span><span class="p">))</span>
+    <span class="k">return</span> <span class="n">ret</span></div>
+
+
+<div class="viewcode-block" id="get_interval_ahead_statistics"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.Measures.get_interval_ahead_statistics">[docs]</a><span class="k">def</span> <span class="nf">get_interval_ahead_statistics</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">intervals</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Condensate all measures for point interval forecasters</span>
+
+<span class="sd">    :param data: test data</span>
+<span class="sd">    :param intervals: predicted intervals for each datapoint</span>
+<span class="sd">    :param kwargs:</span>
+<span class="sd">    :return: a list with the sharpness, resolution, coverage, .05 pinball mean,</span>
+<span class="sd">            .25 pinball mean, .75 pinball mean and .95 pinball mean.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">l</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">intervals</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">)</span> <span class="o">!=</span> <span class="n">l</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span><span class="s2">&quot;Data and intervals have different lenghts!&quot;</span><span class="p">)</span>
+
+    <span class="n">lags</span> <span class="o">=</span> <span class="p">{}</span>
+
+    <span class="k">for</span> <span class="n">lag</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">l</span><span class="p">):</span>
+        <span class="n">ret</span> <span class="o">=</span> <span class="p">{}</span>
+        <span class="n">datum</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="n">lag</span><span class="p">]</span>
+        <span class="n">interval</span> <span class="o">=</span> <span class="n">intervals</span><span class="p">[</span><span class="n">lag</span><span class="p">]</span>
+        <span class="n">ret</span><span class="p">[</span><span class="s1">&#39;steps&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">lag</span>
+        <span class="n">ret</span><span class="p">[</span><span class="s1">&#39;method&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="s1">&#39;&#39;</span>
+        <span class="n">ret</span><span class="p">[</span><span class="s1">&#39;sharpness&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="nb">round</span><span class="p">(</span><span class="n">interval</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">interval</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="mi">2</span><span class="p">)</span>
+        <span class="n">ret</span><span class="p">[</span><span class="s1">&#39;coverage&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="mi">1</span> <span class="k">if</span> <span class="n">interval</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">&lt;=</span> <span class="n">datum</span> <span class="o">&lt;=</span> <span class="n">interval</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="k">else</span> <span class="mi">0</span>
+        <span class="n">ret</span><span class="p">[</span><span class="s1">&#39;pinball05&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="nb">round</span><span class="p">(</span><span class="n">pinball</span><span class="p">(</span><span class="mf">0.05</span><span class="p">,</span> <span class="n">datum</span><span class="p">,</span> <span class="n">interval</span><span class="p">[</span><span class="mi">0</span><span class="p">]),</span> <span class="mi">2</span><span class="p">)</span>
+        <span class="n">ret</span><span class="p">[</span><span class="s1">&#39;pinball25&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="nb">round</span><span class="p">(</span><span class="n">pinball</span><span class="p">(</span><span class="mf">0.25</span><span class="p">,</span> <span class="n">datum</span><span class="p">,</span> <span class="n">interval</span><span class="p">[</span><span class="mi">0</span><span class="p">]),</span> <span class="mi">2</span><span class="p">)</span>
+        <span class="n">ret</span><span class="p">[</span><span class="s1">&#39;pinball75&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="nb">round</span><span class="p">(</span><span class="n">pinball</span><span class="p">(</span><span class="mf">0.75</span><span class="p">,</span> <span class="n">datum</span><span class="p">,</span> <span class="n">interval</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span> <span class="mi">2</span><span class="p">)</span>
+        <span class="n">ret</span><span class="p">[</span><span class="s1">&#39;pinball95&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="nb">round</span><span class="p">(</span><span class="n">pinball</span><span class="p">(</span><span class="mf">0.95</span><span class="p">,</span> <span class="n">datum</span><span class="p">,</span> <span class="n">interval</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span> <span class="mi">2</span><span class="p">)</span>
+        <span class="n">ret</span><span class="p">[</span><span class="s1">&#39;winkler05&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="nb">round</span><span class="p">(</span><span class="n">winkler_score</span><span class="p">(</span><span class="mf">0.05</span><span class="p">,</span> <span class="n">datum</span><span class="p">,</span> <span class="n">interval</span><span class="p">),</span> <span class="mi">2</span><span class="p">)</span>
+        <span class="n">ret</span><span class="p">[</span><span class="s1">&#39;winkler25&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="nb">round</span><span class="p">(</span><span class="n">winkler_score</span><span class="p">(</span><span class="mf">0.25</span><span class="p">,</span> <span class="n">datum</span><span class="p">,</span> <span class="n">interval</span><span class="p">),</span> <span class="mi">2</span><span class="p">)</span>
+        <span class="n">lags</span><span class="p">[</span><span class="n">lag</span><span class="p">]</span> <span class="o">=</span> <span class="n">ret</span>
+
+    <span class="k">return</span> <span class="n">lags</span></div>
+
+
+<div class="viewcode-block" id="get_distribution_statistics"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.Measures.get_distribution_statistics">[docs]</a><span class="k">def</span> <span class="nf">get_distribution_statistics</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">model</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Get CRPS statistic and time for a forecasting model</span>
+
+<span class="sd">    :param data: test data</span>
+<span class="sd">    :param model: FTS model with probabilistic forecasting capability</span>
+<span class="sd">    :param kwargs:</span>
+<span class="sd">    :return: a list with the CRPS and execution time</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">steps_ahead</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;steps_ahead&#39;</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+    <span class="n">kwargs</span><span class="p">[</span><span class="s1">&#39;type&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="s1">&#39;distribution&#39;</span>
+
+    <span class="n">ret</span> <span class="o">=</span> <span class="nb">list</span><span class="p">()</span>
+
+    <span class="k">if</span> <span class="n">steps_ahead</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
+        <span class="n">_s1</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
+        <span class="n">forecasts</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+        <span class="n">_e1</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
+        <span class="k">if</span> <span class="n">model</span><span class="o">.</span><span class="n">is_multivariate</span><span class="p">:</span>
+            <span class="n">data</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="n">model</span><span class="o">.</span><span class="n">target_variable</span><span class="o">.</span><span class="n">data_label</span><span class="p">]</span><span class="o">.</span><span class="n">values</span>
+        <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">crps</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="n">model</span><span class="o">.</span><span class="n">max_lag</span><span class="p">:],</span> <span class="n">forecasts</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">]),</span> <span class="mi">3</span><span class="p">))</span>
+        <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">_e1</span> <span class="o">-</span> <span class="n">_s1</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>
+        <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">brier_score</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="n">model</span><span class="o">.</span><span class="n">max_lag</span><span class="p">:],</span> <span class="n">forecasts</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">]),</span> <span class="mi">3</span><span class="p">))</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">skip</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;steps_ahead_sampler&#39;</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+        <span class="n">forecasts</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="n">_s1</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">max_lag</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">)</span> <span class="o">-</span> <span class="n">steps_ahead</span><span class="p">,</span> <span class="n">skip</span><span class="p">):</span>
+            <span class="n">sample</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="n">k</span> <span class="o">-</span> <span class="n">model</span><span class="o">.</span><span class="n">max_lag</span><span class="p">:</span> <span class="n">k</span><span class="p">]</span>
+            <span class="n">tmp</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">sample</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+            <span class="n">forecasts</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">tmp</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
+        <span class="n">_e1</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
+
+        <span class="n">start</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">max_lag</span> <span class="o">+</span> <span class="n">steps_ahead</span>
+        <span class="k">if</span> <span class="n">model</span><span class="o">.</span><span class="n">is_multivariate</span><span class="p">:</span>
+            <span class="n">data</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="n">model</span><span class="o">.</span><span class="n">target_variable</span><span class="o">.</span><span class="n">data_label</span><span class="p">]</span><span class="o">.</span><span class="n">values</span>
+        <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">crps</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="n">start</span><span class="p">:</span><span class="o">-</span><span class="mi">1</span><span class="p">:</span><span class="n">skip</span><span class="p">],</span> <span class="n">forecasts</span><span class="p">),</span> <span class="mi">3</span><span class="p">))</span>
+        <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">_e1</span> <span class="o">-</span> <span class="n">_s1</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>
+        <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">brier_score</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="n">start</span><span class="p">:</span><span class="o">-</span><span class="mi">1</span><span class="p">:</span><span class="n">skip</span><span class="p">],</span> <span class="n">forecasts</span><span class="p">),</span> <span class="mi">3</span><span class="p">))</span>
+    <span class="k">return</span> <span class="n">ret</span></div>
+
+
+<div class="viewcode-block" id="get_distribution_ahead_statistics"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.Measures.get_distribution_ahead_statistics">[docs]</a><span class="k">def</span> <span class="nf">get_distribution_ahead_statistics</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">distributions</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Get CRPS statistic and time for a forecasting model</span>
+
+<span class="sd">    :param data: test data</span>
+<span class="sd">    :param model: FTS model with probabilistic forecasting capability</span>
+<span class="sd">    :param kwargs:</span>
+<span class="sd">    :return: a list with the CRPS and execution time</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">l</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">distributions</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">)</span> <span class="o">!=</span> <span class="n">l</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span><span class="s2">&quot;Data and distributions have different lenghts!&quot;</span><span class="p">)</span>
+
+    <span class="n">lags</span> <span class="o">=</span> <span class="p">{}</span>
+
+    <span class="k">for</span> <span class="n">lag</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">l</span><span class="p">):</span>
+        <span class="n">ret</span> <span class="o">=</span> <span class="p">{}</span>
+        <span class="n">datum</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="n">lag</span><span class="p">]</span>
+        <span class="n">dist</span> <span class="o">=</span> <span class="n">distributions</span><span class="p">[</span><span class="n">lag</span><span class="p">]</span>
+        <span class="n">ret</span><span class="p">[</span><span class="s1">&#39;steps&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">lag</span>
+        <span class="n">ret</span><span class="p">[</span><span class="s1">&#39;method&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="s1">&#39;&#39;</span>
+        <span class="n">ret</span><span class="p">[</span><span class="s1">&#39;crps&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="nb">round</span><span class="p">(</span><span class="n">crps</span><span class="p">(</span><span class="n">datum</span><span class="p">,</span> <span class="n">dist</span><span class="p">),</span> <span class="mi">3</span><span class="p">)</span>
+        <span class="n">ret</span><span class="p">[</span><span class="s1">&#39;brier&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="nb">round</span><span class="p">(</span><span class="n">brier_score</span><span class="p">(</span><span class="n">datum</span><span class="p">,</span> <span class="n">dist</span><span class="p">),</span> <span class="mi">3</span><span class="p">)</span>
+        <span class="n">ret</span><span class="p">[</span><span class="s1">&#39;log&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="nb">round</span><span class="p">(</span><span class="n">logarithm_score</span><span class="p">(</span><span class="n">datum</span><span class="p">,</span> <span class="n">dist</span><span class="p">),</span> <span class="mi">3</span><span class="p">)</span>
+        <span class="n">lags</span><span class="p">[</span><span class="n">lag</span><span class="p">]</span> <span class="o">=</span> <span class="n">ret</span>
+
+    <span class="k">return</span> <span class="n">lags</span></div>
+</pre></div>
+
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@@ -0,0 +1,221 @@
+
+
+<!doctype html>
+
+<html>
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+    <title>pyFTS.benchmarks.ResidualAnalysis &#8212; pyFTS 1.6 documentation</title>
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+        <li class="nav-item nav-item-this"><a href="">pyFTS.benchmarks.ResidualAnalysis</a></li> 
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+            
+  <h1>Source code for pyFTS.benchmarks.ResidualAnalysis</h1><div class="highlight"><pre>
+<span></span><span class="ch">#!/usr/bin/python</span>
+<span class="c1"># -*- coding: utf8 -*-</span>
+
+<span class="sd">&quot;&quot;&quot;Residual Analysis methods&quot;&quot;&quot;</span>
+
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+<span class="kn">import</span> <span class="nn">matplotlib</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="kn">from</span> <span class="nn">pyFTS.common</span> <span class="kn">import</span> <span class="n">Transformations</span><span class="p">,</span><span class="n">Util</span>
+<span class="kn">from</span> <span class="nn">pyFTS.benchmarks</span> <span class="kn">import</span> <span class="n">Measures</span>
+<span class="kn">from</span> <span class="nn">scipy</span> <span class="kn">import</span> <span class="n">stats</span>
+
+
+<div class="viewcode-block" id="residuals"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.ResidualAnalysis.residuals">[docs]</a><span class="k">def</span> <span class="nf">residuals</span><span class="p">(</span><span class="n">targets</span><span class="p">,</span> <span class="n">forecasts</span><span class="p">,</span> <span class="n">order</span><span class="o">=</span><span class="mi">1</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;First order residuals&quot;&quot;&quot;</span>
+    <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">targets</span><span class="p">[</span><span class="n">order</span><span class="p">:])</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">forecasts</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span></div>
+
+
+<div class="viewcode-block" id="ljung_box_test"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.ResidualAnalysis.ljung_box_test">[docs]</a><span class="k">def</span> <span class="nf">ljung_box_test</span><span class="p">(</span><span class="n">residuals</span><span class="p">,</span> <span class="n">lags</span><span class="o">=</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">],</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.5</span><span class="p">):</span>
+    <span class="kn">from</span> <span class="nn">statsmodels.stats.diagnostic</span> <span class="kn">import</span> <span class="n">acorr_ljungbox</span>
+    <span class="kn">from</span> <span class="nn">scipy.stats</span> <span class="kn">import</span> <span class="n">chi2</span>
+    
+    <span class="n">stat</span><span class="p">,</span> <span class="n">pval</span> <span class="o">=</span> <span class="n">acorr_ljungbox</span><span class="p">(</span><span class="n">residuals</span><span class="p">,</span> <span class="n">lags</span><span class="o">=</span><span class="n">lags</span><span class="p">)</span>
+    
+    <span class="n">rows</span> <span class="o">=</span> <span class="p">[]</span>
+
+    <span class="k">for</span> <span class="n">ct</span><span class="p">,</span> <span class="n">Q</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">stat</span><span class="p">):</span>
+      <span class="n">lag</span> <span class="o">=</span> <span class="n">ct</span><span class="o">+</span><span class="mi">1</span>
+      <span class="n">p_value</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">-</span> <span class="n">chi2</span><span class="o">.</span><span class="n">cdf</span><span class="p">(</span><span class="n">Q</span><span class="p">,</span> <span class="n">df</span><span class="o">=</span><span class="n">lag</span><span class="p">)</span>
+      <span class="n">critical_value</span> <span class="o">=</span> <span class="n">chi2</span><span class="o">.</span><span class="n">ppf</span><span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">alpha</span><span class="p">,</span> <span class="n">df</span><span class="o">=</span><span class="n">lag</span><span class="p">)</span>
+      <span class="n">rows</span><span class="o">.</span><span class="n">append</span><span class="p">([</span><span class="n">lag</span><span class="p">,</span> <span class="n">Q</span><span class="p">,</span> <span class="n">p_value</span><span class="p">,</span> <span class="n">critical_value</span><span class="p">,</span> <span class="s1">&#39;H0 accepted&#39;</span> <span class="k">if</span> <span class="n">Q</span> <span class="o">&gt;</span> <span class="n">critical_value</span> <span class="k">else</span> <span class="s1">&#39;H0 rejected&#39;</span><span class="p">])</span>
+        
+    <span class="k">return</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">rows</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;Lag&#39;</span><span class="p">,</span><span class="s1">&#39;Statistic&#39;</span><span class="p">,</span><span class="s1">&#39;p-Value&#39;</span><span class="p">,</span><span class="s1">&#39;Critical Value&#39;</span><span class="p">,</span> <span class="s1">&#39;Result&#39;</span><span class="p">])</span></div>
+ 
+
+<div class="viewcode-block" id="compare_residuals"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.ResidualAnalysis.compare_residuals">[docs]</a><span class="k">def</span> <span class="nf">compare_residuals</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">models</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=.</span><span class="mi">05</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Compare residual&#39;s statistics of several models</span>
+
+<span class="sd">    :param data: test data</span>
+<span class="sd">    :param models: </span>
+<span class="sd">    :return: a Pandas dataframe with the Box-Ljung statistic for each model</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="kn">from</span> <span class="nn">statsmodels.stats.diagnostic</span> <span class="kn">import</span> <span class="n">acorr_ljungbox</span>
+    <span class="n">rows</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="n">columns</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;Model&quot;</span><span class="p">,</span><span class="s2">&quot;Order&quot;</span><span class="p">,</span><span class="s2">&quot;AVG&quot;</span><span class="p">,</span><span class="s2">&quot;STD&quot;</span><span class="p">,</span><span class="s2">&quot;Box-Ljung&quot;</span><span class="p">,</span><span class="s2">&quot;p-value&quot;</span><span class="p">,</span><span class="s2">&quot;Result&quot;</span><span class="p">]</span>
+    <span class="k">for</span> <span class="n">mfts</span> <span class="ow">in</span> <span class="n">models</span><span class="p">:</span>
+        <span class="n">forecasts</span> <span class="o">=</span> <span class="n">mfts</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+        <span class="n">res</span> <span class="o">=</span> <span class="n">residuals</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">forecasts</span><span class="p">,</span> <span class="n">mfts</span><span class="o">.</span><span class="n">order</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span>
+        <span class="n">mu</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">res</span><span class="p">)</span>
+        <span class="n">sig</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">std</span><span class="p">(</span><span class="n">res</span><span class="p">)</span>
+        <span class="n">row</span> <span class="o">=</span> <span class="p">[</span><span class="n">mfts</span><span class="o">.</span><span class="n">shortname</span><span class="p">,</span> <span class="n">mfts</span><span class="o">.</span><span class="n">order</span><span class="p">,</span> <span class="n">mu</span><span class="p">,</span> <span class="n">sig</span><span class="p">]</span>
+        <span class="n">stat</span><span class="p">,</span> <span class="n">pval</span> <span class="o">=</span> <span class="n">acorr_ljungbox</span><span class="p">(</span><span class="n">res</span><span class="p">)</span>
+        <span class="n">test</span> <span class="o">=</span> <span class="s1">&#39;H0 Accepted&#39;</span> <span class="k">if</span> <span class="n">pval</span> <span class="o">&gt;</span> <span class="n">alpha</span> <span class="k">else</span> <span class="s1">&#39;H0 Rejected&#39;</span>
+        <span class="n">row</span><span class="o">.</span><span class="n">extend</span><span class="p">([</span><span class="n">stat</span><span class="p">,</span> <span class="n">pval</span><span class="p">,</span> <span class="n">test</span><span class="p">])</span>
+        <span class="n">rows</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">row</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">rows</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="n">columns</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="plot_residuals_by_model"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.ResidualAnalysis.plot_residuals_by_model">[docs]</a><span class="k">def</span> <span class="nf">plot_residuals_by_model</span><span class="p">(</span><span class="n">targets</span><span class="p">,</span> <span class="n">models</span><span class="p">,</span> <span class="n">tam</span><span class="o">=</span><span class="p">[</span><span class="mi">8</span><span class="p">,</span> <span class="mi">8</span><span class="p">],</span> <span class="n">save</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">file</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+    <span class="kn">import</span> <span class="nn">scipy</span> <span class="k">as</span> <span class="nn">sp</span>
+    
+    <span class="n">fig</span><span class="p">,</span> <span class="n">axes</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">nrows</span><span class="o">=</span><span class="nb">len</span><span class="p">(</span><span class="n">models</span><span class="p">),</span> <span class="n">ncols</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="n">tam</span><span class="p">)</span>
+
+    <span class="k">for</span> <span class="n">c</span><span class="p">,</span> <span class="n">mfts</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">models</span><span class="p">,</span> <span class="n">start</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
+        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">models</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
+            <span class="n">ax</span> <span class="o">=</span> <span class="n">axes</span><span class="p">[</span><span class="n">c</span><span class="p">]</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">ax</span> <span class="o">=</span> <span class="n">axes</span>
+        <span class="n">forecasts</span> <span class="o">=</span> <span class="n">mfts</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">targets</span><span class="p">)</span>
+        <span class="n">res</span> <span class="o">=</span> <span class="n">residuals</span><span class="p">(</span><span class="n">targets</span><span class="p">,</span> <span class="n">forecasts</span><span class="p">,</span> <span class="n">mfts</span><span class="o">.</span><span class="n">order</span><span class="p">)</span>
+        <span class="n">mu</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">res</span><span class="p">)</span>
+        <span class="n">sig</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">std</span><span class="p">(</span><span class="n">res</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">c</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span> <span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">&quot;Residuals&quot;</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="s1">&#39;large&#39;</span><span class="p">)</span>
+        <span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="n">mfts</span><span class="o">.</span><span class="n">shortname</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="s1">&#39;large&#39;</span><span class="p">)</span>
+        <span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39; &#39;</span><span class="p">)</span>
+        <span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">res</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">c</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span> <span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">&quot;Autocorrelation&quot;</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="s1">&#39;large&#39;</span><span class="p">)</span>
+        <span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;ACS&#39;</span><span class="p">)</span>
+        <span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;Lag&#39;</span><span class="p">)</span>
+        <span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">acorr</span><span class="p">(</span><span class="n">res</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">c</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span> <span class="n">ax</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">&quot;Histogram&quot;</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="s1">&#39;large&#39;</span><span class="p">)</span>
+        <span class="n">ax</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;Freq&#39;</span><span class="p">)</span>
+        <span class="n">ax</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;Bins&#39;</span><span class="p">)</span>
+        <span class="n">ax</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">hist</span><span class="p">(</span><span class="n">res</span><span class="p">)</span>
+        
+        <span class="k">if</span> <span class="n">c</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span> <span class="n">ax</span><span class="p">[</span><span class="mi">3</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">&quot;QQ Plot&quot;</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="s1">&#39;large&#39;</span><span class="p">)</span>
+        
+        <span class="n">_</span><span class="p">,</span> <span class="p">(</span><span class="n">__</span><span class="p">,</span> <span class="n">___</span><span class="p">,</span> <span class="n">r</span><span class="p">)</span> <span class="o">=</span> <span class="n">sp</span><span class="o">.</span><span class="n">stats</span><span class="o">.</span><span class="n">probplot</span><span class="p">(</span><span class="n">res</span><span class="p">,</span> <span class="n">plot</span><span class="o">=</span><span class="n">ax</span><span class="p">[</span><span class="mi">3</span><span class="p">],</span> <span class="n">fit</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+
+    <span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
+
+    <span class="n">Util</span><span class="o">.</span><span class="n">show_and_save_image</span><span class="p">(</span><span class="n">fig</span><span class="p">,</span> <span class="n">file</span><span class="p">,</span> <span class="n">save</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="single_plot_residuals"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.ResidualAnalysis.single_plot_residuals">[docs]</a><span class="k">def</span> <span class="nf">single_plot_residuals</span><span class="p">(</span><span class="n">res</span><span class="p">,</span> <span class="n">order</span><span class="p">,</span> <span class="n">tam</span><span class="o">=</span><span class="p">[</span><span class="mi">10</span><span class="p">,</span> <span class="mi">7</span><span class="p">],</span> <span class="n">save</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">file</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+    <span class="kn">import</span> <span class="nn">scipy</span> <span class="k">as</span> <span class="nn">sp</span>
+    
+    <span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">nrows</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">ncols</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="n">tam</span><span class="p">)</span>
+
+    <span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">&quot;Residuals&quot;</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="s1">&#39;large&#39;</span><span class="p">)</span>
+    <span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">res</span><span class="p">)</span>
+
+    <span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">&quot;Autocorrelation&quot;</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="s1">&#39;large&#39;</span><span class="p">)</span>
+    <span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;ACF&#39;</span><span class="p">)</span>
+    <span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;Lag&#39;</span><span class="p">)</span>
+    <span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">acorr</span><span class="p">(</span><span class="n">res</span><span class="p">)</span>
+
+    <span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">&quot;Histogram&quot;</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="s1">&#39;large&#39;</span><span class="p">)</span>
+    <span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;Freq&#39;</span><span class="p">)</span>
+    <span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;Bins&#39;</span><span class="p">)</span>
+    <span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">hist</span><span class="p">(</span><span class="n">res</span><span class="p">)</span>
+    
+    <span class="n">_</span><span class="p">,</span> <span class="p">(</span><span class="n">__</span><span class="p">,</span> <span class="n">___</span><span class="p">,</span> <span class="n">r</span><span class="p">)</span> <span class="o">=</span> <span class="n">sp</span><span class="o">.</span><span class="n">stats</span><span class="o">.</span><span class="n">probplot</span><span class="p">(</span><span class="n">res</span><span class="p">,</span> <span class="n">plot</span><span class="o">=</span><span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="mi">1</span><span class="p">],</span> <span class="n">fit</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+
+    <span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
+
+    <span class="n">Util</span><span class="o">.</span><span class="n">show_and_save_image</span><span class="p">(</span><span class="n">fig</span><span class="p">,</span> <span class="n">file</span><span class="p">,</span> <span class="n">save</span><span class="p">)</span></div>
+</pre></div>
+
+            <div class="clearer"></div>
+          </div>
+        </div>
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+      <div class="sphinxsidebar" role="navigation" aria-label="main navigation">
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diff --git a/docs/build/html/_modules/pyFTS/benchmarks/Tests.html b/docs/build/html/_modules/pyFTS/benchmarks/Tests.html
new file mode 100644
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@@ -0,0 +1,266 @@
+
+
+<!doctype html>
+
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+        <li class="nav-item nav-item-this"><a href="">pyFTS.benchmarks.Tests</a></li> 
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+            
+  <h1>Source code for pyFTS.benchmarks.Tests</h1><div class="highlight"><pre>
+<span></span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+
+<span class="kn">from</span> <span class="nn">pyFTS.benchmarks.Measures</span> <span class="kn">import</span> <span class="n">acf</span>
+
+
+<div class="viewcode-block" id="BoxPierceStatistic"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.Tests.BoxPierceStatistic">[docs]</a><span class="k">def</span> <span class="nf">BoxPierceStatistic</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">h</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Q Statistic for Box-Pierce test</span>
+
+<span class="sd">    :param data:</span>
+<span class="sd">    :param h:</span>
+<span class="sd">    :return:</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">n</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+    <span class="n">s</span> <span class="o">=</span> <span class="mi">0</span>
+    <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">h</span> <span class="o">+</span> <span class="mi">1</span><span class="p">):</span>
+        <span class="n">r</span> <span class="o">=</span> <span class="n">acf</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">k</span><span class="p">)</span>
+        <span class="n">s</span> <span class="o">+=</span> <span class="n">r</span> <span class="o">**</span> <span class="mi">2</span>
+    <span class="k">return</span> <span class="n">n</span> <span class="o">*</span> <span class="n">s</span></div>
+
+
+<div class="viewcode-block" id="BoxLjungStatistic"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.Tests.BoxLjungStatistic">[docs]</a><span class="k">def</span> <span class="nf">BoxLjungStatistic</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">h</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Q Statistic for Ljung–Box test</span>
+
+<span class="sd">    :param data:</span>
+<span class="sd">    :param h:</span>
+<span class="sd">    :return:</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">n</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+    <span class="n">s</span> <span class="o">=</span> <span class="mi">0</span>
+    <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">h</span> <span class="o">+</span> <span class="mi">1</span><span class="p">):</span>
+        <span class="n">r</span> <span class="o">=</span> <span class="n">acf</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">k</span><span class="p">)</span>
+        <span class="n">s</span> <span class="o">+=</span> <span class="n">r</span> <span class="o">**</span> <span class="mi">2</span> <span class="o">/</span> <span class="p">(</span><span class="n">n</span> <span class="o">-</span> <span class="n">k</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">n</span> <span class="o">*</span> <span class="p">(</span><span class="n">n</span> <span class="o">-</span> <span class="mi">2</span><span class="p">)</span> <span class="o">*</span> <span class="n">s</span></div>
+
+
+<div class="viewcode-block" id="format_experiment_table"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.Tests.format_experiment_table">[docs]</a><span class="k">def</span> <span class="nf">format_experiment_table</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">exclude</span><span class="o">=</span><span class="p">[],</span> <span class="n">replace</span><span class="o">=</span><span class="p">{},</span> <span class="n">csv</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">std</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
+    <span class="n">rows</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="n">columns</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="n">datasets</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">Dataset</span><span class="o">.</span><span class="n">unique</span><span class="p">()</span>
+    <span class="n">models</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">Model</span><span class="o">.</span><span class="n">unique</span><span class="p">()</span>
+    <span class="k">for</span> <span class="n">model</span> <span class="ow">in</span> <span class="n">models</span><span class="p">:</span>
+        <span class="n">test</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">any</span><span class="p">([</span><span class="n">model</span><span class="o">.</span><span class="n">rfind</span><span class="p">(</span><span class="n">k</span><span class="p">)</span> <span class="o">!=</span> <span class="o">-</span><span class="mi">1</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">exclude</span><span class="p">])</span> <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">exclude</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span> <span class="k">else</span> <span class="kc">False</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="n">test</span><span class="p">:</span>
+            <span class="n">columns</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">model</span><span class="p">)</span>
+
+    <span class="k">for</span> <span class="n">dataset</span> <span class="ow">in</span> <span class="n">datasets</span><span class="p">:</span>
+        <span class="n">row</span> <span class="o">=</span> <span class="p">[</span><span class="n">dataset</span><span class="p">]</span>
+        <span class="k">if</span> <span class="n">std</span><span class="p">:</span>
+            <span class="n">row_std</span> <span class="o">=</span> <span class="p">[</span><span class="n">dataset</span><span class="p">]</span>
+        <span class="k">for</span> <span class="n">model</span> <span class="ow">in</span> <span class="n">columns</span><span class="p">:</span>
+            <span class="n">avg</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nanmin</span><span class="p">(</span><span class="n">df</span><span class="p">[(</span><span class="n">df</span><span class="o">.</span><span class="n">Dataset</span> <span class="o">==</span> <span class="n">dataset</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">df</span><span class="o">.</span><span class="n">Model</span> <span class="o">==</span> <span class="n">model</span><span class="p">)][</span><span class="s2">&quot;AVG&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">)</span>
+            <span class="n">row</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">avg</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>
+            <span class="k">if</span> <span class="n">std</span><span class="p">:</span>
+                <span class="n">_std</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nanmin</span><span class="p">(</span><span class="n">df</span><span class="p">[(</span><span class="n">df</span><span class="o">.</span><span class="n">Dataset</span> <span class="o">==</span> <span class="n">dataset</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">df</span><span class="o">.</span><span class="n">Model</span> <span class="o">==</span> <span class="n">model</span><span class="p">)][</span><span class="s2">&quot;STD&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">)</span>
+                <span class="n">row_std</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s2">&quot;(&quot;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">_std</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span> <span class="o">+</span> <span class="s2">&quot;)&quot;</span><span class="p">)</span>
+
+        <span class="n">rows</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">row</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">std</span><span class="p">:</span>
+            <span class="n">rows</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">row_std</span><span class="p">)</span>
+
+    <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">columns</span><span class="p">)):</span>
+        <span class="k">if</span> <span class="n">columns</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="ow">in</span> <span class="n">replace</span><span class="p">:</span>
+            <span class="n">columns</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="n">replace</span><span class="p">[</span><span class="n">columns</span><span class="p">[</span><span class="n">k</span><span class="p">]]</span>
+
+    <span class="n">columns</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="s2">&quot;dataset&quot;</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="n">csv</span><span class="p">:</span>
+        <span class="n">header</span> <span class="o">=</span> <span class="s2">&quot;&quot;</span>
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">columns</span><span class="p">)):</span>
+            <span class="k">if</span> <span class="n">k</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
+                <span class="n">header</span> <span class="o">+=</span> <span class="s2">&quot;,&quot;</span>
+            <span class="n">header</span> <span class="o">+=</span> <span class="n">columns</span><span class="p">[</span><span class="n">k</span><span class="p">]</span>
+
+        <span class="n">body</span> <span class="o">=</span> <span class="s2">&quot;&quot;</span>
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">rows</span><span class="p">)):</span>
+            <span class="n">row</span> <span class="o">=</span> <span class="s2">&quot;&quot;</span>
+            <span class="k">for</span> <span class="n">w</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">rows</span><span class="p">[</span><span class="n">k</span><span class="p">])):</span>
+                <span class="k">if</span> <span class="n">w</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
+                    <span class="n">row</span> <span class="o">+=</span> <span class="s2">&quot;,&quot;</span>
+                <span class="n">row</span> <span class="o">+=</span> <span class="nb">str</span><span class="p">(</span><span class="n">rows</span><span class="p">[</span><span class="n">k</span><span class="p">][</span><span class="n">w</span><span class="p">])</span>
+            <span class="n">body</span> <span class="o">+=</span> <span class="s1">&#39;</span><span class="se">\n</span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">row</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">header</span> <span class="o">+</span> <span class="n">body</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">ret</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">rows</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="n">columns</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">ret</span></div>
+
+
+<div class="viewcode-block" id="test_mean_equality"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.Tests.test_mean_equality">[docs]</a><span class="k">def</span> <span class="nf">test_mean_equality</span><span class="p">(</span><span class="n">tests</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=.</span><span class="mi">05</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s1">&#39;friedman&#39;</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Test for the equality of the means, with alpha confidence level.</span>
+
+<span class="sd">    H_0: There&#39;s no significant difference between the means</span>
+<span class="sd">    H_1: There is at least one significant difference between the means</span>
+
+<span class="sd">    :param tests:</span>
+<span class="sd">    :param alpha:</span>
+<span class="sd">    :param method:</span>
+<span class="sd">    :return:</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="kn">from</span> <span class="nn">stac.stac</span> <span class="kn">import</span> <span class="n">nonparametric_tests</span> <span class="k">as</span> <span class="n">npt</span>
+
+    <span class="n">methods</span> <span class="o">=</span> <span class="n">tests</span><span class="o">.</span><span class="n">columns</span><span class="p">[</span><span class="mi">1</span><span class="p">:]</span>
+
+    <span class="n">values</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">methods</span><span class="p">:</span>
+        <span class="n">values</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">tests</span><span class="p">[</span><span class="n">k</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="n">method</span><span class="o">==</span><span class="s1">&#39;quade&#39;</span><span class="p">:</span>
+        <span class="n">f_value</span><span class="p">,</span> <span class="n">p_value</span><span class="p">,</span> <span class="n">rankings</span><span class="p">,</span> <span class="n">pivots</span> <span class="o">=</span> <span class="n">npt</span><span class="o">.</span><span class="n">quade_test</span><span class="p">(</span><span class="o">*</span><span class="n">values</span><span class="p">)</span>
+    <span class="k">elif</span> <span class="n">method</span><span class="o">==</span><span class="s1">&#39;friedman&#39;</span><span class="p">:</span>
+        <span class="n">f_value</span><span class="p">,</span> <span class="n">p_value</span><span class="p">,</span> <span class="n">rankings</span><span class="p">,</span> <span class="n">pivots</span> <span class="o">=</span> <span class="n">npt</span><span class="o">.</span><span class="n">friedman_aligned_ranks_test</span><span class="p">(</span><span class="o">*</span><span class="n">values</span><span class="p">)</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span><span class="s1">&#39;Unknown test method!&#39;</span><span class="p">)</span>
+
+    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;F-Value: </span><span class="si">{}</span><span class="s2"> </span><span class="se">\t</span><span class="s2">p-Value: </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">f_value</span><span class="p">,</span> <span class="n">p_value</span><span class="p">))</span>
+
+    <span class="k">if</span> <span class="n">p_value</span> <span class="o">&lt;</span> <span class="n">alpha</span><span class="p">:</span>
+        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">H0 is rejected!</span><span class="se">\n</span><span class="s2">&quot;</span><span class="p">)</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">H0 is accepted!</span><span class="se">\n</span><span class="s2">&quot;</span><span class="p">)</span>
+
+    <span class="n">post_hoc</span> <span class="o">=</span> <span class="p">{}</span>
+    <span class="n">rows</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">methods</span><span class="p">)):</span>
+        <span class="n">rows</span><span class="o">.</span><span class="n">append</span><span class="p">([</span><span class="n">methods</span><span class="p">[</span><span class="n">k</span><span class="p">],</span> <span class="n">rankings</span><span class="p">[</span><span class="n">k</span><span class="p">]])</span>
+        <span class="n">post_hoc</span><span class="p">[</span><span class="n">methods</span><span class="p">[</span><span class="n">k</span><span class="p">]]</span> <span class="o">=</span> <span class="n">pivots</span><span class="p">[</span><span class="n">k</span><span class="p">]</span>
+
+    <span class="k">return</span> <span class="p">[</span><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">rows</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;METHOD&#39;</span><span class="p">,</span> <span class="s1">&#39;RANK&#39;</span><span class="p">])</span><span class="o">.</span><span class="n">sort_values</span><span class="p">([</span><span class="s1">&#39;RANK&#39;</span><span class="p">]),</span> <span class="n">post_hoc</span><span class="p">]</span></div>
+
+
+<div class="viewcode-block" id="post_hoc_tests"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.Tests.post_hoc_tests">[docs]</a><span class="k">def</span> <span class="nf">post_hoc_tests</span><span class="p">(</span><span class="n">post_hoc</span><span class="p">,</span> <span class="n">control_method</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=.</span><span class="mi">05</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s1">&#39;finner&#39;</span><span class="p">):</span>
+    <span class="sd">&#39;&#39;&#39;</span>
+<span class="sd">    Finner paired post-hoc test with NSFTS as control method.</span>
+
+<span class="sd">    $H_0$: There is no significant difference between the means</span>
+
+<span class="sd">    $H_1$: There is a significant difference between the means</span>
+
+<span class="sd">    :param post_hoc:</span>
+<span class="sd">    :param control_method:</span>
+<span class="sd">    :param alpha:</span>
+<span class="sd">    :param method:</span>
+<span class="sd">    :return:</span>
+<span class="sd">    &#39;&#39;&#39;</span>
+    <span class="kn">from</span> <span class="nn">stac.stac</span> <span class="kn">import</span> <span class="n">nonparametric_tests</span> <span class="k">as</span> <span class="n">npt</span>
+
+    <span class="k">if</span> <span class="n">method</span> <span class="o">==</span> <span class="s1">&#39;bonferroni_dunn&#39;</span><span class="p">:</span>
+        <span class="n">comparisons</span><span class="p">,</span> <span class="n">z_values</span><span class="p">,</span> <span class="n">p_values</span><span class="p">,</span> <span class="n">adj_p_values</span> <span class="o">=</span> <span class="n">npt</span><span class="o">.</span><span class="n">bonferroni_dunn_test</span><span class="p">(</span><span class="n">post_hoc</span><span class="p">,</span><span class="n">control_method</span><span class="p">)</span>
+    <span class="k">elif</span> <span class="n">method</span> <span class="o">==</span> <span class="s1">&#39;holm&#39;</span><span class="p">:</span>
+        <span class="n">comparisons</span><span class="p">,</span> <span class="n">z_values</span><span class="p">,</span> <span class="n">p_values</span><span class="p">,</span> <span class="n">adj_p_values</span> <span class="o">=</span> <span class="n">npt</span><span class="o">.</span><span class="n">holm_test</span><span class="p">(</span><span class="n">post_hoc</span><span class="p">,</span><span class="n">control_method</span><span class="p">)</span>
+    <span class="k">elif</span>  <span class="n">method</span> <span class="o">==</span> <span class="s1">&#39;finner&#39;</span><span class="p">:</span>
+        <span class="n">comparisons</span><span class="p">,</span> <span class="n">z_values</span><span class="p">,</span> <span class="n">p_values</span><span class="p">,</span> <span class="n">adj_p_values</span> <span class="o">=</span> <span class="n">npt</span><span class="o">.</span><span class="n">finner_test</span><span class="p">(</span><span class="n">post_hoc</span><span class="p">,</span> <span class="n">control_method</span><span class="p">)</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span><span class="s1">&#39;Unknown test method!&#39;</span><span class="p">)</span>
+
+    <span class="n">rows</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">comparisons</span><span class="p">)):</span>
+        <span class="n">test</span> <span class="o">=</span> <span class="s1">&#39;H0 Accepted&#39;</span> <span class="k">if</span> <span class="n">adj_p_values</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">&gt;</span> <span class="n">alpha</span> <span class="k">else</span> <span class="s1">&#39;H0 Rejected&#39;</span>
+        <span class="n">rows</span><span class="o">.</span><span class="n">append</span><span class="p">([</span><span class="n">comparisons</span><span class="p">[</span><span class="n">k</span><span class="p">],</span> <span class="n">z_values</span><span class="p">[</span><span class="n">k</span><span class="p">],</span> <span class="n">p_values</span><span class="p">[</span><span class="n">k</span><span class="p">],</span> <span class="n">adj_p_values</span><span class="p">[</span><span class="n">k</span><span class="p">],</span> <span class="n">test</span><span class="p">])</span>
+
+    <span class="k">return</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">rows</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;COMPARISON&#39;</span><span class="p">,</span> <span class="s1">&#39;Z-VALUE&#39;</span><span class="p">,</span> <span class="s1">&#39;P-VALUE&#39;</span><span class="p">,</span> <span class="s1">&#39;ADJUSTED P-VALUE&#39;</span><span class="p">,</span> <span class="s1">&#39;Result&#39;</span><span class="p">])</span></div>
+
+</pre></div>
+
+            <div class="clearer"></div>
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+
+
+<!doctype html>
+
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+        <li class="nav-item nav-item-this"><a href="">pyFTS.benchmarks.Util</a></li> 
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+            
+  <h1>Source code for pyFTS.benchmarks.Util</h1><div class="highlight"><pre>
+<span></span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">Facilities for pyFTS Benchmark module</span>
+<span class="sd">&quot;&quot;&quot;</span>
+
+<span class="kn">import</span> <span class="nn">matplotlib</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="kn">import</span> <span class="nn">matplotlib.cm</span> <span class="k">as</span> <span class="nn">cmx</span>
+<span class="kn">import</span> <span class="nn">matplotlib.colors</span> <span class="k">as</span> <span class="nn">pltcolors</span>
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+<span class="kn">import</span> <span class="nn">sqlite3</span>
+<span class="c1">#from mpl_toolkits.mplot3d import Axes3D</span>
+
+
+<span class="kn">from</span> <span class="nn">copy</span> <span class="kn">import</span> <span class="n">deepcopy</span>
+<span class="kn">from</span> <span class="nn">pyFTS.common</span> <span class="kn">import</span> <span class="n">Util</span>
+
+
+<div class="viewcode-block" id="open_benchmark_db"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.Util.open_benchmark_db">[docs]</a><span class="k">def</span> <span class="nf">open_benchmark_db</span><span class="p">(</span><span class="n">name</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Open a connection with a Sqlite database designed to store benchmark results.</span>
+
+<span class="sd">    :param name: database filenem</span>
+<span class="sd">    :return: a sqlite3 database connection</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">conn</span> <span class="o">=</span> <span class="n">sqlite3</span><span class="o">.</span><span class="n">connect</span><span class="p">(</span><span class="n">name</span><span class="p">)</span>
+
+    <span class="c1">#performance optimizations</span>
+    <span class="n">conn</span><span class="o">.</span><span class="n">execute</span><span class="p">(</span><span class="s2">&quot;PRAGMA journal_mode = WAL&quot;</span><span class="p">)</span>
+    <span class="n">conn</span><span class="o">.</span><span class="n">execute</span><span class="p">(</span><span class="s2">&quot;PRAGMA synchronous = NORMAL&quot;</span><span class="p">)</span>
+
+    <span class="n">create_benchmark_tables</span><span class="p">(</span><span class="n">conn</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">conn</span></div>
+
+
+<div class="viewcode-block" id="create_benchmark_tables"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.Util.create_benchmark_tables">[docs]</a><span class="k">def</span> <span class="nf">create_benchmark_tables</span><span class="p">(</span><span class="n">conn</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Create a sqlite3 table designed to store benchmark results.</span>
+
+<span class="sd">    :param conn: a sqlite3 database connection</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">c</span> <span class="o">=</span> <span class="n">conn</span><span class="o">.</span><span class="n">cursor</span><span class="p">()</span>
+
+    <span class="n">c</span><span class="o">.</span><span class="n">execute</span><span class="p">(</span><span class="s1">&#39;&#39;&#39;CREATE TABLE if not exists benchmarks(</span>
+<span class="s1">                 ID integer primary key, Date int, Dataset text, Tag text, </span>
+<span class="s1">                 Type text, Model text, Transformation text, &#39;Order&#39; int, </span>
+<span class="s1">                 Scheme text, Partitions int,</span>
+<span class="s1">                 Size int, Steps int, Method text, Measure text, Value real)&#39;&#39;&#39;</span><span class="p">)</span>
+
+    <span class="n">conn</span><span class="o">.</span><span class="n">commit</span><span class="p">()</span></div>
+
+
+<div class="viewcode-block" id="insert_benchmark"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.Util.insert_benchmark">[docs]</a><span class="k">def</span> <span class="nf">insert_benchmark</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">conn</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Insert benchmark data on database</span>
+
+<span class="sd">    :param data: a tuple with the benchmark data with format:</span>
+
+<span class="sd">    ID: integer incremental primary key</span>
+<span class="sd">    Date: Date/hour of benchmark execution</span>
+<span class="sd">    Dataset: Identify on which dataset the dataset was performed</span>
+<span class="sd">    Tag: a user defined word that indentify a benchmark set</span>
+<span class="sd">    Type: forecasting type (point, interval, distribution)</span>
+<span class="sd">    Model: FTS model</span>
+<span class="sd">    Transformation: The name of data transformation, if one was used</span>
+<span class="sd">    Order: the order of the FTS method</span>
+<span class="sd">    Scheme: UoD partitioning scheme</span>
+<span class="sd">    Partitions: Number of partitions</span>
+<span class="sd">    Size: Number of rules of the FTS model</span>
+<span class="sd">    Steps: prediction horizon, i. e., the number of steps ahead</span>
+<span class="sd">    Measure: accuracy measure</span>
+<span class="sd">    Value: the measure value</span>
+
+<span class="sd">    :param conn: a sqlite3 database connection</span>
+<span class="sd">    :return:</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">c</span> <span class="o">=</span> <span class="n">conn</span><span class="o">.</span><span class="n">cursor</span><span class="p">()</span>
+
+    <span class="n">c</span><span class="o">.</span><span class="n">execute</span><span class="p">(</span><span class="s2">&quot;INSERT INTO benchmarks(Date, Dataset, Tag, Type, Model, &quot;</span>
+              <span class="o">+</span> <span class="s2">&quot;Transformation, &#39;Order&#39;, Scheme, Partitions, &quot;</span>
+              <span class="o">+</span> <span class="s2">&quot;Size, Steps, Method, Measure, Value) &quot;</span>
+              <span class="o">+</span> <span class="s2">&quot;VALUES(datetime(&#39;now&#39;),?,?,?,?,?,?,?,?,?,?,?,?,?)&quot;</span><span class="p">,</span> <span class="n">data</span><span class="p">)</span>
+    <span class="n">conn</span><span class="o">.</span><span class="n">commit</span><span class="p">()</span></div>
+
+
+<div class="viewcode-block" id="process_common_data"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.Util.process_common_data">[docs]</a><span class="k">def</span> <span class="nf">process_common_data</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">tag</span><span class="p">,</span> <span class="nb">type</span><span class="p">,</span> <span class="n">job</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Wraps benchmark information on a tuple for sqlite database</span>
+
+<span class="sd">    :param dataset: benchmark dataset</span>
+<span class="sd">    :param tag: benchmark set alias</span>
+<span class="sd">    :param type: forecasting type</span>
+<span class="sd">    :param job: a dictionary with benchmark data</span>
+<span class="sd">    :return: tuple for sqlite database</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">model</span> <span class="o">=</span> <span class="n">job</span><span class="p">[</span><span class="s2">&quot;obj&quot;</span><span class="p">]</span>
+    <span class="k">if</span> <span class="n">model</span><span class="o">.</span><span class="n">benchmark_only</span><span class="p">:</span>
+        <span class="n">data</span> <span class="o">=</span> <span class="p">[</span><span class="n">dataset</span><span class="p">,</span> <span class="n">tag</span><span class="p">,</span> <span class="nb">type</span><span class="p">,</span> <span class="n">model</span><span class="o">.</span><span class="n">shortname</span><span class="p">,</span>
+                <span class="nb">str</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">transformations</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">transformations</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span> <span class="k">else</span> <span class="kc">None</span><span class="p">,</span>
+                <span class="n">model</span><span class="o">.</span><span class="n">order</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span>
+                <span class="kc">None</span><span class="p">]</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">data</span> <span class="o">=</span> <span class="p">[</span><span class="n">dataset</span><span class="p">,</span> <span class="n">tag</span><span class="p">,</span> <span class="nb">type</span><span class="p">,</span> <span class="n">model</span><span class="o">.</span><span class="n">shortname</span><span class="p">,</span>
+                <span class="nb">str</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">transformation</span><span class="p">)</span> <span class="k">if</span> <span class="n">model</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">transformation</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">else</span> <span class="kc">None</span><span class="p">,</span>
+                <span class="n">model</span><span class="o">.</span><span class="n">order</span><span class="p">,</span> <span class="n">model</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">name</span><span class="p">,</span> <span class="nb">str</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">partitions</span><span class="p">),</span>
+                <span class="nb">len</span><span class="p">(</span><span class="n">model</span><span class="p">)]</span>
+
+    <span class="k">return</span> <span class="n">data</span></div>
+
+
+<div class="viewcode-block" id="process_common_data2"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.Util.process_common_data2">[docs]</a><span class="k">def</span> <span class="nf">process_common_data2</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">tag</span><span class="p">,</span> <span class="nb">type</span><span class="p">,</span> <span class="n">job</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Wraps benchmark information on a tuple for sqlite database</span>
+
+<span class="sd">    :param dataset: benchmark dataset</span>
+<span class="sd">    :param tag: benchmark set alias</span>
+<span class="sd">    :param type: forecasting type</span>
+<span class="sd">    :param job: a dictionary with benchmark data</span>
+<span class="sd">    :return: tuple for sqlite database</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">data</span> <span class="o">=</span> <span class="p">[</span><span class="n">dataset</span><span class="p">,</span> <span class="n">tag</span><span class="p">,</span> <span class="nb">type</span><span class="p">,</span>
+            <span class="n">job</span><span class="p">[</span><span class="s1">&#39;model&#39;</span><span class="p">],</span>
+            <span class="n">job</span><span class="p">[</span><span class="s1">&#39;transformation&#39;</span><span class="p">],</span>
+            <span class="n">job</span><span class="p">[</span><span class="s1">&#39;order&#39;</span><span class="p">],</span>
+            <span class="n">job</span><span class="p">[</span><span class="s1">&#39;partitioner&#39;</span><span class="p">],</span>
+            <span class="n">job</span><span class="p">[</span><span class="s1">&#39;partitions&#39;</span><span class="p">],</span>
+            <span class="n">job</span><span class="p">[</span><span class="s1">&#39;size&#39;</span><span class="p">]</span>
+            <span class="p">]</span>
+
+    <span class="k">return</span> <span class="n">data</span></div>
+
+
+<div class="viewcode-block" id="get_dataframe_from_bd"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.Util.get_dataframe_from_bd">[docs]</a><span class="k">def</span> <span class="nf">get_dataframe_from_bd</span><span class="p">(</span><span class="n">file</span><span class="p">,</span> <span class="nb">filter</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Query the sqlite benchmark database and return a pandas dataframe with the results</span>
+
+<span class="sd">    :param file: the url of the benchmark database</span>
+<span class="sd">    :param filter: sql conditions to filter</span>
+<span class="sd">    :return: pandas dataframe with the query results</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">con</span> <span class="o">=</span> <span class="n">sqlite3</span><span class="o">.</span><span class="n">connect</span><span class="p">(</span><span class="n">file</span><span class="p">)</span>
+    <span class="n">sql</span> <span class="o">=</span> <span class="s2">&quot;SELECT * from benchmarks&quot;</span>
+    <span class="k">if</span> <span class="nb">filter</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="n">sql</span> <span class="o">+=</span> <span class="s2">&quot; WHERE &quot;</span> <span class="o">+</span> <span class="nb">filter</span>
+    <span class="k">return</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_sql_query</span><span class="p">(</span><span class="n">sql</span><span class="p">,</span> <span class="n">con</span><span class="p">)</span></div>
+
+
+
+<div class="viewcode-block" id="extract_measure"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.Util.extract_measure">[docs]</a><span class="k">def</span> <span class="nf">extract_measure</span><span class="p">(</span><span class="n">dataframe</span><span class="p">,</span> <span class="n">measure</span><span class="p">,</span> <span class="n">data_columns</span><span class="p">):</span>
+    <span class="k">if</span> <span class="ow">not</span> <span class="n">dataframe</span><span class="o">.</span><span class="n">empty</span><span class="p">:</span>
+        <span class="n">df</span> <span class="o">=</span> <span class="n">dataframe</span><span class="p">[(</span><span class="n">dataframe</span><span class="o">.</span><span class="n">Measure</span> <span class="o">==</span> <span class="n">measure</span><span class="p">)][</span><span class="n">data_columns</span><span class="p">]</span>
+        <span class="n">tmp</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">to_dict</span><span class="p">(</span><span class="n">orient</span><span class="o">=</span><span class="s2">&quot;records&quot;</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
+        <span class="n">ret</span> <span class="o">=</span> <span class="p">[</span><span class="n">k</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">tmp</span><span class="o">.</span><span class="n">values</span><span class="p">()</span> <span class="k">if</span> <span class="ow">not</span> <span class="n">np</span><span class="o">.</span><span class="n">isnan</span><span class="p">(</span><span class="n">k</span><span class="p">)]</span>
+        <span class="k">return</span> <span class="n">ret</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">return</span> <span class="kc">None</span></div>
+
+
+<div class="viewcode-block" id="find_best"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.Util.find_best">[docs]</a><span class="k">def</span> <span class="nf">find_best</span><span class="p">(</span><span class="n">dataframe</span><span class="p">,</span> <span class="n">criteria</span><span class="p">,</span> <span class="n">ascending</span><span class="p">):</span>
+    <span class="n">models</span> <span class="o">=</span> <span class="n">dataframe</span><span class="o">.</span><span class="n">Model</span><span class="o">.</span><span class="n">unique</span><span class="p">()</span>
+    <span class="n">orders</span> <span class="o">=</span> <span class="n">dataframe</span><span class="o">.</span><span class="n">Order</span><span class="o">.</span><span class="n">unique</span><span class="p">()</span>
+    <span class="n">ret</span> <span class="o">=</span> <span class="p">{}</span>
+    <span class="k">for</span> <span class="n">m</span> <span class="ow">in</span> <span class="n">models</span><span class="p">:</span>
+        <span class="k">for</span> <span class="n">o</span> <span class="ow">in</span> <span class="n">orders</span><span class="p">:</span>
+            <span class="n">mod</span> <span class="o">=</span> <span class="p">{}</span>
+            <span class="n">df</span> <span class="o">=</span> <span class="n">dataframe</span><span class="p">[(</span><span class="n">dataframe</span><span class="o">.</span><span class="n">Model</span> <span class="o">==</span> <span class="n">m</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">dataframe</span><span class="o">.</span><span class="n">Order</span> <span class="o">==</span> <span class="n">o</span><span class="p">)]</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="n">by</span><span class="o">=</span><span class="n">criteria</span><span class="p">,</span> <span class="n">ascending</span><span class="o">=</span><span class="n">ascending</span><span class="p">)</span>
+            <span class="k">if</span> <span class="ow">not</span> <span class="n">df</span><span class="o">.</span><span class="n">empty</span><span class="p">:</span>
+                <span class="n">_key</span> <span class="o">=</span> <span class="nb">str</span><span class="p">(</span><span class="n">m</span><span class="p">)</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">o</span><span class="p">)</span>
+                <span class="n">best</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">df</span><span class="o">.</span><span class="n">index</span><span class="p">[</span><span class="mi">0</span><span class="p">]]</span>
+                <span class="n">mod</span><span class="p">[</span><span class="s1">&#39;Model&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">m</span>
+                <span class="n">mod</span><span class="p">[</span><span class="s1">&#39;Order&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">o</span>
+                <span class="n">mod</span><span class="p">[</span><span class="s1">&#39;Scheme&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">best</span><span class="p">[</span><span class="s2">&quot;Scheme&quot;</span><span class="p">]</span>
+                <span class="n">mod</span><span class="p">[</span><span class="s1">&#39;Partitions&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">best</span><span class="p">[</span><span class="s2">&quot;Partitions&quot;</span><span class="p">]</span>
+
+                <span class="n">ret</span><span class="p">[</span><span class="n">_key</span><span class="p">]</span> <span class="o">=</span> <span class="n">mod</span>
+
+    <span class="k">return</span> <span class="n">ret</span></div>
+
+
+<div class="viewcode-block" id="simple_synthetic_dataframe"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.Util.simple_synthetic_dataframe">[docs]</a><span class="k">def</span> <span class="nf">simple_synthetic_dataframe</span><span class="p">(</span><span class="n">file</span><span class="p">,</span> <span class="n">tag</span><span class="p">,</span> <span class="n">measure</span><span class="p">,</span> <span class="n">sql</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+    <span class="sd">&#39;&#39;&#39;</span>
+<span class="sd">    Read experiments results from sqlite3 database in &#39;file&#39;, make a synthesis of the results</span>
+<span class="sd">    of the metric &#39;measure&#39; with the same &#39;tag&#39;, returning a Pandas DataFrame with the mean results.</span>
+
+<span class="sd">    :param file: sqlite3 database file name</span>
+<span class="sd">    :param tag: common tag of the experiments</span>
+<span class="sd">    :param measure: metric to synthetize</span>
+<span class="sd">    :return: Pandas DataFrame with the mean results</span>
+<span class="sd">    &#39;&#39;&#39;</span>
+    <span class="n">df</span> <span class="o">=</span> <span class="n">get_dataframe_from_bd</span><span class="p">(</span><span class="n">file</span><span class="p">,</span><span class="s2">&quot;tag = &#39;</span><span class="si">{}</span><span class="s2">&#39; and measure = &#39;</span><span class="si">{}</span><span class="s2">&#39; </span><span class="si">{}</span><span class="s2">&quot;</span>
+                              <span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">tag</span><span class="p">,</span> <span class="n">measure</span><span class="p">,</span>
+                                      <span class="s1">&#39;&#39;</span> <span class="k">if</span> <span class="n">sql</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="s1">&#39;and </span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">sql</span><span class="p">)))</span>
+    <span class="n">data</span> <span class="o">=</span> <span class="p">[]</span>
+
+    <span class="n">models</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">Model</span><span class="o">.</span><span class="n">unique</span><span class="p">()</span>
+    <span class="n">datasets</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">Dataset</span><span class="o">.</span><span class="n">unique</span><span class="p">()</span>
+    <span class="k">for</span> <span class="n">dataset</span> <span class="ow">in</span> <span class="n">datasets</span><span class="p">:</span>
+        <span class="k">for</span> <span class="n">model</span> <span class="ow">in</span> <span class="n">models</span><span class="p">:</span>
+            <span class="n">_filter</span> <span class="o">=</span> <span class="p">(</span><span class="n">df</span><span class="o">.</span><span class="n">Dataset</span> <span class="o">==</span> <span class="n">dataset</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">df</span><span class="o">.</span><span class="n">Model</span> <span class="o">==</span> <span class="n">model</span><span class="p">)</span>
+            <span class="n">avg</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nanmean</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="n">_filter</span><span class="p">]</span><span class="o">.</span><span class="n">Value</span><span class="p">)</span>
+            <span class="n">std</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nanstd</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="n">_filter</span><span class="p">]</span><span class="o">.</span><span class="n">Value</span><span class="p">)</span>
+            <span class="n">data</span><span class="o">.</span><span class="n">append</span><span class="p">([</span><span class="n">dataset</span><span class="p">,</span> <span class="n">model</span><span class="p">,</span> <span class="n">avg</span><span class="p">,</span> <span class="n">std</span><span class="p">])</span>
+
+    <span class="n">dat</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;Dataset&#39;</span><span class="p">,</span> <span class="s1">&#39;Model&#39;</span><span class="p">,</span> <span class="s1">&#39;AVG&#39;</span><span class="p">,</span> <span class="s1">&#39;STD&#39;</span><span class="p">])</span>
+    <span class="n">dat</span> <span class="o">=</span> <span class="n">dat</span><span class="o">.</span><span class="n">sort_values</span><span class="p">([</span><span class="s1">&#39;AVG&#39;</span><span class="p">,</span> <span class="s1">&#39;STD&#39;</span><span class="p">])</span>
+
+    <span class="n">best</span> <span class="o">=</span> <span class="p">[]</span>
+
+    <span class="k">for</span> <span class="n">dataset</span> <span class="ow">in</span> <span class="n">datasets</span><span class="p">:</span>
+        <span class="k">for</span> <span class="n">model</span> <span class="ow">in</span> <span class="n">models</span><span class="p">:</span>
+            <span class="n">ix</span> <span class="o">=</span> <span class="n">dat</span><span class="p">[(</span><span class="n">dat</span><span class="o">.</span><span class="n">Dataset</span> <span class="o">==</span> <span class="n">dataset</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">dat</span><span class="o">.</span><span class="n">Model</span> <span class="o">==</span> <span class="n">model</span><span class="p">)]</span><span class="o">.</span><span class="n">index</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+            <span class="n">best</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">ix</span><span class="p">)</span>
+
+    <span class="n">ret</span> <span class="o">=</span> <span class="n">dat</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">best</span><span class="p">]</span><span class="o">.</span><span class="n">sort_values</span><span class="p">([</span><span class="s1">&#39;AVG&#39;</span><span class="p">,</span> <span class="s1">&#39;STD&#39;</span><span class="p">])</span>
+    <span class="n">ret</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="s1">&#39;Dataset&#39;</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">ret</span></div>
+
+
+<div class="viewcode-block" id="analytic_tabular_dataframe"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.Util.analytic_tabular_dataframe">[docs]</a><span class="k">def</span> <span class="nf">analytic_tabular_dataframe</span><span class="p">(</span><span class="n">dataframe</span><span class="p">):</span>
+    <span class="n">experiments</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">dataframe</span><span class="o">.</span><span class="n">columns</span><span class="p">)</span> <span class="o">-</span> <span class="nb">len</span><span class="p">(</span><span class="n">base_dataframe_columns</span><span class="p">())</span> <span class="o">-</span> <span class="mi">1</span>
+    <span class="n">models</span> <span class="o">=</span> <span class="n">dataframe</span><span class="o">.</span><span class="n">Model</span><span class="o">.</span><span class="n">unique</span><span class="p">()</span>
+    <span class="n">orders</span> <span class="o">=</span> <span class="n">dataframe</span><span class="o">.</span><span class="n">Order</span><span class="o">.</span><span class="n">unique</span><span class="p">()</span>
+    <span class="n">schemes</span> <span class="o">=</span> <span class="n">dataframe</span><span class="o">.</span><span class="n">Scheme</span><span class="o">.</span><span class="n">unique</span><span class="p">()</span>
+    <span class="n">partitions</span> <span class="o">=</span> <span class="n">dataframe</span><span class="o">.</span><span class="n">Partitions</span><span class="o">.</span><span class="n">unique</span><span class="p">()</span>
+    <span class="n">steps</span> <span class="o">=</span> <span class="n">dataframe</span><span class="o">.</span><span class="n">Steps</span><span class="o">.</span><span class="n">unique</span><span class="p">()</span>
+    <span class="n">measures</span> <span class="o">=</span> <span class="n">dataframe</span><span class="o">.</span><span class="n">Measure</span><span class="o">.</span><span class="n">unique</span><span class="p">()</span>
+    <span class="n">data_columns</span> <span class="o">=</span> <span class="n">analytical_data_columns</span><span class="p">(</span><span class="n">experiments</span><span class="p">)</span>
+
+    <span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
+
+    <span class="k">for</span> <span class="n">m</span> <span class="ow">in</span> <span class="n">models</span><span class="p">:</span>
+        <span class="k">for</span> <span class="n">o</span> <span class="ow">in</span> <span class="n">orders</span><span class="p">:</span>
+            <span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="n">schemes</span><span class="p">:</span>
+                <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">partitions</span><span class="p">:</span>
+                    <span class="k">for</span> <span class="n">st</span> <span class="ow">in</span> <span class="n">steps</span><span class="p">:</span>
+                        <span class="k">for</span> <span class="n">ms</span> <span class="ow">in</span> <span class="n">measures</span><span class="p">:</span>
+                            <span class="n">df</span> <span class="o">=</span> <span class="n">dataframe</span><span class="p">[(</span><span class="n">dataframe</span><span class="o">.</span><span class="n">Model</span> <span class="o">==</span> <span class="n">m</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">dataframe</span><span class="o">.</span><span class="n">Order</span> <span class="o">==</span> <span class="n">o</span><span class="p">)</span>
+                                           <span class="o">&amp;</span> <span class="p">(</span><span class="n">dataframe</span><span class="o">.</span><span class="n">Scheme</span> <span class="o">==</span> <span class="n">s</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">dataframe</span><span class="o">.</span><span class="n">Partitions</span> <span class="o">==</span> <span class="n">p</span><span class="p">)</span>
+                                           <span class="o">&amp;</span> <span class="p">(</span><span class="n">dataframe</span><span class="o">.</span><span class="n">Steps</span> <span class="o">==</span> <span class="n">st</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">dataframe</span><span class="o">.</span><span class="n">Measure</span> <span class="o">==</span> <span class="n">ms</span><span class="p">)</span> <span class="p">]</span>
+
+                            <span class="k">if</span> <span class="ow">not</span> <span class="n">df</span><span class="o">.</span><span class="n">empty</span><span class="p">:</span>
+                                <span class="k">for</span> <span class="n">col</span> <span class="ow">in</span> <span class="n">data_columns</span><span class="p">:</span>
+                                    <span class="n">mod</span> <span class="o">=</span> <span class="p">[</span><span class="n">m</span><span class="p">,</span> <span class="n">o</span><span class="p">,</span> <span class="n">s</span><span class="p">,</span> <span class="n">p</span><span class="p">,</span> <span class="n">st</span><span class="p">,</span> <span class="n">ms</span><span class="p">,</span> <span class="n">df</span><span class="p">[</span><span class="n">col</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">[</span><span class="mi">0</span><span class="p">]]</span>
+                                    <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">mod</span><span class="p">)</span>
+
+    <span class="n">dat</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">ret</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="n">tabular_dataframe_columns</span><span class="p">())</span>
+    <span class="k">return</span> <span class="n">dat</span></div>
+
+
+<div class="viewcode-block" id="tabular_dataframe_columns"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.Util.tabular_dataframe_columns">[docs]</a><span class="k">def</span> <span class="nf">tabular_dataframe_columns</span><span class="p">():</span>
+        <span class="k">return</span> <span class="p">[</span><span class="s2">&quot;Model&quot;</span><span class="p">,</span> <span class="s2">&quot;Order&quot;</span><span class="p">,</span> <span class="s2">&quot;Scheme&quot;</span><span class="p">,</span> <span class="s2">&quot;Partitions&quot;</span><span class="p">,</span> <span class="s2">&quot;Steps&quot;</span><span class="p">,</span> <span class="s2">&quot;Measure&quot;</span><span class="p">,</span> <span class="s2">&quot;Value&quot;</span><span class="p">]</span></div>
+
+
+<div class="viewcode-block" id="base_dataframe_columns"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.Util.base_dataframe_columns">[docs]</a><span class="k">def</span> <span class="nf">base_dataframe_columns</span><span class="p">():</span>
+    <span class="k">return</span> <span class="p">[</span><span class="s2">&quot;Model&quot;</span><span class="p">,</span> <span class="s2">&quot;Order&quot;</span><span class="p">,</span> <span class="s2">&quot;Scheme&quot;</span><span class="p">,</span> <span class="s2">&quot;Partitions&quot;</span><span class="p">,</span> <span class="s2">&quot;Size&quot;</span><span class="p">,</span> <span class="s2">&quot;Steps&quot;</span><span class="p">,</span> <span class="s2">&quot;Method&quot;</span><span class="p">]</span></div>
+
+<div class="viewcode-block" id="point_dataframe_synthetic_columns"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.Util.point_dataframe_synthetic_columns">[docs]</a><span class="k">def</span> <span class="nf">point_dataframe_synthetic_columns</span><span class="p">():</span>
+    <span class="k">return</span> <span class="n">base_dataframe_columns</span><span class="p">()</span><span class="o">.</span><span class="n">extend</span><span class="p">([</span><span class="s2">&quot;RMSEAVG&quot;</span><span class="p">,</span> <span class="s2">&quot;RMSESTD&quot;</span><span class="p">,</span>
+            <span class="s2">&quot;SMAPEAVG&quot;</span><span class="p">,</span> <span class="s2">&quot;SMAPESTD&quot;</span><span class="p">,</span> <span class="s2">&quot;UAVG&quot;</span><span class="p">,</span><span class="s2">&quot;USTD&quot;</span><span class="p">,</span> <span class="s2">&quot;TIMEAVG&quot;</span><span class="p">,</span> <span class="s2">&quot;TIMESTD&quot;</span><span class="p">])</span></div>
+
+
+<div class="viewcode-block" id="point_dataframe_analytic_columns"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.Util.point_dataframe_analytic_columns">[docs]</a><span class="k">def</span> <span class="nf">point_dataframe_analytic_columns</span><span class="p">(</span><span class="n">experiments</span><span class="p">):</span>
+    <span class="n">columns</span> <span class="o">=</span> <span class="p">[</span><span class="nb">str</span><span class="p">(</span><span class="n">k</span><span class="p">)</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">experiments</span><span class="p">)]</span>
+    <span class="n">columns</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="s2">&quot;Model&quot;</span><span class="p">)</span>
+    <span class="n">columns</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="s2">&quot;Order&quot;</span><span class="p">)</span>
+    <span class="n">columns</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="s2">&quot;Scheme&quot;</span><span class="p">)</span>
+    <span class="n">columns</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="s2">&quot;Partitions&quot;</span><span class="p">)</span>
+    <span class="n">columns</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="s2">&quot;Size&quot;</span><span class="p">)</span>
+    <span class="n">columns</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="s2">&quot;Steps&quot;</span><span class="p">)</span>
+    <span class="n">columns</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="mi">6</span><span class="p">,</span> <span class="s2">&quot;Method&quot;</span><span class="p">)</span>
+    <span class="n">columns</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="mi">7</span><span class="p">,</span> <span class="s2">&quot;Measure&quot;</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">columns</span></div>
+
+
+<div class="viewcode-block" id="save_dataframe_point"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.Util.save_dataframe_point">[docs]</a><span class="k">def</span> <span class="nf">save_dataframe_point</span><span class="p">(</span><span class="n">experiments</span><span class="p">,</span> <span class="n">file</span><span class="p">,</span> <span class="n">objs</span><span class="p">,</span> <span class="n">rmse</span><span class="p">,</span> <span class="n">save</span><span class="p">,</span> <span class="n">synthetic</span><span class="p">,</span> <span class="n">smape</span><span class="p">,</span> <span class="n">times</span><span class="p">,</span> <span class="n">u</span><span class="p">,</span> <span class="n">steps</span><span class="p">,</span> <span class="n">method</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Create a dataframe to store the benchmark results</span>
+
+<span class="sd">    :param experiments: dictionary with the execution results</span>
+<span class="sd">    :param file: </span>
+<span class="sd">    :param objs: </span>
+<span class="sd">    :param rmse: </span>
+<span class="sd">    :param save: </span>
+<span class="sd">    :param synthetic: </span>
+<span class="sd">    :param smape: </span>
+<span class="sd">    :param times: </span>
+<span class="sd">    :param u: </span>
+<span class="sd">    :return: </span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
+
+    <span class="k">if</span> <span class="n">synthetic</span><span class="p">:</span>
+
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">objs</span><span class="o">.</span><span class="n">keys</span><span class="p">()):</span>
+            <span class="k">try</span><span class="p">:</span>
+                <span class="n">mod</span> <span class="o">=</span> <span class="p">[]</span>
+                <span class="n">mfts</span> <span class="o">=</span> <span class="n">objs</span><span class="p">[</span><span class="n">k</span><span class="p">]</span>
+                <span class="n">mod</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">mfts</span><span class="o">.</span><span class="n">shortname</span><span class="p">)</span>
+                <span class="n">mod</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">mfts</span><span class="o">.</span><span class="n">order</span><span class="p">)</span>
+                <span class="k">if</span> <span class="ow">not</span> <span class="n">mfts</span><span class="o">.</span><span class="n">benchmark_only</span><span class="p">:</span>
+                    <span class="n">mod</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">mfts</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">name</span><span class="p">)</span>
+                    <span class="n">mod</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">mfts</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">partitions</span><span class="p">)</span>
+                    <span class="n">mod</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">mfts</span><span class="p">))</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">mod</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s1">&#39;-&#39;</span><span class="p">)</span>
+                    <span class="n">mod</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s1">&#39;-&#39;</span><span class="p">)</span>
+                    <span class="n">mod</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s1">&#39;-&#39;</span><span class="p">)</span>
+                <span class="n">mod</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">steps</span><span class="p">[</span><span class="n">k</span><span class="p">])</span>
+                <span class="n">mod</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">method</span><span class="p">[</span><span class="n">k</span><span class="p">])</span>
+                <span class="n">mod</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">nanmean</span><span class="p">(</span><span class="n">rmse</span><span class="p">[</span><span class="n">k</span><span class="p">]),</span> <span class="mi">2</span><span class="p">))</span>
+                <span class="n">mod</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">nanstd</span><span class="p">(</span><span class="n">rmse</span><span class="p">[</span><span class="n">k</span><span class="p">]),</span> <span class="mi">2</span><span class="p">))</span>
+                <span class="n">mod</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">nanmean</span><span class="p">(</span><span class="n">smape</span><span class="p">[</span><span class="n">k</span><span class="p">]),</span> <span class="mi">2</span><span class="p">))</span>
+                <span class="n">mod</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">nanstd</span><span class="p">(</span><span class="n">smape</span><span class="p">[</span><span class="n">k</span><span class="p">]),</span> <span class="mi">2</span><span class="p">))</span>
+                <span class="n">mod</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">nanmean</span><span class="p">(</span><span class="n">u</span><span class="p">[</span><span class="n">k</span><span class="p">]),</span> <span class="mi">2</span><span class="p">))</span>
+                <span class="n">mod</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">nanstd</span><span class="p">(</span><span class="n">u</span><span class="p">[</span><span class="n">k</span><span class="p">]),</span> <span class="mi">2</span><span class="p">))</span>
+                <span class="n">mod</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">nanmean</span><span class="p">(</span><span class="n">times</span><span class="p">[</span><span class="n">k</span><span class="p">]),</span> <span class="mi">4</span><span class="p">))</span>
+                <span class="n">mod</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">nanstd</span><span class="p">(</span><span class="n">times</span><span class="p">[</span><span class="n">k</span><span class="p">]),</span> <span class="mi">4</span><span class="p">))</span>
+                <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">mod</span><span class="p">)</span>
+            <span class="k">except</span> <span class="ne">Exception</span> <span class="k">as</span> <span class="n">ex</span><span class="p">:</span>
+                <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Erro ao salvar &quot;</span><span class="p">,</span> <span class="n">k</span><span class="p">)</span>
+                <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Exceção &quot;</span><span class="p">,</span> <span class="n">ex</span><span class="p">)</span>
+
+        <span class="n">columns</span> <span class="o">=</span> <span class="n">point_dataframe_synthetic_columns</span><span class="p">()</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">objs</span><span class="o">.</span><span class="n">keys</span><span class="p">()):</span>
+            <span class="k">try</span><span class="p">:</span>
+                <span class="n">mfts</span> <span class="o">=</span> <span class="n">objs</span><span class="p">[</span><span class="n">k</span><span class="p">]</span>
+                <span class="n">n</span> <span class="o">=</span> <span class="n">mfts</span><span class="o">.</span><span class="n">shortname</span>
+                <span class="n">o</span> <span class="o">=</span> <span class="n">mfts</span><span class="o">.</span><span class="n">order</span>
+                <span class="k">if</span> <span class="ow">not</span> <span class="n">mfts</span><span class="o">.</span><span class="n">benchmark_only</span><span class="p">:</span>
+                    <span class="n">s</span> <span class="o">=</span> <span class="n">mfts</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">name</span>
+                    <span class="n">p</span> <span class="o">=</span> <span class="n">mfts</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">partitions</span>
+                    <span class="n">l</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">mfts</span><span class="p">)</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">s</span> <span class="o">=</span> <span class="s1">&#39;-&#39;</span>
+                    <span class="n">p</span> <span class="o">=</span> <span class="s1">&#39;-&#39;</span>
+                    <span class="n">l</span> <span class="o">=</span> <span class="s1">&#39;-&#39;</span>
+                <span class="n">st</span> <span class="o">=</span> <span class="n">steps</span><span class="p">[</span><span class="n">k</span><span class="p">]</span>
+                <span class="n">mt</span> <span class="o">=</span> <span class="n">method</span><span class="p">[</span><span class="n">k</span><span class="p">]</span>
+                <span class="n">tmp</span> <span class="o">=</span> <span class="p">[</span><span class="n">n</span><span class="p">,</span> <span class="n">o</span><span class="p">,</span> <span class="n">s</span><span class="p">,</span> <span class="n">p</span><span class="p">,</span> <span class="n">l</span><span class="p">,</span> <span class="n">st</span><span class="p">,</span> <span class="n">mt</span><span class="p">,</span> <span class="s1">&#39;RMSE&#39;</span><span class="p">]</span>
+                <span class="n">tmp</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">rmse</span><span class="p">[</span><span class="n">k</span><span class="p">])</span>
+                <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">deepcopy</span><span class="p">(</span><span class="n">tmp</span><span class="p">))</span>
+                <span class="n">tmp</span> <span class="o">=</span> <span class="p">[</span><span class="n">n</span><span class="p">,</span> <span class="n">o</span><span class="p">,</span> <span class="n">s</span><span class="p">,</span> <span class="n">p</span><span class="p">,</span> <span class="n">l</span><span class="p">,</span> <span class="n">st</span><span class="p">,</span> <span class="n">mt</span><span class="p">,</span> <span class="s1">&#39;SMAPE&#39;</span><span class="p">]</span>
+                <span class="n">tmp</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">smape</span><span class="p">[</span><span class="n">k</span><span class="p">])</span>
+                <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">deepcopy</span><span class="p">(</span><span class="n">tmp</span><span class="p">))</span>
+                <span class="n">tmp</span> <span class="o">=</span> <span class="p">[</span><span class="n">n</span><span class="p">,</span> <span class="n">o</span><span class="p">,</span> <span class="n">s</span><span class="p">,</span> <span class="n">p</span><span class="p">,</span> <span class="n">l</span><span class="p">,</span> <span class="n">st</span><span class="p">,</span> <span class="n">mt</span><span class="p">,</span> <span class="s1">&#39;U&#39;</span><span class="p">]</span>
+                <span class="n">tmp</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">u</span><span class="p">[</span><span class="n">k</span><span class="p">])</span>
+                <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">deepcopy</span><span class="p">(</span><span class="n">tmp</span><span class="p">))</span>
+                <span class="n">tmp</span> <span class="o">=</span> <span class="p">[</span><span class="n">n</span><span class="p">,</span> <span class="n">o</span><span class="p">,</span> <span class="n">s</span><span class="p">,</span> <span class="n">p</span><span class="p">,</span> <span class="n">l</span><span class="p">,</span> <span class="n">st</span><span class="p">,</span> <span class="n">mt</span><span class="p">,</span> <span class="s1">&#39;TIME&#39;</span><span class="p">]</span>
+                <span class="n">tmp</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">times</span><span class="p">[</span><span class="n">k</span><span class="p">])</span>
+                <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">deepcopy</span><span class="p">(</span><span class="n">tmp</span><span class="p">))</span>
+            <span class="k">except</span> <span class="ne">Exception</span> <span class="k">as</span> <span class="n">ex</span><span class="p">:</span>
+                <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Erro ao salvar &quot;</span><span class="p">,</span> <span class="n">k</span><span class="p">)</span>
+                <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Exceção &quot;</span><span class="p">,</span> <span class="n">ex</span><span class="p">)</span>
+        <span class="n">columns</span> <span class="o">=</span> <span class="n">point_dataframe_analytic_columns</span><span class="p">(</span><span class="n">experiments</span><span class="p">)</span>
+    <span class="k">try</span><span class="p">:</span>
+        <span class="n">dat</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">ret</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="n">columns</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">save</span><span class="p">:</span> <span class="n">dat</span><span class="o">.</span><span class="n">to_csv</span><span class="p">(</span><span class="n">Util</span><span class="o">.</span><span class="n">uniquefilename</span><span class="p">(</span><span class="n">file</span><span class="p">),</span> <span class="n">sep</span><span class="o">=</span><span class="s2">&quot;;&quot;</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">dat</span>
+    <span class="k">except</span> <span class="ne">Exception</span> <span class="k">as</span> <span class="n">ex</span><span class="p">:</span>
+        <span class="nb">print</span><span class="p">(</span><span class="n">ex</span><span class="p">)</span>
+        <span class="nb">print</span><span class="p">(</span><span class="n">experiments</span><span class="p">)</span>
+        <span class="nb">print</span><span class="p">(</span><span class="n">columns</span><span class="p">)</span>
+        <span class="nb">print</span><span class="p">(</span><span class="n">ret</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="cast_dataframe_to_synthetic"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.Util.cast_dataframe_to_synthetic">[docs]</a><span class="k">def</span> <span class="nf">cast_dataframe_to_synthetic</span><span class="p">(</span><span class="n">infile</span><span class="p">,</span> <span class="n">outfile</span><span class="p">,</span> <span class="n">experiments</span><span class="p">,</span> <span class="nb">type</span><span class="p">):</span>
+    <span class="k">if</span> <span class="nb">type</span> <span class="o">==</span> <span class="s1">&#39;point&#39;</span><span class="p">:</span>
+        <span class="n">analytic_columns</span> <span class="o">=</span> <span class="n">point_dataframe_analytic_columns</span>
+        <span class="n">synthetic_columns</span> <span class="o">=</span> <span class="n">point_dataframe_synthetic_columns</span>
+        <span class="n">synthetize_measures</span> <span class="o">=</span> <span class="n">cast_dataframe_to_synthetic_point</span>
+    <span class="k">elif</span> <span class="nb">type</span> <span class="o">==</span> <span class="s1">&#39;interval&#39;</span><span class="p">:</span>
+        <span class="n">analytic_columns</span> <span class="o">=</span> <span class="n">interval_dataframe_analytic_columns</span>
+        <span class="n">synthetic_columns</span> <span class="o">=</span> <span class="n">interval_dataframe_synthetic_columns</span>
+        <span class="n">synthetize_measures</span> <span class="o">=</span> <span class="n">cast_dataframe_to_synthetic_interval</span>
+    <span class="k">elif</span> <span class="nb">type</span> <span class="o">==</span> <span class="s1">&#39;distribution&#39;</span><span class="p">:</span>
+        <span class="n">analytic_columns</span> <span class="o">=</span> <span class="n">probabilistic_dataframe_analytic_columns</span>
+        <span class="n">synthetic_columns</span> <span class="o">=</span> <span class="n">probabilistic_dataframe_synthetic_columns</span>
+        <span class="n">synthetize_measures</span> <span class="o">=</span> <span class="n">cast_dataframe_to_synthetic_probabilistic</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Type parameter has an unknown value!&quot;</span><span class="p">)</span>
+
+    <span class="n">columns</span> <span class="o">=</span> <span class="n">analytic_columns</span><span class="p">(</span><span class="n">experiments</span><span class="p">)</span>
+    <span class="n">dat</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">infile</span><span class="p">,</span> <span class="n">sep</span><span class="o">=</span><span class="s2">&quot;;&quot;</span><span class="p">,</span> <span class="n">usecols</span><span class="o">=</span><span class="n">columns</span><span class="p">)</span>
+    <span class="n">models</span> <span class="o">=</span> <span class="n">dat</span><span class="o">.</span><span class="n">Model</span><span class="o">.</span><span class="n">unique</span><span class="p">()</span>
+    <span class="n">orders</span> <span class="o">=</span> <span class="n">dat</span><span class="o">.</span><span class="n">Order</span><span class="o">.</span><span class="n">unique</span><span class="p">()</span>
+    <span class="n">schemes</span> <span class="o">=</span> <span class="n">dat</span><span class="o">.</span><span class="n">Scheme</span><span class="o">.</span><span class="n">unique</span><span class="p">()</span>
+    <span class="n">partitions</span> <span class="o">=</span> <span class="n">dat</span><span class="o">.</span><span class="n">Partitions</span><span class="o">.</span><span class="n">unique</span><span class="p">()</span>
+    <span class="n">steps</span> <span class="o">=</span> <span class="n">dat</span><span class="o">.</span><span class="n">Steps</span><span class="o">.</span><span class="n">unique</span><span class="p">()</span>
+    <span class="n">methods</span> <span class="o">=</span> <span class="n">dat</span><span class="o">.</span><span class="n">Method</span><span class="o">.</span><span class="n">unique</span><span class="p">()</span>
+
+    <span class="n">data_columns</span> <span class="o">=</span> <span class="n">analytical_data_columns</span><span class="p">(</span><span class="n">experiments</span><span class="p">)</span>
+
+    <span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
+
+    <span class="k">for</span> <span class="n">m</span> <span class="ow">in</span> <span class="n">models</span><span class="p">:</span>
+        <span class="k">for</span> <span class="n">o</span> <span class="ow">in</span> <span class="n">orders</span><span class="p">:</span>
+            <span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="n">schemes</span><span class="p">:</span>
+                <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">partitions</span><span class="p">:</span>
+                    <span class="k">for</span> <span class="n">st</span> <span class="ow">in</span> <span class="n">steps</span><span class="p">:</span>
+                        <span class="k">for</span> <span class="n">mt</span> <span class="ow">in</span> <span class="n">methods</span><span class="p">:</span>
+                            <span class="n">df</span> <span class="o">=</span> <span class="n">dat</span><span class="p">[(</span><span class="n">dat</span><span class="o">.</span><span class="n">Model</span> <span class="o">==</span> <span class="n">m</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">dat</span><span class="o">.</span><span class="n">Order</span> <span class="o">==</span> <span class="n">o</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">dat</span><span class="o">.</span><span class="n">Scheme</span> <span class="o">==</span> <span class="n">s</span><span class="p">)</span> <span class="o">&amp;</span>
+                                     <span class="p">(</span><span class="n">dat</span><span class="o">.</span><span class="n">Partitions</span> <span class="o">==</span> <span class="n">p</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">dat</span><span class="o">.</span><span class="n">Steps</span> <span class="o">==</span> <span class="n">st</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">dat</span><span class="o">.</span><span class="n">Method</span> <span class="o">==</span> <span class="n">mt</span><span class="p">)]</span>
+                            <span class="k">if</span> <span class="ow">not</span> <span class="n">df</span><span class="o">.</span><span class="n">empty</span><span class="p">:</span>
+                                <span class="n">mod</span> <span class="o">=</span> <span class="n">synthetize_measures</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">data_columns</span><span class="p">)</span>
+                                <span class="n">mod</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">m</span><span class="p">)</span>
+                                <span class="n">mod</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">o</span><span class="p">)</span>
+                                <span class="n">mod</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="n">s</span><span class="p">)</span>
+                                <span class="n">mod</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="n">p</span><span class="p">)</span>
+                                <span class="n">mod</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="n">df</span><span class="o">.</span><span class="n">iat</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">5</span><span class="p">])</span>
+                                <span class="n">mod</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="n">st</span><span class="p">)</span>
+                                <span class="n">mod</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="mi">6</span><span class="p">,</span> <span class="n">mt</span><span class="p">)</span>
+                                <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">mod</span><span class="p">)</span>
+
+    <span class="n">dat</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">ret</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="n">synthetic_columns</span><span class="p">())</span>
+    <span class="n">dat</span><span class="o">.</span><span class="n">to_csv</span><span class="p">(</span><span class="n">outfile</span><span class="p">,</span> <span class="n">sep</span><span class="o">=</span><span class="s2">&quot;;&quot;</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="cast_dataframe_to_synthetic_point"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.Util.cast_dataframe_to_synthetic_point">[docs]</a><span class="k">def</span> <span class="nf">cast_dataframe_to_synthetic_point</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">data_columns</span><span class="p">):</span>
+    <span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="n">rmse</span> <span class="o">=</span> <span class="n">extract_measure</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="s1">&#39;RMSE&#39;</span><span class="p">,</span> <span class="n">data_columns</span><span class="p">)</span>
+    <span class="n">smape</span> <span class="o">=</span> <span class="n">extract_measure</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="s1">&#39;SMAPE&#39;</span><span class="p">,</span> <span class="n">data_columns</span><span class="p">)</span>
+    <span class="n">u</span> <span class="o">=</span> <span class="n">extract_measure</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="s1">&#39;U&#39;</span><span class="p">,</span> <span class="n">data_columns</span><span class="p">)</span>
+    <span class="n">times</span> <span class="o">=</span> <span class="n">extract_measure</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="s1">&#39;TIME&#39;</span><span class="p">,</span> <span class="n">data_columns</span><span class="p">)</span>
+    <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">nanmean</span><span class="p">(</span><span class="n">rmse</span><span class="p">),</span> <span class="mi">2</span><span class="p">))</span>
+    <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">nanstd</span><span class="p">(</span><span class="n">rmse</span><span class="p">),</span> <span class="mi">2</span><span class="p">))</span>
+    <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">nanmean</span><span class="p">(</span><span class="n">smape</span><span class="p">),</span> <span class="mi">2</span><span class="p">))</span>
+    <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">nanstd</span><span class="p">(</span><span class="n">smape</span><span class="p">),</span> <span class="mi">2</span><span class="p">))</span>
+    <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">nanmean</span><span class="p">(</span><span class="n">u</span><span class="p">),</span> <span class="mi">2</span><span class="p">))</span>
+    <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">nanstd</span><span class="p">(</span><span class="n">u</span><span class="p">),</span> <span class="mi">2</span><span class="p">))</span>
+    <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">nanmean</span><span class="p">(</span><span class="n">times</span><span class="p">),</span> <span class="mi">4</span><span class="p">))</span>
+    <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">nanstd</span><span class="p">(</span><span class="n">times</span><span class="p">),</span> <span class="mi">4</span><span class="p">))</span>
+
+    <span class="k">return</span> <span class="n">ret</span></div>
+
+
+<div class="viewcode-block" id="analytical_data_columns"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.Util.analytical_data_columns">[docs]</a><span class="k">def</span> <span class="nf">analytical_data_columns</span><span class="p">(</span><span class="n">experiments</span><span class="p">):</span>
+    <span class="n">data_columns</span> <span class="o">=</span> <span class="p">[</span><span class="nb">str</span><span class="p">(</span><span class="n">k</span><span class="p">)</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">experiments</span><span class="p">)]</span>
+    <span class="k">return</span> <span class="n">data_columns</span></div>
+
+
+<div class="viewcode-block" id="scale_params"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.Util.scale_params">[docs]</a><span class="k">def</span> <span class="nf">scale_params</span><span class="p">(</span><span class="n">data</span><span class="p">):</span>
+    <span class="n">vmin</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nanmin</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+    <span class="n">vlen</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nanmax</span><span class="p">(</span><span class="n">data</span><span class="p">)</span> <span class="o">-</span> <span class="n">vmin</span>
+    <span class="k">return</span> <span class="p">(</span><span class="n">vmin</span><span class="p">,</span> <span class="n">vlen</span><span class="p">)</span></div>
+
+
+
+<div class="viewcode-block" id="scale"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.Util.scale">[docs]</a><span class="k">def</span> <span class="nf">scale</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">params</span><span class="p">):</span>
+    <span class="n">ndata</span> <span class="o">=</span> <span class="p">[(</span><span class="n">k</span><span class="o">-</span><span class="n">params</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span><span class="o">/</span><span class="n">params</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">data</span><span class="p">]</span>
+    <span class="k">return</span> <span class="n">ndata</span></div>
+
+
+<div class="viewcode-block" id="stats"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.Util.stats">[docs]</a><span class="k">def</span> <span class="nf">stats</span><span class="p">(</span><span class="n">measure</span><span class="p">,</span> <span class="n">data</span><span class="p">):</span>
+    <span class="nb">print</span><span class="p">(</span><span class="n">measure</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">nanmean</span><span class="p">(</span><span class="n">data</span><span class="p">),</span> <span class="n">np</span><span class="o">.</span><span class="n">nanstd</span><span class="p">(</span><span class="n">data</span><span class="p">))</span></div>
+
+
+<div class="viewcode-block" id="unified_scaled_point"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.Util.unified_scaled_point">[docs]</a><span class="k">def</span> <span class="nf">unified_scaled_point</span><span class="p">(</span><span class="n">experiments</span><span class="p">,</span> <span class="n">tam</span><span class="p">,</span> <span class="n">save</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">file</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+                         <span class="n">sort_columns</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;UAVG&#39;</span><span class="p">,</span> <span class="s1">&#39;RMSEAVG&#39;</span><span class="p">,</span> <span class="s1">&#39;USTD&#39;</span><span class="p">,</span> <span class="s1">&#39;RMSESTD&#39;</span><span class="p">],</span>
+                         <span class="n">sort_ascend</span><span class="o">=</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span><span class="n">save_best</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+                         <span class="n">ignore</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">replace</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+
+    <span class="n">fig</span><span class="p">,</span> <span class="n">axes</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">nrows</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">ncols</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="n">tam</span><span class="p">)</span>
+
+    <span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">&#39;RMSE&#39;</span><span class="p">)</span>
+    <span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">&#39;SMAPE&#39;</span><span class="p">)</span>
+    <span class="n">axes</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">&#39;U Statistic&#39;</span><span class="p">)</span>
+
+    <span class="n">models</span> <span class="o">=</span> <span class="p">{}</span>
+
+    <span class="k">for</span> <span class="n">experiment</span> <span class="ow">in</span> <span class="n">experiments</span><span class="p">:</span>
+
+        <span class="n">mdl</span> <span class="o">=</span> <span class="p">{}</span>
+
+        <span class="n">dat_syn</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">experiment</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">sep</span><span class="o">=</span><span class="s2">&quot;;&quot;</span><span class="p">,</span> <span class="n">usecols</span><span class="o">=</span><span class="n">point_dataframe_synthetic_columns</span><span class="p">())</span>
+
+        <span class="n">bests</span> <span class="o">=</span> <span class="n">find_best</span><span class="p">(</span><span class="n">dat_syn</span><span class="p">,</span> <span class="n">sort_columns</span><span class="p">,</span> <span class="n">sort_ascend</span><span class="p">)</span>
+
+        <span class="n">dat_ana</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">experiment</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">sep</span><span class="o">=</span><span class="s2">&quot;;&quot;</span><span class="p">,</span> <span class="n">usecols</span><span class="o">=</span><span class="n">point_dataframe_analytic_columns</span><span class="p">(</span><span class="n">experiment</span><span class="p">[</span><span class="mi">2</span><span class="p">]))</span>
+
+        <span class="n">rmse</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="n">smape</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="n">u</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="n">times</span> <span class="o">=</span> <span class="p">[]</span>
+
+        <span class="n">data_columns</span> <span class="o">=</span> <span class="n">analytical_data_columns</span><span class="p">(</span><span class="n">experiment</span><span class="p">[</span><span class="mi">2</span><span class="p">])</span>
+
+        <span class="k">for</span> <span class="n">b</span> <span class="ow">in</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">bests</span><span class="o">.</span><span class="n">keys</span><span class="p">()):</span>
+            <span class="k">if</span> <span class="n">check_ignore_list</span><span class="p">(</span><span class="n">b</span><span class="p">,</span> <span class="n">ignore</span><span class="p">):</span>
+                <span class="k">continue</span>
+
+            <span class="k">if</span> <span class="n">b</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">models</span><span class="p">:</span>
+                <span class="n">models</span><span class="p">[</span><span class="n">b</span><span class="p">]</span> <span class="o">=</span> <span class="p">{}</span>
+                <span class="n">models</span><span class="p">[</span><span class="n">b</span><span class="p">][</span><span class="s1">&#39;rmse&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
+                <span class="n">models</span><span class="p">[</span><span class="n">b</span><span class="p">][</span><span class="s1">&#39;smape&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
+                <span class="n">models</span><span class="p">[</span><span class="n">b</span><span class="p">][</span><span class="s1">&#39;u&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
+                <span class="n">models</span><span class="p">[</span><span class="n">b</span><span class="p">][</span><span class="s1">&#39;times&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
+
+            <span class="k">if</span> <span class="n">b</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">mdl</span><span class="p">:</span>
+                <span class="n">mdl</span><span class="p">[</span><span class="n">b</span><span class="p">]</span> <span class="o">=</span> <span class="p">{}</span>
+                <span class="n">mdl</span><span class="p">[</span><span class="n">b</span><span class="p">][</span><span class="s1">&#39;rmse&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
+                <span class="n">mdl</span><span class="p">[</span><span class="n">b</span><span class="p">][</span><span class="s1">&#39;smape&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
+                <span class="n">mdl</span><span class="p">[</span><span class="n">b</span><span class="p">][</span><span class="s1">&#39;u&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
+                <span class="n">mdl</span><span class="p">[</span><span class="n">b</span><span class="p">][</span><span class="s1">&#39;times&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
+
+            <span class="n">best</span> <span class="o">=</span> <span class="n">bests</span><span class="p">[</span><span class="n">b</span><span class="p">]</span>
+            <span class="n">tmp</span> <span class="o">=</span> <span class="n">dat_ana</span><span class="p">[(</span><span class="n">dat_ana</span><span class="o">.</span><span class="n">Model</span> <span class="o">==</span> <span class="n">best</span><span class="p">[</span><span class="s2">&quot;Model&quot;</span><span class="p">])</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">dat_ana</span><span class="o">.</span><span class="n">Order</span> <span class="o">==</span> <span class="n">best</span><span class="p">[</span><span class="s2">&quot;Order&quot;</span><span class="p">])</span>
+                    <span class="o">&amp;</span> <span class="p">(</span><span class="n">dat_ana</span><span class="o">.</span><span class="n">Scheme</span> <span class="o">==</span> <span class="n">best</span><span class="p">[</span><span class="s2">&quot;Scheme&quot;</span><span class="p">])</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">dat_ana</span><span class="o">.</span><span class="n">Partitions</span> <span class="o">==</span> <span class="n">best</span><span class="p">[</span><span class="s2">&quot;Partitions&quot;</span><span class="p">])]</span>
+            <span class="n">tmpl</span> <span class="o">=</span> <span class="n">extract_measure</span><span class="p">(</span><span class="n">tmp</span><span class="p">,</span><span class="s1">&#39;RMSE&#39;</span><span class="p">,</span><span class="n">data_columns</span><span class="p">)</span>
+            <span class="n">mdl</span><span class="p">[</span><span class="n">b</span><span class="p">][</span><span class="s1">&#39;rmse&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span> <span class="n">tmpl</span> <span class="p">)</span>
+            <span class="n">rmse</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span> <span class="n">tmpl</span> <span class="p">)</span>
+            <span class="n">tmpl</span> <span class="o">=</span> <span class="n">extract_measure</span><span class="p">(</span><span class="n">tmp</span><span class="p">,</span> <span class="s1">&#39;SMAPE&#39;</span><span class="p">,</span> <span class="n">data_columns</span><span class="p">)</span>
+            <span class="n">mdl</span><span class="p">[</span><span class="n">b</span><span class="p">][</span><span class="s1">&#39;smape&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">tmpl</span><span class="p">)</span>
+            <span class="n">smape</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">tmpl</span><span class="p">)</span>
+            <span class="n">tmpl</span> <span class="o">=</span> <span class="n">extract_measure</span><span class="p">(</span><span class="n">tmp</span><span class="p">,</span> <span class="s1">&#39;U&#39;</span><span class="p">,</span> <span class="n">data_columns</span><span class="p">)</span>
+            <span class="n">mdl</span><span class="p">[</span><span class="n">b</span><span class="p">][</span><span class="s1">&#39;u&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">tmpl</span><span class="p">)</span>
+            <span class="n">u</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">tmpl</span><span class="p">)</span>
+            <span class="n">tmpl</span> <span class="o">=</span> <span class="n">extract_measure</span><span class="p">(</span><span class="n">tmp</span><span class="p">,</span> <span class="s1">&#39;TIME&#39;</span><span class="p">,</span> <span class="n">data_columns</span><span class="p">)</span>
+            <span class="n">mdl</span><span class="p">[</span><span class="n">b</span><span class="p">][</span><span class="s1">&#39;times&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">tmpl</span><span class="p">)</span>
+            <span class="n">times</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">tmpl</span><span class="p">)</span>
+
+            <span class="n">models</span><span class="p">[</span><span class="n">b</span><span class="p">][</span><span class="s1">&#39;label&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">check_replace_list</span><span class="p">(</span><span class="n">best</span><span class="p">[</span><span class="s2">&quot;Model&quot;</span><span class="p">]</span> <span class="o">+</span> <span class="s2">&quot; &quot;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">best</span><span class="p">[</span><span class="s2">&quot;Order&quot;</span><span class="p">]),</span> <span class="n">replace</span><span class="p">)</span>
+
+        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;GLOBAL&quot;</span><span class="p">)</span>
+        <span class="n">rmse_param</span> <span class="o">=</span> <span class="n">scale_params</span><span class="p">(</span><span class="n">rmse</span><span class="p">)</span>
+        <span class="n">stats</span><span class="p">(</span><span class="s2">&quot;rmse&quot;</span><span class="p">,</span> <span class="n">rmse</span><span class="p">)</span>
+        <span class="n">smape_param</span> <span class="o">=</span> <span class="n">scale_params</span><span class="p">(</span><span class="n">smape</span><span class="p">)</span>
+        <span class="n">stats</span><span class="p">(</span><span class="s2">&quot;smape&quot;</span><span class="p">,</span> <span class="n">smape</span><span class="p">)</span>
+        <span class="n">u_param</span> <span class="o">=</span> <span class="n">scale_params</span><span class="p">(</span><span class="n">u</span><span class="p">)</span>
+        <span class="n">stats</span><span class="p">(</span><span class="s2">&quot;u&quot;</span><span class="p">,</span> <span class="n">u</span><span class="p">)</span>
+        <span class="n">times_param</span> <span class="o">=</span> <span class="n">scale_params</span><span class="p">(</span><span class="n">times</span><span class="p">)</span>
+
+        <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">models</span><span class="o">.</span><span class="n">keys</span><span class="p">()):</span>
+            <span class="n">models</span><span class="p">[</span><span class="n">key</span><span class="p">][</span><span class="s1">&#39;rmse&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span> <span class="n">scale</span><span class="p">(</span><span class="n">mdl</span><span class="p">[</span><span class="n">key</span><span class="p">][</span><span class="s1">&#39;rmse&#39;</span><span class="p">],</span> <span class="n">rmse_param</span><span class="p">)</span> <span class="p">)</span>
+            <span class="n">models</span><span class="p">[</span><span class="n">key</span><span class="p">][</span><span class="s1">&#39;smape&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span> <span class="n">scale</span><span class="p">(</span><span class="n">mdl</span><span class="p">[</span><span class="n">key</span><span class="p">][</span><span class="s1">&#39;smape&#39;</span><span class="p">],</span> <span class="n">smape_param</span><span class="p">)</span> <span class="p">)</span>
+            <span class="n">models</span><span class="p">[</span><span class="n">key</span><span class="p">][</span><span class="s1">&#39;u&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span> <span class="n">scale</span><span class="p">(</span><span class="n">mdl</span><span class="p">[</span><span class="n">key</span><span class="p">][</span><span class="s1">&#39;u&#39;</span><span class="p">],</span> <span class="n">u_param</span><span class="p">)</span> <span class="p">)</span>
+            <span class="n">models</span><span class="p">[</span><span class="n">key</span><span class="p">][</span><span class="s1">&#39;times&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span> <span class="n">scale</span><span class="p">(</span><span class="n">mdl</span><span class="p">[</span><span class="n">key</span><span class="p">][</span><span class="s1">&#39;times&#39;</span><span class="p">],</span> <span class="n">times_param</span><span class="p">)</span> <span class="p">)</span>
+
+    <span class="n">rmse</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="n">smape</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="n">u</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="n">times</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="n">labels</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">models</span><span class="o">.</span><span class="n">keys</span><span class="p">()):</span>
+        <span class="nb">print</span><span class="p">(</span><span class="n">key</span><span class="p">)</span>
+        <span class="n">rmse</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">models</span><span class="p">[</span><span class="n">key</span><span class="p">][</span><span class="s1">&#39;rmse&#39;</span><span class="p">])</span>
+        <span class="n">stats</span><span class="p">(</span><span class="s2">&quot;rmse&quot;</span><span class="p">,</span> <span class="n">models</span><span class="p">[</span><span class="n">key</span><span class="p">][</span><span class="s1">&#39;rmse&#39;</span><span class="p">])</span>
+        <span class="n">smape</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">models</span><span class="p">[</span><span class="n">key</span><span class="p">][</span><span class="s1">&#39;smape&#39;</span><span class="p">])</span>
+        <span class="n">stats</span><span class="p">(</span><span class="s2">&quot;smape&quot;</span><span class="p">,</span> <span class="n">models</span><span class="p">[</span><span class="n">key</span><span class="p">][</span><span class="s1">&#39;smape&#39;</span><span class="p">])</span>
+        <span class="n">u</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">models</span><span class="p">[</span><span class="n">key</span><span class="p">][</span><span class="s1">&#39;u&#39;</span><span class="p">])</span>
+        <span class="n">stats</span><span class="p">(</span><span class="s2">&quot;u&quot;</span><span class="p">,</span> <span class="n">models</span><span class="p">[</span><span class="n">key</span><span class="p">][</span><span class="s1">&#39;u&#39;</span><span class="p">])</span>
+        <span class="n">times</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">models</span><span class="p">[</span><span class="n">key</span><span class="p">][</span><span class="s1">&#39;times&#39;</span><span class="p">])</span>
+        <span class="n">labels</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">models</span><span class="p">[</span><span class="n">key</span><span class="p">][</span><span class="s1">&#39;label&#39;</span><span class="p">])</span>
+
+    <span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">boxplot</span><span class="p">(</span><span class="n">rmse</span><span class="p">,</span> <span class="n">labels</span><span class="o">=</span><span class="n">labels</span><span class="p">,</span> <span class="n">autorange</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">showmeans</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+    <span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">&quot;RMSE&quot;</span><span class="p">)</span>
+    <span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">boxplot</span><span class="p">(</span><span class="n">smape</span><span class="p">,</span> <span class="n">labels</span><span class="o">=</span><span class="n">labels</span><span class="p">,</span> <span class="n">autorange</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">showmeans</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+    <span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">&quot;SMAPE&quot;</span><span class="p">)</span>
+    <span class="n">axes</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">boxplot</span><span class="p">(</span><span class="n">u</span><span class="p">,</span> <span class="n">labels</span><span class="o">=</span><span class="n">labels</span><span class="p">,</span> <span class="n">autorange</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">showmeans</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+    <span class="n">axes</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">&quot;U Statistic&quot;</span><span class="p">)</span>
+
+    <span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
+
+    <span class="n">Util</span><span class="o">.</span><span class="n">show_and_save_image</span><span class="p">(</span><span class="n">fig</span><span class="p">,</span> <span class="n">file</span><span class="p">,</span> <span class="n">save</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="plot_dataframe_point"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.Util.plot_dataframe_point">[docs]</a><span class="k">def</span> <span class="nf">plot_dataframe_point</span><span class="p">(</span><span class="n">file_synthetic</span><span class="p">,</span> <span class="n">file_analytic</span><span class="p">,</span> <span class="n">experiments</span><span class="p">,</span> <span class="n">tam</span><span class="p">,</span> <span class="n">save</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">file</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+                         <span class="n">sort_columns</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;UAVG&#39;</span><span class="p">,</span> <span class="s1">&#39;RMSEAVG&#39;</span><span class="p">,</span> <span class="s1">&#39;USTD&#39;</span><span class="p">,</span> <span class="s1">&#39;RMSESTD&#39;</span><span class="p">],</span>
+                         <span class="n">sort_ascend</span><span class="o">=</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span><span class="n">save_best</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+                         <span class="n">ignore</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span><span class="n">replace</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+
+    <span class="n">fig</span><span class="p">,</span> <span class="n">axes</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">nrows</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">ncols</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="n">tam</span><span class="p">)</span>
+
+    <span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">&#39;RMSE&#39;</span><span class="p">)</span>
+    <span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">&#39;SMAPE&#39;</span><span class="p">)</span>
+    <span class="n">axes</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">&#39;U Statistic&#39;</span><span class="p">)</span>
+
+    <span class="n">dat_syn</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">file_synthetic</span><span class="p">,</span> <span class="n">sep</span><span class="o">=</span><span class="s2">&quot;;&quot;</span><span class="p">,</span> <span class="n">usecols</span><span class="o">=</span><span class="n">point_dataframe_synthetic_columns</span><span class="p">())</span>
+
+    <span class="n">bests</span> <span class="o">=</span> <span class="n">find_best</span><span class="p">(</span><span class="n">dat_syn</span><span class="p">,</span> <span class="n">sort_columns</span><span class="p">,</span> <span class="n">sort_ascend</span><span class="p">)</span>
+
+    <span class="n">dat_ana</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">file_analytic</span><span class="p">,</span> <span class="n">sep</span><span class="o">=</span><span class="s2">&quot;;&quot;</span><span class="p">,</span> <span class="n">usecols</span><span class="o">=</span><span class="n">point_dataframe_analytic_columns</span><span class="p">(</span><span class="n">experiments</span><span class="p">))</span>
+
+    <span class="n">data_columns</span> <span class="o">=</span> <span class="n">analytical_data_columns</span><span class="p">(</span><span class="n">experiments</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="n">save_best</span><span class="p">:</span>
+        <span class="n">dat</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="o">.</span><span class="n">from_dict</span><span class="p">(</span><span class="n">bests</span><span class="p">,</span> <span class="n">orient</span><span class="o">=</span><span class="s1">&#39;index&#39;</span><span class="p">)</span>
+        <span class="n">dat</span><span class="o">.</span><span class="n">to_csv</span><span class="p">(</span><span class="n">Util</span><span class="o">.</span><span class="n">uniquefilename</span><span class="p">(</span><span class="n">file_synthetic</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s2">&quot;synthetic&quot;</span><span class="p">,</span><span class="s2">&quot;best&quot;</span><span class="p">)),</span> <span class="n">sep</span><span class="o">=</span><span class="s2">&quot;;&quot;</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+
+    <span class="n">rmse</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="n">smape</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="n">u</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="n">times</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="n">labels</span> <span class="o">=</span> <span class="p">[]</span>
+
+    <span class="k">for</span> <span class="n">b</span> <span class="ow">in</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">bests</span><span class="o">.</span><span class="n">keys</span><span class="p">()):</span>
+        <span class="k">if</span> <span class="n">check_ignore_list</span><span class="p">(</span><span class="n">b</span><span class="p">,</span> <span class="n">ignore</span><span class="p">):</span>
+            <span class="k">continue</span>
+
+        <span class="n">best</span> <span class="o">=</span> <span class="n">bests</span><span class="p">[</span><span class="n">b</span><span class="p">]</span>
+        <span class="n">tmp</span> <span class="o">=</span> <span class="n">dat_ana</span><span class="p">[(</span><span class="n">dat_ana</span><span class="o">.</span><span class="n">Model</span> <span class="o">==</span> <span class="n">best</span><span class="p">[</span><span class="s2">&quot;Model&quot;</span><span class="p">])</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">dat_ana</span><span class="o">.</span><span class="n">Order</span> <span class="o">==</span> <span class="n">best</span><span class="p">[</span><span class="s2">&quot;Order&quot;</span><span class="p">])</span>
+                <span class="o">&amp;</span> <span class="p">(</span><span class="n">dat_ana</span><span class="o">.</span><span class="n">Scheme</span> <span class="o">==</span> <span class="n">best</span><span class="p">[</span><span class="s2">&quot;Scheme&quot;</span><span class="p">])</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">dat_ana</span><span class="o">.</span><span class="n">Partitions</span> <span class="o">==</span> <span class="n">best</span><span class="p">[</span><span class="s2">&quot;Partitions&quot;</span><span class="p">])]</span>
+        <span class="n">rmse</span><span class="o">.</span><span class="n">append</span><span class="p">(</span> <span class="n">extract_measure</span><span class="p">(</span><span class="n">tmp</span><span class="p">,</span><span class="s1">&#39;RMSE&#39;</span><span class="p">,</span><span class="n">data_columns</span><span class="p">)</span> <span class="p">)</span>
+        <span class="n">smape</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">extract_measure</span><span class="p">(</span><span class="n">tmp</span><span class="p">,</span> <span class="s1">&#39;SMAPE&#39;</span><span class="p">,</span> <span class="n">data_columns</span><span class="p">))</span>
+        <span class="n">u</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">extract_measure</span><span class="p">(</span><span class="n">tmp</span><span class="p">,</span> <span class="s1">&#39;U&#39;</span><span class="p">,</span> <span class="n">data_columns</span><span class="p">))</span>
+        <span class="n">times</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">extract_measure</span><span class="p">(</span><span class="n">tmp</span><span class="p">,</span> <span class="s1">&#39;TIME&#39;</span><span class="p">,</span> <span class="n">data_columns</span><span class="p">))</span>
+
+        <span class="n">labels</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">check_replace_list</span><span class="p">(</span><span class="n">best</span><span class="p">[</span><span class="s2">&quot;Model&quot;</span><span class="p">]</span> <span class="o">+</span> <span class="s2">&quot; &quot;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">best</span><span class="p">[</span><span class="s2">&quot;Order&quot;</span><span class="p">]),</span><span class="n">replace</span><span class="p">))</span>
+
+    <span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">boxplot</span><span class="p">(</span><span class="n">rmse</span><span class="p">,</span> <span class="n">labels</span><span class="o">=</span><span class="n">labels</span><span class="p">,</span> <span class="n">autorange</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">showmeans</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+    <span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">&quot;RMSE&quot;</span><span class="p">)</span>
+    <span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">boxplot</span><span class="p">(</span><span class="n">smape</span><span class="p">,</span> <span class="n">labels</span><span class="o">=</span><span class="n">labels</span><span class="p">,</span> <span class="n">autorange</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">showmeans</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+    <span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">&quot;SMAPE&quot;</span><span class="p">)</span>
+    <span class="n">axes</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">boxplot</span><span class="p">(</span><span class="n">u</span><span class="p">,</span> <span class="n">labels</span><span class="o">=</span><span class="n">labels</span><span class="p">,</span> <span class="n">autorange</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">showmeans</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+    <span class="n">axes</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">&quot;U Statistic&quot;</span><span class="p">)</span>
+
+    <span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
+
+    <span class="n">Util</span><span class="o">.</span><span class="n">show_and_save_image</span><span class="p">(</span><span class="n">fig</span><span class="p">,</span> <span class="n">file</span><span class="p">,</span> <span class="n">save</span><span class="p">)</span></div>
+
+
+
+<div class="viewcode-block" id="check_replace_list"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.Util.check_replace_list">[docs]</a><span class="k">def</span> <span class="nf">check_replace_list</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">replace</span><span class="p">):</span>
+    <span class="k">if</span> <span class="n">replace</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="k">for</span> <span class="n">r</span> <span class="ow">in</span> <span class="n">replace</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">r</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="ow">in</span> <span class="n">m</span><span class="p">:</span>
+                <span class="k">return</span> <span class="n">r</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+    <span class="k">return</span> <span class="n">m</span></div>
+
+
+
+<div class="viewcode-block" id="check_ignore_list"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.Util.check_ignore_list">[docs]</a><span class="k">def</span> <span class="nf">check_ignore_list</span><span class="p">(</span><span class="n">b</span><span class="p">,</span> <span class="n">ignore</span><span class="p">):</span>
+    <span class="n">flag</span> <span class="o">=</span> <span class="kc">False</span>
+    <span class="k">if</span> <span class="n">ignore</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">ignore</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">b</span><span class="p">:</span>
+                <span class="n">flag</span> <span class="o">=</span> <span class="kc">True</span>
+    <span class="k">return</span> <span class="n">flag</span></div>
+
+
+<div class="viewcode-block" id="save_dataframe_interval"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.Util.save_dataframe_interval">[docs]</a><span class="k">def</span> <span class="nf">save_dataframe_interval</span><span class="p">(</span><span class="n">coverage</span><span class="p">,</span> <span class="n">experiments</span><span class="p">,</span> <span class="n">file</span><span class="p">,</span> <span class="n">objs</span><span class="p">,</span> <span class="n">resolution</span><span class="p">,</span> <span class="n">save</span><span class="p">,</span> <span class="n">sharpness</span><span class="p">,</span> <span class="n">synthetic</span><span class="p">,</span> <span class="n">times</span><span class="p">,</span>
+                            <span class="n">q05</span><span class="p">,</span> <span class="n">q25</span><span class="p">,</span> <span class="n">q75</span><span class="p">,</span> <span class="n">q95</span><span class="p">,</span> <span class="n">steps</span><span class="p">,</span> <span class="n">method</span><span class="p">):</span>
+    <span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="k">if</span> <span class="n">synthetic</span><span class="p">:</span>
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">objs</span><span class="o">.</span><span class="n">keys</span><span class="p">()):</span>
+            <span class="n">mod</span> <span class="o">=</span> <span class="p">[]</span>
+            <span class="n">mfts</span> <span class="o">=</span> <span class="n">objs</span><span class="p">[</span><span class="n">k</span><span class="p">]</span>
+            <span class="n">mod</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">mfts</span><span class="o">.</span><span class="n">shortname</span><span class="p">)</span>
+            <span class="n">mod</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">mfts</span><span class="o">.</span><span class="n">order</span><span class="p">)</span>
+            <span class="n">l</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">mfts</span><span class="p">)</span>
+            <span class="k">if</span> <span class="ow">not</span> <span class="n">mfts</span><span class="o">.</span><span class="n">benchmark_only</span><span class="p">:</span>
+                <span class="n">mod</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">mfts</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">name</span><span class="p">)</span>
+                <span class="n">mod</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">mfts</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">partitions</span><span class="p">)</span>
+                <span class="n">mod</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">l</span><span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">mod</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s1">&#39;-&#39;</span><span class="p">)</span>
+                <span class="n">mod</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s1">&#39;-&#39;</span><span class="p">)</span>
+                <span class="n">mod</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s1">&#39;-&#39;</span><span class="p">)</span>
+            <span class="n">mod</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">steps</span><span class="p">[</span><span class="n">k</span><span class="p">])</span>
+            <span class="n">mod</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">method</span><span class="p">[</span><span class="n">k</span><span class="p">])</span>
+            <span class="n">mod</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">nanmean</span><span class="p">(</span><span class="n">sharpness</span><span class="p">[</span><span class="n">k</span><span class="p">]),</span> <span class="mi">2</span><span class="p">))</span>
+            <span class="n">mod</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">nanstd</span><span class="p">(</span><span class="n">sharpness</span><span class="p">[</span><span class="n">k</span><span class="p">]),</span> <span class="mi">2</span><span class="p">))</span>
+            <span class="n">mod</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">nanmean</span><span class="p">(</span><span class="n">resolution</span><span class="p">[</span><span class="n">k</span><span class="p">]),</span> <span class="mi">2</span><span class="p">))</span>
+            <span class="n">mod</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">nanstd</span><span class="p">(</span><span class="n">resolution</span><span class="p">[</span><span class="n">k</span><span class="p">]),</span> <span class="mi">2</span><span class="p">))</span>
+            <span class="n">mod</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">nanmean</span><span class="p">(</span><span class="n">coverage</span><span class="p">[</span><span class="n">k</span><span class="p">]),</span> <span class="mi">2</span><span class="p">))</span>
+            <span class="n">mod</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">nanstd</span><span class="p">(</span><span class="n">coverage</span><span class="p">[</span><span class="n">k</span><span class="p">]),</span> <span class="mi">2</span><span class="p">))</span>
+            <span class="n">mod</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">nanmean</span><span class="p">(</span><span class="n">times</span><span class="p">[</span><span class="n">k</span><span class="p">]),</span> <span class="mi">2</span><span class="p">))</span>
+            <span class="n">mod</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">nanstd</span><span class="p">(</span><span class="n">times</span><span class="p">[</span><span class="n">k</span><span class="p">]),</span> <span class="mi">2</span><span class="p">))</span>
+            <span class="n">mod</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">nanmean</span><span class="p">(</span><span class="n">q05</span><span class="p">[</span><span class="n">k</span><span class="p">]),</span> <span class="mi">2</span><span class="p">))</span>
+            <span class="n">mod</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">nanstd</span><span class="p">(</span><span class="n">q05</span><span class="p">[</span><span class="n">k</span><span class="p">]),</span> <span class="mi">2</span><span class="p">))</span>
+            <span class="n">mod</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">nanmean</span><span class="p">(</span><span class="n">q25</span><span class="p">[</span><span class="n">k</span><span class="p">]),</span> <span class="mi">2</span><span class="p">))</span>
+            <span class="n">mod</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">nanstd</span><span class="p">(</span><span class="n">q25</span><span class="p">[</span><span class="n">k</span><span class="p">]),</span> <span class="mi">2</span><span class="p">))</span>
+            <span class="n">mod</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">nanmean</span><span class="p">(</span><span class="n">q75</span><span class="p">[</span><span class="n">k</span><span class="p">]),</span> <span class="mi">2</span><span class="p">))</span>
+            <span class="n">mod</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">nanstd</span><span class="p">(</span><span class="n">q75</span><span class="p">[</span><span class="n">k</span><span class="p">]),</span> <span class="mi">2</span><span class="p">))</span>
+            <span class="n">mod</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">nanmean</span><span class="p">(</span><span class="n">q95</span><span class="p">[</span><span class="n">k</span><span class="p">]),</span> <span class="mi">2</span><span class="p">))</span>
+            <span class="n">mod</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">nanstd</span><span class="p">(</span><span class="n">q95</span><span class="p">[</span><span class="n">k</span><span class="p">]),</span> <span class="mi">2</span><span class="p">))</span>
+            <span class="n">mod</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">l</span><span class="p">)</span>
+            <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">mod</span><span class="p">)</span>
+
+        <span class="n">columns</span> <span class="o">=</span> <span class="n">interval_dataframe_synthetic_columns</span><span class="p">()</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">objs</span><span class="o">.</span><span class="n">keys</span><span class="p">()):</span>
+            <span class="k">try</span><span class="p">:</span>
+                <span class="n">mfts</span> <span class="o">=</span> <span class="n">objs</span><span class="p">[</span><span class="n">k</span><span class="p">]</span>
+                <span class="n">n</span> <span class="o">=</span> <span class="n">mfts</span><span class="o">.</span><span class="n">shortname</span>
+                <span class="n">o</span> <span class="o">=</span> <span class="n">mfts</span><span class="o">.</span><span class="n">order</span>
+                <span class="k">if</span> <span class="ow">not</span> <span class="n">mfts</span><span class="o">.</span><span class="n">benchmark_only</span><span class="p">:</span>
+                    <span class="n">s</span> <span class="o">=</span> <span class="n">mfts</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">name</span>
+                    <span class="n">p</span> <span class="o">=</span> <span class="n">mfts</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">partitions</span>
+                    <span class="n">l</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">mfts</span><span class="p">)</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">s</span> <span class="o">=</span> <span class="s1">&#39;-&#39;</span>
+                    <span class="n">p</span> <span class="o">=</span> <span class="s1">&#39;-&#39;</span>
+                    <span class="n">l</span> <span class="o">=</span> <span class="s1">&#39;-&#39;</span>
+                <span class="n">st</span> <span class="o">=</span> <span class="n">steps</span><span class="p">[</span><span class="n">k</span><span class="p">]</span>
+                <span class="n">mt</span> <span class="o">=</span> <span class="n">method</span><span class="p">[</span><span class="n">k</span><span class="p">]</span>
+                <span class="n">tmp</span> <span class="o">=</span> <span class="p">[</span><span class="n">n</span><span class="p">,</span> <span class="n">o</span><span class="p">,</span> <span class="n">s</span><span class="p">,</span> <span class="n">p</span><span class="p">,</span> <span class="n">l</span><span class="p">,</span> <span class="n">st</span><span class="p">,</span> <span class="n">mt</span><span class="p">,</span> <span class="s1">&#39;Sharpness&#39;</span><span class="p">]</span>
+                <span class="n">tmp</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">sharpness</span><span class="p">[</span><span class="n">k</span><span class="p">])</span>
+                <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">deepcopy</span><span class="p">(</span><span class="n">tmp</span><span class="p">))</span>
+                <span class="n">tmp</span> <span class="o">=</span> <span class="p">[</span><span class="n">n</span><span class="p">,</span> <span class="n">o</span><span class="p">,</span> <span class="n">s</span><span class="p">,</span> <span class="n">p</span><span class="p">,</span> <span class="n">l</span><span class="p">,</span> <span class="n">st</span><span class="p">,</span> <span class="n">mt</span><span class="p">,</span> <span class="s1">&#39;Resolution&#39;</span><span class="p">]</span>
+                <span class="n">tmp</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">resolution</span><span class="p">[</span><span class="n">k</span><span class="p">])</span>
+                <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">deepcopy</span><span class="p">(</span><span class="n">tmp</span><span class="p">))</span>
+                <span class="n">tmp</span> <span class="o">=</span> <span class="p">[</span><span class="n">n</span><span class="p">,</span> <span class="n">o</span><span class="p">,</span> <span class="n">s</span><span class="p">,</span> <span class="n">p</span><span class="p">,</span> <span class="n">l</span><span class="p">,</span> <span class="n">st</span><span class="p">,</span> <span class="n">mt</span><span class="p">,</span> <span class="s1">&#39;Coverage&#39;</span><span class="p">]</span>
+                <span class="n">tmp</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">coverage</span><span class="p">[</span><span class="n">k</span><span class="p">])</span>
+                <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">deepcopy</span><span class="p">(</span><span class="n">tmp</span><span class="p">))</span>
+                <span class="n">tmp</span> <span class="o">=</span> <span class="p">[</span><span class="n">n</span><span class="p">,</span> <span class="n">o</span><span class="p">,</span> <span class="n">s</span><span class="p">,</span> <span class="n">p</span><span class="p">,</span> <span class="n">l</span><span class="p">,</span> <span class="n">st</span><span class="p">,</span> <span class="n">mt</span><span class="p">,</span> <span class="s1">&#39;TIME&#39;</span><span class="p">]</span>
+                <span class="n">tmp</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">times</span><span class="p">[</span><span class="n">k</span><span class="p">])</span>
+                <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">deepcopy</span><span class="p">(</span><span class="n">tmp</span><span class="p">))</span>
+                <span class="n">tmp</span> <span class="o">=</span> <span class="p">[</span><span class="n">n</span><span class="p">,</span> <span class="n">o</span><span class="p">,</span> <span class="n">s</span><span class="p">,</span> <span class="n">p</span><span class="p">,</span> <span class="n">l</span><span class="p">,</span> <span class="n">st</span><span class="p">,</span> <span class="n">mt</span><span class="p">,</span> <span class="s1">&#39;Q05&#39;</span><span class="p">]</span>
+                <span class="n">tmp</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">q05</span><span class="p">[</span><span class="n">k</span><span class="p">])</span>
+                <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">deepcopy</span><span class="p">(</span><span class="n">tmp</span><span class="p">))</span>
+                <span class="n">tmp</span> <span class="o">=</span> <span class="p">[</span><span class="n">n</span><span class="p">,</span> <span class="n">o</span><span class="p">,</span> <span class="n">s</span><span class="p">,</span> <span class="n">p</span><span class="p">,</span> <span class="n">l</span><span class="p">,</span> <span class="n">st</span><span class="p">,</span> <span class="n">mt</span><span class="p">,</span> <span class="s1">&#39;Q25&#39;</span><span class="p">]</span>
+                <span class="n">tmp</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">q25</span><span class="p">[</span><span class="n">k</span><span class="p">])</span>
+                <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">deepcopy</span><span class="p">(</span><span class="n">tmp</span><span class="p">))</span>
+                <span class="n">tmp</span> <span class="o">=</span> <span class="p">[</span><span class="n">n</span><span class="p">,</span> <span class="n">o</span><span class="p">,</span> <span class="n">s</span><span class="p">,</span> <span class="n">p</span><span class="p">,</span> <span class="n">l</span><span class="p">,</span> <span class="n">st</span><span class="p">,</span> <span class="n">mt</span><span class="p">,</span> <span class="s1">&#39;Q75&#39;</span><span class="p">]</span>
+                <span class="n">tmp</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">q75</span><span class="p">[</span><span class="n">k</span><span class="p">])</span>
+                <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">deepcopy</span><span class="p">(</span><span class="n">tmp</span><span class="p">))</span>
+                <span class="n">tmp</span> <span class="o">=</span> <span class="p">[</span><span class="n">n</span><span class="p">,</span> <span class="n">o</span><span class="p">,</span> <span class="n">s</span><span class="p">,</span> <span class="n">p</span><span class="p">,</span> <span class="n">l</span><span class="p">,</span> <span class="n">st</span><span class="p">,</span> <span class="n">mt</span><span class="p">,</span> <span class="s1">&#39;Q95&#39;</span><span class="p">]</span>
+                <span class="n">tmp</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">q95</span><span class="p">[</span><span class="n">k</span><span class="p">])</span>
+                <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">deepcopy</span><span class="p">(</span><span class="n">tmp</span><span class="p">))</span>
+            <span class="k">except</span> <span class="ne">Exception</span> <span class="k">as</span> <span class="n">ex</span><span class="p">:</span>
+                <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Erro ao salvar &quot;</span><span class="p">,</span> <span class="n">k</span><span class="p">)</span>
+                <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Exceção &quot;</span><span class="p">,</span> <span class="n">ex</span><span class="p">)</span>
+        <span class="n">columns</span> <span class="o">=</span> <span class="n">interval_dataframe_analytic_columns</span><span class="p">(</span><span class="n">experiments</span><span class="p">)</span>
+    <span class="n">dat</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">ret</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="n">columns</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">save</span><span class="p">:</span> <span class="n">dat</span><span class="o">.</span><span class="n">to_csv</span><span class="p">(</span><span class="n">Util</span><span class="o">.</span><span class="n">uniquefilename</span><span class="p">(</span><span class="n">file</span><span class="p">),</span> <span class="n">sep</span><span class="o">=</span><span class="s2">&quot;;&quot;</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">dat</span></div>
+
+
+<div class="viewcode-block" id="interval_dataframe_analytic_columns"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.Util.interval_dataframe_analytic_columns">[docs]</a><span class="k">def</span> <span class="nf">interval_dataframe_analytic_columns</span><span class="p">(</span><span class="n">experiments</span><span class="p">):</span>
+    <span class="n">columns</span> <span class="o">=</span> <span class="p">[</span><span class="nb">str</span><span class="p">(</span><span class="n">k</span><span class="p">)</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">experiments</span><span class="p">)]</span>
+    <span class="n">columns</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="s2">&quot;Model&quot;</span><span class="p">)</span>
+    <span class="n">columns</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="s2">&quot;Order&quot;</span><span class="p">)</span>
+    <span class="n">columns</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="s2">&quot;Scheme&quot;</span><span class="p">)</span>
+    <span class="n">columns</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="s2">&quot;Partitions&quot;</span><span class="p">)</span>
+    <span class="n">columns</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="s2">&quot;Size&quot;</span><span class="p">)</span>
+    <span class="n">columns</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="s2">&quot;Steps&quot;</span><span class="p">)</span>
+    <span class="n">columns</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="mi">6</span><span class="p">,</span> <span class="s2">&quot;Method&quot;</span><span class="p">)</span>
+    <span class="n">columns</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="mi">7</span><span class="p">,</span> <span class="s2">&quot;Measure&quot;</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">columns</span></div>
+
+
+
+<div class="viewcode-block" id="interval_dataframe_synthetic_columns"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.Util.interval_dataframe_synthetic_columns">[docs]</a><span class="k">def</span> <span class="nf">interval_dataframe_synthetic_columns</span><span class="p">():</span>
+    <span class="n">columns</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;Model&quot;</span><span class="p">,</span> <span class="s2">&quot;Order&quot;</span><span class="p">,</span> <span class="s2">&quot;Scheme&quot;</span><span class="p">,</span> <span class="s2">&quot;Partitions&quot;</span><span class="p">,</span><span class="s2">&quot;SIZE&quot;</span><span class="p">,</span> <span class="s2">&quot;Steps&quot;</span><span class="p">,</span><span class="s2">&quot;Method&quot;</span> <span class="s2">&quot;SHARPAVG&quot;</span><span class="p">,</span> <span class="s2">&quot;SHARPSTD&quot;</span><span class="p">,</span> <span class="s2">&quot;RESAVG&quot;</span><span class="p">,</span> <span class="s2">&quot;RESSTD&quot;</span><span class="p">,</span> <span class="s2">&quot;COVAVG&quot;</span><span class="p">,</span>
+               <span class="s2">&quot;COVSTD&quot;</span><span class="p">,</span> <span class="s2">&quot;TIMEAVG&quot;</span><span class="p">,</span> <span class="s2">&quot;TIMESTD&quot;</span><span class="p">,</span> <span class="s2">&quot;Q05AVG&quot;</span><span class="p">,</span> <span class="s2">&quot;Q05STD&quot;</span><span class="p">,</span> <span class="s2">&quot;Q25AVG&quot;</span><span class="p">,</span> <span class="s2">&quot;Q25STD&quot;</span><span class="p">,</span> <span class="s2">&quot;Q75AVG&quot;</span><span class="p">,</span> <span class="s2">&quot;Q75STD&quot;</span><span class="p">,</span> <span class="s2">&quot;Q95AVG&quot;</span><span class="p">,</span> <span class="s2">&quot;Q95STD&quot;</span><span class="p">]</span>
+    <span class="k">return</span> <span class="n">columns</span></div>
+
+
+<div class="viewcode-block" id="cast_dataframe_to_synthetic_interval"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.Util.cast_dataframe_to_synthetic_interval">[docs]</a><span class="k">def</span> <span class="nf">cast_dataframe_to_synthetic_interval</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">data_columns</span><span class="p">):</span>
+    <span class="n">sharpness</span> <span class="o">=</span> <span class="n">extract_measure</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="s1">&#39;Sharpness&#39;</span><span class="p">,</span> <span class="n">data_columns</span><span class="p">)</span>
+    <span class="n">resolution</span> <span class="o">=</span> <span class="n">extract_measure</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="s1">&#39;Resolution&#39;</span><span class="p">,</span> <span class="n">data_columns</span><span class="p">)</span>
+    <span class="n">coverage</span> <span class="o">=</span> <span class="n">extract_measure</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="s1">&#39;Coverage&#39;</span><span class="p">,</span> <span class="n">data_columns</span><span class="p">)</span>
+    <span class="n">times</span> <span class="o">=</span> <span class="n">extract_measure</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="s1">&#39;TIME&#39;</span><span class="p">,</span> <span class="n">data_columns</span><span class="p">)</span>
+    <span class="n">q05</span> <span class="o">=</span> <span class="n">extract_measure</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="s1">&#39;Q05&#39;</span><span class="p">,</span> <span class="n">data_columns</span><span class="p">)</span>
+    <span class="n">q25</span> <span class="o">=</span> <span class="n">extract_measure</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="s1">&#39;Q25&#39;</span><span class="p">,</span> <span class="n">data_columns</span><span class="p">)</span>
+    <span class="n">q75</span> <span class="o">=</span> <span class="n">extract_measure</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="s1">&#39;Q75&#39;</span><span class="p">,</span> <span class="n">data_columns</span><span class="p">)</span>
+    <span class="n">q95</span> <span class="o">=</span> <span class="n">extract_measure</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="s1">&#39;Q95&#39;</span><span class="p">,</span> <span class="n">data_columns</span><span class="p">)</span>
+    <span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">nanmean</span><span class="p">(</span><span class="n">sharpness</span><span class="p">),</span> <span class="mi">2</span><span class="p">))</span>
+    <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">nanstd</span><span class="p">(</span><span class="n">sharpness</span><span class="p">),</span> <span class="mi">2</span><span class="p">))</span>
+    <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">nanmean</span><span class="p">(</span><span class="n">resolution</span><span class="p">),</span> <span class="mi">2</span><span class="p">))</span>
+    <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">nanstd</span><span class="p">(</span><span class="n">resolution</span><span class="p">),</span> <span class="mi">2</span><span class="p">))</span>
+    <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">nanmean</span><span class="p">(</span><span class="n">coverage</span><span class="p">),</span> <span class="mi">2</span><span class="p">))</span>
+    <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">nanstd</span><span class="p">(</span><span class="n">coverage</span><span class="p">),</span> <span class="mi">2</span><span class="p">))</span>
+    <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">nanmean</span><span class="p">(</span><span class="n">times</span><span class="p">),</span> <span class="mi">4</span><span class="p">))</span>
+    <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">nanstd</span><span class="p">(</span><span class="n">times</span><span class="p">),</span> <span class="mi">4</span><span class="p">))</span>
+    <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">nanmean</span><span class="p">(</span><span class="n">q05</span><span class="p">),</span> <span class="mi">4</span><span class="p">))</span>
+    <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">nanstd</span><span class="p">(</span><span class="n">q05</span><span class="p">),</span> <span class="mi">4</span><span class="p">))</span>
+    <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">nanmean</span><span class="p">(</span><span class="n">q25</span><span class="p">),</span> <span class="mi">4</span><span class="p">))</span>
+    <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">nanstd</span><span class="p">(</span><span class="n">q25</span><span class="p">),</span> <span class="mi">4</span><span class="p">))</span>
+    <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">nanmean</span><span class="p">(</span><span class="n">q75</span><span class="p">),</span> <span class="mi">4</span><span class="p">))</span>
+    <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">nanstd</span><span class="p">(</span><span class="n">q75</span><span class="p">),</span> <span class="mi">4</span><span class="p">))</span>
+    <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">nanmean</span><span class="p">(</span><span class="n">q95</span><span class="p">),</span> <span class="mi">4</span><span class="p">))</span>
+    <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">nanstd</span><span class="p">(</span><span class="n">q95</span><span class="p">),</span> <span class="mi">4</span><span class="p">))</span>
+    <span class="k">return</span> <span class="n">ret</span></div>
+
+
+
+
+<div class="viewcode-block" id="unified_scaled_interval"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.Util.unified_scaled_interval">[docs]</a><span class="k">def</span> <span class="nf">unified_scaled_interval</span><span class="p">(</span><span class="n">experiments</span><span class="p">,</span> <span class="n">tam</span><span class="p">,</span> <span class="n">save</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">file</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+                            <span class="n">sort_columns</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;COVAVG&#39;</span><span class="p">,</span> <span class="s1">&#39;SHARPAVG&#39;</span><span class="p">,</span> <span class="s1">&#39;COVSTD&#39;</span><span class="p">,</span> <span class="s1">&#39;SHARPSTD&#39;</span><span class="p">],</span>
+                            <span class="n">sort_ascend</span><span class="o">=</span><span class="p">[</span><span class="kc">True</span><span class="p">,</span> <span class="kc">False</span><span class="p">,</span> <span class="kc">True</span><span class="p">,</span> <span class="kc">True</span><span class="p">],</span><span class="n">save_best</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+                            <span class="n">ignore</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">replace</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+    <span class="n">fig</span><span class="p">,</span> <span class="n">axes</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">nrows</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">ncols</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="n">tam</span><span class="p">)</span>
+
+    <span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">&#39;Sharpness&#39;</span><span class="p">)</span>
+    <span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">&#39;Resolution&#39;</span><span class="p">)</span>
+    <span class="n">axes</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">&#39;Coverage&#39;</span><span class="p">)</span>
+
+    <span class="n">models</span> <span class="o">=</span> <span class="p">{}</span>
+
+    <span class="k">for</span> <span class="n">experiment</span> <span class="ow">in</span> <span class="n">experiments</span><span class="p">:</span>
+
+        <span class="n">mdl</span> <span class="o">=</span> <span class="p">{}</span>
+
+        <span class="n">dat_syn</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">experiment</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">sep</span><span class="o">=</span><span class="s2">&quot;;&quot;</span><span class="p">,</span> <span class="n">usecols</span><span class="o">=</span><span class="n">interval_dataframe_synthetic_columns</span><span class="p">())</span>
+
+        <span class="n">bests</span> <span class="o">=</span> <span class="n">find_best</span><span class="p">(</span><span class="n">dat_syn</span><span class="p">,</span> <span class="n">sort_columns</span><span class="p">,</span> <span class="n">sort_ascend</span><span class="p">)</span>
+
+        <span class="n">dat_ana</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">experiment</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">sep</span><span class="o">=</span><span class="s2">&quot;;&quot;</span><span class="p">,</span> <span class="n">usecols</span><span class="o">=</span><span class="n">interval_dataframe_analytic_columns</span><span class="p">(</span><span class="n">experiment</span><span class="p">[</span><span class="mi">2</span><span class="p">]))</span>
+
+        <span class="n">sharpness</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="n">resolution</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="n">coverage</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="n">times</span> <span class="o">=</span> <span class="p">[]</span>
+
+        <span class="n">data_columns</span> <span class="o">=</span> <span class="n">analytical_data_columns</span><span class="p">(</span><span class="n">experiment</span><span class="p">[</span><span class="mi">2</span><span class="p">])</span>
+
+        <span class="k">for</span> <span class="n">b</span> <span class="ow">in</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">bests</span><span class="o">.</span><span class="n">keys</span><span class="p">()):</span>
+            <span class="k">if</span> <span class="n">check_ignore_list</span><span class="p">(</span><span class="n">b</span><span class="p">,</span> <span class="n">ignore</span><span class="p">):</span>
+                <span class="k">continue</span>
+
+            <span class="k">if</span> <span class="n">b</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">models</span><span class="p">:</span>
+                <span class="n">models</span><span class="p">[</span><span class="n">b</span><span class="p">]</span> <span class="o">=</span> <span class="p">{}</span>
+                <span class="n">models</span><span class="p">[</span><span class="n">b</span><span class="p">][</span><span class="s1">&#39;sharpness&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
+                <span class="n">models</span><span class="p">[</span><span class="n">b</span><span class="p">][</span><span class="s1">&#39;resolution&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
+                <span class="n">models</span><span class="p">[</span><span class="n">b</span><span class="p">][</span><span class="s1">&#39;coverage&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
+                <span class="n">models</span><span class="p">[</span><span class="n">b</span><span class="p">][</span><span class="s1">&#39;times&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
+
+            <span class="k">if</span> <span class="n">b</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">mdl</span><span class="p">:</span>
+                <span class="n">mdl</span><span class="p">[</span><span class="n">b</span><span class="p">]</span> <span class="o">=</span> <span class="p">{}</span>
+                <span class="n">mdl</span><span class="p">[</span><span class="n">b</span><span class="p">][</span><span class="s1">&#39;sharpness&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
+                <span class="n">mdl</span><span class="p">[</span><span class="n">b</span><span class="p">][</span><span class="s1">&#39;resolution&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
+                <span class="n">mdl</span><span class="p">[</span><span class="n">b</span><span class="p">][</span><span class="s1">&#39;coverage&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
+                <span class="n">mdl</span><span class="p">[</span><span class="n">b</span><span class="p">][</span><span class="s1">&#39;times&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
+
+            <span class="n">best</span> <span class="o">=</span> <span class="n">bests</span><span class="p">[</span><span class="n">b</span><span class="p">]</span>
+            <span class="nb">print</span><span class="p">(</span><span class="n">best</span><span class="p">)</span>
+            <span class="n">tmp</span> <span class="o">=</span> <span class="n">dat_ana</span><span class="p">[(</span><span class="n">dat_ana</span><span class="o">.</span><span class="n">Model</span> <span class="o">==</span> <span class="n">best</span><span class="p">[</span><span class="s2">&quot;Model&quot;</span><span class="p">])</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">dat_ana</span><span class="o">.</span><span class="n">Order</span> <span class="o">==</span> <span class="n">best</span><span class="p">[</span><span class="s2">&quot;Order&quot;</span><span class="p">])</span>
+                          <span class="o">&amp;</span> <span class="p">(</span><span class="n">dat_ana</span><span class="o">.</span><span class="n">Scheme</span> <span class="o">==</span> <span class="n">best</span><span class="p">[</span><span class="s2">&quot;Scheme&quot;</span><span class="p">])</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">dat_ana</span><span class="o">.</span><span class="n">Partitions</span> <span class="o">==</span> <span class="n">best</span><span class="p">[</span><span class="s2">&quot;Partitions&quot;</span><span class="p">])]</span>
+            <span class="n">tmpl</span> <span class="o">=</span> <span class="n">extract_measure</span><span class="p">(</span><span class="n">tmp</span><span class="p">,</span> <span class="s1">&#39;Sharpness&#39;</span><span class="p">,</span> <span class="n">data_columns</span><span class="p">)</span>
+            <span class="n">mdl</span><span class="p">[</span><span class="n">b</span><span class="p">][</span><span class="s1">&#39;sharpness&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">tmpl</span><span class="p">)</span>
+            <span class="n">sharpness</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">tmpl</span><span class="p">)</span>
+            <span class="n">tmpl</span> <span class="o">=</span> <span class="n">extract_measure</span><span class="p">(</span><span class="n">tmp</span><span class="p">,</span> <span class="s1">&#39;Resolution&#39;</span><span class="p">,</span> <span class="n">data_columns</span><span class="p">)</span>
+            <span class="n">mdl</span><span class="p">[</span><span class="n">b</span><span class="p">][</span><span class="s1">&#39;resolution&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">tmpl</span><span class="p">)</span>
+            <span class="n">resolution</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">tmpl</span><span class="p">)</span>
+            <span class="n">tmpl</span> <span class="o">=</span> <span class="n">extract_measure</span><span class="p">(</span><span class="n">tmp</span><span class="p">,</span> <span class="s1">&#39;Coverage&#39;</span><span class="p">,</span> <span class="n">data_columns</span><span class="p">)</span>
+            <span class="n">mdl</span><span class="p">[</span><span class="n">b</span><span class="p">][</span><span class="s1">&#39;coverage&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">tmpl</span><span class="p">)</span>
+            <span class="n">coverage</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">tmpl</span><span class="p">)</span>
+            <span class="n">tmpl</span> <span class="o">=</span> <span class="n">extract_measure</span><span class="p">(</span><span class="n">tmp</span><span class="p">,</span> <span class="s1">&#39;TIME&#39;</span><span class="p">,</span> <span class="n">data_columns</span><span class="p">)</span>
+            <span class="n">mdl</span><span class="p">[</span><span class="n">b</span><span class="p">][</span><span class="s1">&#39;times&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">tmpl</span><span class="p">)</span>
+            <span class="n">times</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">tmpl</span><span class="p">)</span>
+
+            <span class="n">models</span><span class="p">[</span><span class="n">b</span><span class="p">][</span><span class="s1">&#39;label&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">check_replace_list</span><span class="p">(</span><span class="n">best</span><span class="p">[</span><span class="s2">&quot;Model&quot;</span><span class="p">]</span> <span class="o">+</span> <span class="s2">&quot; &quot;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">best</span><span class="p">[</span><span class="s2">&quot;Order&quot;</span><span class="p">]),</span> <span class="n">replace</span><span class="p">)</span>
+
+        <span class="n">sharpness_param</span> <span class="o">=</span> <span class="n">scale_params</span><span class="p">(</span><span class="n">sharpness</span><span class="p">)</span>
+        <span class="n">resolution_param</span> <span class="o">=</span> <span class="n">scale_params</span><span class="p">(</span><span class="n">resolution</span><span class="p">)</span>
+        <span class="n">coverage_param</span> <span class="o">=</span> <span class="n">scale_params</span><span class="p">(</span><span class="n">coverage</span><span class="p">)</span>
+        <span class="n">times_param</span> <span class="o">=</span> <span class="n">scale_params</span><span class="p">(</span><span class="n">times</span><span class="p">)</span>
+
+        <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">models</span><span class="o">.</span><span class="n">keys</span><span class="p">()):</span>
+            <span class="n">models</span><span class="p">[</span><span class="n">key</span><span class="p">][</span><span class="s1">&#39;sharpness&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">scale</span><span class="p">(</span><span class="n">mdl</span><span class="p">[</span><span class="n">key</span><span class="p">][</span><span class="s1">&#39;sharpness&#39;</span><span class="p">],</span> <span class="n">sharpness_param</span><span class="p">))</span>
+            <span class="n">models</span><span class="p">[</span><span class="n">key</span><span class="p">][</span><span class="s1">&#39;resolution&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">scale</span><span class="p">(</span><span class="n">mdl</span><span class="p">[</span><span class="n">key</span><span class="p">][</span><span class="s1">&#39;resolution&#39;</span><span class="p">],</span> <span class="n">resolution_param</span><span class="p">))</span>
+            <span class="n">models</span><span class="p">[</span><span class="n">key</span><span class="p">][</span><span class="s1">&#39;coverage&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">scale</span><span class="p">(</span><span class="n">mdl</span><span class="p">[</span><span class="n">key</span><span class="p">][</span><span class="s1">&#39;coverage&#39;</span><span class="p">],</span> <span class="n">coverage_param</span><span class="p">))</span>
+            <span class="n">models</span><span class="p">[</span><span class="n">key</span><span class="p">][</span><span class="s1">&#39;times&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">scale</span><span class="p">(</span><span class="n">mdl</span><span class="p">[</span><span class="n">key</span><span class="p">][</span><span class="s1">&#39;times&#39;</span><span class="p">],</span> <span class="n">times_param</span><span class="p">))</span>
+
+    <span class="n">sharpness</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="n">resolution</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="n">coverage</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="n">times</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="n">labels</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">models</span><span class="o">.</span><span class="n">keys</span><span class="p">()):</span>
+        <span class="n">sharpness</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">models</span><span class="p">[</span><span class="n">key</span><span class="p">][</span><span class="s1">&#39;sharpness&#39;</span><span class="p">])</span>
+        <span class="n">resolution</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">models</span><span class="p">[</span><span class="n">key</span><span class="p">][</span><span class="s1">&#39;resolution&#39;</span><span class="p">])</span>
+        <span class="n">coverage</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">models</span><span class="p">[</span><span class="n">key</span><span class="p">][</span><span class="s1">&#39;coverage&#39;</span><span class="p">])</span>
+        <span class="n">times</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">models</span><span class="p">[</span><span class="n">key</span><span class="p">][</span><span class="s1">&#39;times&#39;</span><span class="p">])</span>
+        <span class="n">labels</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">models</span><span class="p">[</span><span class="n">key</span><span class="p">][</span><span class="s1">&#39;label&#39;</span><span class="p">])</span>
+
+    <span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">boxplot</span><span class="p">(</span><span class="n">sharpness</span><span class="p">,</span> <span class="n">labels</span><span class="o">=</span><span class="n">labels</span><span class="p">,</span> <span class="n">autorange</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">showmeans</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+    <span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">boxplot</span><span class="p">(</span><span class="n">resolution</span><span class="p">,</span> <span class="n">labels</span><span class="o">=</span><span class="n">labels</span><span class="p">,</span> <span class="n">autorange</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">showmeans</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+    <span class="n">axes</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">boxplot</span><span class="p">(</span><span class="n">coverage</span><span class="p">,</span> <span class="n">labels</span><span class="o">=</span><span class="n">labels</span><span class="p">,</span> <span class="n">autorange</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">showmeans</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+
+    <span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
+
+    <span class="n">Util</span><span class="o">.</span><span class="n">show_and_save_image</span><span class="p">(</span><span class="n">fig</span><span class="p">,</span> <span class="n">file</span><span class="p">,</span> <span class="n">save</span><span class="p">)</span></div>
+
+
+
+<div class="viewcode-block" id="plot_dataframe_interval"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.Util.plot_dataframe_interval">[docs]</a><span class="k">def</span> <span class="nf">plot_dataframe_interval</span><span class="p">(</span><span class="n">file_synthetic</span><span class="p">,</span> <span class="n">file_analytic</span><span class="p">,</span> <span class="n">experiments</span><span class="p">,</span> <span class="n">tam</span><span class="p">,</span> <span class="n">save</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">file</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+                            <span class="n">sort_columns</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;COVAVG&#39;</span><span class="p">,</span> <span class="s1">&#39;SHARPAVG&#39;</span><span class="p">,</span> <span class="s1">&#39;COVSTD&#39;</span><span class="p">,</span> <span class="s1">&#39;SHARPSTD&#39;</span><span class="p">],</span>
+                            <span class="n">sort_ascend</span><span class="o">=</span><span class="p">[</span><span class="kc">True</span><span class="p">,</span> <span class="kc">False</span><span class="p">,</span> <span class="kc">True</span><span class="p">,</span> <span class="kc">True</span><span class="p">],</span><span class="n">save_best</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+                            <span class="n">ignore</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">replace</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+
+    <span class="n">fig</span><span class="p">,</span> <span class="n">axes</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">nrows</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">ncols</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="n">tam</span><span class="p">)</span>
+
+    <span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">&#39;Sharpness&#39;</span><span class="p">)</span>
+    <span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">&#39;Resolution&#39;</span><span class="p">)</span>
+    <span class="n">axes</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">&#39;Coverage&#39;</span><span class="p">)</span>
+
+    <span class="n">dat_syn</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">file_synthetic</span><span class="p">,</span> <span class="n">sep</span><span class="o">=</span><span class="s2">&quot;;&quot;</span><span class="p">,</span> <span class="n">usecols</span><span class="o">=</span><span class="n">interval_dataframe_synthetic_columns</span><span class="p">())</span>
+
+    <span class="n">bests</span> <span class="o">=</span> <span class="n">find_best</span><span class="p">(</span><span class="n">dat_syn</span><span class="p">,</span> <span class="n">sort_columns</span><span class="p">,</span> <span class="n">sort_ascend</span><span class="p">)</span>
+
+    <span class="n">dat_ana</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">file_analytic</span><span class="p">,</span> <span class="n">sep</span><span class="o">=</span><span class="s2">&quot;;&quot;</span><span class="p">,</span> <span class="n">usecols</span><span class="o">=</span><span class="n">interval_dataframe_analytic_columns</span><span class="p">(</span><span class="n">experiments</span><span class="p">))</span>
+
+    <span class="n">data_columns</span> <span class="o">=</span> <span class="n">analytical_data_columns</span><span class="p">(</span><span class="n">experiments</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="n">save_best</span><span class="p">:</span>
+        <span class="n">dat</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="o">.</span><span class="n">from_dict</span><span class="p">(</span><span class="n">bests</span><span class="p">,</span> <span class="n">orient</span><span class="o">=</span><span class="s1">&#39;index&#39;</span><span class="p">)</span>
+        <span class="n">dat</span><span class="o">.</span><span class="n">to_csv</span><span class="p">(</span><span class="n">Util</span><span class="o">.</span><span class="n">uniquefilename</span><span class="p">(</span><span class="n">file_synthetic</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s2">&quot;synthetic&quot;</span><span class="p">,</span><span class="s2">&quot;best&quot;</span><span class="p">)),</span> <span class="n">sep</span><span class="o">=</span><span class="s2">&quot;;&quot;</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+
+    <span class="n">sharpness</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="n">resolution</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="n">coverage</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="n">times</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="n">labels</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="n">bounds_shp</span> <span class="o">=</span> <span class="p">[]</span>
+
+    <span class="k">for</span> <span class="n">b</span> <span class="ow">in</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">bests</span><span class="o">.</span><span class="n">keys</span><span class="p">()):</span>
+        <span class="k">if</span> <span class="n">check_ignore_list</span><span class="p">(</span><span class="n">b</span><span class="p">,</span> <span class="n">ignore</span><span class="p">):</span>
+            <span class="k">continue</span>
+        <span class="n">best</span> <span class="o">=</span> <span class="n">bests</span><span class="p">[</span><span class="n">b</span><span class="p">]</span>
+        <span class="n">df</span> <span class="o">=</span> <span class="n">dat_ana</span><span class="p">[(</span><span class="n">dat_ana</span><span class="o">.</span><span class="n">Model</span> <span class="o">==</span> <span class="n">best</span><span class="p">[</span><span class="s2">&quot;Model&quot;</span><span class="p">])</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">dat_ana</span><span class="o">.</span><span class="n">Order</span> <span class="o">==</span> <span class="n">best</span><span class="p">[</span><span class="s2">&quot;Order&quot;</span><span class="p">])</span>
+                <span class="o">&amp;</span> <span class="p">(</span><span class="n">dat_ana</span><span class="o">.</span><span class="n">Scheme</span> <span class="o">==</span> <span class="n">best</span><span class="p">[</span><span class="s2">&quot;Scheme&quot;</span><span class="p">])</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">dat_ana</span><span class="o">.</span><span class="n">Partitions</span> <span class="o">==</span> <span class="n">best</span><span class="p">[</span><span class="s2">&quot;Partitions&quot;</span><span class="p">])]</span>
+        <span class="n">sharpness</span><span class="o">.</span><span class="n">append</span><span class="p">(</span> <span class="n">extract_measure</span><span class="p">(</span><span class="n">df</span><span class="p">,</span><span class="s1">&#39;Sharpness&#39;</span><span class="p">,</span><span class="n">data_columns</span><span class="p">)</span> <span class="p">)</span>
+        <span class="n">resolution</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">extract_measure</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="s1">&#39;Resolution&#39;</span><span class="p">,</span> <span class="n">data_columns</span><span class="p">))</span>
+        <span class="n">coverage</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">extract_measure</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="s1">&#39;Coverage&#39;</span><span class="p">,</span> <span class="n">data_columns</span><span class="p">))</span>
+        <span class="n">times</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">extract_measure</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="s1">&#39;TIME&#39;</span><span class="p">,</span> <span class="n">data_columns</span><span class="p">))</span>
+        <span class="n">labels</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">check_replace_list</span><span class="p">(</span><span class="n">best</span><span class="p">[</span><span class="s2">&quot;Model&quot;</span><span class="p">]</span> <span class="o">+</span> <span class="s2">&quot; &quot;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">best</span><span class="p">[</span><span class="s2">&quot;Order&quot;</span><span class="p">]),</span> <span class="n">replace</span><span class="p">))</span>
+
+    <span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">boxplot</span><span class="p">(</span><span class="n">sharpness</span><span class="p">,</span> <span class="n">labels</span><span class="o">=</span><span class="n">labels</span><span class="p">,</span> <span class="n">autorange</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">showmeans</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+    <span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">&quot;Sharpness&quot;</span><span class="p">)</span>
+    <span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">boxplot</span><span class="p">(</span><span class="n">resolution</span><span class="p">,</span> <span class="n">labels</span><span class="o">=</span><span class="n">labels</span><span class="p">,</span> <span class="n">autorange</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">showmeans</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+    <span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">&quot;Resolution&quot;</span><span class="p">)</span>
+    <span class="n">axes</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">boxplot</span><span class="p">(</span><span class="n">coverage</span><span class="p">,</span> <span class="n">labels</span><span class="o">=</span><span class="n">labels</span><span class="p">,</span> <span class="n">autorange</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">showmeans</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+    <span class="n">axes</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">&quot;Coverage&quot;</span><span class="p">)</span>
+    <span class="n">axes</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mf">1.1</span><span class="p">])</span>
+
+    <span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
+
+    <span class="n">Util</span><span class="o">.</span><span class="n">show_and_save_image</span><span class="p">(</span><span class="n">fig</span><span class="p">,</span> <span class="n">file</span><span class="p">,</span> <span class="n">save</span><span class="p">)</span></div>
+
+
+
+<div class="viewcode-block" id="unified_scaled_interval_pinball"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.Util.unified_scaled_interval_pinball">[docs]</a><span class="k">def</span> <span class="nf">unified_scaled_interval_pinball</span><span class="p">(</span><span class="n">experiments</span><span class="p">,</span> <span class="n">tam</span><span class="p">,</span> <span class="n">save</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">file</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+                                    <span class="n">sort_columns</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;COVAVG&#39;</span><span class="p">,</span><span class="s1">&#39;SHARPAVG&#39;</span><span class="p">,</span><span class="s1">&#39;COVSTD&#39;</span><span class="p">,</span><span class="s1">&#39;SHARPSTD&#39;</span><span class="p">],</span>
+                                    <span class="n">sort_ascend</span><span class="o">=</span><span class="p">[</span><span class="kc">True</span><span class="p">,</span> <span class="kc">False</span><span class="p">,</span> <span class="kc">True</span><span class="p">,</span> <span class="kc">True</span><span class="p">],</span> <span class="n">save_best</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+                                    <span class="n">ignore</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">replace</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+    <span class="n">fig</span><span class="p">,</span> <span class="n">axes</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">nrows</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">ncols</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="n">tam</span><span class="p">)</span>
+    <span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="sa">r</span><span class="s1">&#39;$\tau=0.05$&#39;</span><span class="p">)</span>
+    <span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="sa">r</span><span class="s1">&#39;$\tau=0.25$&#39;</span><span class="p">)</span>
+    <span class="n">axes</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="sa">r</span><span class="s1">&#39;$\tau=0.75$&#39;</span><span class="p">)</span>
+    <span class="n">axes</span><span class="p">[</span><span class="mi">3</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="sa">r</span><span class="s1">&#39;$\tau=0.95$&#39;</span><span class="p">)</span>
+    <span class="n">models</span> <span class="o">=</span> <span class="p">{}</span>
+
+    <span class="k">for</span> <span class="n">experiment</span> <span class="ow">in</span> <span class="n">experiments</span><span class="p">:</span>
+
+        <span class="n">mdl</span> <span class="o">=</span> <span class="p">{}</span>
+
+        <span class="n">dat_syn</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">experiment</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">sep</span><span class="o">=</span><span class="s2">&quot;;&quot;</span><span class="p">,</span> <span class="n">usecols</span><span class="o">=</span><span class="n">interval_dataframe_synthetic_columns</span><span class="p">())</span>
+
+        <span class="n">bests</span> <span class="o">=</span> <span class="n">find_best</span><span class="p">(</span><span class="n">dat_syn</span><span class="p">,</span> <span class="n">sort_columns</span><span class="p">,</span> <span class="n">sort_ascend</span><span class="p">)</span>
+
+        <span class="n">dat_ana</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">experiment</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">sep</span><span class="o">=</span><span class="s2">&quot;;&quot;</span><span class="p">,</span> <span class="n">usecols</span><span class="o">=</span><span class="n">interval_dataframe_analytic_columns</span><span class="p">(</span><span class="n">experiment</span><span class="p">[</span><span class="mi">2</span><span class="p">]))</span>
+
+        <span class="n">q05</span>	<span class="o">=</span> <span class="p">[]</span>
+        <span class="n">q25</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="n">q75</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="n">q95</span> <span class="o">=</span> <span class="p">[]</span>
+
+        <span class="n">data_columns</span> <span class="o">=</span> <span class="n">analytical_data_columns</span><span class="p">(</span><span class="n">experiment</span><span class="p">[</span><span class="mi">2</span><span class="p">])</span>
+
+        <span class="k">for</span> <span class="n">b</span> <span class="ow">in</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">bests</span><span class="o">.</span><span class="n">keys</span><span class="p">()):</span>
+            <span class="k">if</span> <span class="n">check_ignore_list</span><span class="p">(</span><span class="n">b</span><span class="p">,</span> <span class="n">ignore</span><span class="p">):</span>
+                <span class="k">continue</span>
+
+            <span class="k">if</span> <span class="n">b</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">models</span><span class="p">:</span>
+                <span class="n">models</span><span class="p">[</span><span class="n">b</span><span class="p">]</span> <span class="o">=</span> <span class="p">{}</span>
+                <span class="n">models</span><span class="p">[</span><span class="n">b</span><span class="p">][</span><span class="s1">&#39;q05&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
+                <span class="n">models</span><span class="p">[</span><span class="n">b</span><span class="p">][</span><span class="s1">&#39;q25&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
+                <span class="n">models</span><span class="p">[</span><span class="n">b</span><span class="p">][</span><span class="s1">&#39;q75&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
+                <span class="n">models</span><span class="p">[</span><span class="n">b</span><span class="p">][</span><span class="s1">&#39;q95&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
+
+            <span class="k">if</span> <span class="n">b</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">mdl</span><span class="p">:</span>
+                <span class="n">mdl</span><span class="p">[</span><span class="n">b</span><span class="p">]</span> <span class="o">=</span> <span class="p">{}</span>
+                <span class="n">mdl</span><span class="p">[</span><span class="n">b</span><span class="p">][</span><span class="s1">&#39;q05&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
+                <span class="n">mdl</span><span class="p">[</span><span class="n">b</span><span class="p">][</span><span class="s1">&#39;q25&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
+                <span class="n">mdl</span><span class="p">[</span><span class="n">b</span><span class="p">][</span><span class="s1">&#39;q75&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
+                <span class="n">mdl</span><span class="p">[</span><span class="n">b</span><span class="p">][</span><span class="s1">&#39;q95&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
+
+            <span class="n">best</span> <span class="o">=</span> <span class="n">bests</span><span class="p">[</span><span class="n">b</span><span class="p">]</span>
+            <span class="nb">print</span><span class="p">(</span><span class="n">best</span><span class="p">)</span>
+            <span class="n">tmp</span> <span class="o">=</span> <span class="n">dat_ana</span><span class="p">[(</span><span class="n">dat_ana</span><span class="o">.</span><span class="n">Model</span> <span class="o">==</span> <span class="n">best</span><span class="p">[</span><span class="s2">&quot;Model&quot;</span><span class="p">])</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">dat_ana</span><span class="o">.</span><span class="n">Order</span> <span class="o">==</span> <span class="n">best</span><span class="p">[</span><span class="s2">&quot;Order&quot;</span><span class="p">])</span>
+                          <span class="o">&amp;</span> <span class="p">(</span><span class="n">dat_ana</span><span class="o">.</span><span class="n">Scheme</span> <span class="o">==</span> <span class="n">best</span><span class="p">[</span><span class="s2">&quot;Scheme&quot;</span><span class="p">])</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">dat_ana</span><span class="o">.</span><span class="n">Partitions</span> <span class="o">==</span> <span class="n">best</span><span class="p">[</span><span class="s2">&quot;Partitions&quot;</span><span class="p">])]</span>
+            <span class="n">tmpl</span> <span class="o">=</span> <span class="n">extract_measure</span><span class="p">(</span><span class="n">tmp</span><span class="p">,</span> <span class="s1">&#39;Q05&#39;</span><span class="p">,</span> <span class="n">data_columns</span><span class="p">)</span>
+            <span class="n">mdl</span><span class="p">[</span><span class="n">b</span><span class="p">][</span><span class="s1">&#39;q05&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">tmpl</span><span class="p">)</span>
+            <span class="n">q05</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">tmpl</span><span class="p">)</span>
+            <span class="n">tmpl</span> <span class="o">=</span> <span class="n">extract_measure</span><span class="p">(</span><span class="n">tmp</span><span class="p">,</span> <span class="s1">&#39;Q25&#39;</span><span class="p">,</span> <span class="n">data_columns</span><span class="p">)</span>
+            <span class="n">mdl</span><span class="p">[</span><span class="n">b</span><span class="p">][</span><span class="s1">&#39;q25&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">tmpl</span><span class="p">)</span>
+            <span class="n">q25</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">tmpl</span><span class="p">)</span>
+            <span class="n">tmpl</span> <span class="o">=</span> <span class="n">extract_measure</span><span class="p">(</span><span class="n">tmp</span><span class="p">,</span> <span class="s1">&#39;Q75&#39;</span><span class="p">,</span> <span class="n">data_columns</span><span class="p">)</span>
+            <span class="n">mdl</span><span class="p">[</span><span class="n">b</span><span class="p">][</span><span class="s1">&#39;q75&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">tmpl</span><span class="p">)</span>
+            <span class="n">q75</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">tmpl</span><span class="p">)</span>
+            <span class="n">tmpl</span> <span class="o">=</span> <span class="n">extract_measure</span><span class="p">(</span><span class="n">tmp</span><span class="p">,</span> <span class="s1">&#39;Q95&#39;</span><span class="p">,</span> <span class="n">data_columns</span><span class="p">)</span>
+            <span class="n">mdl</span><span class="p">[</span><span class="n">b</span><span class="p">][</span><span class="s1">&#39;q95&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">tmpl</span><span class="p">)</span>
+            <span class="n">q95</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">tmpl</span><span class="p">)</span>
+
+            <span class="n">models</span><span class="p">[</span><span class="n">b</span><span class="p">][</span><span class="s1">&#39;label&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">check_replace_list</span><span class="p">(</span><span class="n">best</span><span class="p">[</span><span class="s2">&quot;Model&quot;</span><span class="p">]</span> <span class="o">+</span> <span class="s2">&quot; &quot;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">best</span><span class="p">[</span><span class="s2">&quot;Order&quot;</span><span class="p">]),</span> <span class="n">replace</span><span class="p">)</span>
+
+        <span class="n">q05_param</span> <span class="o">=</span> <span class="n">scale_params</span><span class="p">(</span><span class="n">q05</span><span class="p">)</span>
+        <span class="n">q25_param</span> <span class="o">=</span> <span class="n">scale_params</span><span class="p">(</span><span class="n">q25</span><span class="p">)</span>
+        <span class="n">q75_param</span> <span class="o">=</span> <span class="n">scale_params</span><span class="p">(</span><span class="n">q75</span><span class="p">)</span>
+        <span class="n">q95_param</span> <span class="o">=</span> <span class="n">scale_params</span><span class="p">(</span><span class="n">q95</span><span class="p">)</span>
+
+        <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">models</span><span class="o">.</span><span class="n">keys</span><span class="p">()):</span>
+            <span class="n">models</span><span class="p">[</span><span class="n">key</span><span class="p">][</span><span class="s1">&#39;q05&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">scale</span><span class="p">(</span><span class="n">mdl</span><span class="p">[</span><span class="n">key</span><span class="p">][</span><span class="s1">&#39;q05&#39;</span><span class="p">],</span> <span class="n">q05_param</span><span class="p">))</span>
+            <span class="n">models</span><span class="p">[</span><span class="n">key</span><span class="p">][</span><span class="s1">&#39;q25&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">scale</span><span class="p">(</span><span class="n">mdl</span><span class="p">[</span><span class="n">key</span><span class="p">][</span><span class="s1">&#39;q25&#39;</span><span class="p">],</span> <span class="n">q25_param</span><span class="p">))</span>
+            <span class="n">models</span><span class="p">[</span><span class="n">key</span><span class="p">][</span><span class="s1">&#39;q75&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">scale</span><span class="p">(</span><span class="n">mdl</span><span class="p">[</span><span class="n">key</span><span class="p">][</span><span class="s1">&#39;q75&#39;</span><span class="p">],</span> <span class="n">q75_param</span><span class="p">))</span>
+            <span class="n">models</span><span class="p">[</span><span class="n">key</span><span class="p">][</span><span class="s1">&#39;q95&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">scale</span><span class="p">(</span><span class="n">mdl</span><span class="p">[</span><span class="n">key</span><span class="p">][</span><span class="s1">&#39;q95&#39;</span><span class="p">],</span> <span class="n">q95_param</span><span class="p">))</span>
+
+    <span class="n">q05</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="n">q25</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="n">q75</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="n">q95</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="n">labels</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">models</span><span class="o">.</span><span class="n">keys</span><span class="p">()):</span>
+        <span class="n">q05</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">models</span><span class="p">[</span><span class="n">key</span><span class="p">][</span><span class="s1">&#39;q05&#39;</span><span class="p">])</span>
+        <span class="n">q25</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">models</span><span class="p">[</span><span class="n">key</span><span class="p">][</span><span class="s1">&#39;q25&#39;</span><span class="p">])</span>
+        <span class="n">q75</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">models</span><span class="p">[</span><span class="n">key</span><span class="p">][</span><span class="s1">&#39;q75&#39;</span><span class="p">])</span>
+        <span class="n">q95</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">models</span><span class="p">[</span><span class="n">key</span><span class="p">][</span><span class="s1">&#39;q95&#39;</span><span class="p">])</span>
+        <span class="n">labels</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">models</span><span class="p">[</span><span class="n">key</span><span class="p">][</span><span class="s1">&#39;label&#39;</span><span class="p">])</span>
+
+    <span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">boxplot</span><span class="p">(</span><span class="n">q05</span><span class="p">,</span> <span class="n">labels</span><span class="o">=</span><span class="n">labels</span><span class="p">,</span> <span class="n">vert</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">autorange</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">showmeans</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+    <span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">boxplot</span><span class="p">(</span><span class="n">q25</span><span class="p">,</span> <span class="n">labels</span><span class="o">=</span><span class="n">labels</span><span class="p">,</span> <span class="n">vert</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">autorange</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">showmeans</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+    <span class="n">axes</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">boxplot</span><span class="p">(</span><span class="n">q75</span><span class="p">,</span> <span class="n">labels</span><span class="o">=</span><span class="n">labels</span><span class="p">,</span> <span class="n">vert</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">autorange</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">showmeans</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+    <span class="n">axes</span><span class="p">[</span><span class="mi">3</span><span class="p">]</span><span class="o">.</span><span class="n">boxplot</span><span class="p">(</span><span class="n">q95</span><span class="p">,</span> <span class="n">labels</span><span class="o">=</span><span class="n">labels</span><span class="p">,</span> <span class="n">vert</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">autorange</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">showmeans</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+
+    <span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
+
+    <span class="n">Util</span><span class="o">.</span><span class="n">show_and_save_image</span><span class="p">(</span><span class="n">fig</span><span class="p">,</span> <span class="n">file</span><span class="p">,</span> <span class="n">save</span><span class="p">)</span></div>
+
+
+
+<div class="viewcode-block" id="plot_dataframe_interval_pinball"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.Util.plot_dataframe_interval_pinball">[docs]</a><span class="k">def</span> <span class="nf">plot_dataframe_interval_pinball</span><span class="p">(</span><span class="n">file_synthetic</span><span class="p">,</span> <span class="n">file_analytic</span><span class="p">,</span> <span class="n">experiments</span><span class="p">,</span> <span class="n">tam</span><span class="p">,</span> <span class="n">save</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">file</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+                                    <span class="n">sort_columns</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;COVAVG&#39;</span><span class="p">,</span><span class="s1">&#39;SHARPAVG&#39;</span><span class="p">,</span><span class="s1">&#39;COVSTD&#39;</span><span class="p">,</span><span class="s1">&#39;SHARPSTD&#39;</span><span class="p">],</span>
+                                    <span class="n">sort_ascend</span><span class="o">=</span><span class="p">[</span><span class="kc">True</span><span class="p">,</span> <span class="kc">False</span><span class="p">,</span> <span class="kc">True</span><span class="p">,</span> <span class="kc">True</span><span class="p">],</span> <span class="n">save_best</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+                                    <span class="n">ignore</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">replace</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+
+    <span class="n">fig</span><span class="p">,</span> <span class="n">axes</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">nrows</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">ncols</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="n">tam</span><span class="p">)</span>
+    <span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="sa">r</span><span class="s1">&#39;$\tau=0.05$&#39;</span><span class="p">)</span>
+    <span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="sa">r</span><span class="s1">&#39;$\tau=0.25$&#39;</span><span class="p">)</span>
+    <span class="n">axes</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="sa">r</span><span class="s1">&#39;$\tau=0.75$&#39;</span><span class="p">)</span>
+    <span class="n">axes</span><span class="p">[</span><span class="mi">3</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="sa">r</span><span class="s1">&#39;$\tau=0.95$&#39;</span><span class="p">)</span>
+
+    <span class="n">dat_syn</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">file_synthetic</span><span class="p">,</span> <span class="n">sep</span><span class="o">=</span><span class="s2">&quot;;&quot;</span><span class="p">,</span> <span class="n">usecols</span><span class="o">=</span><span class="n">interval_dataframe_synthetic_columns</span><span class="p">())</span>
+
+    <span class="n">bests</span> <span class="o">=</span> <span class="n">find_best</span><span class="p">(</span><span class="n">dat_syn</span><span class="p">,</span> <span class="n">sort_columns</span><span class="p">,</span> <span class="n">sort_ascend</span><span class="p">)</span>
+
+    <span class="n">dat_ana</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">file_analytic</span><span class="p">,</span> <span class="n">sep</span><span class="o">=</span><span class="s2">&quot;;&quot;</span><span class="p">,</span> <span class="n">usecols</span><span class="o">=</span><span class="n">interval_dataframe_analytic_columns</span><span class="p">(</span><span class="n">experiments</span><span class="p">))</span>
+
+    <span class="n">data_columns</span> <span class="o">=</span> <span class="n">analytical_data_columns</span><span class="p">(</span><span class="n">experiments</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="n">save_best</span><span class="p">:</span>
+        <span class="n">dat</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="o">.</span><span class="n">from_dict</span><span class="p">(</span><span class="n">bests</span><span class="p">,</span> <span class="n">orient</span><span class="o">=</span><span class="s1">&#39;index&#39;</span><span class="p">)</span>
+        <span class="n">dat</span><span class="o">.</span><span class="n">to_csv</span><span class="p">(</span><span class="n">Util</span><span class="o">.</span><span class="n">uniquefilename</span><span class="p">(</span><span class="n">file_synthetic</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s2">&quot;synthetic&quot;</span><span class="p">,</span><span class="s2">&quot;best&quot;</span><span class="p">)),</span> <span class="n">sep</span><span class="o">=</span><span class="s2">&quot;;&quot;</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+
+    <span class="n">q05</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="n">q25</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="n">q75</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="n">q95</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="n">labels</span> <span class="o">=</span> <span class="p">[]</span>
+
+    <span class="k">for</span> <span class="n">b</span> <span class="ow">in</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">bests</span><span class="o">.</span><span class="n">keys</span><span class="p">()):</span>
+        <span class="k">if</span> <span class="n">check_ignore_list</span><span class="p">(</span><span class="n">b</span><span class="p">,</span> <span class="n">ignore</span><span class="p">):</span>
+            <span class="k">continue</span>
+        <span class="n">best</span> <span class="o">=</span> <span class="n">bests</span><span class="p">[</span><span class="n">b</span><span class="p">]</span>
+        <span class="n">df</span> <span class="o">=</span> <span class="n">dat_ana</span><span class="p">[(</span><span class="n">dat_ana</span><span class="o">.</span><span class="n">Model</span> <span class="o">==</span> <span class="n">best</span><span class="p">[</span><span class="s2">&quot;Model&quot;</span><span class="p">])</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">dat_ana</span><span class="o">.</span><span class="n">Order</span> <span class="o">==</span> <span class="n">best</span><span class="p">[</span><span class="s2">&quot;Order&quot;</span><span class="p">])</span>
+                <span class="o">&amp;</span> <span class="p">(</span><span class="n">dat_ana</span><span class="o">.</span><span class="n">Scheme</span> <span class="o">==</span> <span class="n">best</span><span class="p">[</span><span class="s2">&quot;Scheme&quot;</span><span class="p">])</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">dat_ana</span><span class="o">.</span><span class="n">Partitions</span> <span class="o">==</span> <span class="n">best</span><span class="p">[</span><span class="s2">&quot;Partitions&quot;</span><span class="p">])]</span>
+        <span class="n">q05</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">extract_measure</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="s1">&#39;Q05&#39;</span><span class="p">,</span> <span class="n">data_columns</span><span class="p">))</span>
+        <span class="n">q25</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">extract_measure</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="s1">&#39;Q25&#39;</span><span class="p">,</span> <span class="n">data_columns</span><span class="p">))</span>
+        <span class="n">q75</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">extract_measure</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="s1">&#39;Q75&#39;</span><span class="p">,</span> <span class="n">data_columns</span><span class="p">))</span>
+        <span class="n">q95</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">extract_measure</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="s1">&#39;Q95&#39;</span><span class="p">,</span> <span class="n">data_columns</span><span class="p">))</span>
+        <span class="n">labels</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">check_replace_list</span><span class="p">(</span><span class="n">best</span><span class="p">[</span><span class="s2">&quot;Model&quot;</span><span class="p">]</span> <span class="o">+</span> <span class="s2">&quot; &quot;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">best</span><span class="p">[</span><span class="s2">&quot;Order&quot;</span><span class="p">]),</span> <span class="n">replace</span><span class="p">))</span>
+
+    <span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">boxplot</span><span class="p">(</span><span class="n">q05</span><span class="p">,</span> <span class="n">labels</span><span class="o">=</span><span class="n">labels</span><span class="p">,</span> <span class="n">vert</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">autorange</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">showmeans</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+    <span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">boxplot</span><span class="p">(</span><span class="n">q25</span><span class="p">,</span> <span class="n">labels</span><span class="o">=</span><span class="n">labels</span><span class="p">,</span> <span class="n">vert</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">autorange</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">showmeans</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+    <span class="n">axes</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">boxplot</span><span class="p">(</span><span class="n">q75</span><span class="p">,</span> <span class="n">labels</span><span class="o">=</span><span class="n">labels</span><span class="p">,</span> <span class="n">vert</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">autorange</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">showmeans</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+    <span class="n">axes</span><span class="p">[</span><span class="mi">3</span><span class="p">]</span><span class="o">.</span><span class="n">boxplot</span><span class="p">(</span><span class="n">q95</span><span class="p">,</span> <span class="n">labels</span><span class="o">=</span><span class="n">labels</span><span class="p">,</span> <span class="n">vert</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">autorange</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">showmeans</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+
+    <span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
+
+    <span class="n">Util</span><span class="o">.</span><span class="n">show_and_save_image</span><span class="p">(</span><span class="n">fig</span><span class="p">,</span> <span class="n">file</span><span class="p">,</span> <span class="n">save</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="save_dataframe_probabilistic"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.Util.save_dataframe_probabilistic">[docs]</a><span class="k">def</span> <span class="nf">save_dataframe_probabilistic</span><span class="p">(</span><span class="n">experiments</span><span class="p">,</span> <span class="n">file</span><span class="p">,</span> <span class="n">objs</span><span class="p">,</span> <span class="n">crps</span><span class="p">,</span> <span class="n">times</span><span class="p">,</span> <span class="n">save</span><span class="p">,</span> <span class="n">synthetic</span><span class="p">,</span> <span class="n">steps</span><span class="p">,</span> <span class="n">method</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Save benchmark results for m-step ahead probabilistic forecasters </span>
+<span class="sd">    :param experiments: </span>
+<span class="sd">    :param file: </span>
+<span class="sd">    :param objs: </span>
+<span class="sd">    :param crps_interval: </span>
+<span class="sd">    :param crps_distr: </span>
+<span class="sd">    :param times: </span>
+<span class="sd">    :param times2: </span>
+<span class="sd">    :param save: </span>
+<span class="sd">    :param synthetic: </span>
+<span class="sd">    :return: </span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
+
+    <span class="k">if</span> <span class="n">synthetic</span><span class="p">:</span>
+
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">objs</span><span class="o">.</span><span class="n">keys</span><span class="p">()):</span>
+            <span class="k">try</span><span class="p">:</span>
+                <span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
+                <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">objs</span><span class="o">.</span><span class="n">keys</span><span class="p">()):</span>
+                    <span class="k">try</span><span class="p">:</span>
+                        <span class="n">mod</span> <span class="o">=</span> <span class="p">[]</span>
+                        <span class="n">mfts</span> <span class="o">=</span> <span class="n">objs</span><span class="p">[</span><span class="n">k</span><span class="p">]</span>
+                        <span class="n">mod</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">mfts</span><span class="o">.</span><span class="n">shortname</span><span class="p">)</span>
+                        <span class="n">mod</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">mfts</span><span class="o">.</span><span class="n">order</span><span class="p">)</span>
+                        <span class="k">if</span> <span class="ow">not</span> <span class="n">mfts</span><span class="o">.</span><span class="n">benchmark_only</span><span class="p">:</span>
+                            <span class="n">mod</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">mfts</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">name</span><span class="p">)</span>
+                            <span class="n">mod</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">mfts</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">partitions</span><span class="p">)</span>
+                            <span class="n">mod</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">mfts</span><span class="p">))</span>
+                        <span class="k">else</span><span class="p">:</span>
+                            <span class="n">mod</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s1">&#39;-&#39;</span><span class="p">)</span>
+                            <span class="n">mod</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s1">&#39;-&#39;</span><span class="p">)</span>
+                            <span class="n">mod</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s1">&#39;-&#39;</span><span class="p">)</span>
+                        <span class="n">mod</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">steps</span><span class="p">[</span><span class="n">k</span><span class="p">])</span>
+                        <span class="n">mod</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">method</span><span class="p">[</span><span class="n">k</span><span class="p">])</span>
+                        <span class="n">mod</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">nanmean</span><span class="p">(</span><span class="n">crps</span><span class="p">[</span><span class="n">k</span><span class="p">]),</span> <span class="mi">2</span><span class="p">))</span>
+                        <span class="n">mod</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">nanstd</span><span class="p">(</span><span class="n">crps</span><span class="p">[</span><span class="n">k</span><span class="p">]),</span> <span class="mi">2</span><span class="p">))</span>
+                        <span class="n">mod</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">nanmean</span><span class="p">(</span><span class="n">times</span><span class="p">[</span><span class="n">k</span><span class="p">]),</span> <span class="mi">4</span><span class="p">))</span>
+                        <span class="n">mod</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">nanstd</span><span class="p">(</span><span class="n">times</span><span class="p">[</span><span class="n">k</span><span class="p">]),</span> <span class="mi">4</span><span class="p">))</span>
+                        <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">mod</span><span class="p">)</span>
+                    <span class="k">except</span> <span class="ne">Exception</span> <span class="k">as</span> <span class="n">e</span><span class="p">:</span>
+                        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Erro: </span><span class="si">%s</span><span class="s1">&#39;</span> <span class="o">%</span> <span class="n">e</span><span class="p">)</span>
+            <span class="k">except</span> <span class="ne">Exception</span> <span class="k">as</span> <span class="n">ex</span><span class="p">:</span>
+                <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Erro ao salvar &quot;</span><span class="p">,</span> <span class="n">k</span><span class="p">)</span>
+                <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Exceção &quot;</span><span class="p">,</span> <span class="n">ex</span><span class="p">)</span>
+
+        <span class="n">columns</span> <span class="o">=</span> <span class="n">probabilistic_dataframe_synthetic_columns</span><span class="p">()</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">objs</span><span class="o">.</span><span class="n">keys</span><span class="p">()):</span>
+            <span class="k">try</span><span class="p">:</span>
+                <span class="n">mfts</span> <span class="o">=</span> <span class="n">objs</span><span class="p">[</span><span class="n">k</span><span class="p">]</span>
+                <span class="n">n</span> <span class="o">=</span> <span class="n">mfts</span><span class="o">.</span><span class="n">shortname</span>
+                <span class="n">o</span> <span class="o">=</span> <span class="n">mfts</span><span class="o">.</span><span class="n">order</span>
+                <span class="k">if</span> <span class="ow">not</span> <span class="n">mfts</span><span class="o">.</span><span class="n">benchmark_only</span><span class="p">:</span>
+                    <span class="n">s</span> <span class="o">=</span> <span class="n">mfts</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">name</span>
+                    <span class="n">p</span> <span class="o">=</span> <span class="n">mfts</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">partitions</span>
+                    <span class="n">l</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">mfts</span><span class="p">)</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">s</span> <span class="o">=</span> <span class="s1">&#39;-&#39;</span>
+                    <span class="n">p</span> <span class="o">=</span> <span class="s1">&#39;-&#39;</span>
+                    <span class="n">l</span> <span class="o">=</span> <span class="s1">&#39;-&#39;</span>
+                <span class="n">st</span> <span class="o">=</span> <span class="n">steps</span><span class="p">[</span><span class="n">k</span><span class="p">]</span>
+                <span class="n">mt</span> <span class="o">=</span> <span class="n">method</span><span class="p">[</span><span class="n">k</span><span class="p">]</span>
+                <span class="n">tmp</span> <span class="o">=</span> <span class="p">[</span><span class="n">n</span><span class="p">,</span> <span class="n">o</span><span class="p">,</span> <span class="n">s</span><span class="p">,</span> <span class="n">p</span><span class="p">,</span> <span class="n">l</span><span class="p">,</span> <span class="n">st</span><span class="p">,</span> <span class="n">mt</span><span class="p">,</span> <span class="s1">&#39;CRPS&#39;</span><span class="p">]</span>
+                <span class="n">tmp</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">crps</span><span class="p">[</span><span class="n">k</span><span class="p">])</span>
+                <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">deepcopy</span><span class="p">(</span><span class="n">tmp</span><span class="p">))</span>
+                <span class="n">tmp</span> <span class="o">=</span> <span class="p">[</span><span class="n">n</span><span class="p">,</span> <span class="n">o</span><span class="p">,</span> <span class="n">s</span><span class="p">,</span> <span class="n">p</span><span class="p">,</span> <span class="n">l</span><span class="p">,</span> <span class="n">st</span><span class="p">,</span> <span class="n">mt</span><span class="p">,</span> <span class="s1">&#39;TIME&#39;</span><span class="p">]</span>
+                <span class="n">tmp</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">times</span><span class="p">[</span><span class="n">k</span><span class="p">])</span>
+                <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">deepcopy</span><span class="p">(</span><span class="n">tmp</span><span class="p">))</span>
+            <span class="k">except</span> <span class="ne">Exception</span> <span class="k">as</span> <span class="n">ex</span><span class="p">:</span>
+                <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Erro ao salvar &quot;</span><span class="p">,</span> <span class="n">k</span><span class="p">)</span>
+                <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Exceção &quot;</span><span class="p">,</span> <span class="n">ex</span><span class="p">)</span>
+        <span class="n">columns</span> <span class="o">=</span> <span class="n">probabilistic_dataframe_analytic_columns</span><span class="p">(</span><span class="n">experiments</span><span class="p">)</span>
+    <span class="n">dat</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">ret</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="n">columns</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">save</span><span class="p">:</span> <span class="n">dat</span><span class="o">.</span><span class="n">to_csv</span><span class="p">(</span><span class="n">Util</span><span class="o">.</span><span class="n">uniquefilename</span><span class="p">(</span><span class="n">file</span><span class="p">),</span> <span class="n">sep</span><span class="o">=</span><span class="s2">&quot;;&quot;</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">dat</span></div>
+
+
+<div class="viewcode-block" id="probabilistic_dataframe_analytic_columns"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.Util.probabilistic_dataframe_analytic_columns">[docs]</a><span class="k">def</span> <span class="nf">probabilistic_dataframe_analytic_columns</span><span class="p">(</span><span class="n">experiments</span><span class="p">):</span>
+    <span class="n">columns</span> <span class="o">=</span> <span class="p">[</span><span class="nb">str</span><span class="p">(</span><span class="n">k</span><span class="p">)</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">experiments</span><span class="p">)]</span>
+    <span class="n">columns</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="s2">&quot;Model&quot;</span><span class="p">)</span>
+    <span class="n">columns</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="s2">&quot;Order&quot;</span><span class="p">)</span>
+    <span class="n">columns</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="s2">&quot;Scheme&quot;</span><span class="p">)</span>
+    <span class="n">columns</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="s2">&quot;Partitions&quot;</span><span class="p">)</span>
+    <span class="n">columns</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="s2">&quot;Size&quot;</span><span class="p">)</span>
+    <span class="n">columns</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="s2">&quot;Steps&quot;</span><span class="p">)</span>
+    <span class="n">columns</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="mi">6</span><span class="p">,</span> <span class="s2">&quot;Method&quot;</span><span class="p">)</span>
+    <span class="n">columns</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="mi">7</span><span class="p">,</span> <span class="s2">&quot;Measure&quot;</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">columns</span></div>
+
+
+<div class="viewcode-block" id="probabilistic_dataframe_synthetic_columns"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.Util.probabilistic_dataframe_synthetic_columns">[docs]</a><span class="k">def</span> <span class="nf">probabilistic_dataframe_synthetic_columns</span><span class="p">():</span>
+    <span class="n">columns</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;Model&quot;</span><span class="p">,</span> <span class="s2">&quot;Order&quot;</span><span class="p">,</span> <span class="s2">&quot;Scheme&quot;</span><span class="p">,</span> <span class="s2">&quot;Partitions&quot;</span><span class="p">,</span><span class="s2">&quot;Size&quot;</span><span class="p">,</span> <span class="s2">&quot;Steps&quot;</span><span class="p">,</span> <span class="s2">&quot;Method&quot;</span><span class="p">,</span> <span class="s2">&quot;CRPSAVG&quot;</span><span class="p">,</span> <span class="s2">&quot;CRPSSTD&quot;</span><span class="p">,</span>
+               <span class="s2">&quot;TIMEAVG&quot;</span><span class="p">,</span> <span class="s2">&quot;TIMESTD&quot;</span><span class="p">]</span>
+    <span class="k">return</span> <span class="n">columns</span></div>
+
+
+<div class="viewcode-block" id="cast_dataframe_to_synthetic_probabilistic"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.Util.cast_dataframe_to_synthetic_probabilistic">[docs]</a><span class="k">def</span> <span class="nf">cast_dataframe_to_synthetic_probabilistic</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">data_columns</span><span class="p">):</span>
+    <span class="n">crps1</span> <span class="o">=</span> <span class="n">extract_measure</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="s1">&#39;CRPS&#39;</span><span class="p">,</span> <span class="n">data_columns</span><span class="p">)</span>
+    <span class="n">times1</span> <span class="o">=</span> <span class="n">extract_measure</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="s1">&#39;TIME&#39;</span><span class="p">,</span> <span class="n">data_columns</span><span class="p">)</span>
+    <span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">nanmean</span><span class="p">(</span><span class="n">crps1</span><span class="p">),</span> <span class="mi">2</span><span class="p">))</span>
+    <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">nanstd</span><span class="p">(</span><span class="n">crps1</span><span class="p">),</span> <span class="mi">2</span><span class="p">))</span>
+    <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">nanmean</span><span class="p">(</span><span class="n">times1</span><span class="p">),</span> <span class="mi">2</span><span class="p">))</span>
+    <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">nanstd</span><span class="p">(</span><span class="n">times1</span><span class="p">),</span> <span class="mi">2</span><span class="p">))</span>
+    <span class="k">return</span> <span class="n">ret</span></div>
+
+
+<div class="viewcode-block" id="unified_scaled_probabilistic"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.Util.unified_scaled_probabilistic">[docs]</a><span class="k">def</span> <span class="nf">unified_scaled_probabilistic</span><span class="p">(</span><span class="n">experiments</span><span class="p">,</span> <span class="n">tam</span><span class="p">,</span> <span class="n">save</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">file</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+                                 <span class="n">sort_columns</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;CRPSAVG&#39;</span><span class="p">,</span> <span class="s1">&#39;CRPSSTD&#39;</span><span class="p">],</span>
+                                 <span class="n">sort_ascend</span><span class="o">=</span><span class="p">[</span><span class="kc">True</span><span class="p">,</span> <span class="kc">True</span><span class="p">],</span> <span class="n">save_best</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+                                 <span class="n">ignore</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">replace</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+    <span class="n">fig</span><span class="p">,</span> <span class="n">axes</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">nrows</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">ncols</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="n">tam</span><span class="p">)</span>
+
+    <span class="n">axes</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">&#39;CRPS&#39;</span><span class="p">)</span>
+    <span class="c1">#axes[1].set_title(&#39;CRPS Distribution Ahead&#39;)</span>
+
+    <span class="n">models</span> <span class="o">=</span> <span class="p">{}</span>
+
+    <span class="k">for</span> <span class="n">experiment</span> <span class="ow">in</span> <span class="n">experiments</span><span class="p">:</span>
+
+        <span class="nb">print</span><span class="p">(</span><span class="n">experiment</span><span class="p">)</span>
+
+        <span class="n">mdl</span> <span class="o">=</span> <span class="p">{}</span>
+
+        <span class="n">dat_syn</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">experiment</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">sep</span><span class="o">=</span><span class="s2">&quot;;&quot;</span><span class="p">,</span> <span class="n">usecols</span><span class="o">=</span><span class="n">probabilistic_dataframe_synthetic_columns</span><span class="p">())</span>
+
+        <span class="n">bests</span> <span class="o">=</span> <span class="n">find_best</span><span class="p">(</span><span class="n">dat_syn</span><span class="p">,</span> <span class="n">sort_columns</span><span class="p">,</span> <span class="n">sort_ascend</span><span class="p">)</span>
+
+        <span class="n">dat_ana</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">experiment</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">sep</span><span class="o">=</span><span class="s2">&quot;;&quot;</span><span class="p">,</span> <span class="n">usecols</span><span class="o">=</span><span class="n">probabilistic_dataframe_analytic_columns</span><span class="p">(</span><span class="n">experiment</span><span class="p">[</span><span class="mi">2</span><span class="p">]))</span>
+
+        <span class="n">crps1</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="n">crps2</span> <span class="o">=</span> <span class="p">[]</span>
+
+        <span class="n">data_columns</span> <span class="o">=</span> <span class="n">analytical_data_columns</span><span class="p">(</span><span class="n">experiment</span><span class="p">[</span><span class="mi">2</span><span class="p">])</span>
+
+        <span class="k">for</span> <span class="n">b</span> <span class="ow">in</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">bests</span><span class="o">.</span><span class="n">keys</span><span class="p">()):</span>
+            <span class="k">if</span> <span class="n">check_ignore_list</span><span class="p">(</span><span class="n">b</span><span class="p">,</span> <span class="n">ignore</span><span class="p">):</span>
+                <span class="k">continue</span>
+
+            <span class="k">if</span> <span class="n">b</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">models</span><span class="p">:</span>
+                <span class="n">models</span><span class="p">[</span><span class="n">b</span><span class="p">]</span> <span class="o">=</span> <span class="p">{}</span>
+                <span class="n">models</span><span class="p">[</span><span class="n">b</span><span class="p">][</span><span class="s1">&#39;crps1&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
+                <span class="n">models</span><span class="p">[</span><span class="n">b</span><span class="p">][</span><span class="s1">&#39;crps2&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
+
+            <span class="k">if</span> <span class="n">b</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">mdl</span><span class="p">:</span>
+                <span class="n">mdl</span><span class="p">[</span><span class="n">b</span><span class="p">]</span> <span class="o">=</span> <span class="p">{}</span>
+                <span class="n">mdl</span><span class="p">[</span><span class="n">b</span><span class="p">][</span><span class="s1">&#39;crps1&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
+                <span class="n">mdl</span><span class="p">[</span><span class="n">b</span><span class="p">][</span><span class="s1">&#39;crps2&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
+
+            <span class="n">best</span> <span class="o">=</span> <span class="n">bests</span><span class="p">[</span><span class="n">b</span><span class="p">]</span>
+
+            <span class="nb">print</span><span class="p">(</span><span class="n">best</span><span class="p">)</span>
+
+            <span class="n">tmp</span> <span class="o">=</span> <span class="n">dat_ana</span><span class="p">[(</span><span class="n">dat_ana</span><span class="o">.</span><span class="n">Model</span> <span class="o">==</span> <span class="n">best</span><span class="p">[</span><span class="s2">&quot;Model&quot;</span><span class="p">])</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">dat_ana</span><span class="o">.</span><span class="n">Order</span> <span class="o">==</span> <span class="n">best</span><span class="p">[</span><span class="s2">&quot;Order&quot;</span><span class="p">])</span>
+                          <span class="o">&amp;</span> <span class="p">(</span><span class="n">dat_ana</span><span class="o">.</span><span class="n">Scheme</span> <span class="o">==</span> <span class="n">best</span><span class="p">[</span><span class="s2">&quot;Scheme&quot;</span><span class="p">])</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">dat_ana</span><span class="o">.</span><span class="n">Partitions</span> <span class="o">==</span> <span class="n">best</span><span class="p">[</span><span class="s2">&quot;Partitions&quot;</span><span class="p">])]</span>
+            <span class="n">tmpl</span> <span class="o">=</span> <span class="n">extract_measure</span><span class="p">(</span><span class="n">tmp</span><span class="p">,</span> <span class="s1">&#39;CRPS_Interval&#39;</span><span class="p">,</span> <span class="n">data_columns</span><span class="p">)</span>
+            <span class="n">mdl</span><span class="p">[</span><span class="n">b</span><span class="p">][</span><span class="s1">&#39;crps1&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">tmpl</span><span class="p">)</span>
+            <span class="n">crps1</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">tmpl</span><span class="p">)</span>
+            <span class="n">tmpl</span> <span class="o">=</span> <span class="n">extract_measure</span><span class="p">(</span><span class="n">tmp</span><span class="p">,</span> <span class="s1">&#39;CRPS_Distribution&#39;</span><span class="p">,</span> <span class="n">data_columns</span><span class="p">)</span>
+            <span class="n">mdl</span><span class="p">[</span><span class="n">b</span><span class="p">][</span><span class="s1">&#39;crps2&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">tmpl</span><span class="p">)</span>
+            <span class="n">crps2</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">tmpl</span><span class="p">)</span>
+
+            <span class="n">models</span><span class="p">[</span><span class="n">b</span><span class="p">][</span><span class="s1">&#39;label&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">check_replace_list</span><span class="p">(</span><span class="n">best</span><span class="p">[</span><span class="s2">&quot;Model&quot;</span><span class="p">]</span> <span class="o">+</span> <span class="s2">&quot; &quot;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">best</span><span class="p">[</span><span class="s2">&quot;Order&quot;</span><span class="p">]),</span> <span class="n">replace</span><span class="p">)</span>
+
+        <span class="n">crps1_param</span> <span class="o">=</span> <span class="n">scale_params</span><span class="p">(</span><span class="n">crps1</span><span class="p">)</span>
+        <span class="n">crps2_param</span> <span class="o">=</span> <span class="n">scale_params</span><span class="p">(</span><span class="n">crps2</span><span class="p">)</span>
+
+        <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">mdl</span><span class="o">.</span><span class="n">keys</span><span class="p">()):</span>
+            <span class="nb">print</span><span class="p">(</span><span class="n">key</span><span class="p">)</span>
+            <span class="n">models</span><span class="p">[</span><span class="n">key</span><span class="p">][</span><span class="s1">&#39;crps1&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">scale</span><span class="p">(</span><span class="n">mdl</span><span class="p">[</span><span class="n">key</span><span class="p">][</span><span class="s1">&#39;crps1&#39;</span><span class="p">],</span> <span class="n">crps1_param</span><span class="p">))</span>
+            <span class="n">models</span><span class="p">[</span><span class="n">key</span><span class="p">][</span><span class="s1">&#39;crps2&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">scale</span><span class="p">(</span><span class="n">mdl</span><span class="p">[</span><span class="n">key</span><span class="p">][</span><span class="s1">&#39;crps2&#39;</span><span class="p">],</span> <span class="n">crps2_param</span><span class="p">))</span>
+
+    <span class="n">crps1</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="n">crps2</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="n">labels</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">models</span><span class="o">.</span><span class="n">keys</span><span class="p">()):</span>
+        <span class="n">crps1</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">models</span><span class="p">[</span><span class="n">key</span><span class="p">][</span><span class="s1">&#39;crps1&#39;</span><span class="p">])</span>
+        <span class="n">crps2</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">models</span><span class="p">[</span><span class="n">key</span><span class="p">][</span><span class="s1">&#39;crps2&#39;</span><span class="p">])</span>
+        <span class="n">labels</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">models</span><span class="p">[</span><span class="n">key</span><span class="p">][</span><span class="s1">&#39;label&#39;</span><span class="p">])</span>
+
+    <span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">boxplot</span><span class="p">(</span><span class="n">crps1</span><span class="p">,</span> <span class="n">labels</span><span class="o">=</span><span class="n">labels</span><span class="p">,</span> <span class="n">autorange</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">showmeans</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+    <span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">boxplot</span><span class="p">(</span><span class="n">crps2</span><span class="p">,</span> <span class="n">labels</span><span class="o">=</span><span class="n">labels</span><span class="p">,</span> <span class="n">autorange</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">showmeans</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+
+    <span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
+
+    <span class="n">Util</span><span class="o">.</span><span class="n">show_and_save_image</span><span class="p">(</span><span class="n">fig</span><span class="p">,</span> <span class="n">file</span><span class="p">,</span> <span class="n">save</span><span class="p">)</span></div>
+
+
+
+<div class="viewcode-block" id="plot_dataframe_probabilistic"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.Util.plot_dataframe_probabilistic">[docs]</a><span class="k">def</span> <span class="nf">plot_dataframe_probabilistic</span><span class="p">(</span><span class="n">file_synthetic</span><span class="p">,</span> <span class="n">file_analytic</span><span class="p">,</span> <span class="n">experiments</span><span class="p">,</span> <span class="n">tam</span><span class="p">,</span> <span class="n">save</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">file</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+                                 <span class="n">sort_columns</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;CRPS1AVG&#39;</span><span class="p">,</span> <span class="s1">&#39;CRPS2AVG&#39;</span><span class="p">,</span> <span class="s1">&#39;CRPS1STD&#39;</span><span class="p">,</span> <span class="s1">&#39;CRPS2STD&#39;</span><span class="p">],</span>
+                                 <span class="n">sort_ascend</span><span class="o">=</span><span class="p">[</span><span class="kc">True</span><span class="p">,</span> <span class="kc">True</span><span class="p">,</span> <span class="kc">True</span><span class="p">,</span> <span class="kc">True</span><span class="p">],</span> <span class="n">save_best</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+                                 <span class="n">ignore</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">replace</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+
+    <span class="n">fig</span><span class="p">,</span> <span class="n">axes</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">nrows</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">ncols</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="n">tam</span><span class="p">)</span>
+
+    <span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">&#39;CRPS&#39;</span><span class="p">)</span>
+    <span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">&#39;CRPS&#39;</span><span class="p">)</span>
+
+    <span class="n">dat_syn</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">file_synthetic</span><span class="p">,</span> <span class="n">sep</span><span class="o">=</span><span class="s2">&quot;;&quot;</span><span class="p">,</span> <span class="n">usecols</span><span class="o">=</span><span class="n">probabilistic_dataframe_synthetic_columns</span><span class="p">())</span>
+
+    <span class="n">bests</span> <span class="o">=</span> <span class="n">find_best</span><span class="p">(</span><span class="n">dat_syn</span><span class="p">,</span> <span class="n">sort_columns</span><span class="p">,</span> <span class="n">sort_ascend</span><span class="p">)</span>
+
+    <span class="n">dat_ana</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">file_analytic</span><span class="p">,</span> <span class="n">sep</span><span class="o">=</span><span class="s2">&quot;;&quot;</span><span class="p">,</span> <span class="n">usecols</span><span class="o">=</span><span class="n">probabilistic_dataframe_analytic_columns</span><span class="p">(</span><span class="n">experiments</span><span class="p">))</span>
+
+    <span class="n">data_columns</span> <span class="o">=</span> <span class="n">analytical_data_columns</span><span class="p">(</span><span class="n">experiments</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="n">save_best</span><span class="p">:</span>
+        <span class="n">dat</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="o">.</span><span class="n">from_dict</span><span class="p">(</span><span class="n">bests</span><span class="p">,</span> <span class="n">orient</span><span class="o">=</span><span class="s1">&#39;index&#39;</span><span class="p">)</span>
+        <span class="n">dat</span><span class="o">.</span><span class="n">to_csv</span><span class="p">(</span><span class="n">Util</span><span class="o">.</span><span class="n">uniquefilename</span><span class="p">(</span><span class="n">file_synthetic</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s2">&quot;synthetic&quot;</span><span class="p">,</span><span class="s2">&quot;best&quot;</span><span class="p">)),</span> <span class="n">sep</span><span class="o">=</span><span class="s2">&quot;;&quot;</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+
+    <span class="n">crps1</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="n">crps2</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="n">labels</span> <span class="o">=</span> <span class="p">[]</span>
+
+    <span class="k">for</span> <span class="n">b</span> <span class="ow">in</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">bests</span><span class="o">.</span><span class="n">keys</span><span class="p">()):</span>
+        <span class="k">if</span> <span class="n">check_ignore_list</span><span class="p">(</span><span class="n">b</span><span class="p">,</span> <span class="n">ignore</span><span class="p">):</span>
+            <span class="k">continue</span>
+        <span class="n">best</span> <span class="o">=</span> <span class="n">bests</span><span class="p">[</span><span class="n">b</span><span class="p">]</span>
+        <span class="n">df</span> <span class="o">=</span> <span class="n">dat_ana</span><span class="p">[(</span><span class="n">dat_ana</span><span class="o">.</span><span class="n">Model</span> <span class="o">==</span> <span class="n">best</span><span class="p">[</span><span class="s2">&quot;Model&quot;</span><span class="p">])</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">dat_ana</span><span class="o">.</span><span class="n">Order</span> <span class="o">==</span> <span class="n">best</span><span class="p">[</span><span class="s2">&quot;Order&quot;</span><span class="p">])</span>
+                <span class="o">&amp;</span> <span class="p">(</span><span class="n">dat_ana</span><span class="o">.</span><span class="n">Scheme</span> <span class="o">==</span> <span class="n">best</span><span class="p">[</span><span class="s2">&quot;Scheme&quot;</span><span class="p">])</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">dat_ana</span><span class="o">.</span><span class="n">Partitions</span> <span class="o">==</span> <span class="n">best</span><span class="p">[</span><span class="s2">&quot;Partitions&quot;</span><span class="p">])]</span>
+        <span class="n">crps1</span><span class="o">.</span><span class="n">append</span><span class="p">(</span> <span class="n">extract_measure</span><span class="p">(</span><span class="n">df</span><span class="p">,</span><span class="s1">&#39;CRPS_Interval&#39;</span><span class="p">,</span><span class="n">data_columns</span><span class="p">)</span> <span class="p">)</span>
+        <span class="n">crps2</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">extract_measure</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="s1">&#39;CRPS_Distribution&#39;</span><span class="p">,</span> <span class="n">data_columns</span><span class="p">))</span>
+        <span class="n">labels</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">check_replace_list</span><span class="p">(</span><span class="n">best</span><span class="p">[</span><span class="s2">&quot;Model&quot;</span><span class="p">]</span> <span class="o">+</span> <span class="s2">&quot; &quot;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">best</span><span class="p">[</span><span class="s2">&quot;Order&quot;</span><span class="p">]),</span> <span class="n">replace</span><span class="p">))</span>
+
+    <span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">boxplot</span><span class="p">(</span><span class="n">crps1</span><span class="p">,</span> <span class="n">labels</span><span class="o">=</span><span class="n">labels</span><span class="p">,</span> <span class="n">autorange</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">showmeans</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+    <span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">boxplot</span><span class="p">(</span><span class="n">crps2</span><span class="p">,</span> <span class="n">labels</span><span class="o">=</span><span class="n">labels</span><span class="p">,</span> <span class="n">autorange</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">showmeans</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+
+    <span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
+    <span class="n">Util</span><span class="o">.</span><span class="n">show_and_save_image</span><span class="p">(</span><span class="n">fig</span><span class="p">,</span> <span class="n">file</span><span class="p">,</span> <span class="n">save</span><span class="p">)</span></div>
+
+</pre></div>
+
+            <div class="clearer"></div>
+          </div>
+        </div>
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+      <div class="sphinxsidebar" role="navigation" aria-label="main navigation">
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+<script>$('#searchbox').show(0);</script>
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+      <h3>Navigation</h3>
+      <ul>
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+             >index</a></li>
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+             >modules</a> |</li>
+        <li class="nav-item nav-item-0"><a href="../../../index.html">pyFTS 1.6 documentation</a> &#187;</li>
+          <li class="nav-item nav-item-1"><a href="../../index.html" >Module code</a> &#187;</li>
+        <li class="nav-item nav-item-this"><a href="">pyFTS.benchmarks.Util</a></li> 
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+      Created using <a href="https://www.sphinx-doc.org/">Sphinx</a> 3.1.2.
+    </div>
+  </body>
+</html>
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diff --git a/docs/build/html/_modules/pyFTS/benchmarks/arima.html b/docs/build/html/_modules/pyFTS/benchmarks/arima.html
new file mode 100644
index 0000000..9c8248f
--- /dev/null
+++ b/docs/build/html/_modules/pyFTS/benchmarks/arima.html
@@ -0,0 +1,320 @@
+
+
+<!doctype html>
+
+<html>
+  <head>
+    <meta charset="utf-8" />
+    <meta name="viewport" content="width=device-width, initial-scale=1.0"><script type="text/javascript">
+
+      var _gaq = _gaq || [];
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+        var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s);
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+    <title>pyFTS.benchmarks.arima &#8212; pyFTS 1.6 documentation</title>
+    <link rel="stylesheet" href="../../../_static/bizstyle.css" type="text/css" />
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+    <div class="related" role="navigation" aria-label="related navigation">
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+             >modules</a> |</li>
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+          <li class="nav-item nav-item-1"><a href="../../index.html" accesskey="U">Module code</a> &#187;</li>
+        <li class="nav-item nav-item-this"><a href="">pyFTS.benchmarks.arima</a></li> 
+      </ul>
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+
+    <div class="document">
+      <div class="documentwrapper">
+        <div class="bodywrapper">
+          <div class="body" role="main">
+            
+  <h1>Source code for pyFTS.benchmarks.arima</h1><div class="highlight"><pre>
+<span></span><span class="ch">#!/usr/bin/python</span>
+<span class="c1"># -*- coding: utf8 -*-</span>
+
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+<span class="kn">from</span> <span class="nn">statsmodels.tsa.arima_model</span> <span class="kn">import</span> <span class="n">ARIMA</span> <span class="k">as</span> <span class="n">stats_arima</span>
+<span class="kn">import</span> <span class="nn">scipy.stats</span> <span class="k">as</span> <span class="nn">st</span>
+<span class="kn">from</span> <span class="nn">pyFTS.common</span> <span class="kn">import</span> <span class="n">SortedCollection</span><span class="p">,</span> <span class="n">fts</span>
+<span class="kn">from</span> <span class="nn">pyFTS.probabilistic</span> <span class="kn">import</span> <span class="n">ProbabilityDistribution</span>
+
+
+<div class="viewcode-block" id="ARIMA"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.arima.ARIMA">[docs]</a><span class="k">class</span> <span class="nc">ARIMA</span><span class="p">(</span><span class="n">fts</span><span class="o">.</span><span class="n">FTS</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Façade for statsmodels.tsa.arima_model</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">(</span><span class="n">ARIMA</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="s2">&quot;ARIMA&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">detail</span> <span class="o">=</span> <span class="s2">&quot;Auto Regressive Integrated Moving Average&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">is_high_order</span> <span class="o">=</span> <span class="kc">True</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">has_point_forecasting</span> <span class="o">=</span> <span class="kc">True</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">has_interval_forecasting</span> <span class="o">=</span> <span class="kc">True</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">has_probability_forecasting</span> <span class="o">=</span> <span class="kc">True</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">uod_clip</span> <span class="o">=</span> <span class="kc">False</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">model</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">model_fit</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">trained_data</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">p</span> <span class="o">=</span> <span class="mi">1</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">d</span> <span class="o">=</span> <span class="mi">0</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">q</span> <span class="o">=</span> <span class="mi">0</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">benchmark_only</span> <span class="o">=</span> <span class="kc">True</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">min_order</span> <span class="o">=</span> <span class="mi">1</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">alpha</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;alpha&quot;</span><span class="p">,</span> <span class="mf">0.05</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">order</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;order&quot;</span><span class="p">,</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">))</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_decompose_order</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">_decompose_order</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">order</span><span class="p">):</span>
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">order</span><span class="p">,</span> <span class="p">(</span><span class="nb">tuple</span><span class="p">,</span> <span class="nb">set</span><span class="p">,</span> <span class="nb">list</span><span class="p">)):</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">p</span> <span class="o">=</span> <span class="n">order</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">d</span> <span class="o">=</span> <span class="n">order</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">q</span> <span class="o">=</span> <span class="n">order</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">order</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">p</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">q</span> <span class="o">+</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">q</span> <span class="o">-</span> <span class="mi">1</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">q</span> <span class="o">&gt;</span> <span class="mi">0</span> <span class="k">else</span> <span class="mi">0</span><span class="p">)</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">d</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">transformations</span><span class="p">)</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">shortname</span> <span class="o">=</span> <span class="s2">&quot;ARIMA(</span><span class="si">{}</span><span class="s2">,</span><span class="si">{}</span><span class="s2">,</span><span class="si">{}</span><span class="s2">)-</span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">p</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">d</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">q</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">alpha</span><span class="p">)</span>
+
+<div class="viewcode-block" id="ARIMA.train"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.arima.ARIMA.train">[docs]</a>    <span class="k">def</span> <span class="nf">train</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+
+        <span class="k">if</span> <span class="s1">&#39;order&#39;</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="p">:</span>
+            <span class="n">order</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s1">&#39;order&#39;</span><span class="p">)</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_decompose_order</span><span class="p">(</span><span class="n">order</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">indexer</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">data</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">indexer</span><span class="o">.</span><span class="n">get_data</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+
+        <span class="k">try</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">model</span> <span class="o">=</span>  <span class="n">stats_arima</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">order</span><span class="o">=</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">p</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">d</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">q</span><span class="p">))</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">model_fit</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">disp</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+        <span class="k">except</span> <span class="ne">Exception</span> <span class="k">as</span> <span class="n">ex</span><span class="p">:</span>
+            <span class="nb">print</span><span class="p">(</span><span class="n">ex</span><span class="p">)</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">model_fit</span> <span class="o">=</span> <span class="kc">None</span></div>
+
+<div class="viewcode-block" id="ARIMA.ar"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.arima.ARIMA.ar">[docs]</a>    <span class="k">def</span> <span class="nf">ar</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">):</span>
+        <span class="k">return</span> <span class="n">data</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">model_fit</span><span class="o">.</span><span class="n">arparams</span><span class="p">)</span></div>
+
+<div class="viewcode-block" id="ARIMA.ma"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.arima.ARIMA.ma">[docs]</a>    <span class="k">def</span> <span class="nf">ma</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">):</span>
+        <span class="k">return</span> <span class="n">data</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">model_fit</span><span class="o">.</span><span class="n">maparams</span><span class="p">)</span></div>
+
+<div class="viewcode-block" id="ARIMA.forecast"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.arima.ARIMA.forecast">[docs]</a>    <span class="k">def</span> <span class="nf">forecast</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ndata</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">model_fit</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
+
+        <span class="n">ndata</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">ndata</span><span class="p">)</span>
+
+        <span class="n">l</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">ndata</span><span class="p">)</span>
+
+        <span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
+
+        <span class="n">ar</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="bp">self</span><span class="o">.</span><span class="n">ar</span><span class="p">(</span><span class="n">ndata</span><span class="p">[</span><span class="n">k</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">p</span><span class="p">:</span> <span class="n">k</span><span class="p">])</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">p</span><span class="p">,</span> <span class="n">l</span><span class="o">+</span><span class="mi">1</span><span class="p">)])</span> <span class="c1">#+1 to forecast one step ahead given all available lags</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">q</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="n">residuals</span> <span class="o">=</span> <span class="n">ndata</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">p</span><span class="o">-</span><span class="mi">1</span><span class="p">:]</span> <span class="o">-</span> <span class="n">ar</span>
+
+            <span class="n">ma</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="bp">self</span><span class="o">.</span><span class="n">ma</span><span class="p">(</span><span class="n">residuals</span><span class="p">[</span><span class="n">k</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">q</span><span class="p">:</span> <span class="n">k</span><span class="p">])</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">q</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">residuals</span><span class="p">)</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)])</span>
+
+            <span class="n">ret</span> <span class="o">=</span> <span class="n">ar</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">q</span> <span class="o">-</span> <span class="mi">1</span><span class="p">:]</span> <span class="o">+</span> <span class="n">ma</span>
+            <span class="n">ret</span> <span class="o">=</span> <span class="n">ret</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">q</span><span class="p">:]</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">ret</span> <span class="o">=</span> <span class="n">ar</span>
+
+        <span class="c1">#ret = self.apply_inverse_transformations(ret, params=[data[self.order - 1:]])        nforecasts = np.array(forecasts)</span>
+
+        <span class="k">return</span> <span class="n">ret</span></div>
+
+<div class="viewcode-block" id="ARIMA.forecast_interval"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.arima.ARIMA.forecast_interval">[docs]</a>    <span class="k">def</span> <span class="nf">forecast_interval</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">model_fit</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
+
+        <span class="k">if</span> <span class="s1">&#39;alpha&#39;</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="p">:</span>
+            <span class="n">alpha</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;alpha&#39;</span><span class="p">,</span><span class="mf">0.05</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">alpha</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">alpha</span>
+
+        <span class="n">sigma</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">model_fit</span><span class="o">.</span><span class="n">sigma2</span><span class="p">)</span>
+
+        <span class="n">l</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+
+        <span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
+
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">,</span> <span class="n">l</span><span class="o">+</span><span class="mi">1</span><span class="p">):</span>
+            <span class="n">tmp</span> <span class="o">=</span> <span class="p">[]</span>
+
+            <span class="n">sample</span> <span class="o">=</span> <span class="p">[</span><span class="n">data</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">k</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">,</span> <span class="n">k</span><span class="p">)]</span>
+
+            <span class="n">mean</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">forecast</span><span class="p">(</span><span class="n">sample</span><span class="p">)</span>
+
+            <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">mean</span><span class="p">,(</span><span class="nb">list</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">)):</span>
+                <span class="n">mean</span> <span class="o">=</span> <span class="n">mean</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+
+            <span class="n">tmp</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">mean</span> <span class="o">+</span> <span class="n">st</span><span class="o">.</span><span class="n">norm</span><span class="o">.</span><span class="n">ppf</span><span class="p">(</span><span class="n">alpha</span><span class="p">)</span> <span class="o">*</span> <span class="n">sigma</span><span class="p">)</span>
+            <span class="n">tmp</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">mean</span> <span class="o">+</span> <span class="n">st</span><span class="o">.</span><span class="n">norm</span><span class="o">.</span><span class="n">ppf</span><span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">alpha</span><span class="p">)</span> <span class="o">*</span> <span class="n">sigma</span><span class="p">)</span>
+
+            <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">tmp</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">ret</span></div>
+
+<div class="viewcode-block" id="ARIMA.forecast_ahead_interval"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.arima.ARIMA.forecast_ahead_interval">[docs]</a>    <span class="k">def</span> <span class="nf">forecast_ahead_interval</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ndata</span><span class="p">,</span> <span class="n">steps</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">model_fit</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
+
+        <span class="k">if</span> <span class="s1">&#39;alpha&#39;</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="p">:</span>
+            <span class="n">alpha</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;alpha&#39;</span><span class="p">,</span><span class="mf">0.05</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">alpha</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">alpha</span>
+
+        <span class="n">smoothing</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;smoothing&quot;</span><span class="p">,</span><span class="mf">0.5</span><span class="p">)</span>
+
+        <span class="n">sigma</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">model_fit</span><span class="o">.</span><span class="n">sigma2</span><span class="p">)</span>
+
+        <span class="n">l</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">ndata</span><span class="p">)</span>
+
+        <span class="n">nmeans</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">forecast_ahead</span><span class="p">(</span><span class="n">ndata</span><span class="p">,</span> <span class="n">steps</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+
+        <span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
+
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">steps</span><span class="p">):</span>
+            <span class="n">tmp</span> <span class="o">=</span> <span class="p">[]</span>
+
+            <span class="n">hsigma</span> <span class="o">=</span> <span class="p">(</span><span class="mi">1</span> <span class="o">+</span> <span class="n">k</span><span class="o">*</span><span class="n">smoothing</span><span class="p">)</span><span class="o">*</span><span class="n">sigma</span>
+
+            <span class="n">tmp</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">nmeans</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">+</span> <span class="n">st</span><span class="o">.</span><span class="n">norm</span><span class="o">.</span><span class="n">ppf</span><span class="p">(</span><span class="n">alpha</span><span class="p">)</span> <span class="o">*</span> <span class="n">hsigma</span><span class="p">)</span>
+            <span class="n">tmp</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">nmeans</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">+</span> <span class="n">st</span><span class="o">.</span><span class="n">norm</span><span class="o">.</span><span class="n">ppf</span><span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">alpha</span><span class="p">)</span> <span class="o">*</span> <span class="n">hsigma</span><span class="p">)</span>
+
+            <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">tmp</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">ret</span><span class="p">[</span><span class="o">-</span><span class="n">steps</span><span class="p">:]</span></div>
+
+<div class="viewcode-block" id="ARIMA.forecast_distribution"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.arima.ARIMA.forecast_distribution">[docs]</a>    <span class="k">def</span> <span class="nf">forecast_distribution</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+
+        <span class="n">sigma</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">model_fit</span><span class="o">.</span><span class="n">sigma2</span><span class="p">)</span>
+
+        <span class="n">l</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+
+        <span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
+
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">,</span> <span class="n">l</span> <span class="o">+</span> <span class="mi">1</span><span class="p">):</span>
+            <span class="n">sample</span> <span class="o">=</span> <span class="p">[</span><span class="n">data</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">k</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">,</span> <span class="n">k</span><span class="p">)]</span>
+
+            <span class="n">mean</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">forecast</span><span class="p">(</span><span class="n">sample</span><span class="p">)</span>
+
+            <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">mean</span><span class="p">,</span> <span class="p">(</span><span class="nb">list</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">)):</span>
+                <span class="n">mean</span> <span class="o">=</span> <span class="n">mean</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+
+            <span class="n">dist</span> <span class="o">=</span> <span class="n">ProbabilityDistribution</span><span class="o">.</span><span class="n">ProbabilityDistribution</span><span class="p">(</span><span class="nb">type</span><span class="o">=</span><span class="s2">&quot;histogram&quot;</span><span class="p">,</span> <span class="n">uod</span><span class="o">=</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">original_min</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">original_max</span><span class="p">])</span>
+            <span class="n">intervals</span> <span class="o">=</span> <span class="p">[]</span>
+            <span class="k">for</span> <span class="n">alpha</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mf">0.05</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">,</span> <span class="mf">0.05</span><span class="p">):</span>
+
+                <span class="n">qt1</span> <span class="o">=</span> <span class="n">mean</span> <span class="o">+</span> <span class="n">st</span><span class="o">.</span><span class="n">norm</span><span class="o">.</span><span class="n">ppf</span><span class="p">(</span><span class="n">alpha</span><span class="p">)</span> <span class="o">*</span> <span class="n">sigma</span>
+                <span class="n">qt2</span> <span class="o">=</span> <span class="n">mean</span> <span class="o">+</span> <span class="n">st</span><span class="o">.</span><span class="n">norm</span><span class="o">.</span><span class="n">ppf</span><span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">alpha</span><span class="p">)</span> <span class="o">*</span> <span class="n">sigma</span>
+
+                <span class="n">intervals</span><span class="o">.</span><span class="n">append</span><span class="p">([</span><span class="n">qt1</span><span class="p">,</span> <span class="n">qt2</span><span class="p">])</span>
+
+            <span class="n">dist</span><span class="o">.</span><span class="n">append_interval</span><span class="p">(</span><span class="n">intervals</span><span class="p">)</span>
+
+            <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">dist</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">ret</span></div>
+
+
+<div class="viewcode-block" id="ARIMA.forecast_ahead_distribution"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.arima.ARIMA.forecast_ahead_distribution">[docs]</a>    <span class="k">def</span> <span class="nf">forecast_ahead_distribution</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">steps</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="n">smoothing</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;smoothing&quot;</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">)</span>
+
+        <span class="n">sigma</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">model_fit</span><span class="o">.</span><span class="n">sigma2</span><span class="p">)</span>
+
+        <span class="n">l</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+
+        <span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
+
+        <span class="n">nmeans</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">forecast_ahead</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">steps</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">steps</span><span class="p">):</span>
+            <span class="n">dist</span> <span class="o">=</span> <span class="n">ProbabilityDistribution</span><span class="o">.</span><span class="n">ProbabilityDistribution</span><span class="p">(</span><span class="nb">type</span><span class="o">=</span><span class="s2">&quot;histogram&quot;</span><span class="p">,</span>
+                                                                   <span class="n">uod</span><span class="o">=</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">original_min</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">original_max</span><span class="p">])</span>
+            <span class="n">intervals</span> <span class="o">=</span> <span class="p">[]</span>
+            <span class="k">for</span> <span class="n">alpha</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mf">0.05</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">,</span> <span class="mf">0.05</span><span class="p">):</span>
+                <span class="n">tmp</span> <span class="o">=</span> <span class="p">[]</span>
+
+                <span class="n">hsigma</span> <span class="o">=</span> <span class="p">(</span><span class="mi">1</span> <span class="o">+</span> <span class="n">k</span> <span class="o">*</span> <span class="n">smoothing</span><span class="p">)</span> <span class="o">*</span> <span class="n">sigma</span>
+
+                <span class="n">tmp</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">nmeans</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">+</span> <span class="n">st</span><span class="o">.</span><span class="n">norm</span><span class="o">.</span><span class="n">ppf</span><span class="p">(</span><span class="n">alpha</span><span class="p">)</span> <span class="o">*</span> <span class="n">hsigma</span><span class="p">)</span>
+                <span class="n">tmp</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">nmeans</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">+</span> <span class="n">st</span><span class="o">.</span><span class="n">norm</span><span class="o">.</span><span class="n">ppf</span><span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">alpha</span><span class="p">)</span> <span class="o">*</span> <span class="n">hsigma</span><span class="p">)</span>
+
+                <span class="n">intervals</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">tmp</span><span class="p">)</span>
+
+            <span class="n">dist</span><span class="o">.</span><span class="n">append_interval</span><span class="p">(</span><span class="n">intervals</span><span class="p">)</span>
+
+            <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">dist</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">ret</span><span class="p">[</span><span class="o">-</span><span class="n">steps</span><span class="p">:]</span></div></div>
+
+</pre></div>
+
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new file mode 100644
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@@ -0,0 +1,1910 @@
+
+
+<!doctype html>
+
+<html>
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+        <li class="nav-item nav-item-this"><a href="">pyFTS.benchmarks.benchmarks</a></li> 
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+            
+  <h1>Source code for pyFTS.benchmarks.benchmarks</h1><div class="highlight"><pre>
+<span></span><span class="ch">#!/usr/bin/python</span>
+<span class="c1"># -*- coding: utf8 -*-</span>
+
+<span class="sd">&quot;&quot;&quot;Benchmarks methods for FTS methods&quot;&quot;&quot;</span>
+
+
+<span class="kn">import</span> <span class="nn">datetime</span>
+<span class="kn">import</span> <span class="nn">time</span>
+<span class="kn">from</span> <span class="nn">copy</span> <span class="kn">import</span> <span class="n">deepcopy</span>
+<span class="kn">import</span> <span class="nn">traceback</span>
+
+<span class="kn">import</span> <span class="nn">matplotlib</span> <span class="k">as</span> <span class="nn">plt</span>
+
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">from</span> <span class="nn">mpl_toolkits.mplot3d</span> <span class="kn">import</span> <span class="n">Axes3D</span>
+<span class="kn">from</span> <span class="nn">itertools</span> <span class="kn">import</span> <span class="n">product</span>
+
+<span class="kn">from</span> <span class="nn">pyFTS.common</span> <span class="kn">import</span> <span class="n">Transformations</span>
+<span class="kn">from</span> <span class="nn">pyFTS.models</span> <span class="kn">import</span> <span class="n">song</span><span class="p">,</span> <span class="n">chen</span><span class="p">,</span> <span class="n">yu</span><span class="p">,</span> <span class="n">ismailefendi</span><span class="p">,</span> <span class="n">sadaei</span><span class="p">,</span> <span class="n">hofts</span><span class="p">,</span> <span class="n">pwfts</span><span class="p">,</span> <span class="n">ifts</span><span class="p">,</span> <span class="n">cheng</span><span class="p">,</span> <span class="n">hwang</span>
+<span class="kn">from</span> <span class="nn">pyFTS.models.multivariate</span> <span class="kn">import</span> <span class="n">mvfts</span><span class="p">,</span> <span class="n">wmvfts</span><span class="p">,</span> <span class="n">cmvfts</span>
+<span class="kn">from</span> <span class="nn">pyFTS.models.ensemble</span> <span class="kn">import</span> <span class="n">ensemble</span>
+<span class="kn">from</span> <span class="nn">pyFTS.benchmarks</span> <span class="kn">import</span> <span class="n">Measures</span><span class="p">,</span> <span class="n">naive</span><span class="p">,</span> <span class="n">arima</span><span class="p">,</span> <span class="n">ResidualAnalysis</span><span class="p">,</span> <span class="n">quantreg</span><span class="p">,</span> <span class="n">knn</span>
+<span class="kn">from</span> <span class="nn">pyFTS.benchmarks</span> <span class="kn">import</span> <span class="n">Util</span> <span class="k">as</span> <span class="n">bUtil</span>
+<span class="kn">from</span> <span class="nn">pyFTS.common</span> <span class="kn">import</span> <span class="n">Util</span> <span class="k">as</span> <span class="n">cUtil</span>
+<span class="c1"># from sklearn.cross_validation import KFold</span>
+<span class="kn">from</span> <span class="nn">pyFTS.partitioners</span> <span class="kn">import</span> <span class="n">Grid</span>
+
+<span class="n">colors</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;grey&#39;</span><span class="p">,</span> <span class="s1">&#39;darkgrey&#39;</span><span class="p">,</span> <span class="s1">&#39;rosybrown&#39;</span><span class="p">,</span> <span class="s1">&#39;maroon&#39;</span><span class="p">,</span> <span class="s1">&#39;red&#39;</span><span class="p">,</span><span class="s1">&#39;orange&#39;</span><span class="p">,</span> <span class="s1">&#39;gold&#39;</span><span class="p">,</span> <span class="s1">&#39;yellow&#39;</span><span class="p">,</span> <span class="s1">&#39;olive&#39;</span><span class="p">,</span> <span class="s1">&#39;green&#39;</span><span class="p">,</span>
+          <span class="s1">&#39;darkgreen&#39;</span><span class="p">,</span> <span class="s1">&#39;cyan&#39;</span><span class="p">,</span> <span class="s1">&#39;lightblue&#39;</span><span class="p">,</span><span class="s1">&#39;blue&#39;</span><span class="p">,</span> <span class="s1">&#39;darkblue&#39;</span><span class="p">,</span> <span class="s1">&#39;purple&#39;</span><span class="p">,</span> <span class="s1">&#39;darkviolet&#39;</span> <span class="p">]</span>
+
+<span class="n">ncol</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">colors</span><span class="p">)</span>
+
+<span class="n">styles</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;-&#39;</span><span class="p">,</span><span class="s1">&#39;--&#39;</span><span class="p">,</span><span class="s1">&#39;-.&#39;</span><span class="p">,</span><span class="s1">&#39;:&#39;</span><span class="p">,</span><span class="s1">&#39;.&#39;</span><span class="p">]</span>
+
+<span class="n">nsty</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">styles</span><span class="p">)</span>
+
+
+<span class="k">def</span> <span class="nf">__pop</span><span class="p">(</span><span class="n">key</span><span class="p">,</span> <span class="n">default</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">):</span>
+    <span class="k">if</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="p">:</span>
+        <span class="k">return</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="n">key</span><span class="p">)</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">return</span> <span class="n">default</span>
+
+
+<div class="viewcode-block" id="get_benchmark_point_methods"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.benchmarks.get_benchmark_point_methods">[docs]</a><span class="k">def</span> <span class="nf">get_benchmark_point_methods</span><span class="p">():</span>
+    <span class="sd">&quot;&quot;&quot;Return all non FTS methods for point forecasting&quot;&quot;&quot;</span>
+    <span class="k">return</span> <span class="p">[</span><span class="n">naive</span><span class="o">.</span><span class="n">Naive</span><span class="p">,</span> <span class="n">arima</span><span class="o">.</span><span class="n">ARIMA</span><span class="p">,</span> <span class="n">quantreg</span><span class="o">.</span><span class="n">QuantileRegression</span><span class="p">]</span></div>
+
+
+<div class="viewcode-block" id="get_point_methods"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.benchmarks.get_point_methods">[docs]</a><span class="k">def</span> <span class="nf">get_point_methods</span><span class="p">():</span>
+    <span class="sd">&quot;&quot;&quot;Return all FTS methods for point forecasting&quot;&quot;&quot;</span>
+    <span class="k">return</span> <span class="p">[</span><span class="n">song</span><span class="o">.</span><span class="n">ConventionalFTS</span><span class="p">,</span> <span class="n">chen</span><span class="o">.</span><span class="n">ConventionalFTS</span><span class="p">,</span> <span class="n">yu</span><span class="o">.</span><span class="n">WeightedFTS</span><span class="p">,</span> <span class="n">ismailefendi</span><span class="o">.</span><span class="n">ImprovedWeightedFTS</span><span class="p">,</span>
+            <span class="n">cheng</span><span class="o">.</span><span class="n">TrendWeightedFTS</span><span class="p">,</span> <span class="n">sadaei</span><span class="o">.</span><span class="n">ExponentialyWeightedFTS</span><span class="p">,</span>
+            <span class="n">hofts</span><span class="o">.</span><span class="n">HighOrderFTS</span><span class="p">,</span> <span class="n">hofts</span><span class="o">.</span><span class="n">WeightedHighOrderFTS</span><span class="p">,</span> <span class="n">hwang</span><span class="o">.</span><span class="n">HighOrderFTS</span><span class="p">,</span>
+            <span class="n">pwfts</span><span class="o">.</span><span class="n">ProbabilisticWeightedFTS</span><span class="p">]</span></div>
+
+
+<div class="viewcode-block" id="get_point_multivariate_methods"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.benchmarks.get_point_multivariate_methods">[docs]</a><span class="k">def</span> <span class="nf">get_point_multivariate_methods</span><span class="p">():</span>
+    <span class="sd">&quot;&quot;&quot;Return all multivariate FTS methods por point forecasting&quot;&quot;&quot;</span>
+    <span class="k">return</span> <span class="p">[</span><span class="n">mvfts</span><span class="o">.</span><span class="n">MVFTS</span><span class="p">,</span> <span class="n">wmvfts</span><span class="o">.</span><span class="n">WeightedMVFTS</span><span class="p">,</span> <span class="n">cmvfts</span><span class="o">.</span><span class="n">ClusteredMVFTS</span><span class="p">]</span></div>
+
+
+<div class="viewcode-block" id="get_benchmark_interval_methods"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.benchmarks.get_benchmark_interval_methods">[docs]</a><span class="k">def</span> <span class="nf">get_benchmark_interval_methods</span><span class="p">():</span>
+    <span class="sd">&quot;&quot;&quot;Return all non FTS methods for point_to_interval forecasting&quot;&quot;&quot;</span>
+    <span class="k">return</span> <span class="p">[</span> <span class="n">arima</span><span class="o">.</span><span class="n">ARIMA</span><span class="p">,</span> <span class="n">quantreg</span><span class="o">.</span><span class="n">QuantileRegression</span><span class="p">]</span></div>
+
+
+<div class="viewcode-block" id="get_interval_methods"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.benchmarks.get_interval_methods">[docs]</a><span class="k">def</span> <span class="nf">get_interval_methods</span><span class="p">():</span>
+    <span class="sd">&quot;&quot;&quot;Return all FTS methods for point_to_interval forecasting&quot;&quot;&quot;</span>
+    <span class="k">return</span> <span class="p">[</span><span class="n">ifts</span><span class="o">.</span><span class="n">IntervalFTS</span><span class="p">,</span> <span class="n">ifts</span><span class="o">.</span><span class="n">WeightedIntervalFTS</span><span class="p">,</span> <span class="n">pwfts</span><span class="o">.</span><span class="n">ProbabilisticWeightedFTS</span><span class="p">]</span></div>
+
+
+<div class="viewcode-block" id="get_probabilistic_methods"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.benchmarks.get_probabilistic_methods">[docs]</a><span class="k">def</span> <span class="nf">get_probabilistic_methods</span><span class="p">():</span>
+    <span class="sd">&quot;&quot;&quot;Return all FTS methods for probabilistic forecasting&quot;&quot;&quot;</span>
+    <span class="k">return</span> <span class="p">[</span><span class="n">ensemble</span><span class="o">.</span><span class="n">AllMethodEnsembleFTS</span><span class="p">,</span> <span class="n">pwfts</span><span class="o">.</span><span class="n">ProbabilisticWeightedFTS</span><span class="p">]</span></div>
+
+
+<div class="viewcode-block" id="get_benchmark_probabilistic_methods"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.benchmarks.get_benchmark_probabilistic_methods">[docs]</a><span class="k">def</span> <span class="nf">get_benchmark_probabilistic_methods</span><span class="p">():</span>
+    <span class="sd">&quot;&quot;&quot;Return all FTS methods for probabilistic forecasting&quot;&quot;&quot;</span>
+    <span class="k">return</span> <span class="p">[</span><span class="n">arima</span><span class="o">.</span><span class="n">ARIMA</span><span class="p">,</span> <span class="n">quantreg</span><span class="o">.</span><span class="n">QuantileRegression</span><span class="p">,</span> <span class="n">knn</span><span class="o">.</span><span class="n">KNearestNeighbors</span><span class="p">]</span></div>
+
+
+<div class="viewcode-block" id="multivariate_sliding_window_benchmarks2"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.benchmarks.multivariate_sliding_window_benchmarks2">[docs]</a><span class="k">def</span> <span class="nf">multivariate_sliding_window_benchmarks2</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">windowsize</span><span class="p">,</span> <span class="n">train</span><span class="o">=</span><span class="mf">0.8</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+    <span class="kn">from</span> <span class="nn">pyFTS.models.multivariate</span> <span class="kn">import</span> <span class="n">common</span><span class="p">,</span> <span class="n">variable</span><span class="p">,</span> <span class="n">mvfts</span>
+
+    <span class="n">tag</span> <span class="o">=</span> <span class="n">__pop</span><span class="p">(</span><span class="s1">&#39;tag&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">)</span>
+    <span class="n">dataset</span> <span class="o">=</span> <span class="n">__pop</span><span class="p">(</span><span class="s1">&#39;dataset&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">)</span>
+
+    <span class="n">distributed</span> <span class="o">=</span> <span class="n">__pop</span><span class="p">(</span><span class="s1">&#39;distributed&#39;</span><span class="p">,</span> <span class="kc">False</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">)</span>
+
+    <span class="n">variables</span> <span class="o">=</span> <span class="n">__pop</span><span class="p">(</span><span class="s1">&#39;variables&#39;</span><span class="p">,</span> <span class="p">{},</span> <span class="n">kwargs</span><span class="p">)</span>
+
+    <span class="n">target_variable</span> <span class="o">=</span> <span class="n">__pop</span><span class="p">(</span><span class="s1">&#39;target_variable&#39;</span><span class="p">,</span> <span class="s1">&#39;&#39;</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">)</span>
+
+    <span class="nb">type</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;type&quot;</span><span class="p">,</span> <span class="s1">&#39;point&#39;</span><span class="p">)</span>
+
+    <span class="n">steps_ahead</span> <span class="o">=</span> <span class="n">__pop</span><span class="p">(</span><span class="s1">&#39;steps_ahead&#39;</span><span class="p">,</span> <span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">kwargs</span><span class="p">)</span>
+
+    <span class="n">steps_ahead</span> <span class="o">=</span> <span class="p">[</span><span class="n">k</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">steps_ahead</span><span class="p">]</span>
+
+    <span class="n">fts_methods</span> <span class="o">=</span> <span class="n">__pop</span><span class="p">(</span><span class="s1">&#39;methods&#39;</span><span class="p">,</span> <span class="p">[],</span> <span class="n">kwargs</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="n">fts_methods</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="n">methods_parameters</span> <span class="o">=</span> <span class="n">__pop</span><span class="p">(</span><span class="s1">&#39;methods_parameters&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="nb">type</span> <span class="o">==</span> <span class="s1">&#39;point&#39;</span><span class="p">:</span>
+        <span class="n">experiment_method</span> <span class="o">=</span> <span class="n">mv_run_point2</span>
+        <span class="n">synthesis_method</span> <span class="o">=</span> <span class="n">process_point_jobs2</span>
+    <span class="k">elif</span> <span class="nb">type</span> <span class="o">==</span> <span class="s1">&#39;interval&#39;</span><span class="p">:</span>
+        <span class="n">experiment_method</span> <span class="o">=</span> <span class="n">mv_run_interval2</span>
+        <span class="n">synthesis_method</span> <span class="o">=</span> <span class="n">process_interval_jobs2</span>
+    <span class="k">elif</span> <span class="nb">type</span> <span class="o">==</span> <span class="s1">&#39;distribution&#39;</span><span class="p">:</span>
+        <span class="n">experiment_method</span> <span class="o">=</span> <span class="n">mv_run_probabilistic2</span>
+        <span class="n">synthesis_method</span> <span class="o">=</span> <span class="n">process_probabilistic_jobs2</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Type parameter has a unkown value!&quot;</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="n">distributed</span><span class="p">:</span>
+        <span class="kn">import</span> <span class="nn">pyFTS.distributed.dispy</span> <span class="k">as</span> <span class="nn">dispy</span>
+
+        <span class="n">nodes</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;nodes&quot;</span><span class="p">,</span> <span class="p">[</span><span class="s1">&#39;127.0.0.1&#39;</span><span class="p">])</span>
+        <span class="n">cluster</span><span class="p">,</span> <span class="n">http_server</span> <span class="o">=</span> <span class="n">dispy</span><span class="o">.</span><span class="n">start_dispy_cluster</span><span class="p">(</span><span class="n">experiment_method</span><span class="p">,</span> <span class="n">nodes</span><span class="p">)</span>
+
+    <span class="n">inc</span> <span class="o">=</span> <span class="n">__pop</span><span class="p">(</span><span class="s2">&quot;inc&quot;</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">)</span>
+
+    <span class="n">file</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;file&#39;</span><span class="p">,</span> <span class="s2">&quot;benchmarks.db&quot;</span><span class="p">)</span>
+
+    <span class="n">conn</span> <span class="o">=</span> <span class="n">bUtil</span><span class="o">.</span><span class="n">open_benchmark_db</span><span class="p">(</span><span class="n">file</span><span class="p">)</span>
+
+    <span class="n">jobs</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="k">for</span> <span class="n">ct</span><span class="p">,</span> <span class="n">train</span><span class="p">,</span> <span class="n">test</span> <span class="ow">in</span> <span class="n">cUtil</span><span class="o">.</span><span class="n">sliding_window</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">windowsize</span><span class="p">,</span> <span class="n">train</span><span class="p">,</span> <span class="n">inc</span><span class="o">=</span><span class="n">inc</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="k">for</span> <span class="nb">id</span><span class="p">,</span> <span class="n">fts_method</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">fts_methods</span><span class="p">):</span>
+
+            <span class="n">kwargs</span><span class="p">[</span><span class="s1">&#39;steps_ahead&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">steps_ahead</span><span class="p">)</span>
+            <span class="n">parameters</span> <span class="o">=</span> <span class="p">{}</span>
+            <span class="k">if</span> <span class="n">methods_parameters</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="n">parameters</span> <span class="o">=</span> <span class="n">methods_parameters</span><span class="p">[</span><span class="nb">id</span><span class="p">]</span>
+
+            <span class="nb">vars</span> <span class="o">=</span> <span class="p">[]</span>
+            <span class="n">tvar</span> <span class="o">=</span> <span class="kc">None</span>
+
+            <span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">value</span> <span class="ow">in</span> <span class="n">variables</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+                <span class="n">var</span> <span class="o">=</span> <span class="n">variable</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="n">key</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="n">train</span><span class="p">,</span> <span class="o">**</span><span class="n">value</span><span class="p">)</span>
+                <span class="nb">vars</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">var</span><span class="p">)</span>
+                <span class="k">if</span> <span class="n">key</span> <span class="o">==</span> <span class="n">target_variable</span><span class="p">:</span>
+                    <span class="n">tvar</span> <span class="o">=</span> <span class="n">var</span>
+
+
+            <span class="n">model</span> <span class="o">=</span> <span class="n">fts_method</span><span class="p">(</span><span class="n">explanatory_variables</span><span class="o">=</span><span class="nb">vars</span><span class="p">,</span> <span class="n">target_variable</span><span class="o">=</span><span class="n">tvar</span><span class="p">,</span>
+                               <span class="o">**</span><span class="n">parameters</span><span class="p">)</span>
+
+            <span class="k">if</span> <span class="ow">not</span> <span class="n">distributed</span><span class="p">:</span>
+                <span class="k">try</span><span class="p">:</span>
+                    <span class="n">job</span> <span class="o">=</span> <span class="n">experiment_method</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">train</span><span class="p">,</span> <span class="n">test</span><span class="p">,</span> <span class="n">ct</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+                    <span class="n">synthesis_method</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">tag</span><span class="p">,</span> <span class="n">job</span><span class="p">,</span> <span class="n">conn</span><span class="p">)</span>
+                <span class="k">except</span> <span class="ne">Exception</span> <span class="k">as</span> <span class="n">ex</span><span class="p">:</span>
+                    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;EXCEPTION! &#39;</span><span class="p">,</span> <span class="n">fts_method</span><span class="p">)</span>
+                    <span class="n">traceback</span><span class="o">.</span><span class="n">print_exc</span><span class="p">()</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">job</span> <span class="o">=</span> <span class="n">cluster</span><span class="o">.</span><span class="n">submit</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">train</span><span class="p">,</span> <span class="n">test</span><span class="p">,</span> <span class="n">ct</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+                <span class="n">job</span><span class="o">.</span><span class="n">id</span> <span class="o">=</span> <span class="nb">id</span>
+                <span class="n">jobs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">job</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="n">distributed</span><span class="p">:</span>
+        <span class="k">for</span> <span class="n">job</span> <span class="ow">in</span> <span class="n">jobs</span><span class="p">:</span>
+            <span class="n">job</span><span class="p">()</span>
+            <span class="k">if</span> <span class="n">job</span><span class="o">.</span><span class="n">status</span> <span class="o">==</span> <span class="n">dispy</span><span class="o">.</span><span class="n">dispy</span><span class="o">.</span><span class="n">DispyJob</span><span class="o">.</span><span class="n">Finished</span> <span class="ow">and</span> <span class="n">job</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="n">tmp</span> <span class="o">=</span> <span class="n">job</span><span class="o">.</span><span class="n">result</span>
+                <span class="n">synthesis_method</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">tag</span><span class="p">,</span> <span class="n">tmp</span><span class="p">,</span> <span class="n">conn</span><span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;status&quot;</span><span class="p">,</span> <span class="n">job</span><span class="o">.</span><span class="n">status</span><span class="p">)</span>
+                <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;result&quot;</span><span class="p">,</span> <span class="n">job</span><span class="o">.</span><span class="n">result</span><span class="p">)</span>
+                <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;stdout&quot;</span><span class="p">,</span> <span class="n">job</span><span class="o">.</span><span class="n">stdout</span><span class="p">)</span>
+                <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;stderr&quot;</span><span class="p">,</span> <span class="n">job</span><span class="o">.</span><span class="n">exception</span><span class="p">)</span>
+
+        <span class="n">cluster</span><span class="o">.</span><span class="n">wait</span><span class="p">()</span>  <span class="c1"># wait for all jobs to finish</span>
+        <span class="n">dispy</span><span class="o">.</span><span class="n">stop_dispy_cluster</span><span class="p">(</span><span class="n">cluster</span><span class="p">,</span> <span class="n">http_server</span><span class="p">)</span>
+
+    <span class="n">conn</span><span class="o">.</span><span class="n">close</span><span class="p">()</span></div>
+
+
+<div class="viewcode-block" id="sliding_window_benchmarks2"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.benchmarks.sliding_window_benchmarks2">[docs]</a><span class="k">def</span> <span class="nf">sliding_window_benchmarks2</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">windowsize</span><span class="p">,</span> <span class="n">train</span><span class="o">=</span><span class="mf">0.8</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+    <span class="n">tag</span> <span class="o">=</span> <span class="n">__pop</span><span class="p">(</span><span class="s1">&#39;tag&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">)</span>
+    <span class="n">dataset</span> <span class="o">=</span> <span class="n">__pop</span><span class="p">(</span><span class="s1">&#39;dataset&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">)</span>
+
+    <span class="n">distributed</span> <span class="o">=</span> <span class="n">__pop</span><span class="p">(</span><span class="s1">&#39;distributed&#39;</span><span class="p">,</span> <span class="kc">False</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">)</span>
+
+    <span class="n">transformations</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;transformations&#39;</span><span class="p">,</span> <span class="p">[</span><span class="kc">None</span><span class="p">])</span>
+
+    <span class="nb">type</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;type&quot;</span><span class="p">,</span> <span class="s1">&#39;point&#39;</span><span class="p">)</span>
+
+    <span class="n">orders</span> <span class="o">=</span> <span class="n">__pop</span><span class="p">(</span><span class="s2">&quot;orders&quot;</span><span class="p">,</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span> <span class="n">kwargs</span><span class="p">)</span>
+
+    <span class="n">partitioners_methods</span> <span class="o">=</span> <span class="n">__pop</span><span class="p">(</span><span class="s2">&quot;partitioners_methods&quot;</span><span class="p">,</span> <span class="p">[</span><span class="n">Grid</span><span class="o">.</span><span class="n">GridPartitioner</span><span class="p">],</span> <span class="n">kwargs</span><span class="p">)</span>
+    <span class="n">partitions</span> <span class="o">=</span> <span class="n">__pop</span><span class="p">(</span><span class="s2">&quot;partitions&quot;</span><span class="p">,</span> <span class="p">[</span><span class="mi">10</span><span class="p">],</span> <span class="n">kwargs</span><span class="p">)</span>
+
+    <span class="n">partitions</span> <span class="o">=</span> <span class="p">[</span><span class="n">k</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">partitions</span><span class="p">]</span>
+
+    <span class="n">steps_ahead</span> <span class="o">=</span> <span class="n">__pop</span><span class="p">(</span><span class="s1">&#39;steps_ahead&#39;</span><span class="p">,</span> <span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">kwargs</span><span class="p">)</span>
+
+    <span class="n">steps_ahead</span> <span class="o">=</span> <span class="p">[</span><span class="n">k</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">steps_ahead</span><span class="p">]</span>
+
+    <span class="n">fts_methods</span> <span class="o">=</span> <span class="n">__pop</span><span class="p">(</span><span class="s1">&#39;methods&#39;</span><span class="p">,</span> <span class="p">[],</span> <span class="n">kwargs</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="n">fts_methods</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="n">methods_parameters</span> <span class="o">=</span> <span class="n">__pop</span><span class="p">(</span><span class="s1">&#39;methods_parameters&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">)</span>
+
+    <span class="n">ix_methods</span> <span class="o">=</span> <span class="p">[</span><span class="n">k</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">fts_methods</span><span class="p">))]</span>
+
+    <span class="n">benchmark_models</span> <span class="o">=</span> <span class="n">__pop</span><span class="p">(</span><span class="s2">&quot;benchmark_models&quot;</span><span class="p">,</span> <span class="kc">False</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">)</span>
+    <span class="n">benchmark_methods</span> <span class="o">=</span> <span class="n">__pop</span><span class="p">(</span><span class="s2">&quot;benchmark_methods&quot;</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">)</span>
+    <span class="n">benchmark_methods_parameters</span> <span class="o">=</span> <span class="n">__pop</span><span class="p">(</span><span class="s2">&quot;benchmark_methods_parameters&quot;</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="nb">type</span> <span class="o">==</span> <span class="s1">&#39;point&#39;</span><span class="p">:</span>
+        <span class="n">experiment_method</span> <span class="o">=</span> <span class="n">run_point2</span>
+        <span class="n">synthesis_method</span> <span class="o">=</span> <span class="n">process_point_jobs2</span>
+    <span class="k">elif</span> <span class="nb">type</span> <span class="o">==</span> <span class="s1">&#39;interval&#39;</span><span class="p">:</span>
+        <span class="n">experiment_method</span> <span class="o">=</span> <span class="n">run_interval2</span>
+        <span class="n">synthesis_method</span> <span class="o">=</span> <span class="n">process_interval_jobs2</span>
+    <span class="k">elif</span> <span class="nb">type</span> <span class="o">==</span> <span class="s1">&#39;distribution&#39;</span><span class="p">:</span>
+        <span class="n">experiment_method</span> <span class="o">=</span> <span class="n">run_probabilistic2</span>
+        <span class="n">synthesis_method</span> <span class="o">=</span> <span class="n">process_probabilistic_jobs2</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Type parameter has a unkown value!&quot;</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="n">distributed</span><span class="p">:</span>
+        <span class="kn">import</span> <span class="nn">pyFTS.distributed.dispy</span> <span class="k">as</span> <span class="nn">dispy</span>
+
+        <span class="n">nodes</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;nodes&quot;</span><span class="p">,</span> <span class="p">[</span><span class="s1">&#39;127.0.0.1&#39;</span><span class="p">])</span>
+        <span class="n">cluster</span><span class="p">,</span> <span class="n">http_server</span> <span class="o">=</span> <span class="n">dispy</span><span class="o">.</span><span class="n">start_dispy_cluster</span><span class="p">(</span><span class="n">experiment_method</span><span class="p">,</span> <span class="n">nodes</span><span class="p">)</span>
+
+    <span class="n">inc</span> <span class="o">=</span> <span class="n">__pop</span><span class="p">(</span><span class="s2">&quot;inc&quot;</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">)</span>
+
+    <span class="n">file</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;file&#39;</span><span class="p">,</span> <span class="s2">&quot;benchmarks.db&quot;</span><span class="p">)</span>
+
+    <span class="n">conn</span> <span class="o">=</span> <span class="n">bUtil</span><span class="o">.</span><span class="n">open_benchmark_db</span><span class="p">(</span><span class="n">file</span><span class="p">)</span>
+
+    <span class="n">jobs</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="k">for</span> <span class="n">ct</span><span class="p">,</span> <span class="n">train</span><span class="p">,</span> <span class="n">test</span> <span class="ow">in</span> <span class="n">cUtil</span><span class="o">.</span><span class="n">sliding_window</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">windowsize</span><span class="p">,</span> <span class="n">train</span><span class="p">,</span> <span class="n">inc</span><span class="o">=</span><span class="n">inc</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+
+        <span class="k">if</span> <span class="n">benchmark_models</span><span class="p">:</span>
+            <span class="k">for</span> <span class="n">bm</span><span class="p">,</span> <span class="n">method</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">benchmark_methods</span><span class="p">):</span>
+                <span class="n">kwargs</span><span class="p">[</span><span class="s1">&#39;steps_ahead&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">steps_ahead</span><span class="p">)</span>
+                <span class="n">kwargs</span><span class="p">[</span><span class="s1">&#39;parameters&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">benchmark_methods_parameters</span><span class="p">[</span><span class="n">bm</span><span class="p">]</span>
+
+                <span class="k">if</span> <span class="ow">not</span> <span class="n">distributed</span><span class="p">:</span>
+                    <span class="k">try</span><span class="p">:</span>
+                        <span class="n">job</span> <span class="o">=</span> <span class="n">experiment_method</span><span class="p">(</span><span class="n">method</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="n">train</span><span class="p">,</span> <span class="n">test</span><span class="p">,</span> <span class="n">ct</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+                        <span class="n">synthesis_method</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">tag</span><span class="p">,</span> <span class="n">job</span><span class="p">,</span> <span class="n">conn</span><span class="p">)</span>
+                    <span class="k">except</span> <span class="ne">Exception</span> <span class="k">as</span> <span class="n">ex</span><span class="p">:</span>
+                        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;EXCEPTION! &#39;</span><span class="p">,</span> <span class="n">method</span><span class="p">,</span> <span class="n">benchmark_methods_parameters</span><span class="p">[</span><span class="n">bm</span><span class="p">])</span>
+                        <span class="n">traceback</span><span class="o">.</span><span class="n">print_exc</span><span class="p">()</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">job</span> <span class="o">=</span> <span class="n">cluster</span><span class="o">.</span><span class="n">submit</span><span class="p">(</span><span class="n">method</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="n">train</span><span class="p">,</span> <span class="n">test</span><span class="p">,</span> <span class="n">ct</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+                    <span class="n">jobs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">job</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">fts_methods</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">params</span> <span class="o">=</span> <span class="p">[</span><span class="n">ix_methods</span><span class="p">,</span> <span class="n">orders</span><span class="p">,</span> <span class="n">partitioners_methods</span><span class="p">,</span> <span class="n">partitions</span><span class="p">,</span> <span class="n">transformations</span><span class="p">]</span>
+            <span class="k">for</span> <span class="nb">id</span><span class="p">,</span> <span class="n">instance</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">product</span><span class="p">(</span><span class="o">*</span><span class="n">params</span><span class="p">)):</span>
+                <span class="n">fts_method</span> <span class="o">=</span> <span class="n">fts_methods</span><span class="p">[</span><span class="n">instance</span><span class="p">[</span><span class="mi">0</span><span class="p">]]</span>
+                <span class="n">kwargs</span><span class="p">[</span><span class="s1">&#39;steps_ahead&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">steps_ahead</span><span class="p">)</span>
+                <span class="k">if</span> <span class="n">methods_parameters</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                    <span class="n">kwargs</span><span class="p">[</span><span class="s1">&#39;parameters&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">methods_parameters</span><span class="p">[</span><span class="n">instance</span><span class="p">[</span><span class="mi">0</span><span class="p">]]</span>
+                <span class="k">if</span> <span class="ow">not</span> <span class="n">distributed</span><span class="p">:</span>
+                    <span class="k">try</span><span class="p">:</span>
+                        <span class="n">job</span> <span class="o">=</span> <span class="n">experiment_method</span><span class="p">(</span><span class="n">fts_method</span><span class="p">,</span> <span class="n">instance</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">instance</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span> <span class="n">instance</span><span class="p">[</span><span class="mi">3</span><span class="p">],</span> <span class="n">instance</span><span class="p">[</span><span class="mi">4</span><span class="p">],</span> <span class="n">train</span><span class="p">,</span> <span class="n">test</span><span class="p">,</span> <span class="n">ct</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+                        <span class="n">synthesis_method</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">tag</span><span class="p">,</span> <span class="n">job</span><span class="p">,</span> <span class="n">conn</span><span class="p">)</span>
+                    <span class="k">except</span> <span class="ne">Exception</span> <span class="k">as</span> <span class="n">ex</span><span class="p">:</span>
+                        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;EXCEPTION! &#39;</span><span class="p">,</span> <span class="n">instance</span><span class="p">)</span>
+                        <span class="n">traceback</span><span class="o">.</span><span class="n">print_exc</span><span class="p">()</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">job</span> <span class="o">=</span> <span class="n">cluster</span><span class="o">.</span><span class="n">submit</span><span class="p">(</span><span class="n">fts_method</span><span class="p">,</span> <span class="n">instance</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">instance</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span> <span class="n">instance</span><span class="p">[</span><span class="mi">3</span><span class="p">],</span> <span class="n">instance</span><span class="p">[</span><span class="mi">4</span><span class="p">],</span> <span class="n">train</span><span class="p">,</span> <span class="n">test</span><span class="p">,</span> <span class="n">ct</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+                    <span class="n">job</span><span class="o">.</span><span class="n">id</span> <span class="o">=</span> <span class="nb">id</span>
+                    <span class="n">jobs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">job</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="n">distributed</span><span class="p">:</span>
+        <span class="k">for</span> <span class="n">job</span> <span class="ow">in</span> <span class="n">jobs</span><span class="p">:</span>
+            <span class="n">job</span><span class="p">()</span>
+            <span class="k">if</span> <span class="n">job</span><span class="o">.</span><span class="n">status</span> <span class="o">==</span> <span class="n">dispy</span><span class="o">.</span><span class="n">dispy</span><span class="o">.</span><span class="n">DispyJob</span><span class="o">.</span><span class="n">Finished</span> <span class="ow">and</span> <span class="n">job</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="n">tmp</span> <span class="o">=</span> <span class="n">job</span><span class="o">.</span><span class="n">result</span>
+                <span class="n">synthesis_method</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">tag</span><span class="p">,</span> <span class="n">tmp</span><span class="p">,</span> <span class="n">conn</span><span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;status&quot;</span><span class="p">,</span> <span class="n">job</span><span class="o">.</span><span class="n">status</span><span class="p">)</span>
+                <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;result&quot;</span><span class="p">,</span> <span class="n">job</span><span class="o">.</span><span class="n">result</span><span class="p">)</span>
+                <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;stdout&quot;</span><span class="p">,</span> <span class="n">job</span><span class="o">.</span><span class="n">stdout</span><span class="p">)</span>
+                <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;stderr&quot;</span><span class="p">,</span> <span class="n">job</span><span class="o">.</span><span class="n">exception</span><span class="p">)</span>
+
+        <span class="n">cluster</span><span class="o">.</span><span class="n">wait</span><span class="p">()</span>  <span class="c1"># wait for all jobs to finish</span>
+        <span class="n">dispy</span><span class="o">.</span><span class="n">stop_dispy_cluster</span><span class="p">(</span><span class="n">cluster</span><span class="p">,</span> <span class="n">http_server</span><span class="p">)</span>
+
+    <span class="n">conn</span><span class="o">.</span><span class="n">close</span><span class="p">()</span></div>
+
+
+<div class="viewcode-block" id="sliding_window_benchmarks"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.benchmarks.sliding_window_benchmarks">[docs]</a><span class="k">def</span> <span class="nf">sliding_window_benchmarks</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">windowsize</span><span class="p">,</span> <span class="n">train</span><span class="o">=</span><span class="mf">0.8</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Sliding window benchmarks for FTS forecasters.</span>
+
+<span class="sd">    For each data window, a train and test datasets will be splitted. For each train split, number of</span>
+<span class="sd">    partitions and partitioning method will be created a partitioner model. And for each partitioner, order,</span>
+<span class="sd">    steps ahead and FTS method a foreasting model will be trained.</span>
+
+<span class="sd">    Then all trained models are benchmarked on the test data and the metrics are stored on a sqlite3 database</span>
+<span class="sd">    (identified by the &#39;file&#39; parameter) for posterior analysis.</span>
+
+<span class="sd">    All these process can be distributed on a dispy cluster, setting the atributed &#39;distributed&#39; to true and</span>
+<span class="sd">    informing the list of dispy nodes on &#39;nodes&#39; parameter.</span>
+
+<span class="sd">    The number of experiments is determined by &#39;windowsize&#39; and &#39;inc&#39; parameters.</span>
+
+<span class="sd">    :param data: test data</span>
+<span class="sd">    :param windowsize: size of sliding window</span>
+<span class="sd">    :param train: percentual of sliding window data used to train the models</span>
+<span class="sd">    :param kwargs: dict, optional arguments</span>
+
+<span class="sd">    :keyword benchmark_methods:  a list with Non FTS models to benchmark. The default is None.</span>
+<span class="sd">    :keyword benchmark_methods_parameters:  a list with Non FTS models parameters. The default is None.</span>
+<span class="sd">    :keyword benchmark_models: A boolean value indicating if external FTS methods will be used on benchmark. The default is False.</span>
+<span class="sd">    :keyword build_methods: A boolean value indicating if the default FTS methods will be used on benchmark. The default is True.</span>
+<span class="sd">    :keyword dataset: the dataset name to identify the current set of benchmarks results on database.</span>
+<span class="sd">    :keyword distributed: A boolean value indicating if the forecasting procedure will be distributed in a dispy cluster. . The default is False</span>
+<span class="sd">    :keyword file: file path to save the results. The default is benchmarks.db.</span>
+<span class="sd">    :keyword inc: a float on interval [0,1] indicating the percentage of the windowsize to move the window</span>
+<span class="sd">    :keyword methods: a list with FTS class names. The default depends on the forecasting type and contains the list of all FTS methods.</span>
+<span class="sd">    :keyword models: a list with prebuilt FTS objects. The default is None.</span>
+<span class="sd">    :keyword nodes: a list with the dispy cluster nodes addresses. The default is [127.0.0.1].</span>
+<span class="sd">    :keyword orders: a list with orders of the models (for high order models). The default is [1,2,3].</span>
+<span class="sd">    :keyword partitions: a list with the numbers of partitions on the Universe of Discourse. The default is [10].</span>
+<span class="sd">    :keyword partitioners_models: a list with prebuilt Universe of Discourse partitioners objects. The default is None.</span>
+<span class="sd">    :keyword partitioners_methods: a list with Universe of Discourse partitioners class names. The default is [partitioners.Grid.GridPartitioner].</span>
+<span class="sd">    :keyword progress: If true a progress bar will be displayed during the benchmarks. The default is False.</span>
+<span class="sd">    :keyword start: in the multi step forecasting, the index of the data where to start forecasting. The default is 0.</span>
+<span class="sd">    :keyword steps_ahead: a list with  the forecasting horizons, i. e., the number of steps ahead to forecast. The default is 1.</span>
+<span class="sd">    :keyword tag: a name to identify the current set of benchmarks results on database.</span>
+<span class="sd">    :keyword type: the forecasting type, one of these values: point(default), interval or distribution. The default is point.</span>
+<span class="sd">    :keyword transformations: a list with data transformations do apply . The default is [None].</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">tag</span> <span class="o">=</span> <span class="n">__pop</span><span class="p">(</span><span class="s1">&#39;tag&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">)</span>
+    <span class="n">dataset</span> <span class="o">=</span> <span class="n">__pop</span><span class="p">(</span><span class="s1">&#39;dataset&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">)</span>
+
+    <span class="n">distributed</span> <span class="o">=</span> <span class="n">__pop</span><span class="p">(</span><span class="s1">&#39;distributed&#39;</span><span class="p">,</span> <span class="kc">False</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">)</span>
+
+    <span class="n">transformations</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;transformations&#39;</span><span class="p">,</span> <span class="p">[</span><span class="kc">None</span><span class="p">])</span>
+    <span class="n">progress</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;progress&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
+    <span class="nb">type</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;type&quot;</span><span class="p">,</span> <span class="s1">&#39;point&#39;</span><span class="p">)</span>
+
+    <span class="n">orders</span> <span class="o">=</span> <span class="n">__pop</span><span class="p">(</span><span class="s2">&quot;orders&quot;</span><span class="p">,</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">],</span> <span class="n">kwargs</span><span class="p">)</span>
+
+    <span class="n">partitioners_models</span> <span class="o">=</span> <span class="n">__pop</span><span class="p">(</span><span class="s2">&quot;partitioners_models&quot;</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">)</span>
+    <span class="n">partitioners_methods</span> <span class="o">=</span> <span class="n">__pop</span><span class="p">(</span><span class="s2">&quot;partitioners_methods&quot;</span><span class="p">,</span> <span class="p">[</span><span class="n">Grid</span><span class="o">.</span><span class="n">GridPartitioner</span><span class="p">],</span> <span class="n">kwargs</span><span class="p">)</span>
+    <span class="n">partitions</span> <span class="o">=</span> <span class="n">__pop</span><span class="p">(</span><span class="s2">&quot;partitions&quot;</span><span class="p">,</span> <span class="p">[</span><span class="mi">10</span><span class="p">],</span> <span class="n">kwargs</span><span class="p">)</span>
+
+    <span class="n">steps_ahead</span> <span class="o">=</span> <span class="n">__pop</span><span class="p">(</span><span class="s1">&#39;steps_ahead&#39;</span><span class="p">,</span> <span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">kwargs</span><span class="p">)</span>
+
+    <span class="n">methods</span> <span class="o">=</span> <span class="n">__pop</span><span class="p">(</span><span class="s1">&#39;methods&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">)</span>
+
+    <span class="n">models</span> <span class="o">=</span> <span class="n">__pop</span><span class="p">(</span><span class="s1">&#39;models&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">)</span>
+
+    <span class="n">pool</span> <span class="o">=</span> <span class="p">[]</span> <span class="k">if</span> <span class="n">models</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">models</span>
+
+    <span class="k">if</span> <span class="n">methods</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="k">if</span> <span class="nb">type</span>  <span class="o">==</span> <span class="s1">&#39;point&#39;</span><span class="p">:</span>
+            <span class="n">methods</span> <span class="o">=</span> <span class="n">get_point_methods</span><span class="p">()</span>
+        <span class="k">elif</span> <span class="nb">type</span> <span class="o">==</span> <span class="s1">&#39;interval&#39;</span><span class="p">:</span>
+            <span class="n">methods</span> <span class="o">=</span> <span class="n">get_interval_methods</span><span class="p">()</span>
+        <span class="k">elif</span> <span class="nb">type</span> <span class="o">==</span> <span class="s1">&#39;distribution&#39;</span><span class="p">:</span>
+            <span class="n">methods</span> <span class="o">=</span> <span class="n">get_probabilistic_methods</span><span class="p">()</span>
+
+    <span class="n">build_methods</span> <span class="o">=</span> <span class="n">__pop</span><span class="p">(</span><span class="s2">&quot;build_methods&quot;</span><span class="p">,</span> <span class="kc">True</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="n">build_methods</span><span class="p">:</span>
+        <span class="k">for</span> <span class="n">method</span> <span class="ow">in</span> <span class="n">methods</span><span class="p">:</span>
+            <span class="n">mfts</span> <span class="o">=</span> <span class="n">method</span><span class="p">()</span>
+
+            <span class="k">if</span> <span class="n">mfts</span><span class="o">.</span><span class="n">is_high_order</span><span class="p">:</span>
+                <span class="k">for</span> <span class="n">order</span> <span class="ow">in</span> <span class="n">orders</span><span class="p">:</span>
+                    <span class="k">if</span> <span class="n">order</span> <span class="o">&gt;=</span> <span class="n">mfts</span><span class="o">.</span><span class="n">min_order</span><span class="p">:</span>
+                        <span class="n">mfts</span> <span class="o">=</span> <span class="n">method</span><span class="p">()</span>
+                        <span class="n">mfts</span><span class="o">.</span><span class="n">order</span> <span class="o">=</span> <span class="n">order</span>
+                        <span class="n">pool</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">mfts</span><span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">mfts</span><span class="o">.</span><span class="n">order</span> <span class="o">=</span> <span class="mi">1</span>
+                <span class="n">pool</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">mfts</span><span class="p">)</span>
+
+    <span class="n">benchmark_models</span> <span class="o">=</span> <span class="n">__pop</span><span class="p">(</span><span class="s2">&quot;benchmark_models&quot;</span><span class="p">,</span> <span class="kc">False</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="n">benchmark_models</span> <span class="o">!=</span> <span class="kc">False</span><span class="p">:</span>
+
+        <span class="n">benchmark_methods</span> <span class="o">=</span> <span class="n">__pop</span><span class="p">(</span><span class="s2">&quot;benchmark_methods&quot;</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">)</span>
+        <span class="n">benchmark_methods_parameters</span> <span class="o">=</span> <span class="n">__pop</span><span class="p">(</span><span class="s2">&quot;benchmark_methods_parameters&quot;</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">)</span>
+
+        <span class="n">benchmark_pool</span> <span class="o">=</span> <span class="p">[]</span> <span class="k">if</span> <span class="p">(</span> <span class="n">benchmark_models</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">or</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">benchmark_models</span><span class="p">,</span> <span class="nb">list</span><span class="p">))</span> \
+            <span class="k">else</span> <span class="n">benchmark_models</span>
+
+        <span class="k">if</span> <span class="n">benchmark_models</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">benchmark_methods</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">if</span> <span class="nb">type</span> <span class="o">==</span> <span class="s1">&#39;point&#39;</span><span class="ow">or</span> <span class="nb">type</span>  <span class="o">==</span> <span class="s1">&#39;partition&#39;</span><span class="p">:</span>
+                <span class="n">benchmark_methods</span> <span class="o">=</span> <span class="n">get_benchmark_point_methods</span><span class="p">()</span>
+            <span class="k">elif</span> <span class="nb">type</span> <span class="o">==</span> <span class="s1">&#39;interval&#39;</span><span class="p">:</span>
+                <span class="n">benchmark_methods</span> <span class="o">=</span> <span class="n">get_benchmark_interval_methods</span><span class="p">()</span>
+            <span class="k">elif</span> <span class="nb">type</span> <span class="o">==</span> <span class="s1">&#39;distribution&#39;</span><span class="p">:</span>
+                <span class="n">benchmark_methods</span> <span class="o">=</span> <span class="n">get_benchmark_probabilistic_methods</span><span class="p">()</span>
+
+        <span class="k">if</span> <span class="n">benchmark_methods</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">for</span> <span class="n">transformation</span> <span class="ow">in</span> <span class="n">transformations</span><span class="p">:</span>
+                <span class="k">for</span> <span class="n">count</span><span class="p">,</span> <span class="n">model</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">benchmark_methods</span><span class="p">,</span> <span class="n">start</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
+                    <span class="n">par</span> <span class="o">=</span> <span class="n">benchmark_methods_parameters</span><span class="p">[</span><span class="n">count</span><span class="p">]</span>
+                    <span class="n">mfts</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="o">**</span><span class="n">par</span><span class="p">)</span>
+                    <span class="n">mfts</span><span class="o">.</span><span class="n">append_transformation</span><span class="p">(</span><span class="n">transformation</span><span class="p">)</span>
+                    <span class="n">benchmark_pool</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">mfts</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="nb">type</span> <span class="o">==</span> <span class="s1">&#39;point&#39;</span><span class="p">:</span>
+        <span class="n">experiment_method</span> <span class="o">=</span> <span class="n">run_point</span>
+        <span class="n">synthesis_method</span> <span class="o">=</span> <span class="n">process_point_jobs</span>
+    <span class="k">elif</span> <span class="nb">type</span> <span class="o">==</span> <span class="s1">&#39;interval&#39;</span><span class="p">:</span>
+        <span class="n">experiment_method</span> <span class="o">=</span> <span class="n">run_interval</span>
+        <span class="n">synthesis_method</span> <span class="o">=</span> <span class="n">process_interval_jobs</span>
+    <span class="k">elif</span> <span class="nb">type</span> <span class="o">==</span> <span class="s1">&#39;distribution&#39;</span><span class="p">:</span>
+        <span class="n">experiment_method</span> <span class="o">=</span> <span class="n">run_probabilistic</span>
+        <span class="n">synthesis_method</span> <span class="o">=</span> <span class="n">process_probabilistic_jobs</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Type parameter has a unkown value!&quot;</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="n">distributed</span><span class="p">:</span>
+        <span class="kn">import</span> <span class="nn">pyFTS.distributed.dispy</span> <span class="k">as</span> <span class="nn">dispy</span>
+
+        <span class="n">nodes</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;nodes&quot;</span><span class="p">,</span> <span class="p">[</span><span class="s1">&#39;127.0.0.1&#39;</span><span class="p">])</span>
+        <span class="n">cluster</span><span class="p">,</span> <span class="n">http_server</span> <span class="o">=</span> <span class="n">dispy</span><span class="o">.</span><span class="n">start_dispy_cluster</span><span class="p">(</span><span class="n">experiment_method</span><span class="p">,</span> <span class="n">nodes</span><span class="p">)</span>
+
+    <span class="n">jobs</span> <span class="o">=</span> <span class="p">[]</span>
+
+    <span class="n">inc</span> <span class="o">=</span> <span class="n">__pop</span><span class="p">(</span><span class="s2">&quot;inc&quot;</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="n">progress</span><span class="p">:</span>
+        <span class="kn">from</span> <span class="nn">tqdm</span> <span class="kn">import</span> <span class="n">tqdm</span>
+        <span class="n">_tdata</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">)</span> <span class="o">/</span> <span class="p">(</span><span class="n">windowsize</span> <span class="o">*</span> <span class="n">inc</span><span class="p">)</span>
+        <span class="n">_tasks</span> <span class="o">=</span> <span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">partitioners_models</span><span class="p">)</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="n">orders</span><span class="p">)</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="n">partitions</span><span class="p">)</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="n">transformations</span><span class="p">)</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="n">steps_ahead</span><span class="p">))</span>
+        <span class="n">_tbcmk</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">benchmark_pool</span><span class="p">)</span><span class="o">*</span><span class="nb">len</span><span class="p">(</span><span class="n">steps_ahead</span><span class="p">)</span>
+        <span class="n">progressbar</span> <span class="o">=</span> <span class="n">tqdm</span><span class="p">(</span><span class="n">total</span><span class="o">=</span><span class="n">_tdata</span><span class="o">*</span><span class="n">_tasks</span> <span class="o">+</span> <span class="n">_tdata</span><span class="o">*</span><span class="n">_tbcmk</span><span class="p">,</span> <span class="n">desc</span><span class="o">=</span><span class="s2">&quot;Benchmarks:&quot;</span><span class="p">)</span>
+
+    <span class="n">file</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;file&#39;</span><span class="p">,</span> <span class="s2">&quot;benchmarks.db&quot;</span><span class="p">)</span>
+
+    <span class="n">conn</span> <span class="o">=</span> <span class="n">bUtil</span><span class="o">.</span><span class="n">open_benchmark_db</span><span class="p">(</span><span class="n">file</span><span class="p">)</span>
+
+    <span class="k">for</span> <span class="n">ct</span><span class="p">,</span> <span class="n">train</span><span class="p">,</span> <span class="n">test</span> <span class="ow">in</span> <span class="n">cUtil</span><span class="o">.</span><span class="n">sliding_window</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">windowsize</span><span class="p">,</span> <span class="n">train</span><span class="p">,</span> <span class="n">inc</span><span class="o">=</span><span class="n">inc</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="k">if</span> <span class="n">benchmark_models</span> <span class="o">!=</span> <span class="kc">False</span><span class="p">:</span>
+            <span class="k">for</span> <span class="n">model</span> <span class="ow">in</span> <span class="n">benchmark_pool</span><span class="p">:</span>
+                <span class="k">for</span> <span class="n">step</span> <span class="ow">in</span> <span class="n">steps_ahead</span><span class="p">:</span>
+
+                    <span class="n">kwargs</span><span class="p">[</span><span class="s1">&#39;steps_ahead&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">step</span>
+
+                    <span class="k">if</span> <span class="ow">not</span> <span class="n">distributed</span><span class="p">:</span>
+                        <span class="k">if</span> <span class="n">progress</span><span class="p">:</span>
+                            <span class="n">progressbar</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
+                        <span class="k">try</span><span class="p">:</span>
+                            <span class="n">job</span> <span class="o">=</span> <span class="n">experiment_method</span><span class="p">(</span><span class="n">deepcopy</span><span class="p">(</span><span class="n">model</span><span class="p">),</span> <span class="kc">None</span><span class="p">,</span> <span class="n">train</span><span class="p">,</span> <span class="n">test</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+                            <span class="n">synthesis_method</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">tag</span><span class="p">,</span> <span class="n">job</span><span class="p">,</span> <span class="n">conn</span><span class="p">)</span>
+                        <span class="k">except</span> <span class="ne">Exception</span> <span class="k">as</span> <span class="n">ex</span><span class="p">:</span>
+                            <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;EXCEPTION! &#39;</span><span class="p">,</span> <span class="n">model</span><span class="o">.</span><span class="n">shortname</span><span class="p">,</span> <span class="n">model</span><span class="o">.</span><span class="n">order</span><span class="p">)</span>
+                            <span class="n">traceback</span><span class="o">.</span><span class="n">print_exc</span><span class="p">()</span>
+                    <span class="k">else</span><span class="p">:</span>
+                        <span class="n">job</span> <span class="o">=</span> <span class="n">cluster</span><span class="o">.</span><span class="n">submit</span><span class="p">(</span><span class="n">deepcopy</span><span class="p">(</span><span class="n">model</span><span class="p">),</span> <span class="kc">None</span><span class="p">,</span> <span class="n">train</span><span class="p">,</span> <span class="n">test</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+                        <span class="n">jobs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">job</span><span class="p">)</span>
+
+        <span class="n">partitioners_pool</span> <span class="o">=</span> <span class="p">[]</span>
+
+        <span class="k">if</span> <span class="n">partitioners_models</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+
+            <span class="k">for</span> <span class="n">transformation</span> <span class="ow">in</span> <span class="n">transformations</span><span class="p">:</span>
+
+                <span class="k">for</span> <span class="n">partition</span> <span class="ow">in</span> <span class="n">partitions</span><span class="p">:</span>
+
+                    <span class="k">for</span> <span class="n">partitioner</span> <span class="ow">in</span> <span class="n">partitioners_methods</span><span class="p">:</span>
+
+                        <span class="n">data_train_fs</span> <span class="o">=</span> <span class="n">partitioner</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">train</span><span class="p">,</span> <span class="n">npart</span><span class="o">=</span><span class="n">partition</span><span class="p">,</span> <span class="n">transformation</span><span class="o">=</span><span class="n">transformation</span><span class="p">)</span>
+
+                        <span class="n">partitioners_pool</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">data_train_fs</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">partitioners_pool</span> <span class="o">=</span> <span class="n">partitioners_models</span>
+
+        <span class="k">for</span> <span class="n">step</span> <span class="ow">in</span> <span class="n">steps_ahead</span><span class="p">:</span>
+
+            <span class="k">for</span> <span class="n">partitioner</span> <span class="ow">in</span> <span class="n">partitioners_pool</span><span class="p">:</span>
+
+                <span class="k">for</span> <span class="n">_id</span><span class="p">,</span> <span class="n">model</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">pool</span><span class="p">,</span><span class="n">start</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
+
+                    <span class="n">kwargs</span><span class="p">[</span><span class="s1">&#39;steps_ahead&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">step</span>
+
+                    <span class="k">if</span> <span class="ow">not</span> <span class="n">distributed</span><span class="p">:</span>
+                        <span class="k">if</span> <span class="n">progress</span><span class="p">:</span>
+                            <span class="n">progressbar</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
+                        <span class="k">try</span><span class="p">:</span>
+                            <span class="n">job</span> <span class="o">=</span> <span class="n">experiment_method</span><span class="p">(</span><span class="n">deepcopy</span><span class="p">(</span><span class="n">model</span><span class="p">),</span> <span class="n">deepcopy</span><span class="p">(</span><span class="n">partitioner</span><span class="p">),</span> <span class="n">train</span><span class="p">,</span> <span class="n">test</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+                            <span class="n">synthesis_method</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">tag</span><span class="p">,</span> <span class="n">job</span><span class="p">,</span> <span class="n">conn</span><span class="p">)</span>
+                        <span class="k">except</span> <span class="ne">Exception</span> <span class="k">as</span> <span class="n">ex</span><span class="p">:</span>
+                            <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;EXCEPTION! &#39;</span><span class="p">,</span><span class="n">model</span><span class="o">.</span><span class="n">shortname</span><span class="p">,</span> <span class="n">model</span><span class="o">.</span><span class="n">order</span><span class="p">,</span> <span class="n">partitioner</span><span class="o">.</span><span class="n">name</span><span class="p">,</span>
+                                  <span class="n">partitioner</span><span class="o">.</span><span class="n">partitions</span><span class="p">,</span> <span class="nb">str</span><span class="p">(</span><span class="n">partitioner</span><span class="o">.</span><span class="n">transformation</span><span class="p">))</span>
+                            <span class="n">traceback</span><span class="o">.</span><span class="n">print_exc</span><span class="p">()</span>
+                    <span class="k">else</span><span class="p">:</span>
+                        <span class="n">job</span> <span class="o">=</span> <span class="n">cluster</span><span class="o">.</span><span class="n">submit</span><span class="p">(</span><span class="n">deepcopy</span><span class="p">(</span><span class="n">model</span><span class="p">),</span> <span class="n">deepcopy</span><span class="p">(</span><span class="n">partitioner</span><span class="p">),</span> <span class="n">train</span><span class="p">,</span> <span class="n">test</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+                        <span class="n">job</span><span class="o">.</span><span class="n">id</span> <span class="o">=</span> <span class="nb">id</span>  <span class="c1"># associate an ID to identify jobs (if needed later)</span>
+                        <span class="n">jobs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">job</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="n">progress</span><span class="p">:</span>
+        <span class="n">progressbar</span><span class="o">.</span><span class="n">close</span><span class="p">()</span>
+
+    <span class="k">if</span> <span class="n">distributed</span><span class="p">:</span>
+
+        <span class="k">for</span> <span class="n">job</span> <span class="ow">in</span> <span class="n">jobs</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">progress</span><span class="p">:</span>
+                <span class="n">progressbar</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
+            <span class="n">job</span><span class="p">()</span>
+            <span class="k">if</span> <span class="n">job</span><span class="o">.</span><span class="n">status</span> <span class="o">==</span> <span class="n">dispy</span><span class="o">.</span><span class="n">dispy</span><span class="o">.</span><span class="n">DispyJob</span><span class="o">.</span><span class="n">Finished</span> <span class="ow">and</span> <span class="n">job</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="n">tmp</span> <span class="o">=</span> <span class="n">job</span><span class="o">.</span><span class="n">result</span>
+                <span class="n">synthesis_method</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">tag</span><span class="p">,</span> <span class="n">tmp</span><span class="p">,</span> <span class="n">conn</span><span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;status&quot;</span><span class="p">,</span><span class="n">job</span><span class="o">.</span><span class="n">status</span><span class="p">)</span>
+                <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;result&quot;</span><span class="p">,</span><span class="n">job</span><span class="o">.</span><span class="n">result</span><span class="p">)</span>
+                <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;stdout&quot;</span><span class="p">,</span><span class="n">job</span><span class="o">.</span><span class="n">stdout</span><span class="p">)</span>
+                <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;stderr&quot;</span><span class="p">,</span><span class="n">job</span><span class="o">.</span><span class="n">exception</span><span class="p">)</span>
+
+        <span class="n">cluster</span><span class="o">.</span><span class="n">wait</span><span class="p">()</span>  <span class="c1"># wait for all jobs to finish</span>
+
+        <span class="n">dispy</span><span class="o">.</span><span class="n">stop_dispy_cluster</span><span class="p">(</span><span class="n">cluster</span><span class="p">,</span> <span class="n">http_server</span><span class="p">)</span>
+
+    <span class="n">conn</span><span class="o">.</span><span class="n">close</span><span class="p">()</span></div>
+
+
+<div class="viewcode-block" id="run_point"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.benchmarks.run_point">[docs]</a><span class="k">def</span> <span class="nf">run_point</span><span class="p">(</span><span class="n">mfts</span><span class="p">,</span> <span class="n">partitioner</span><span class="p">,</span> <span class="n">train_data</span><span class="p">,</span> <span class="n">test_data</span><span class="p">,</span> <span class="n">window_key</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Run the point forecasting benchmarks</span>
+
+<span class="sd">    :param mfts: FTS model</span>
+<span class="sd">    :param partitioner: Universe of Discourse partitioner</span>
+<span class="sd">    :param train_data: data used to train the model</span>
+<span class="sd">    :param test_data: ata used to test the model</span>
+<span class="sd">    :param window_key: id of the sliding window</span>
+<span class="sd">    :param transformation: data transformation</span>
+<span class="sd">    :param indexer: seasonal indexer</span>
+<span class="sd">    :return: a dictionary with the benchmark results</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="kn">import</span> <span class="nn">time</span>
+    <span class="kn">from</span> <span class="nn">pyFTS.models</span> <span class="kn">import</span> <span class="n">yu</span><span class="p">,</span> <span class="n">chen</span><span class="p">,</span> <span class="n">hofts</span><span class="p">,</span> <span class="n">pwfts</span><span class="p">,</span><span class="n">ismailefendi</span><span class="p">,</span><span class="n">sadaei</span><span class="p">,</span> <span class="n">song</span><span class="p">,</span> <span class="n">cheng</span><span class="p">,</span> <span class="n">hwang</span>
+    <span class="kn">from</span> <span class="nn">pyFTS.partitioners</span> <span class="kn">import</span> <span class="n">Grid</span><span class="p">,</span> <span class="n">Entropy</span><span class="p">,</span> <span class="n">FCM</span>
+    <span class="kn">from</span> <span class="nn">pyFTS.benchmarks</span> <span class="kn">import</span> <span class="n">Measures</span><span class="p">,</span> <span class="n">naive</span><span class="p">,</span> <span class="n">arima</span><span class="p">,</span> <span class="n">quantreg</span>
+    <span class="kn">from</span> <span class="nn">pyFTS.common</span> <span class="kn">import</span> <span class="n">Transformations</span>
+
+    <span class="n">tmp</span> <span class="o">=</span> <span class="p">[</span><span class="n">song</span><span class="o">.</span><span class="n">ConventionalFTS</span><span class="p">,</span> <span class="n">chen</span><span class="o">.</span><span class="n">ConventionalFTS</span><span class="p">,</span> <span class="n">yu</span><span class="o">.</span><span class="n">WeightedFTS</span><span class="p">,</span> <span class="n">ismailefendi</span><span class="o">.</span><span class="n">ImprovedWeightedFTS</span><span class="p">,</span>
+           <span class="n">cheng</span><span class="o">.</span><span class="n">TrendWeightedFTS</span><span class="p">,</span> <span class="n">sadaei</span><span class="o">.</span><span class="n">ExponentialyWeightedFTS</span><span class="p">,</span> <span class="n">hofts</span><span class="o">.</span><span class="n">HighOrderFTS</span><span class="p">,</span> <span class="n">hwang</span><span class="o">.</span><span class="n">HighOrderFTS</span><span class="p">,</span>
+           <span class="n">pwfts</span><span class="o">.</span><span class="n">ProbabilisticWeightedFTS</span><span class="p">]</span>
+
+    <span class="n">tmp2</span> <span class="o">=</span> <span class="p">[</span><span class="n">Grid</span><span class="o">.</span><span class="n">GridPartitioner</span><span class="p">,</span> <span class="n">Entropy</span><span class="o">.</span><span class="n">EntropyPartitioner</span><span class="p">,</span> <span class="n">FCM</span><span class="o">.</span><span class="n">FCMPartitioner</span><span class="p">]</span>
+
+    <span class="n">tmp4</span> <span class="o">=</span> <span class="p">[</span><span class="n">naive</span><span class="o">.</span><span class="n">Naive</span><span class="p">,</span> <span class="n">arima</span><span class="o">.</span><span class="n">ARIMA</span><span class="p">,</span> <span class="n">quantreg</span><span class="o">.</span><span class="n">QuantileRegression</span><span class="p">]</span>
+
+    <span class="n">tmp3</span> <span class="o">=</span> <span class="p">[</span><span class="n">Measures</span><span class="o">.</span><span class="n">get_point_statistics</span><span class="p">]</span>
+
+    <span class="n">tmp5</span> <span class="o">=</span> <span class="p">[</span><span class="n">Transformations</span><span class="o">.</span><span class="n">Differential</span><span class="p">]</span>
+
+    <span class="n">indexer</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;indexer&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
+
+    <span class="n">steps_ahead</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;steps_ahead&#39;</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+    <span class="n">method</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;method&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="n">mfts</span><span class="o">.</span><span class="n">benchmark_only</span><span class="p">:</span>
+        <span class="n">_key</span> <span class="o">=</span> <span class="n">mfts</span><span class="o">.</span><span class="n">shortname</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">mfts</span><span class="o">.</span><span class="n">order</span> <span class="k">if</span> <span class="n">mfts</span><span class="o">.</span><span class="n">order</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">else</span> <span class="s2">&quot;&quot;</span><span class="p">)</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">pttr</span> <span class="o">=</span> <span class="nb">str</span><span class="p">(</span><span class="n">partitioner</span><span class="o">.</span><span class="vm">__module__</span><span class="p">)</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;.&#39;</span><span class="p">)[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
+        <span class="n">_key</span> <span class="o">=</span> <span class="n">mfts</span><span class="o">.</span><span class="n">shortname</span> <span class="o">+</span> <span class="s2">&quot; n = &quot;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">mfts</span><span class="o">.</span><span class="n">order</span><span class="p">)</span> <span class="o">+</span> <span class="s2">&quot; &quot;</span> <span class="o">+</span> <span class="n">pttr</span> <span class="o">+</span> <span class="s2">&quot; q = &quot;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">partitioner</span><span class="o">.</span><span class="n">partitions</span><span class="p">)</span>
+        <span class="n">mfts</span><span class="o">.</span><span class="n">partitioner</span> <span class="o">=</span> <span class="n">partitioner</span>
+        <span class="n">mfts</span><span class="o">.</span><span class="n">append_transformation</span><span class="p">(</span><span class="n">partitioner</span><span class="o">.</span><span class="n">transformation</span><span class="p">)</span>
+
+    <span class="n">_key</span> <span class="o">+=</span> <span class="nb">str</span><span class="p">(</span><span class="n">steps_ahead</span><span class="p">)</span>
+    <span class="n">_key</span> <span class="o">+=</span> <span class="nb">str</span><span class="p">(</span><span class="n">method</span><span class="p">)</span> <span class="k">if</span> <span class="n">method</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">else</span> <span class="s2">&quot;&quot;</span>
+
+    <span class="n">_start</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
+    <span class="n">mfts</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">train_data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+    <span class="n">_end</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
+    <span class="n">times</span> <span class="o">=</span> <span class="n">_end</span> <span class="o">-</span> <span class="n">_start</span>
+
+
+    <span class="n">_start</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
+    <span class="n">_rmse</span><span class="p">,</span> <span class="n">_smape</span><span class="p">,</span> <span class="n">_u</span> <span class="o">=</span> <span class="n">Measures</span><span class="o">.</span><span class="n">get_point_statistics</span><span class="p">(</span><span class="n">test_data</span><span class="p">,</span> <span class="n">mfts</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+    <span class="n">_end</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
+    <span class="n">times</span> <span class="o">+=</span> <span class="n">_end</span> <span class="o">-</span> <span class="n">_start</span>
+
+    <span class="n">ret</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;key&#39;</span><span class="p">:</span> <span class="n">_key</span><span class="p">,</span> <span class="s1">&#39;obj&#39;</span><span class="p">:</span> <span class="n">mfts</span><span class="p">,</span> <span class="s1">&#39;rmse&#39;</span><span class="p">:</span> <span class="n">_rmse</span><span class="p">,</span> <span class="s1">&#39;smape&#39;</span><span class="p">:</span> <span class="n">_smape</span><span class="p">,</span> <span class="s1">&#39;u&#39;</span><span class="p">:</span> <span class="n">_u</span><span class="p">,</span> <span class="s1">&#39;time&#39;</span><span class="p">:</span> <span class="n">times</span><span class="p">,</span> <span class="s1">&#39;window&#39;</span><span class="p">:</span> <span class="n">window_key</span><span class="p">,</span>
+           <span class="s1">&#39;steps&#39;</span><span class="p">:</span> <span class="n">steps_ahead</span><span class="p">,</span> <span class="s1">&#39;method&#39;</span><span class="p">:</span> <span class="n">method</span><span class="p">}</span>
+
+    <span class="k">return</span> <span class="n">ret</span></div>
+
+
+<div class="viewcode-block" id="run_interval"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.benchmarks.run_interval">[docs]</a><span class="k">def</span> <span class="nf">run_interval</span><span class="p">(</span><span class="n">mfts</span><span class="p">,</span> <span class="n">partitioner</span><span class="p">,</span> <span class="n">train_data</span><span class="p">,</span> <span class="n">test_data</span><span class="p">,</span> <span class="n">window_key</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Run the interval forecasting benchmarks</span>
+
+<span class="sd">    :param mfts: FTS model</span>
+<span class="sd">    :param partitioner: Universe of Discourse partitioner</span>
+<span class="sd">    :param train_data: data used to train the model</span>
+<span class="sd">    :param test_data: ata used to test the model</span>
+<span class="sd">    :param window_key: id of the sliding window</span>
+<span class="sd">    :param transformation: data transformation</span>
+<span class="sd">    :param indexer: seasonal indexer</span>
+<span class="sd">    :return: a dictionary with the benchmark results</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="kn">import</span> <span class="nn">time</span>
+    <span class="kn">from</span> <span class="nn">pyFTS.models</span> <span class="kn">import</span> <span class="n">hofts</span><span class="p">,</span><span class="n">ifts</span><span class="p">,</span><span class="n">pwfts</span>
+    <span class="kn">from</span> <span class="nn">pyFTS.partitioners</span> <span class="kn">import</span> <span class="n">Grid</span><span class="p">,</span> <span class="n">Entropy</span><span class="p">,</span> <span class="n">FCM</span>
+    <span class="kn">from</span> <span class="nn">pyFTS.benchmarks</span> <span class="kn">import</span> <span class="n">Measures</span><span class="p">,</span> <span class="n">arima</span><span class="p">,</span> <span class="n">quantreg</span>
+
+    <span class="n">tmp</span> <span class="o">=</span> <span class="p">[</span><span class="n">hofts</span><span class="o">.</span><span class="n">HighOrderFTS</span><span class="p">,</span> <span class="n">ifts</span><span class="o">.</span><span class="n">IntervalFTS</span><span class="p">,</span>  <span class="n">pwfts</span><span class="o">.</span><span class="n">ProbabilisticWeightedFTS</span><span class="p">]</span>
+
+    <span class="n">tmp2</span> <span class="o">=</span> <span class="p">[</span><span class="n">Grid</span><span class="o">.</span><span class="n">GridPartitioner</span><span class="p">,</span> <span class="n">Entropy</span><span class="o">.</span><span class="n">EntropyPartitioner</span><span class="p">,</span> <span class="n">FCM</span><span class="o">.</span><span class="n">FCMPartitioner</span><span class="p">]</span>
+
+    <span class="n">tmp4</span> <span class="o">=</span> <span class="p">[</span><span class="n">arima</span><span class="o">.</span><span class="n">ARIMA</span><span class="p">,</span> <span class="n">quantreg</span><span class="o">.</span><span class="n">QuantileRegression</span><span class="p">]</span>
+
+    <span class="n">tmp3</span> <span class="o">=</span> <span class="p">[</span><span class="n">Measures</span><span class="o">.</span><span class="n">get_interval_statistics</span><span class="p">]</span>
+
+    <span class="n">steps_ahead</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;steps_ahead&#39;</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+    <span class="n">method</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;method&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="n">mfts</span><span class="o">.</span><span class="n">benchmark_only</span><span class="p">:</span>
+        <span class="n">_key</span> <span class="o">=</span> <span class="n">mfts</span><span class="o">.</span><span class="n">shortname</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">mfts</span><span class="o">.</span><span class="n">order</span> <span class="k">if</span> <span class="n">mfts</span><span class="o">.</span><span class="n">order</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">else</span> <span class="s2">&quot;&quot;</span><span class="p">)</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">mfts</span><span class="o">.</span><span class="n">alpha</span><span class="p">)</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">pttr</span> <span class="o">=</span> <span class="nb">str</span><span class="p">(</span><span class="n">partitioner</span><span class="o">.</span><span class="vm">__module__</span><span class="p">)</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;.&#39;</span><span class="p">)[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
+        <span class="n">_key</span> <span class="o">=</span> <span class="n">mfts</span><span class="o">.</span><span class="n">shortname</span> <span class="o">+</span> <span class="s2">&quot; n = &quot;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">mfts</span><span class="o">.</span><span class="n">order</span><span class="p">)</span> <span class="o">+</span> <span class="s2">&quot; &quot;</span> <span class="o">+</span> <span class="n">pttr</span> <span class="o">+</span> <span class="s2">&quot; q = &quot;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">partitioner</span><span class="o">.</span><span class="n">partitions</span><span class="p">)</span>
+        <span class="n">mfts</span><span class="o">.</span><span class="n">partitioner</span> <span class="o">=</span> <span class="n">partitioner</span>
+        <span class="n">mfts</span><span class="o">.</span><span class="n">append_transformation</span><span class="p">(</span><span class="n">partitioner</span><span class="o">.</span><span class="n">transformation</span><span class="p">)</span>
+
+    <span class="n">_key</span> <span class="o">+=</span> <span class="nb">str</span><span class="p">(</span><span class="n">steps_ahead</span><span class="p">)</span>
+    <span class="n">_key</span> <span class="o">+=</span> <span class="nb">str</span><span class="p">(</span><span class="n">method</span><span class="p">)</span> <span class="k">if</span> <span class="n">method</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">else</span> <span class="s2">&quot;&quot;</span>
+
+    <span class="n">_start</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
+    <span class="n">mfts</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">train_data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+    <span class="n">_end</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
+    <span class="n">times</span> <span class="o">=</span> <span class="n">_end</span> <span class="o">-</span> <span class="n">_start</span>
+
+    <span class="n">_start</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
+    <span class="c1">#_sharp, _res, _cov, _q05, _q25, _q75, _q95, _w05, _w25</span>
+    <span class="n">metrics</span> <span class="o">=</span> <span class="n">Measures</span><span class="o">.</span><span class="n">get_interval_statistics</span><span class="p">(</span><span class="n">test_data</span><span class="p">,</span> <span class="n">mfts</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+    <span class="n">_end</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
+    <span class="n">times</span> <span class="o">+=</span> <span class="n">_end</span> <span class="o">-</span> <span class="n">_start</span>
+
+    <span class="n">ret</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;key&#39;</span><span class="p">:</span> <span class="n">_key</span><span class="p">,</span> <span class="s1">&#39;obj&#39;</span><span class="p">:</span> <span class="n">mfts</span><span class="p">,</span> <span class="s1">&#39;sharpness&#39;</span><span class="p">:</span> <span class="n">metrics</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="s1">&#39;resolution&#39;</span><span class="p">:</span> <span class="n">metrics</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="s1">&#39;coverage&#39;</span><span class="p">:</span> <span class="n">metrics</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span>
+           <span class="s1">&#39;time&#39;</span><span class="p">:</span> <span class="n">times</span><span class="p">,</span><span class="s1">&#39;Q05&#39;</span><span class="p">:</span> <span class="n">metrics</span><span class="p">[</span><span class="mi">3</span><span class="p">],</span> <span class="s1">&#39;Q25&#39;</span><span class="p">:</span> <span class="n">metrics</span><span class="p">[</span><span class="mi">4</span><span class="p">],</span> <span class="s1">&#39;Q75&#39;</span><span class="p">:</span> <span class="n">metrics</span><span class="p">[</span><span class="mi">5</span><span class="p">],</span> <span class="s1">&#39;Q95&#39;</span><span class="p">:</span> <span class="n">metrics</span><span class="p">[</span><span class="mi">6</span><span class="p">],</span>
+           <span class="s1">&#39;winkler05&#39;</span><span class="p">:</span> <span class="n">metrics</span><span class="p">[</span><span class="mi">7</span><span class="p">],</span> <span class="s1">&#39;winkler25&#39;</span><span class="p">:</span> <span class="n">metrics</span><span class="p">[</span><span class="mi">8</span><span class="p">],</span>
+           <span class="s1">&#39;window&#39;</span><span class="p">:</span> <span class="n">window_key</span><span class="p">,</span><span class="s1">&#39;steps&#39;</span><span class="p">:</span> <span class="n">steps_ahead</span><span class="p">,</span> <span class="s1">&#39;method&#39;</span><span class="p">:</span> <span class="n">method</span><span class="p">}</span>
+
+    <span class="k">return</span> <span class="n">ret</span></div>
+
+
+<div class="viewcode-block" id="run_probabilistic"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.benchmarks.run_probabilistic">[docs]</a><span class="k">def</span> <span class="nf">run_probabilistic</span><span class="p">(</span><span class="n">mfts</span><span class="p">,</span> <span class="n">partitioner</span><span class="p">,</span> <span class="n">train_data</span><span class="p">,</span> <span class="n">test_data</span><span class="p">,</span> <span class="n">window_key</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Run the probabilistic forecasting benchmarks</span>
+
+<span class="sd">    :param mfts: FTS model</span>
+<span class="sd">    :param partitioner: Universe of Discourse partitioner</span>
+<span class="sd">    :param train_data: data used to train the model</span>
+<span class="sd">    :param test_data: ata used to test the model</span>
+<span class="sd">    :param steps:</span>
+<span class="sd">    :param resolution:</span>
+<span class="sd">    :param window_key: id of the sliding window</span>
+<span class="sd">    :param transformation: data transformation</span>
+<span class="sd">    :param indexer: seasonal indexer</span>
+<span class="sd">    :return: a dictionary with the benchmark results</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="kn">import</span> <span class="nn">time</span>
+    <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+    <span class="kn">from</span> <span class="nn">pyFTS.models</span> <span class="kn">import</span> <span class="n">hofts</span><span class="p">,</span> <span class="n">ifts</span><span class="p">,</span> <span class="n">pwfts</span>
+    <span class="kn">from</span> <span class="nn">pyFTS.models.ensemble</span> <span class="kn">import</span> <span class="n">ensemble</span>
+    <span class="kn">from</span> <span class="nn">pyFTS.partitioners</span> <span class="kn">import</span> <span class="n">Grid</span><span class="p">,</span> <span class="n">Entropy</span><span class="p">,</span> <span class="n">FCM</span>
+    <span class="kn">from</span> <span class="nn">pyFTS.benchmarks</span> <span class="kn">import</span> <span class="n">Measures</span><span class="p">,</span> <span class="n">arima</span><span class="p">,</span> <span class="n">quantreg</span><span class="p">,</span> <span class="n">knn</span>
+    <span class="kn">from</span> <span class="nn">pyFTS.models.seasonal</span> <span class="kn">import</span> <span class="n">SeasonalIndexer</span>
+
+    <span class="n">tmp</span> <span class="o">=</span> <span class="p">[</span><span class="n">hofts</span><span class="o">.</span><span class="n">HighOrderFTS</span><span class="p">,</span> <span class="n">ifts</span><span class="o">.</span><span class="n">IntervalFTS</span><span class="p">,</span> <span class="n">pwfts</span><span class="o">.</span><span class="n">ProbabilisticWeightedFTS</span><span class="p">,</span> <span class="n">arima</span><span class="o">.</span><span class="n">ARIMA</span><span class="p">,</span>
+           <span class="n">ensemble</span><span class="o">.</span><span class="n">AllMethodEnsembleFTS</span><span class="p">,</span> <span class="n">knn</span><span class="o">.</span><span class="n">KNearestNeighbors</span><span class="p">]</span>
+
+    <span class="n">tmp2</span> <span class="o">=</span> <span class="p">[</span><span class="n">Grid</span><span class="o">.</span><span class="n">GridPartitioner</span><span class="p">,</span> <span class="n">Entropy</span><span class="o">.</span><span class="n">EntropyPartitioner</span><span class="p">,</span> <span class="n">FCM</span><span class="o">.</span><span class="n">FCMPartitioner</span><span class="p">]</span>
+
+    <span class="n">tmp3</span> <span class="o">=</span> <span class="p">[</span><span class="n">Measures</span><span class="o">.</span><span class="n">get_distribution_statistics</span><span class="p">,</span> <span class="n">SeasonalIndexer</span><span class="o">.</span><span class="n">SeasonalIndexer</span><span class="p">,</span> <span class="n">SeasonalIndexer</span><span class="o">.</span><span class="n">LinearSeasonalIndexer</span><span class="p">]</span>
+
+    <span class="n">indexer</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;indexer&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
+
+    <span class="n">steps_ahead</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;steps_ahead&#39;</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+    <span class="n">method</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;method&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="n">mfts</span><span class="o">.</span><span class="n">benchmark_only</span><span class="p">:</span>
+        <span class="n">_key</span> <span class="o">=</span> <span class="n">mfts</span><span class="o">.</span><span class="n">shortname</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">mfts</span><span class="o">.</span><span class="n">order</span> <span class="k">if</span> <span class="n">mfts</span><span class="o">.</span><span class="n">order</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">else</span> <span class="s2">&quot;&quot;</span><span class="p">)</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">mfts</span><span class="o">.</span><span class="n">alpha</span><span class="p">)</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">pttr</span> <span class="o">=</span> <span class="nb">str</span><span class="p">(</span><span class="n">partitioner</span><span class="o">.</span><span class="vm">__module__</span><span class="p">)</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;.&#39;</span><span class="p">)[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
+        <span class="n">_key</span> <span class="o">=</span> <span class="n">mfts</span><span class="o">.</span><span class="n">shortname</span> <span class="o">+</span> <span class="s2">&quot; n = &quot;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">mfts</span><span class="o">.</span><span class="n">order</span><span class="p">)</span> <span class="o">+</span> <span class="s2">&quot; &quot;</span> <span class="o">+</span> <span class="n">pttr</span> <span class="o">+</span> <span class="s2">&quot; q = &quot;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">partitioner</span><span class="o">.</span><span class="n">partitions</span><span class="p">)</span>
+        <span class="n">mfts</span><span class="o">.</span><span class="n">partitioner</span> <span class="o">=</span> <span class="n">partitioner</span>
+        <span class="n">mfts</span><span class="o">.</span><span class="n">append_transformation</span><span class="p">(</span><span class="n">partitioner</span><span class="o">.</span><span class="n">transformation</span><span class="p">)</span>
+
+    <span class="n">_key</span> <span class="o">+=</span> <span class="nb">str</span><span class="p">(</span><span class="n">steps_ahead</span><span class="p">)</span>
+    <span class="n">_key</span> <span class="o">+=</span> <span class="nb">str</span><span class="p">(</span><span class="n">method</span><span class="p">)</span> <span class="k">if</span> <span class="n">method</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">else</span> <span class="s2">&quot;&quot;</span>
+
+    <span class="k">if</span> <span class="n">mfts</span><span class="o">.</span><span class="n">has_seasonality</span><span class="p">:</span>
+        <span class="n">mfts</span><span class="o">.</span><span class="n">indexer</span> <span class="o">=</span> <span class="n">indexer</span>
+
+    <span class="n">_start</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
+    <span class="n">mfts</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">train_data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+    <span class="n">_end</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
+    <span class="n">times</span> <span class="o">=</span> <span class="n">_end</span> <span class="o">-</span> <span class="n">_start</span>
+
+    <span class="n">_crps1</span><span class="p">,</span> <span class="n">_t1</span><span class="p">,</span> <span class="n">_brier</span> <span class="o">=</span> <span class="n">Measures</span><span class="o">.</span><span class="n">get_distribution_statistics</span><span class="p">(</span><span class="n">test_data</span><span class="p">,</span> <span class="n">mfts</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+    <span class="n">_t1</span> <span class="o">+=</span> <span class="n">times</span>
+
+    <span class="n">ret</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;key&#39;</span><span class="p">:</span> <span class="n">_key</span><span class="p">,</span> <span class="s1">&#39;obj&#39;</span><span class="p">:</span> <span class="n">mfts</span><span class="p">,</span> <span class="s1">&#39;crps&#39;</span><span class="p">:</span> <span class="n">_crps1</span><span class="p">,</span> <span class="s1">&#39;time&#39;</span><span class="p">:</span> <span class="n">_t1</span><span class="p">,</span> <span class="s1">&#39;brier&#39;</span><span class="p">:</span> <span class="n">_brier</span><span class="p">,</span> <span class="s1">&#39;window&#39;</span><span class="p">:</span> <span class="n">window_key</span><span class="p">,</span>
+           <span class="s1">&#39;steps&#39;</span><span class="p">:</span> <span class="n">steps_ahead</span><span class="p">,</span> <span class="s1">&#39;method&#39;</span><span class="p">:</span> <span class="n">method</span><span class="p">}</span>
+
+    <span class="k">return</span> <span class="n">ret</span></div>
+
+
+<span class="k">def</span> <span class="nf">__build_model</span><span class="p">(</span><span class="n">fts_method</span><span class="p">,</span> <span class="n">order</span><span class="p">,</span> <span class="n">parameters</span><span class="p">,</span> <span class="n">partitioner_method</span><span class="p">,</span> <span class="n">partitions</span><span class="p">,</span> <span class="n">train_data</span><span class="p">,</span> <span class="n">transformation</span><span class="p">):</span>
+    <span class="n">mfts</span> <span class="o">=</span> <span class="n">fts_method</span><span class="p">(</span><span class="o">**</span><span class="n">parameters</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">mfts</span><span class="o">.</span><span class="n">benchmark_only</span> <span class="ow">or</span> <span class="n">mfts</span><span class="o">.</span><span class="n">is_wrapper</span><span class="p">:</span>
+        <span class="n">pttr</span> <span class="o">=</span> <span class="s1">&#39;&#39;</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">fs</span> <span class="o">=</span> <span class="n">partitioner_method</span><span class="p">(</span><span class="n">npart</span><span class="o">=</span><span class="n">partitions</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="n">train_data</span><span class="p">,</span> <span class="n">transformation</span><span class="o">=</span><span class="n">transformation</span><span class="p">)</span>
+        <span class="n">pttr</span> <span class="o">=</span> <span class="nb">str</span><span class="p">(</span><span class="n">fs</span><span class="o">.</span><span class="vm">__module__</span><span class="p">)</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;.&#39;</span><span class="p">)[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
+        <span class="k">if</span> <span class="n">order</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
+            <span class="n">mfts</span> <span class="o">=</span> <span class="n">fts_method</span><span class="p">(</span><span class="n">partitioner</span><span class="o">=</span><span class="n">fs</span><span class="p">,</span> <span class="n">order</span><span class="o">=</span><span class="n">order</span><span class="p">,</span> <span class="o">**</span><span class="n">parameters</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">mfts</span><span class="o">.</span><span class="n">partitioner</span> <span class="o">=</span> <span class="n">fs</span>
+
+        <span class="k">if</span> <span class="n">transformation</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">mfts</span><span class="o">.</span><span class="n">append_transformation</span><span class="p">(</span><span class="n">transformation</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">mfts</span><span class="p">,</span> <span class="n">pttr</span>
+
+
+<div class="viewcode-block" id="mv_run_point2"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.benchmarks.mv_run_point2">[docs]</a><span class="k">def</span> <span class="nf">mv_run_point2</span><span class="p">(</span><span class="n">mfts</span><span class="p">,</span> <span class="n">train_data</span><span class="p">,</span> <span class="n">test_data</span><span class="p">,</span> <span class="n">window_key</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+    <span class="kn">import</span> <span class="nn">time</span>
+    <span class="kn">from</span> <span class="nn">pyFTS.models</span> <span class="kn">import</span> <span class="n">hofts</span><span class="p">,</span> <span class="n">ifts</span><span class="p">,</span> <span class="n">pwfts</span>
+    <span class="kn">from</span> <span class="nn">pyFTS.models.multivariate</span> <span class="kn">import</span> <span class="n">mvfts</span><span class="p">,</span> <span class="n">wmvfts</span><span class="p">,</span> <span class="n">granular</span>
+    <span class="kn">from</span> <span class="nn">pyFTS.partitioners</span> <span class="kn">import</span> <span class="n">Grid</span><span class="p">,</span> <span class="n">Entropy</span><span class="p">,</span> <span class="n">FCM</span>
+    <span class="kn">from</span> <span class="nn">pyFTS.benchmarks</span> <span class="kn">import</span> <span class="n">Measures</span><span class="p">,</span> <span class="n">arima</span><span class="p">,</span> <span class="n">quantreg</span><span class="p">,</span> <span class="n">BSTS</span><span class="p">,</span> <span class="n">benchmarks</span>
+
+    <span class="n">tmp</span> <span class="o">=</span> <span class="p">[</span><span class="n">hofts</span><span class="o">.</span><span class="n">HighOrderFTS</span><span class="p">,</span> <span class="n">ifts</span><span class="o">.</span><span class="n">IntervalFTS</span><span class="p">,</span> <span class="n">ifts</span><span class="o">.</span><span class="n">WeightedIntervalFTS</span><span class="p">,</span> <span class="n">pwfts</span><span class="o">.</span><span class="n">ProbabilisticWeightedFTS</span><span class="p">]</span>
+
+    <span class="n">tmp2</span> <span class="o">=</span> <span class="p">[</span><span class="n">Grid</span><span class="o">.</span><span class="n">GridPartitioner</span><span class="p">,</span> <span class="n">mvfts</span><span class="o">.</span><span class="n">MVFTS</span><span class="p">,</span> <span class="n">wmvfts</span><span class="o">.</span><span class="n">WeightedMVFTS</span><span class="p">,</span> <span class="n">granular</span><span class="o">.</span><span class="n">GranularWMVFTS</span><span class="p">]</span>
+
+    <span class="n">tmp4</span> <span class="o">=</span> <span class="p">[</span><span class="n">arima</span><span class="o">.</span><span class="n">ARIMA</span><span class="p">,</span> <span class="n">quantreg</span><span class="o">.</span><span class="n">QuantileRegression</span><span class="p">,</span> <span class="n">BSTS</span><span class="o">.</span><span class="n">ARIMA</span><span class="p">]</span>
+
+    <span class="n">tmp3</span> <span class="o">=</span> <span class="p">[</span><span class="n">Measures</span><span class="o">.</span><span class="n">get_interval_statistics</span><span class="p">]</span>
+
+    <span class="n">steps_ahead</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;steps_ahead&#39;</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+    <span class="n">method</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;method&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
+
+    <span class="n">_start</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
+    <span class="n">mfts</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">train_data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+    <span class="n">_end</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
+    <span class="n">times</span> <span class="o">=</span> <span class="n">_end</span> <span class="o">-</span> <span class="n">_start</span>
+
+    <span class="k">if</span> <span class="n">steps_ahead</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
+
+        <span class="n">_start</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
+        <span class="n">_rmse</span><span class="p">,</span> <span class="n">_smape</span><span class="p">,</span> <span class="n">_u</span> <span class="o">=</span> <span class="n">Measures</span><span class="o">.</span><span class="n">get_point_statistics</span><span class="p">(</span><span class="n">test_data</span><span class="p">,</span> <span class="n">mfts</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+        <span class="n">_end</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
+        <span class="n">times</span> <span class="o">+=</span> <span class="n">_end</span> <span class="o">-</span> <span class="n">_start</span>
+
+        <span class="n">ret</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;model&#39;</span><span class="p">:</span> <span class="n">mfts</span><span class="o">.</span><span class="n">shortname</span><span class="p">,</span> <span class="s1">&#39;partitioner&#39;</span><span class="p">:</span> <span class="kc">None</span><span class="p">,</span> <span class="s1">&#39;order&#39;</span><span class="p">:</span> <span class="n">mfts</span><span class="o">.</span><span class="n">order</span><span class="p">,</span> <span class="s1">&#39;partitions&#39;</span><span class="p">:</span> <span class="kc">None</span><span class="p">,</span>
+               <span class="s1">&#39;transformation&#39;</span><span class="p">:</span> <span class="kc">None</span><span class="p">,</span>
+               <span class="s1">&#39;size&#39;</span><span class="p">:</span> <span class="nb">len</span><span class="p">(</span><span class="n">mfts</span><span class="p">),</span> <span class="s1">&#39;time&#39;</span><span class="p">:</span> <span class="n">times</span><span class="p">,</span>
+               <span class="s1">&#39;rmse&#39;</span><span class="p">:</span> <span class="n">_rmse</span><span class="p">,</span> <span class="s1">&#39;smape&#39;</span><span class="p">:</span> <span class="n">_smape</span><span class="p">,</span> <span class="s1">&#39;u&#39;</span><span class="p">:</span> <span class="n">_u</span><span class="p">,</span> <span class="s1">&#39;window&#39;</span><span class="p">:</span> <span class="n">window_key</span><span class="p">,</span>
+               <span class="s1">&#39;steps&#39;</span><span class="p">:</span> <span class="n">steps_ahead</span><span class="p">,</span> <span class="s1">&#39;method&#39;</span><span class="p">:</span> <span class="n">method</span><span class="p">}</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">_start</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
+        <span class="n">forecasts</span> <span class="o">=</span> <span class="n">mfts</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">test_data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+        <span class="n">_end</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
+        <span class="n">times</span> <span class="o">+=</span> <span class="n">_end</span> <span class="o">-</span> <span class="n">_start</span>
+
+        <span class="n">tmp_test</span> <span class="o">=</span> <span class="n">test_data</span><span class="p">[</span><span class="n">mfts</span><span class="o">.</span><span class="n">target_variable</span><span class="o">.</span><span class="n">data_label</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">[</span><span class="n">mfts</span><span class="o">.</span><span class="n">order</span><span class="p">:</span><span class="n">mfts</span><span class="o">.</span><span class="n">order</span> <span class="o">+</span> <span class="n">steps_ahead</span><span class="p">]</span>
+
+        <span class="nb">eval</span> <span class="o">=</span> <span class="n">Measures</span><span class="o">.</span><span class="n">get_point_ahead_statistics</span><span class="p">(</span><span class="n">tmp_test</span><span class="p">,</span> <span class="n">forecasts</span><span class="p">)</span>
+
+        <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="nb">eval</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
+            <span class="nb">eval</span><span class="p">[</span><span class="n">key</span><span class="p">][</span><span class="s2">&quot;time&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">times</span>
+            <span class="nb">eval</span><span class="p">[</span><span class="n">key</span><span class="p">][</span><span class="s2">&quot;method&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">method</span>
+
+        <span class="n">ret</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;model&#39;</span><span class="p">:</span> <span class="n">mfts</span><span class="o">.</span><span class="n">shortname</span><span class="p">,</span> <span class="s1">&#39;partitioner&#39;</span><span class="p">:</span> <span class="kc">None</span><span class="p">,</span> <span class="s1">&#39;order&#39;</span><span class="p">:</span> <span class="n">mfts</span><span class="o">.</span><span class="n">order</span><span class="p">,</span> <span class="s1">&#39;partitions&#39;</span><span class="p">:</span> <span class="kc">None</span><span class="p">,</span>
+               <span class="s1">&#39;transformation&#39;</span><span class="p">:</span> <span class="kc">None</span><span class="p">,</span>
+               <span class="s1">&#39;size&#39;</span><span class="p">:</span> <span class="nb">len</span><span class="p">(</span><span class="n">mfts</span><span class="p">),</span> <span class="s1">&#39;time&#39;</span><span class="p">:</span> <span class="n">times</span><span class="p">,</span>
+               <span class="s1">&#39;window&#39;</span><span class="p">:</span> <span class="n">window_key</span><span class="p">,</span> <span class="s1">&#39;steps&#39;</span><span class="p">:</span> <span class="n">steps_ahead</span><span class="p">,</span> <span class="s1">&#39;method&#39;</span><span class="p">:</span> <span class="n">method</span><span class="p">,</span>
+               <span class="s1">&#39;ahead_results&#39;</span><span class="p">:</span> <span class="nb">eval</span>
+               <span class="p">}</span>
+
+    <span class="k">return</span> <span class="n">ret</span></div>
+
+
+<div class="viewcode-block" id="run_point2"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.benchmarks.run_point2">[docs]</a><span class="k">def</span> <span class="nf">run_point2</span><span class="p">(</span><span class="n">fts_method</span><span class="p">,</span> <span class="n">order</span><span class="p">,</span> <span class="n">partitioner_method</span><span class="p">,</span> <span class="n">partitions</span><span class="p">,</span> <span class="n">transformation</span><span class="p">,</span> <span class="n">train_data</span><span class="p">,</span> <span class="n">test_data</span><span class="p">,</span> <span class="n">window_key</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+
+    <span class="kn">import</span> <span class="nn">time</span>
+    <span class="kn">from</span> <span class="nn">pyFTS.models</span> <span class="kn">import</span> <span class="n">yu</span><span class="p">,</span> <span class="n">chen</span><span class="p">,</span> <span class="n">hofts</span><span class="p">,</span> <span class="n">pwfts</span><span class="p">,</span><span class="n">ismailefendi</span><span class="p">,</span><span class="n">sadaei</span><span class="p">,</span> <span class="n">song</span><span class="p">,</span> <span class="n">cheng</span><span class="p">,</span> <span class="n">hwang</span>
+    <span class="kn">from</span> <span class="nn">pyFTS.partitioners</span> <span class="kn">import</span> <span class="n">Grid</span><span class="p">,</span> <span class="n">Entropy</span><span class="p">,</span> <span class="n">FCM</span>
+    <span class="kn">from</span> <span class="nn">pyFTS.benchmarks</span> <span class="kn">import</span> <span class="n">Measures</span><span class="p">,</span> <span class="n">naive</span><span class="p">,</span> <span class="n">arima</span><span class="p">,</span> <span class="n">quantreg</span><span class="p">,</span> <span class="n">benchmarks</span>
+    <span class="kn">from</span> <span class="nn">pyFTS.common</span> <span class="kn">import</span> <span class="n">Transformations</span>
+
+    <span class="n">tmp</span> <span class="o">=</span> <span class="p">[</span><span class="n">song</span><span class="o">.</span><span class="n">ConventionalFTS</span><span class="p">,</span> <span class="n">chen</span><span class="o">.</span><span class="n">ConventionalFTS</span><span class="p">,</span> <span class="n">yu</span><span class="o">.</span><span class="n">WeightedFTS</span><span class="p">,</span> <span class="n">ismailefendi</span><span class="o">.</span><span class="n">ImprovedWeightedFTS</span><span class="p">,</span>
+           <span class="n">cheng</span><span class="o">.</span><span class="n">TrendWeightedFTS</span><span class="p">,</span> <span class="n">sadaei</span><span class="o">.</span><span class="n">ExponentialyWeightedFTS</span><span class="p">,</span> <span class="n">hofts</span><span class="o">.</span><span class="n">HighOrderFTS</span><span class="p">,</span> <span class="n">hwang</span><span class="o">.</span><span class="n">HighOrderFTS</span><span class="p">,</span>
+           <span class="n">pwfts</span><span class="o">.</span><span class="n">ProbabilisticWeightedFTS</span><span class="p">]</span>
+
+    <span class="n">tmp2</span> <span class="o">=</span> <span class="p">[</span><span class="n">Grid</span><span class="o">.</span><span class="n">GridPartitioner</span><span class="p">,</span> <span class="n">Entropy</span><span class="o">.</span><span class="n">EntropyPartitioner</span><span class="p">,</span> <span class="n">FCM</span><span class="o">.</span><span class="n">FCMPartitioner</span><span class="p">]</span>
+
+    <span class="n">tmp4</span> <span class="o">=</span> <span class="p">[</span><span class="n">naive</span><span class="o">.</span><span class="n">Naive</span><span class="p">,</span> <span class="n">arima</span><span class="o">.</span><span class="n">ARIMA</span><span class="p">,</span> <span class="n">quantreg</span><span class="o">.</span><span class="n">QuantileRegression</span><span class="p">]</span>
+
+    <span class="n">tmp3</span> <span class="o">=</span> <span class="p">[</span><span class="n">Measures</span><span class="o">.</span><span class="n">get_point_statistics</span><span class="p">]</span>
+
+    <span class="n">tmp5</span> <span class="o">=</span> <span class="p">[</span><span class="n">Transformations</span><span class="o">.</span><span class="n">Differential</span><span class="p">]</span>
+
+    <span class="n">indexer</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;indexer&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
+
+    <span class="n">steps_ahead</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;steps_ahead&#39;</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+    <span class="n">method</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;method&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
+    <span class="n">parameters</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;parameters&#39;</span><span class="p">,</span> <span class="p">{})</span>
+
+    <span class="n">mfts</span><span class="p">,</span> <span class="n">pttr</span> <span class="o">=</span> <span class="n">benchmarks</span><span class="o">.</span><span class="n">__build_model</span><span class="p">(</span><span class="n">fts_method</span><span class="p">,</span> <span class="n">order</span><span class="p">,</span> <span class="n">parameters</span><span class="p">,</span> <span class="n">partitioner_method</span><span class="p">,</span> <span class="n">partitions</span><span class="p">,</span> <span class="n">train_data</span><span class="p">,</span>
+                               <span class="n">transformation</span><span class="p">)</span>
+
+    <span class="n">_start</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
+    <span class="n">mfts</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">train_data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+    <span class="n">_end</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
+    <span class="n">times</span> <span class="o">=</span> <span class="n">_end</span> <span class="o">-</span> <span class="n">_start</span>
+
+    <span class="k">if</span> <span class="n">steps_ahead</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
+
+        <span class="n">_start</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
+        <span class="n">_rmse</span><span class="p">,</span> <span class="n">_smape</span><span class="p">,</span> <span class="n">_u</span> <span class="o">=</span> <span class="n">Measures</span><span class="o">.</span><span class="n">get_point_statistics</span><span class="p">(</span><span class="n">test_data</span><span class="p">,</span> <span class="n">mfts</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+        <span class="n">_end</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
+        <span class="n">times</span> <span class="o">+=</span> <span class="n">_end</span> <span class="o">-</span> <span class="n">_start</span>
+
+        <span class="n">ret</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;model&#39;</span><span class="p">:</span> <span class="n">mfts</span><span class="o">.</span><span class="n">shortname</span><span class="p">,</span> <span class="s1">&#39;partitioner&#39;</span><span class="p">:</span> <span class="n">pttr</span><span class="p">,</span> <span class="s1">&#39;order&#39;</span><span class="p">:</span> <span class="n">order</span><span class="p">,</span> <span class="s1">&#39;partitions&#39;</span><span class="p">:</span> <span class="n">partitions</span><span class="p">,</span>
+               <span class="s1">&#39;transformation&#39;</span><span class="p">:</span> <span class="s1">&#39;&#39;</span> <span class="k">if</span> <span class="n">transformation</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">transformation</span><span class="o">.</span><span class="n">name</span><span class="p">,</span>
+               <span class="s1">&#39;size&#39;</span><span class="p">:</span> <span class="nb">len</span><span class="p">(</span><span class="n">mfts</span><span class="p">),</span> <span class="s1">&#39;time&#39;</span><span class="p">:</span> <span class="n">times</span><span class="p">,</span>
+               <span class="s1">&#39;rmse&#39;</span><span class="p">:</span> <span class="n">_rmse</span><span class="p">,</span> <span class="s1">&#39;smape&#39;</span><span class="p">:</span> <span class="n">_smape</span><span class="p">,</span> <span class="s1">&#39;u&#39;</span><span class="p">:</span> <span class="n">_u</span><span class="p">,</span> <span class="s1">&#39;window&#39;</span><span class="p">:</span> <span class="n">window_key</span><span class="p">,</span>
+               <span class="s1">&#39;steps&#39;</span><span class="p">:</span> <span class="n">steps_ahead</span><span class="p">,</span> <span class="s1">&#39;method&#39;</span><span class="p">:</span> <span class="n">method</span><span class="p">}</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">_start</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
+        <span class="n">forecasts</span> <span class="o">=</span> <span class="n">mfts</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">test_data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+        <span class="n">_end</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
+        <span class="n">times</span> <span class="o">+=</span> <span class="n">_end</span> <span class="o">-</span> <span class="n">_start</span>
+
+        <span class="nb">eval</span> <span class="o">=</span> <span class="n">Measures</span><span class="o">.</span><span class="n">get_point_ahead_statistics</span><span class="p">(</span><span class="n">test_data</span><span class="p">[</span><span class="n">mfts</span><span class="o">.</span><span class="n">order</span><span class="p">:</span><span class="n">mfts</span><span class="o">.</span><span class="n">order</span><span class="o">+</span><span class="n">steps_ahead</span><span class="p">],</span> <span class="n">forecasts</span><span class="p">)</span>
+
+        <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="nb">eval</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
+            <span class="nb">eval</span><span class="p">[</span><span class="n">key</span><span class="p">][</span><span class="s2">&quot;time&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">times</span>
+            <span class="nb">eval</span><span class="p">[</span><span class="n">key</span><span class="p">][</span><span class="s2">&quot;method&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">method</span>
+
+        <span class="n">ret</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;model&#39;</span><span class="p">:</span> <span class="n">mfts</span><span class="o">.</span><span class="n">shortname</span><span class="p">,</span> <span class="s1">&#39;partitioner&#39;</span><span class="p">:</span> <span class="n">pttr</span><span class="p">,</span> <span class="s1">&#39;order&#39;</span><span class="p">:</span> <span class="n">order</span><span class="p">,</span> <span class="s1">&#39;partitions&#39;</span><span class="p">:</span> <span class="n">partitions</span><span class="p">,</span>
+               <span class="s1">&#39;transformation&#39;</span><span class="p">:</span> <span class="s1">&#39;&#39;</span> <span class="k">if</span> <span class="n">transformation</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">transformation</span><span class="o">.</span><span class="n">name</span><span class="p">,</span>
+               <span class="s1">&#39;size&#39;</span><span class="p">:</span> <span class="nb">len</span><span class="p">(</span><span class="n">mfts</span><span class="p">),</span> <span class="s1">&#39;time&#39;</span><span class="p">:</span> <span class="n">times</span><span class="p">,</span>
+               <span class="s1">&#39;window&#39;</span><span class="p">:</span> <span class="n">window_key</span><span class="p">,</span> <span class="s1">&#39;steps&#39;</span><span class="p">:</span> <span class="n">steps_ahead</span><span class="p">,</span> <span class="s1">&#39;method&#39;</span><span class="p">:</span> <span class="n">method</span><span class="p">,</span>
+               <span class="s1">&#39;ahead_results&#39;</span><span class="p">:</span> <span class="nb">eval</span>
+               <span class="p">}</span>
+
+    <span class="k">return</span> <span class="n">ret</span></div>
+
+
+<div class="viewcode-block" id="mv_run_interval2"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.benchmarks.mv_run_interval2">[docs]</a><span class="k">def</span> <span class="nf">mv_run_interval2</span><span class="p">(</span><span class="n">mfts</span><span class="p">,</span><span class="n">train_data</span><span class="p">,</span> <span class="n">test_data</span><span class="p">,</span> <span class="n">window_key</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+    <span class="kn">import</span> <span class="nn">time</span>
+    <span class="kn">from</span> <span class="nn">pyFTS.models</span> <span class="kn">import</span> <span class="n">hofts</span><span class="p">,</span><span class="n">ifts</span><span class="p">,</span><span class="n">pwfts</span>
+    <span class="kn">from</span> <span class="nn">pyFTS.models.multivariate</span> <span class="kn">import</span> <span class="n">mvfts</span><span class="p">,</span> <span class="n">wmvfts</span><span class="p">,</span> <span class="n">granular</span>
+    <span class="kn">from</span> <span class="nn">pyFTS.partitioners</span> <span class="kn">import</span> <span class="n">Grid</span><span class="p">,</span> <span class="n">Entropy</span><span class="p">,</span> <span class="n">FCM</span>
+    <span class="kn">from</span> <span class="nn">pyFTS.benchmarks</span> <span class="kn">import</span> <span class="n">Measures</span><span class="p">,</span> <span class="n">arima</span><span class="p">,</span> <span class="n">quantreg</span><span class="p">,</span> <span class="n">BSTS</span><span class="p">,</span> <span class="n">benchmarks</span>
+
+    <span class="n">tmp</span> <span class="o">=</span> <span class="p">[</span><span class="n">hofts</span><span class="o">.</span><span class="n">HighOrderFTS</span><span class="p">,</span> <span class="n">ifts</span><span class="o">.</span><span class="n">IntervalFTS</span><span class="p">,</span> <span class="n">ifts</span><span class="o">.</span><span class="n">WeightedIntervalFTS</span><span class="p">,</span>  <span class="n">pwfts</span><span class="o">.</span><span class="n">ProbabilisticWeightedFTS</span><span class="p">]</span>
+
+    <span class="n">tmp2</span> <span class="o">=</span> <span class="p">[</span><span class="n">Grid</span><span class="o">.</span><span class="n">GridPartitioner</span><span class="p">,</span> <span class="n">mvfts</span><span class="o">.</span><span class="n">MVFTS</span><span class="p">,</span> <span class="n">wmvfts</span><span class="o">.</span><span class="n">WeightedMVFTS</span><span class="p">,</span> <span class="n">granular</span><span class="o">.</span><span class="n">GranularWMVFTS</span> <span class="p">]</span>
+
+    <span class="n">tmp4</span> <span class="o">=</span> <span class="p">[</span><span class="n">arima</span><span class="o">.</span><span class="n">ARIMA</span><span class="p">,</span> <span class="n">quantreg</span><span class="o">.</span><span class="n">QuantileRegression</span><span class="p">,</span> <span class="n">BSTS</span><span class="o">.</span><span class="n">ARIMA</span><span class="p">]</span>
+
+    <span class="n">tmp3</span> <span class="o">=</span> <span class="p">[</span><span class="n">Measures</span><span class="o">.</span><span class="n">get_interval_statistics</span><span class="p">]</span>
+
+    <span class="n">steps_ahead</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;steps_ahead&#39;</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+    <span class="n">method</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;method&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
+    <span class="n">parameters</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;parameters&#39;</span><span class="p">,{})</span>
+
+    <span class="n">_start</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
+    <span class="n">mfts</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">train_data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+    <span class="n">_end</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
+    <span class="n">times</span> <span class="o">=</span> <span class="n">_end</span> <span class="o">-</span> <span class="n">_start</span>
+
+    <span class="k">if</span> <span class="n">steps_ahead</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
+
+        <span class="n">_start</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
+        <span class="n">metrics</span> <span class="o">=</span> <span class="n">Measures</span><span class="o">.</span><span class="n">get_interval_statistics</span><span class="p">(</span><span class="n">test_data</span><span class="p">,</span> <span class="n">mfts</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+        <span class="n">_end</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
+        <span class="n">times</span> <span class="o">+=</span> <span class="n">_end</span> <span class="o">-</span> <span class="n">_start</span>
+
+        <span class="n">ret</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;model&#39;</span><span class="p">:</span> <span class="n">mfts</span><span class="o">.</span><span class="n">shortname</span><span class="p">,</span> <span class="s1">&#39;partitioner&#39;</span><span class="p">:</span> <span class="kc">None</span><span class="p">,</span> <span class="s1">&#39;order&#39;</span><span class="p">:</span> <span class="n">mfts</span><span class="o">.</span><span class="n">order</span><span class="p">,</span> <span class="s1">&#39;partitions&#39;</span><span class="p">:</span> <span class="kc">None</span><span class="p">,</span>
+               <span class="s1">&#39;transformation&#39;</span><span class="p">:</span> <span class="kc">None</span><span class="p">,</span>
+               <span class="s1">&#39;size&#39;</span><span class="p">:</span> <span class="nb">len</span><span class="p">(</span><span class="n">mfts</span><span class="p">),</span> <span class="s1">&#39;time&#39;</span><span class="p">:</span> <span class="n">times</span><span class="p">,</span>
+               <span class="s1">&#39;sharpness&#39;</span><span class="p">:</span> <span class="n">metrics</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="s1">&#39;resolution&#39;</span><span class="p">:</span> <span class="n">metrics</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="s1">&#39;coverage&#39;</span><span class="p">:</span> <span class="n">metrics</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span>
+               <span class="s1">&#39;time&#39;</span><span class="p">:</span> <span class="n">times</span><span class="p">,</span><span class="s1">&#39;pinball05&#39;</span><span class="p">:</span> <span class="n">metrics</span><span class="p">[</span><span class="mi">3</span><span class="p">],</span> <span class="s1">&#39;pinball25&#39;</span><span class="p">:</span> <span class="n">metrics</span><span class="p">[</span><span class="mi">4</span><span class="p">],</span> <span class="s1">&#39;pinball75&#39;</span><span class="p">:</span> <span class="n">metrics</span><span class="p">[</span><span class="mi">5</span><span class="p">],</span> <span class="s1">&#39;pinball95&#39;</span><span class="p">:</span> <span class="n">metrics</span><span class="p">[</span><span class="mi">6</span><span class="p">],</span>
+               <span class="s1">&#39;winkler05&#39;</span><span class="p">:</span> <span class="n">metrics</span><span class="p">[</span><span class="mi">7</span><span class="p">],</span> <span class="s1">&#39;winkler25&#39;</span><span class="p">:</span> <span class="n">metrics</span><span class="p">[</span><span class="mi">8</span><span class="p">],</span>
+               <span class="s1">&#39;window&#39;</span><span class="p">:</span> <span class="n">window_key</span><span class="p">,</span><span class="s1">&#39;steps&#39;</span><span class="p">:</span> <span class="n">steps_ahead</span><span class="p">,</span> <span class="s1">&#39;method&#39;</span><span class="p">:</span> <span class="n">method</span><span class="p">}</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">_start</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
+        <span class="n">intervals</span> <span class="o">=</span> <span class="n">mfts</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">test_data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+        <span class="n">_end</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
+        <span class="n">times</span> <span class="o">+=</span> <span class="n">_end</span> <span class="o">-</span> <span class="n">_start</span>
+
+        <span class="n">tmp_test</span> <span class="o">=</span> <span class="n">test_data</span><span class="p">[</span><span class="n">mfts</span><span class="o">.</span><span class="n">target_variable</span><span class="o">.</span><span class="n">data_label</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">[</span><span class="n">mfts</span><span class="o">.</span><span class="n">order</span><span class="p">:</span><span class="n">mfts</span><span class="o">.</span><span class="n">order</span> <span class="o">+</span> <span class="n">steps_ahead</span><span class="p">]</span>
+
+        <span class="nb">eval</span> <span class="o">=</span> <span class="n">Measures</span><span class="o">.</span><span class="n">get_interval_ahead_statistics</span><span class="p">(</span><span class="n">tmp_test</span><span class="p">,</span> <span class="n">intervals</span><span class="p">)</span>
+
+        <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="nb">eval</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
+            <span class="nb">eval</span><span class="p">[</span><span class="n">key</span><span class="p">][</span><span class="s2">&quot;time&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">times</span>
+            <span class="nb">eval</span><span class="p">[</span><span class="n">key</span><span class="p">][</span><span class="s2">&quot;method&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">method</span>
+
+        <span class="n">ret</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;model&#39;</span><span class="p">:</span> <span class="n">mfts</span><span class="o">.</span><span class="n">shortname</span><span class="p">,</span> <span class="s1">&#39;partitioner&#39;</span><span class="p">:</span> <span class="kc">None</span><span class="p">,</span> <span class="s1">&#39;order&#39;</span><span class="p">:</span> <span class="n">mfts</span><span class="o">.</span><span class="n">order</span><span class="p">,</span> <span class="s1">&#39;partitions&#39;</span><span class="p">:</span> <span class="kc">None</span><span class="p">,</span>
+               <span class="s1">&#39;transformation&#39;</span><span class="p">:</span> <span class="kc">None</span><span class="p">,</span>
+               <span class="s1">&#39;size&#39;</span><span class="p">:</span> <span class="nb">len</span><span class="p">(</span><span class="n">mfts</span><span class="p">),</span> <span class="s1">&#39;time&#39;</span><span class="p">:</span> <span class="n">times</span><span class="p">,</span>
+               <span class="s1">&#39;window&#39;</span><span class="p">:</span> <span class="n">window_key</span><span class="p">,</span> <span class="s1">&#39;steps&#39;</span><span class="p">:</span> <span class="n">steps_ahead</span><span class="p">,</span> <span class="s1">&#39;method&#39;</span><span class="p">:</span> <span class="n">method</span><span class="p">,</span>
+               <span class="s1">&#39;ahead_results&#39;</span><span class="p">:</span> <span class="nb">eval</span>
+               <span class="p">}</span>
+
+
+    <span class="k">return</span> <span class="n">ret</span></div>
+
+
+<div class="viewcode-block" id="run_interval2"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.benchmarks.run_interval2">[docs]</a><span class="k">def</span> <span class="nf">run_interval2</span><span class="p">(</span><span class="n">fts_method</span><span class="p">,</span> <span class="n">order</span><span class="p">,</span> <span class="n">partitioner_method</span><span class="p">,</span> <span class="n">partitions</span><span class="p">,</span> <span class="n">transformation</span><span class="p">,</span> <span class="n">train_data</span><span class="p">,</span> <span class="n">test_data</span><span class="p">,</span> <span class="n">window_key</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+    <span class="kn">import</span> <span class="nn">time</span>
+    <span class="kn">from</span> <span class="nn">pyFTS.models</span> <span class="kn">import</span> <span class="n">hofts</span><span class="p">,</span><span class="n">ifts</span><span class="p">,</span><span class="n">pwfts</span>
+    <span class="kn">from</span> <span class="nn">pyFTS.partitioners</span> <span class="kn">import</span> <span class="n">Grid</span><span class="p">,</span> <span class="n">Entropy</span><span class="p">,</span> <span class="n">FCM</span>
+    <span class="kn">from</span> <span class="nn">pyFTS.benchmarks</span> <span class="kn">import</span> <span class="n">Measures</span><span class="p">,</span> <span class="n">arima</span><span class="p">,</span> <span class="n">quantreg</span><span class="p">,</span> <span class="n">BSTS</span><span class="p">,</span> <span class="n">benchmarks</span>
+
+    <span class="n">tmp</span> <span class="o">=</span> <span class="p">[</span><span class="n">hofts</span><span class="o">.</span><span class="n">HighOrderFTS</span><span class="p">,</span> <span class="n">ifts</span><span class="o">.</span><span class="n">IntervalFTS</span><span class="p">,</span> <span class="n">ifts</span><span class="o">.</span><span class="n">WeightedIntervalFTS</span><span class="p">,</span>  <span class="n">pwfts</span><span class="o">.</span><span class="n">ProbabilisticWeightedFTS</span><span class="p">]</span>
+
+    <span class="n">tmp2</span> <span class="o">=</span> <span class="p">[</span><span class="n">Grid</span><span class="o">.</span><span class="n">GridPartitioner</span><span class="p">,</span> <span class="n">Entropy</span><span class="o">.</span><span class="n">EntropyPartitioner</span><span class="p">,</span> <span class="n">FCM</span><span class="o">.</span><span class="n">FCMPartitioner</span><span class="p">]</span>
+
+    <span class="n">tmp4</span> <span class="o">=</span> <span class="p">[</span><span class="n">arima</span><span class="o">.</span><span class="n">ARIMA</span><span class="p">,</span> <span class="n">quantreg</span><span class="o">.</span><span class="n">QuantileRegression</span><span class="p">,</span> <span class="n">BSTS</span><span class="o">.</span><span class="n">ARIMA</span><span class="p">]</span>
+
+    <span class="n">tmp3</span> <span class="o">=</span> <span class="p">[</span><span class="n">Measures</span><span class="o">.</span><span class="n">get_interval_statistics</span><span class="p">]</span>
+
+    <span class="n">steps_ahead</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;steps_ahead&#39;</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+    <span class="n">method</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;method&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
+    <span class="n">parameters</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;parameters&#39;</span><span class="p">,{})</span>
+
+    <span class="n">mfts</span><span class="p">,</span> <span class="n">pttr</span> <span class="o">=</span> <span class="n">benchmarks</span><span class="o">.</span><span class="n">__build_model</span><span class="p">(</span><span class="n">fts_method</span><span class="p">,</span> <span class="n">order</span><span class="p">,</span> <span class="n">parameters</span><span class="p">,</span> <span class="n">partitioner_method</span><span class="p">,</span> <span class="n">partitions</span><span class="p">,</span> <span class="n">train_data</span><span class="p">,</span>
+                               <span class="n">transformation</span><span class="p">)</span>
+    <span class="n">_start</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
+    <span class="n">mfts</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">train_data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+    <span class="n">_end</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
+    <span class="n">times</span> <span class="o">=</span> <span class="n">_end</span> <span class="o">-</span> <span class="n">_start</span>
+
+    <span class="k">if</span> <span class="n">steps_ahead</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
+
+        <span class="n">_start</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
+        <span class="n">metrics</span> <span class="o">=</span> <span class="n">Measures</span><span class="o">.</span><span class="n">get_interval_statistics</span><span class="p">(</span><span class="n">test_data</span><span class="p">,</span> <span class="n">mfts</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+        <span class="n">_end</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
+        <span class="n">times</span> <span class="o">+=</span> <span class="n">_end</span> <span class="o">-</span> <span class="n">_start</span>
+
+        <span class="n">ret</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;model&#39;</span><span class="p">:</span> <span class="n">mfts</span><span class="o">.</span><span class="n">shortname</span><span class="p">,</span> <span class="s1">&#39;partitioner&#39;</span><span class="p">:</span> <span class="n">pttr</span><span class="p">,</span> <span class="s1">&#39;order&#39;</span><span class="p">:</span> <span class="n">order</span><span class="p">,</span> <span class="s1">&#39;partitions&#39;</span><span class="p">:</span> <span class="n">partitions</span><span class="p">,</span>
+               <span class="s1">&#39;transformation&#39;</span><span class="p">:</span> <span class="s1">&#39;&#39;</span> <span class="k">if</span> <span class="n">transformation</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">transformation</span><span class="o">.</span><span class="n">name</span><span class="p">,</span>
+               <span class="s1">&#39;size&#39;</span><span class="p">:</span> <span class="nb">len</span><span class="p">(</span><span class="n">mfts</span><span class="p">),</span> <span class="s1">&#39;time&#39;</span><span class="p">:</span> <span class="n">times</span><span class="p">,</span>
+               <span class="s1">&#39;sharpness&#39;</span><span class="p">:</span> <span class="n">metrics</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="s1">&#39;resolution&#39;</span><span class="p">:</span> <span class="n">metrics</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="s1">&#39;coverage&#39;</span><span class="p">:</span> <span class="n">metrics</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span>
+               <span class="s1">&#39;time&#39;</span><span class="p">:</span> <span class="n">times</span><span class="p">,</span><span class="s1">&#39;pinball05&#39;</span><span class="p">:</span> <span class="n">metrics</span><span class="p">[</span><span class="mi">3</span><span class="p">],</span> <span class="s1">&#39;pinball25&#39;</span><span class="p">:</span> <span class="n">metrics</span><span class="p">[</span><span class="mi">4</span><span class="p">],</span> <span class="s1">&#39;pinball75&#39;</span><span class="p">:</span> <span class="n">metrics</span><span class="p">[</span><span class="mi">5</span><span class="p">],</span> <span class="s1">&#39;pinball95&#39;</span><span class="p">:</span> <span class="n">metrics</span><span class="p">[</span><span class="mi">6</span><span class="p">],</span>
+               <span class="s1">&#39;winkler05&#39;</span><span class="p">:</span> <span class="n">metrics</span><span class="p">[</span><span class="mi">7</span><span class="p">],</span> <span class="s1">&#39;winkler25&#39;</span><span class="p">:</span> <span class="n">metrics</span><span class="p">[</span><span class="mi">8</span><span class="p">],</span>
+               <span class="s1">&#39;window&#39;</span><span class="p">:</span> <span class="n">window_key</span><span class="p">,</span><span class="s1">&#39;steps&#39;</span><span class="p">:</span> <span class="n">steps_ahead</span><span class="p">,</span> <span class="s1">&#39;method&#39;</span><span class="p">:</span> <span class="n">method</span><span class="p">}</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">_start</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
+        <span class="n">intervals</span> <span class="o">=</span> <span class="n">mfts</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">test_data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+        <span class="n">_end</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
+        <span class="n">times</span> <span class="o">+=</span> <span class="n">_end</span> <span class="o">-</span> <span class="n">_start</span>
+
+        <span class="nb">eval</span> <span class="o">=</span> <span class="n">Measures</span><span class="o">.</span><span class="n">get_interval_ahead_statistics</span><span class="p">(</span><span class="n">test_data</span><span class="p">[</span><span class="n">mfts</span><span class="o">.</span><span class="n">order</span><span class="p">:</span><span class="n">mfts</span><span class="o">.</span><span class="n">order</span><span class="o">+</span><span class="n">steps_ahead</span><span class="p">],</span> <span class="n">intervals</span><span class="p">)</span>
+
+        <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="nb">eval</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
+            <span class="nb">eval</span><span class="p">[</span><span class="n">key</span><span class="p">][</span><span class="s2">&quot;time&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">times</span>
+            <span class="nb">eval</span><span class="p">[</span><span class="n">key</span><span class="p">][</span><span class="s2">&quot;method&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">method</span>
+
+        <span class="n">ret</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;model&#39;</span><span class="p">:</span> <span class="n">mfts</span><span class="o">.</span><span class="n">shortname</span><span class="p">,</span> <span class="s1">&#39;partitioner&#39;</span><span class="p">:</span> <span class="n">pttr</span><span class="p">,</span> <span class="s1">&#39;order&#39;</span><span class="p">:</span> <span class="n">order</span><span class="p">,</span> <span class="s1">&#39;partitions&#39;</span><span class="p">:</span> <span class="n">partitions</span><span class="p">,</span>
+               <span class="s1">&#39;transformation&#39;</span><span class="p">:</span> <span class="s1">&#39;&#39;</span> <span class="k">if</span> <span class="n">transformation</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">transformation</span><span class="o">.</span><span class="n">name</span><span class="p">,</span>
+               <span class="s1">&#39;size&#39;</span><span class="p">:</span> <span class="nb">len</span><span class="p">(</span><span class="n">mfts</span><span class="p">),</span> <span class="s1">&#39;time&#39;</span><span class="p">:</span> <span class="n">times</span><span class="p">,</span>
+               <span class="s1">&#39;window&#39;</span><span class="p">:</span> <span class="n">window_key</span><span class="p">,</span> <span class="s1">&#39;steps&#39;</span><span class="p">:</span> <span class="n">steps_ahead</span><span class="p">,</span> <span class="s1">&#39;method&#39;</span><span class="p">:</span> <span class="n">method</span><span class="p">,</span>
+               <span class="s1">&#39;ahead_results&#39;</span><span class="p">:</span> <span class="nb">eval</span>
+               <span class="p">}</span>
+
+
+    <span class="k">return</span> <span class="n">ret</span></div>
+
+
+<div class="viewcode-block" id="mv_run_probabilistic2"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.benchmarks.mv_run_probabilistic2">[docs]</a><span class="k">def</span> <span class="nf">mv_run_probabilistic2</span><span class="p">(</span><span class="n">mfts</span><span class="p">,</span> <span class="n">train_data</span><span class="p">,</span> <span class="n">test_data</span><span class="p">,</span> <span class="n">window_key</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+    <span class="kn">import</span> <span class="nn">time</span>
+    <span class="kn">from</span> <span class="nn">pyFTS.models</span> <span class="kn">import</span> <span class="n">hofts</span><span class="p">,</span> <span class="n">ifts</span><span class="p">,</span> <span class="n">pwfts</span>
+    <span class="kn">from</span> <span class="nn">pyFTS.models.multivariate</span> <span class="kn">import</span> <span class="n">mvfts</span><span class="p">,</span> <span class="n">wmvfts</span><span class="p">,</span> <span class="n">granular</span>
+    <span class="kn">from</span> <span class="nn">pyFTS.partitioners</span> <span class="kn">import</span> <span class="n">Grid</span><span class="p">,</span> <span class="n">Entropy</span><span class="p">,</span> <span class="n">FCM</span>
+    <span class="kn">from</span> <span class="nn">pyFTS.benchmarks</span> <span class="kn">import</span> <span class="n">Measures</span><span class="p">,</span> <span class="n">arima</span><span class="p">,</span> <span class="n">quantreg</span><span class="p">,</span> <span class="n">BSTS</span><span class="p">,</span> <span class="n">benchmarks</span>
+
+    <span class="n">tmp</span> <span class="o">=</span> <span class="p">[</span><span class="n">hofts</span><span class="o">.</span><span class="n">HighOrderFTS</span><span class="p">,</span> <span class="n">ifts</span><span class="o">.</span><span class="n">IntervalFTS</span><span class="p">,</span> <span class="n">ifts</span><span class="o">.</span><span class="n">WeightedIntervalFTS</span><span class="p">,</span> <span class="n">pwfts</span><span class="o">.</span><span class="n">ProbabilisticWeightedFTS</span><span class="p">]</span>
+
+    <span class="n">tmp2</span> <span class="o">=</span> <span class="p">[</span><span class="n">Grid</span><span class="o">.</span><span class="n">GridPartitioner</span><span class="p">,</span> <span class="n">mvfts</span><span class="o">.</span><span class="n">MVFTS</span><span class="p">,</span> <span class="n">wmvfts</span><span class="o">.</span><span class="n">WeightedMVFTS</span><span class="p">,</span> <span class="n">granular</span><span class="o">.</span><span class="n">GranularWMVFTS</span><span class="p">]</span>
+
+    <span class="n">tmp4</span> <span class="o">=</span> <span class="p">[</span><span class="n">arima</span><span class="o">.</span><span class="n">ARIMA</span><span class="p">,</span> <span class="n">quantreg</span><span class="o">.</span><span class="n">QuantileRegression</span><span class="p">,</span> <span class="n">BSTS</span><span class="o">.</span><span class="n">ARIMA</span><span class="p">]</span>
+
+    <span class="n">tmp3</span> <span class="o">=</span> <span class="p">[</span><span class="n">Measures</span><span class="o">.</span><span class="n">get_interval_statistics</span><span class="p">]</span>
+
+    <span class="n">steps_ahead</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;steps_ahead&#39;</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+    <span class="n">method</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;method&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
+    <span class="n">parameters</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;parameters&#39;</span><span class="p">,</span> <span class="p">{})</span>
+
+    <span class="n">_start</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
+    <span class="n">mfts</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">train_data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+    <span class="n">_end</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
+    <span class="n">times</span> <span class="o">=</span> <span class="n">_end</span> <span class="o">-</span> <span class="n">_start</span>
+
+    <span class="k">if</span> <span class="n">steps_ahead</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
+
+        <span class="n">_crps1</span><span class="p">,</span> <span class="n">_t1</span><span class="p">,</span> <span class="n">_brier</span> <span class="o">=</span> <span class="n">Measures</span><span class="o">.</span><span class="n">get_distribution_statistics</span><span class="p">(</span><span class="n">test_data</span><span class="p">,</span> <span class="n">mfts</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+        <span class="n">times</span> <span class="o">+=</span> <span class="n">_t1</span>
+
+        <span class="n">ret</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;model&#39;</span><span class="p">:</span> <span class="n">mfts</span><span class="o">.</span><span class="n">shortname</span><span class="p">,</span> <span class="s1">&#39;partitioner&#39;</span><span class="p">:</span> <span class="kc">None</span><span class="p">,</span> <span class="s1">&#39;order&#39;</span><span class="p">:</span> <span class="n">mfts</span><span class="o">.</span><span class="n">order</span><span class="p">,</span> <span class="s1">&#39;partitions&#39;</span><span class="p">:</span> <span class="kc">None</span><span class="p">,</span>
+               <span class="s1">&#39;transformation&#39;</span><span class="p">:</span> <span class="kc">None</span><span class="p">,</span>
+               <span class="s1">&#39;size&#39;</span><span class="p">:</span> <span class="nb">len</span><span class="p">(</span><span class="n">mfts</span><span class="p">),</span> <span class="s1">&#39;time&#39;</span><span class="p">:</span> <span class="n">times</span><span class="p">,</span>
+               <span class="s1">&#39;crps&#39;</span><span class="p">:</span> <span class="n">_crps1</span><span class="p">,</span> <span class="s1">&#39;brier&#39;</span><span class="p">:</span> <span class="n">_brier</span><span class="p">,</span> <span class="s1">&#39;window&#39;</span><span class="p">:</span> <span class="n">window_key</span><span class="p">,</span>
+               <span class="s1">&#39;steps&#39;</span><span class="p">:</span> <span class="n">steps_ahead</span><span class="p">,</span> <span class="s1">&#39;method&#39;</span><span class="p">:</span> <span class="n">method</span><span class="p">}</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">_start</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
+        <span class="n">distributions</span> <span class="o">=</span> <span class="n">mfts</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">test_data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+        <span class="n">_end</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
+        <span class="n">times</span> <span class="o">+=</span> <span class="n">_end</span> <span class="o">-</span> <span class="n">_start</span>
+
+        <span class="n">tmp_test</span> <span class="o">=</span> <span class="n">test_data</span><span class="p">[</span><span class="n">mfts</span><span class="o">.</span><span class="n">target_variable</span><span class="o">.</span><span class="n">data_label</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">[</span><span class="n">mfts</span><span class="o">.</span><span class="n">order</span><span class="p">:</span><span class="n">mfts</span><span class="o">.</span><span class="n">order</span> <span class="o">+</span> <span class="n">steps_ahead</span><span class="p">]</span>
+
+        <span class="nb">eval</span> <span class="o">=</span> <span class="n">Measures</span><span class="o">.</span><span class="n">get_distribution_ahead_statistics</span><span class="p">(</span><span class="n">tmp_test</span><span class="p">,</span> <span class="n">distributions</span><span class="p">)</span>
+
+        <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="nb">eval</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
+            <span class="nb">eval</span><span class="p">[</span><span class="n">key</span><span class="p">][</span><span class="s2">&quot;time&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">times</span>
+            <span class="nb">eval</span><span class="p">[</span><span class="n">key</span><span class="p">][</span><span class="s2">&quot;method&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">method</span>
+
+        <span class="n">ret</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;model&#39;</span><span class="p">:</span> <span class="n">mfts</span><span class="o">.</span><span class="n">shortname</span><span class="p">,</span> <span class="s1">&#39;partitioner&#39;</span><span class="p">:</span> <span class="kc">None</span><span class="p">,</span> <span class="s1">&#39;order&#39;</span><span class="p">:</span> <span class="n">mfts</span><span class="o">.</span><span class="n">order</span><span class="p">,</span> <span class="s1">&#39;partitions&#39;</span><span class="p">:</span> <span class="kc">None</span><span class="p">,</span>
+               <span class="s1">&#39;transformation&#39;</span><span class="p">:</span> <span class="kc">None</span><span class="p">,</span>
+               <span class="s1">&#39;size&#39;</span><span class="p">:</span> <span class="nb">len</span><span class="p">(</span><span class="n">mfts</span><span class="p">),</span> <span class="s1">&#39;time&#39;</span><span class="p">:</span> <span class="n">times</span><span class="p">,</span>
+               <span class="s1">&#39;window&#39;</span><span class="p">:</span> <span class="n">window_key</span><span class="p">,</span> <span class="s1">&#39;steps&#39;</span><span class="p">:</span> <span class="n">steps_ahead</span><span class="p">,</span> <span class="s1">&#39;method&#39;</span><span class="p">:</span> <span class="n">method</span><span class="p">,</span>
+               <span class="s1">&#39;ahead_results&#39;</span><span class="p">:</span> <span class="nb">eval</span>
+               <span class="p">}</span>
+
+    <span class="k">return</span> <span class="n">ret</span></div>
+
+
+
+<div class="viewcode-block" id="run_probabilistic2"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.benchmarks.run_probabilistic2">[docs]</a><span class="k">def</span> <span class="nf">run_probabilistic2</span><span class="p">(</span><span class="n">fts_method</span><span class="p">,</span> <span class="n">order</span><span class="p">,</span> <span class="n">partitioner_method</span><span class="p">,</span> <span class="n">partitions</span><span class="p">,</span> <span class="n">transformation</span><span class="p">,</span> <span class="n">train_data</span><span class="p">,</span> <span class="n">test_data</span><span class="p">,</span> <span class="n">window_key</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+    <span class="kn">import</span> <span class="nn">time</span>
+    <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+    <span class="kn">from</span> <span class="nn">pyFTS.models</span> <span class="kn">import</span> <span class="n">hofts</span><span class="p">,</span> <span class="n">ifts</span><span class="p">,</span> <span class="n">pwfts</span>
+    <span class="kn">from</span> <span class="nn">pyFTS.models.ensemble</span> <span class="kn">import</span> <span class="n">ensemble</span>
+    <span class="kn">from</span> <span class="nn">pyFTS.partitioners</span> <span class="kn">import</span> <span class="n">Grid</span><span class="p">,</span> <span class="n">Entropy</span><span class="p">,</span> <span class="n">FCM</span>
+    <span class="kn">from</span> <span class="nn">pyFTS.benchmarks</span> <span class="kn">import</span> <span class="n">Measures</span><span class="p">,</span> <span class="n">arima</span><span class="p">,</span> <span class="n">quantreg</span><span class="p">,</span> <span class="n">knn</span><span class="p">,</span> <span class="n">benchmarks</span>
+    <span class="kn">from</span> <span class="nn">pyFTS.models.seasonal</span> <span class="kn">import</span> <span class="n">SeasonalIndexer</span>
+
+    <span class="n">tmp</span> <span class="o">=</span> <span class="p">[</span><span class="n">hofts</span><span class="o">.</span><span class="n">HighOrderFTS</span><span class="p">,</span> <span class="n">ifts</span><span class="o">.</span><span class="n">IntervalFTS</span><span class="p">,</span> <span class="n">pwfts</span><span class="o">.</span><span class="n">ProbabilisticWeightedFTS</span><span class="p">,</span> <span class="n">arima</span><span class="o">.</span><span class="n">ARIMA</span><span class="p">,</span>
+           <span class="n">ensemble</span><span class="o">.</span><span class="n">AllMethodEnsembleFTS</span><span class="p">,</span> <span class="n">knn</span><span class="o">.</span><span class="n">KNearestNeighbors</span><span class="p">]</span>
+
+    <span class="n">tmp2</span> <span class="o">=</span> <span class="p">[</span><span class="n">Grid</span><span class="o">.</span><span class="n">GridPartitioner</span><span class="p">,</span> <span class="n">Entropy</span><span class="o">.</span><span class="n">EntropyPartitioner</span><span class="p">,</span> <span class="n">FCM</span><span class="o">.</span><span class="n">FCMPartitioner</span><span class="p">]</span>
+
+    <span class="n">tmp3</span> <span class="o">=</span> <span class="p">[</span><span class="n">Measures</span><span class="o">.</span><span class="n">get_distribution_statistics</span><span class="p">,</span> <span class="n">SeasonalIndexer</span><span class="o">.</span><span class="n">SeasonalIndexer</span><span class="p">,</span> <span class="n">SeasonalIndexer</span><span class="o">.</span><span class="n">LinearSeasonalIndexer</span><span class="p">]</span>
+
+    <span class="n">indexer</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;indexer&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
+
+    <span class="n">steps_ahead</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;steps_ahead&#39;</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+    <span class="n">method</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;method&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
+    <span class="n">parameters</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;parameters&#39;</span><span class="p">,</span> <span class="p">{})</span>
+
+    <span class="n">mfts</span><span class="p">,</span> <span class="n">pttr</span> <span class="o">=</span> <span class="n">benchmarks</span><span class="o">.</span><span class="n">__build_model</span><span class="p">(</span><span class="n">fts_method</span><span class="p">,</span> <span class="n">order</span><span class="p">,</span> <span class="n">parameters</span><span class="p">,</span> <span class="n">partitioner_method</span><span class="p">,</span> <span class="n">partitions</span><span class="p">,</span> <span class="n">train_data</span><span class="p">,</span>
+                               <span class="n">transformation</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">mfts</span><span class="o">.</span><span class="n">has_seasonality</span><span class="p">:</span>
+        <span class="n">mfts</span><span class="o">.</span><span class="n">indexer</span> <span class="o">=</span> <span class="n">indexer</span>
+
+    <span class="n">_start</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
+    <span class="n">mfts</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">train_data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+    <span class="n">_end</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
+    <span class="n">times</span> <span class="o">=</span> <span class="n">_end</span> <span class="o">-</span> <span class="n">_start</span>
+
+    <span class="k">if</span> <span class="n">steps_ahead</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
+
+        <span class="n">_crps1</span><span class="p">,</span> <span class="n">_t1</span><span class="p">,</span> <span class="n">_brier</span> <span class="o">=</span> <span class="n">Measures</span><span class="o">.</span><span class="n">get_distribution_statistics</span><span class="p">(</span><span class="n">test_data</span><span class="p">,</span> <span class="n">mfts</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+        <span class="n">times</span> <span class="o">+=</span> <span class="n">_t1</span>
+
+        <span class="n">ret</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;model&#39;</span><span class="p">:</span> <span class="n">mfts</span><span class="o">.</span><span class="n">shortname</span><span class="p">,</span> <span class="s1">&#39;partitioner&#39;</span><span class="p">:</span> <span class="n">pttr</span><span class="p">,</span> <span class="s1">&#39;order&#39;</span><span class="p">:</span> <span class="n">order</span><span class="p">,</span> <span class="s1">&#39;partitions&#39;</span><span class="p">:</span> <span class="n">partitions</span><span class="p">,</span>
+               <span class="s1">&#39;transformation&#39;</span><span class="p">:</span> <span class="s1">&#39;&#39;</span> <span class="k">if</span> <span class="n">transformation</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">transformation</span><span class="o">.</span><span class="n">name</span><span class="p">,</span>
+               <span class="s1">&#39;size&#39;</span><span class="p">:</span> <span class="nb">len</span><span class="p">(</span><span class="n">mfts</span><span class="p">),</span> <span class="s1">&#39;time&#39;</span><span class="p">:</span> <span class="n">times</span><span class="p">,</span>
+               <span class="s1">&#39;crps&#39;</span><span class="p">:</span> <span class="n">_crps1</span><span class="p">,</span> <span class="s1">&#39;brier&#39;</span><span class="p">:</span> <span class="n">_brier</span><span class="p">,</span> <span class="s1">&#39;window&#39;</span><span class="p">:</span> <span class="n">window_key</span><span class="p">,</span>
+               <span class="s1">&#39;steps&#39;</span><span class="p">:</span> <span class="n">steps_ahead</span><span class="p">,</span> <span class="s1">&#39;method&#39;</span><span class="p">:</span> <span class="n">method</span><span class="p">}</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">_start</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
+        <span class="n">distributions</span> <span class="o">=</span> <span class="n">mfts</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">test_data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+        <span class="n">_end</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
+        <span class="n">times</span> <span class="o">+=</span> <span class="n">_end</span> <span class="o">-</span> <span class="n">_start</span>
+
+        <span class="nb">eval</span> <span class="o">=</span> <span class="n">Measures</span><span class="o">.</span><span class="n">get_distribution_ahead_statistics</span><span class="p">(</span><span class="n">test_data</span><span class="p">[</span><span class="n">mfts</span><span class="o">.</span><span class="n">order</span><span class="p">:</span><span class="n">mfts</span><span class="o">.</span><span class="n">order</span><span class="o">+</span><span class="n">steps_ahead</span><span class="p">],</span> <span class="n">distributions</span><span class="p">)</span>
+
+        <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="nb">eval</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
+            <span class="nb">eval</span><span class="p">[</span><span class="n">key</span><span class="p">][</span><span class="s2">&quot;time&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">times</span>
+            <span class="nb">eval</span><span class="p">[</span><span class="n">key</span><span class="p">][</span><span class="s2">&quot;method&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">method</span>
+
+        <span class="n">ret</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;model&#39;</span><span class="p">:</span> <span class="n">mfts</span><span class="o">.</span><span class="n">shortname</span><span class="p">,</span> <span class="s1">&#39;partitioner&#39;</span><span class="p">:</span> <span class="n">pttr</span><span class="p">,</span> <span class="s1">&#39;order&#39;</span><span class="p">:</span> <span class="n">order</span><span class="p">,</span> <span class="s1">&#39;partitions&#39;</span><span class="p">:</span> <span class="n">partitions</span><span class="p">,</span>
+               <span class="s1">&#39;transformation&#39;</span><span class="p">:</span> <span class="s1">&#39;&#39;</span> <span class="k">if</span> <span class="n">transformation</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">transformation</span><span class="o">.</span><span class="n">name</span><span class="p">,</span>
+               <span class="s1">&#39;size&#39;</span><span class="p">:</span> <span class="nb">len</span><span class="p">(</span><span class="n">mfts</span><span class="p">),</span> <span class="s1">&#39;time&#39;</span><span class="p">:</span> <span class="n">times</span><span class="p">,</span>
+               <span class="s1">&#39;window&#39;</span><span class="p">:</span> <span class="n">window_key</span><span class="p">,</span> <span class="s1">&#39;steps&#39;</span><span class="p">:</span> <span class="n">steps_ahead</span><span class="p">,</span> <span class="s1">&#39;method&#39;</span><span class="p">:</span> <span class="n">method</span><span class="p">,</span>
+               <span class="s1">&#39;ahead_results&#39;</span><span class="p">:</span> <span class="nb">eval</span>
+               <span class="p">}</span>
+
+    <span class="k">return</span> <span class="n">ret</span></div>
+
+
+<div class="viewcode-block" id="common_process_time_jobs"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.benchmarks.common_process_time_jobs">[docs]</a><span class="k">def</span> <span class="nf">common_process_time_jobs</span><span class="p">(</span><span class="n">conn</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">job</span><span class="p">):</span>
+    <span class="n">dta</span> <span class="o">=</span> <span class="n">deepcopy</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+    <span class="n">dta</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">job</span><span class="p">[</span><span class="s1">&#39;steps&#39;</span><span class="p">])</span>
+    <span class="n">dta</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">job</span><span class="p">[</span><span class="s1">&#39;method&#39;</span><span class="p">])</span>
+    <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="p">[</span><span class="s2">&quot;time&quot;</span><span class="p">]:</span>
+        <span class="k">if</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">job</span><span class="p">:</span>
+            <span class="n">data2</span> <span class="o">=</span> <span class="n">deepcopy</span><span class="p">(</span><span class="n">dta</span><span class="p">)</span>
+            <span class="n">data2</span><span class="o">.</span><span class="n">extend</span><span class="p">([</span><span class="n">key</span><span class="p">,</span> <span class="n">job</span><span class="p">[</span><span class="n">key</span><span class="p">]])</span>
+            <span class="n">bUtil</span><span class="o">.</span><span class="n">insert_benchmark</span><span class="p">(</span><span class="n">data2</span><span class="p">,</span> <span class="n">conn</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="common_process_point_jobs"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.benchmarks.common_process_point_jobs">[docs]</a><span class="k">def</span> <span class="nf">common_process_point_jobs</span><span class="p">(</span><span class="n">conn</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">job</span><span class="p">):</span>
+    <span class="n">dta</span> <span class="o">=</span> <span class="n">deepcopy</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+    <span class="n">dta</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">job</span><span class="p">[</span><span class="s1">&#39;steps&#39;</span><span class="p">])</span>
+    <span class="n">dta</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">job</span><span class="p">[</span><span class="s1">&#39;method&#39;</span><span class="p">])</span>
+    <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="p">[</span><span class="s2">&quot;rmse&quot;</span><span class="p">,</span> <span class="s2">&quot;mape&quot;</span><span class="p">,</span> <span class="s2">&quot;u&quot;</span><span class="p">,</span> <span class="s2">&quot;time&quot;</span><span class="p">]:</span>
+        <span class="k">if</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">job</span><span class="p">:</span>
+            <span class="n">data2</span> <span class="o">=</span> <span class="n">deepcopy</span><span class="p">(</span><span class="n">dta</span><span class="p">)</span>
+            <span class="n">data2</span><span class="o">.</span><span class="n">extend</span><span class="p">([</span><span class="n">key</span><span class="p">,</span> <span class="n">job</span><span class="p">[</span><span class="n">key</span><span class="p">]])</span>
+            <span class="n">bUtil</span><span class="o">.</span><span class="n">insert_benchmark</span><span class="p">(</span><span class="n">data2</span><span class="p">,</span> <span class="n">conn</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="process_point_jobs"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.benchmarks.process_point_jobs">[docs]</a><span class="k">def</span> <span class="nf">process_point_jobs</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">tag</span><span class="p">,</span>  <span class="n">job</span><span class="p">,</span> <span class="n">conn</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Extract information from a dictionary with point benchmark results and save it on a database</span>
+
+<span class="sd">    :param dataset: the benchmark dataset name</span>
+<span class="sd">    :param tag: alias for the benchmark group being executed</span>
+<span class="sd">    :param job: a dictionary with the benchmark results</span>
+<span class="sd">    :param conn: a connection to a Sqlite database</span>
+<span class="sd">    :return:</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">data</span> <span class="o">=</span> <span class="n">bUtil</span><span class="o">.</span><span class="n">process_common_data</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">tag</span><span class="p">,</span> <span class="s1">&#39;point&#39;</span><span class="p">,</span><span class="n">job</span><span class="p">)</span>
+
+    <span class="n">common_process_point_jobs</span><span class="p">(</span><span class="n">conn</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">job</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="process_point_jobs2"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.benchmarks.process_point_jobs2">[docs]</a><span class="k">def</span> <span class="nf">process_point_jobs2</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">tag</span><span class="p">,</span>  <span class="n">job</span><span class="p">,</span> <span class="n">conn</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Extract information from a dictionary with point benchmark results and save it on a database</span>
+
+<span class="sd">    :param dataset: the benchmark dataset name</span>
+<span class="sd">    :param tag: alias for the benchmark group being executed</span>
+<span class="sd">    :param job: a dictionary with the benchmark results</span>
+<span class="sd">    :param conn: a connection to a Sqlite database</span>
+<span class="sd">    :return:</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">data</span> <span class="o">=</span> <span class="n">bUtil</span><span class="o">.</span><span class="n">process_common_data2</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">tag</span><span class="p">,</span> <span class="s1">&#39;point&#39;</span><span class="p">,</span><span class="n">job</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="n">job</span><span class="p">[</span><span class="s1">&#39;steps&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
+        <span class="n">common_process_point_jobs</span><span class="p">(</span><span class="n">conn</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">job</span><span class="p">)</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">job</span><span class="p">[</span><span class="s1">&#39;steps&#39;</span><span class="p">]):</span>
+            <span class="n">j2</span> <span class="o">=</span> <span class="n">job</span><span class="p">[</span><span class="s1">&#39;ahead_results&#39;</span><span class="p">][</span><span class="n">k</span><span class="p">]</span>
+            <span class="n">common_process_point_jobs</span><span class="p">(</span><span class="n">conn</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">j2</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="common_process_interval_jobs"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.benchmarks.common_process_interval_jobs">[docs]</a><span class="k">def</span> <span class="nf">common_process_interval_jobs</span><span class="p">(</span><span class="n">conn</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">job</span><span class="p">):</span>
+    <span class="n">dta</span> <span class="o">=</span> <span class="n">deepcopy</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+    <span class="n">dta</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">job</span><span class="p">[</span><span class="s1">&#39;steps&#39;</span><span class="p">])</span>
+    <span class="n">dta</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">job</span><span class="p">[</span><span class="s1">&#39;method&#39;</span><span class="p">])</span>
+    <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="p">[</span><span class="s2">&quot;sharpness&quot;</span><span class="p">,</span><span class="s2">&quot;resolution&quot;</span><span class="p">,</span><span class="s2">&quot;coverage&quot;</span><span class="p">,</span><span class="s2">&quot;time&quot;</span><span class="p">,</span><span class="s2">&quot;pinball05&quot;</span><span class="p">,</span>
+                <span class="s2">&quot;pinball25&quot;</span><span class="p">,</span><span class="s2">&quot;pinball75&quot;</span><span class="p">,</span><span class="s2">&quot;pinball95&quot;</span><span class="p">,</span> <span class="s2">&quot;winkler05&quot;</span><span class="p">,</span> <span class="s2">&quot;winkler25&quot;</span><span class="p">]:</span>
+        <span class="k">if</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">job</span><span class="p">:</span>
+            <span class="n">data2</span> <span class="o">=</span> <span class="n">deepcopy</span><span class="p">(</span><span class="n">dta</span><span class="p">)</span>
+            <span class="n">data2</span><span class="o">.</span><span class="n">extend</span><span class="p">([</span><span class="n">key</span><span class="p">,</span> <span class="n">job</span><span class="p">[</span><span class="n">key</span><span class="p">]])</span>
+            <span class="n">bUtil</span><span class="o">.</span><span class="n">insert_benchmark</span><span class="p">(</span><span class="n">data2</span><span class="p">,</span> <span class="n">conn</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="process_interval_jobs"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.benchmarks.process_interval_jobs">[docs]</a><span class="k">def</span> <span class="nf">process_interval_jobs</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">tag</span><span class="p">,</span> <span class="n">job</span><span class="p">,</span> <span class="n">conn</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Extract information from an dictionary with interval benchmark results and save it on a database</span>
+
+<span class="sd">    :param dataset: the benchmark dataset name</span>
+<span class="sd">    :param tag: alias for the benchmark group being executed</span>
+<span class="sd">    :param job: a dictionary with the benchmark results</span>
+<span class="sd">    :param conn: a connection to a Sqlite database</span>
+<span class="sd">    :return:</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">data</span> <span class="o">=</span> <span class="n">bUtil</span><span class="o">.</span><span class="n">process_common_data</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">tag</span><span class="p">,</span> <span class="s1">&#39;interval&#39;</span><span class="p">,</span> <span class="n">job</span><span class="p">)</span>
+
+    <span class="n">common_process_interval_jobs</span><span class="p">(</span><span class="n">conn</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">job</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="process_interval_jobs2"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.benchmarks.process_interval_jobs2">[docs]</a><span class="k">def</span> <span class="nf">process_interval_jobs2</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">tag</span><span class="p">,</span> <span class="n">job</span><span class="p">,</span> <span class="n">conn</span><span class="p">):</span>
+
+    <span class="n">data</span> <span class="o">=</span> <span class="n">bUtil</span><span class="o">.</span><span class="n">process_common_data2</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">tag</span><span class="p">,</span> <span class="s1">&#39;interval&#39;</span><span class="p">,</span> <span class="n">job</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="n">job</span><span class="p">[</span><span class="s1">&#39;steps&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
+        <span class="n">common_process_interval_jobs</span><span class="p">(</span><span class="n">conn</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">job</span><span class="p">)</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">job</span><span class="p">[</span><span class="s1">&#39;steps&#39;</span><span class="p">]):</span>
+            <span class="n">j2</span> <span class="o">=</span> <span class="n">job</span><span class="p">[</span><span class="s1">&#39;ahead_results&#39;</span><span class="p">][</span><span class="n">k</span><span class="p">]</span>
+            <span class="n">common_process_interval_jobs</span><span class="p">(</span><span class="n">conn</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">j2</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="common_process_probabilistic_jobs"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.benchmarks.common_process_probabilistic_jobs">[docs]</a><span class="k">def</span> <span class="nf">common_process_probabilistic_jobs</span><span class="p">(</span><span class="n">conn</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">job</span><span class="p">):</span>
+    <span class="n">dta</span> <span class="o">=</span> <span class="n">deepcopy</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+    <span class="n">dta</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">job</span><span class="p">[</span><span class="s1">&#39;steps&#39;</span><span class="p">])</span>
+    <span class="n">dta</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">job</span><span class="p">[</span><span class="s1">&#39;method&#39;</span><span class="p">])</span>
+    <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="p">[</span><span class="s2">&quot;crps&quot;</span><span class="p">,</span><span class="s2">&quot;time&quot;</span><span class="p">,</span><span class="s2">&quot;brier&quot;</span><span class="p">]:</span>
+        <span class="k">if</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">job</span><span class="p">:</span>
+            <span class="n">data2</span> <span class="o">=</span> <span class="n">deepcopy</span><span class="p">(</span><span class="n">dta</span><span class="p">)</span>
+            <span class="n">data2</span><span class="o">.</span><span class="n">extend</span><span class="p">([</span><span class="n">key</span><span class="p">,</span> <span class="n">job</span><span class="p">[</span><span class="n">key</span><span class="p">]])</span>
+            <span class="n">bUtil</span><span class="o">.</span><span class="n">insert_benchmark</span><span class="p">(</span><span class="n">data2</span><span class="p">,</span> <span class="n">conn</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="process_probabilistic_jobs"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.benchmarks.process_probabilistic_jobs">[docs]</a><span class="k">def</span> <span class="nf">process_probabilistic_jobs</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">tag</span><span class="p">,</span>  <span class="n">job</span><span class="p">,</span> <span class="n">conn</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Extract information from an dictionary with probabilistic benchmark results and save it on a database</span>
+
+<span class="sd">    :param dataset: the benchmark dataset name</span>
+<span class="sd">    :param tag: alias for the benchmark group being executed</span>
+<span class="sd">    :param job: a dictionary with the benchmark results</span>
+<span class="sd">    :param conn: a connection to a Sqlite database</span>
+<span class="sd">    :return:</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">data</span> <span class="o">=</span> <span class="n">bUtil</span><span class="o">.</span><span class="n">process_common_data</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">tag</span><span class="p">,</span>  <span class="s1">&#39;density&#39;</span><span class="p">,</span> <span class="n">job</span><span class="p">)</span>
+
+    <span class="n">common_process_probabilistic_jobs</span><span class="p">(</span><span class="n">conn</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">job</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="process_probabilistic_jobs2"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.benchmarks.process_probabilistic_jobs2">[docs]</a><span class="k">def</span> <span class="nf">process_probabilistic_jobs2</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">tag</span><span class="p">,</span>  <span class="n">job</span><span class="p">,</span> <span class="n">conn</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Extract information from an dictionary with probabilistic benchmark results and save it on a database</span>
+
+<span class="sd">    :param dataset: the benchmark dataset name</span>
+<span class="sd">    :param tag: alias for the benchmark group being executed</span>
+<span class="sd">    :param job: a dictionary with the benchmark results</span>
+<span class="sd">    :param conn: a connection to a Sqlite database</span>
+<span class="sd">    :return:</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">data</span> <span class="o">=</span> <span class="n">bUtil</span><span class="o">.</span><span class="n">process_common_data2</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">tag</span><span class="p">,</span> <span class="s1">&#39;density&#39;</span><span class="p">,</span> <span class="n">job</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="n">job</span><span class="p">[</span><span class="s1">&#39;steps&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
+        <span class="n">common_process_probabilistic_jobs</span><span class="p">(</span><span class="n">conn</span><span class="p">,</span><span class="n">data</span><span class="p">,</span><span class="n">job</span><span class="p">)</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">job</span><span class="p">[</span><span class="s1">&#39;steps&#39;</span><span class="p">]):</span>
+            <span class="n">j2</span> <span class="o">=</span> <span class="n">job</span><span class="p">[</span><span class="s1">&#39;ahead_results&#39;</span><span class="p">][</span><span class="n">k</span><span class="p">]</span>
+            <span class="n">common_process_probabilistic_jobs</span><span class="p">(</span><span class="n">conn</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">j2</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="print_point_statistics"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.benchmarks.print_point_statistics">[docs]</a><span class="k">def</span> <span class="nf">print_point_statistics</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">models</span><span class="p">,</span> <span class="n">externalmodels</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">externalforecasts</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">indexers</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Run point benchmarks on given models and data and print the results</span>
+
+<span class="sd">    :param data: test data</span>
+<span class="sd">    :param models: a list of FTS models to benchmark</span>
+<span class="sd">    :param externalmodels: a list with benchmark models (façades for other methods)</span>
+<span class="sd">    :param externalforecasts:</span>
+<span class="sd">    :param indexers:</span>
+<span class="sd">    :return:</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">ret</span> <span class="o">=</span> <span class="s2">&quot;Model		&amp; Order     &amp; RMSE		&amp; SMAPE      &amp; Theil&#39;s U		</span><span class="se">\\\\</span><span class="s2"> </span><span class="se">\n</span><span class="s2">&quot;</span>
+    <span class="k">for</span> <span class="n">count</span><span class="p">,</span><span class="n">model</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">models</span><span class="p">,</span><span class="n">start</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
+        <span class="n">_rmse</span><span class="p">,</span> <span class="n">_smape</span><span class="p">,</span> <span class="n">_u</span> <span class="o">=</span> <span class="n">Measures</span><span class="o">.</span><span class="n">get_point_statistics</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">model</span><span class="p">,</span> <span class="n">indexers</span><span class="p">)</span>
+        <span class="n">ret</span> <span class="o">+=</span> <span class="n">model</span><span class="o">.</span><span class="n">shortname</span> <span class="o">+</span> <span class="s2">&quot;		&amp; &quot;</span>
+        <span class="n">ret</span> <span class="o">+=</span> <span class="nb">str</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">order</span><span class="p">)</span> <span class="o">+</span> <span class="s2">&quot;		&amp; &quot;</span>
+        <span class="n">ret</span> <span class="o">+=</span> <span class="nb">str</span><span class="p">(</span><span class="n">_rmse</span><span class="p">)</span> <span class="o">+</span> <span class="s2">&quot;		&amp; &quot;</span>
+        <span class="n">ret</span> <span class="o">+=</span> <span class="nb">str</span><span class="p">(</span><span class="n">_smape</span><span class="p">)</span><span class="o">+</span> <span class="s2">&quot;		&amp; &quot;</span>
+        <span class="n">ret</span> <span class="o">+=</span> <span class="nb">str</span><span class="p">(</span><span class="n">_u</span><span class="p">)</span>
+        <span class="c1">#ret += str(round(Measures.TheilsInequality(np.array(data[fts.order:]), np.array(forecasts[:-1])), 4))</span>
+        <span class="n">ret</span> <span class="o">+=</span> <span class="s2">&quot;	</span><span class="se">\\\\</span><span class="s2"> </span><span class="se">\n</span><span class="s2">&quot;</span>
+    <span class="k">if</span> <span class="n">externalmodels</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="n">l</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">externalmodels</span><span class="p">)</span>
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="n">l</span><span class="p">):</span>
+            <span class="n">ret</span> <span class="o">+=</span> <span class="n">externalmodels</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">+</span> <span class="s2">&quot;		&amp; &quot;</span>
+            <span class="n">ret</span> <span class="o">+=</span> <span class="s2">&quot; 1		&amp; &quot;</span>
+            <span class="n">ret</span> <span class="o">+=</span> <span class="nb">str</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">Measures</span><span class="o">.</span><span class="n">rmse</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">externalforecasts</span><span class="p">[</span><span class="n">k</span><span class="p">][:</span><span class="o">-</span><span class="mi">1</span><span class="p">]),</span> <span class="mi">2</span><span class="p">))</span> <span class="o">+</span> <span class="s2">&quot;		&amp; &quot;</span>
+            <span class="n">ret</span> <span class="o">+=</span> <span class="nb">str</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">Measures</span><span class="o">.</span><span class="n">smape</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">externalforecasts</span><span class="p">[</span><span class="n">k</span><span class="p">][:</span><span class="o">-</span><span class="mi">1</span><span class="p">]),</span> <span class="mi">2</span><span class="p">))</span><span class="o">+</span> <span class="s2">&quot;		&amp; &quot;</span>
+            <span class="n">ret</span> <span class="o">+=</span> <span class="nb">str</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">Measures</span><span class="o">.</span><span class="n">UStatistic</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">externalforecasts</span><span class="p">[</span><span class="n">k</span><span class="p">][:</span><span class="o">-</span><span class="mi">1</span><span class="p">]),</span> <span class="mi">2</span><span class="p">))</span>
+            <span class="n">ret</span> <span class="o">+=</span> <span class="s2">&quot;	</span><span class="se">\\\\</span><span class="s2"> </span><span class="se">\n</span><span class="s2">&quot;</span>
+    <span class="nb">print</span><span class="p">(</span><span class="n">ret</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="print_interval_statistics"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.benchmarks.print_interval_statistics">[docs]</a><span class="k">def</span> <span class="nf">print_interval_statistics</span><span class="p">(</span><span class="n">original</span><span class="p">,</span> <span class="n">models</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Run interval benchmarks on given models and data and print the results</span>
+
+<span class="sd">    :param data: test data</span>
+<span class="sd">    :param models: a list of FTS models to benchmark</span>
+<span class="sd">    :return:</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">ret</span> <span class="o">=</span> <span class="s2">&quot;Model	&amp; Order     &amp; Sharpness		&amp; Resolution		&amp; Coverage &amp; .05  &amp; .25 &amp; .75 &amp; .95	</span><span class="se">\\\\</span><span class="s2"> </span><span class="se">\n</span><span class="s2">&quot;</span>
+    <span class="k">for</span> <span class="n">fts</span> <span class="ow">in</span> <span class="n">models</span><span class="p">:</span>
+        <span class="n">_sharp</span><span class="p">,</span> <span class="n">_res</span><span class="p">,</span> <span class="n">_cov</span><span class="p">,</span> <span class="n">_q5</span><span class="p">,</span> <span class="n">_q25</span><span class="p">,</span> <span class="n">_q75</span><span class="p">,</span> <span class="n">_q95</span>  <span class="o">=</span> <span class="n">Measures</span><span class="o">.</span><span class="n">get_interval_statistics</span><span class="p">(</span><span class="n">original</span><span class="p">,</span> <span class="n">fts</span><span class="p">)</span>
+        <span class="n">ret</span> <span class="o">+=</span> <span class="n">fts</span><span class="o">.</span><span class="n">shortname</span> <span class="o">+</span> <span class="s2">&quot;		&amp; &quot;</span>
+        <span class="n">ret</span> <span class="o">+=</span> <span class="nb">str</span><span class="p">(</span><span class="n">fts</span><span class="o">.</span><span class="n">order</span><span class="p">)</span> <span class="o">+</span> <span class="s2">&quot;		&amp; &quot;</span>
+        <span class="n">ret</span> <span class="o">+=</span> <span class="nb">str</span><span class="p">(</span><span class="n">_sharp</span><span class="p">)</span> <span class="o">+</span> <span class="s2">&quot;		&amp; &quot;</span>
+        <span class="n">ret</span> <span class="o">+=</span> <span class="nb">str</span><span class="p">(</span><span class="n">_res</span><span class="p">)</span> <span class="o">+</span> <span class="s2">&quot;		&amp; &quot;</span>
+        <span class="n">ret</span> <span class="o">+=</span> <span class="nb">str</span><span class="p">(</span><span class="n">_cov</span><span class="p">)</span> <span class="o">+</span> <span class="s2">&quot;        &amp;&quot;</span>
+        <span class="n">ret</span> <span class="o">+=</span> <span class="nb">str</span><span class="p">(</span><span class="n">_q5</span><span class="p">)</span> <span class="o">+</span> <span class="s2">&quot;        &amp;&quot;</span>
+        <span class="n">ret</span> <span class="o">+=</span> <span class="nb">str</span><span class="p">(</span><span class="n">_q25</span><span class="p">)</span> <span class="o">+</span> <span class="s2">&quot;        &amp;&quot;</span>
+        <span class="n">ret</span> <span class="o">+=</span> <span class="nb">str</span><span class="p">(</span><span class="n">_q75</span><span class="p">)</span> <span class="o">+</span> <span class="s2">&quot;        &amp;&quot;</span>
+        <span class="n">ret</span> <span class="o">+=</span> <span class="nb">str</span><span class="p">(</span><span class="n">_q95</span><span class="p">)</span> <span class="o">+</span> <span class="s2">&quot;</span><span class="se">\\\\</span><span class="s2"> </span><span class="se">\n</span><span class="s2">&quot;</span>
+    <span class="nb">print</span><span class="p">(</span><span class="n">ret</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="print_distribution_statistics"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.benchmarks.print_distribution_statistics">[docs]</a><span class="k">def</span> <span class="nf">print_distribution_statistics</span><span class="p">(</span><span class="n">original</span><span class="p">,</span> <span class="n">models</span><span class="p">,</span> <span class="n">steps</span><span class="p">,</span> <span class="n">resolution</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Run probabilistic benchmarks on given models and data and print the results</span>
+
+<span class="sd">    :param data: test data</span>
+<span class="sd">    :param models: a list of FTS models to benchmark</span>
+<span class="sd">    :return:</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">ret</span> <span class="o">=</span> <span class="s2">&quot;Model	&amp; Order     &amp;  Interval &amp; Distribution	</span><span class="se">\\\\</span><span class="s2"> </span><span class="se">\n</span><span class="s2">&quot;</span>
+    <span class="k">for</span> <span class="n">fts</span> <span class="ow">in</span> <span class="n">models</span><span class="p">:</span>
+        <span class="n">_crps1</span><span class="p">,</span> <span class="n">_crps2</span><span class="p">,</span> <span class="n">_t1</span><span class="p">,</span> <span class="n">_t2</span> <span class="o">=</span> <span class="n">Measures</span><span class="o">.</span><span class="n">get_distribution_statistics</span><span class="p">(</span><span class="n">original</span><span class="p">,</span> <span class="n">fts</span><span class="p">,</span> <span class="n">steps</span><span class="p">,</span> <span class="n">resolution</span><span class="p">)</span>
+        <span class="n">ret</span> <span class="o">+=</span> <span class="n">fts</span><span class="o">.</span><span class="n">shortname</span> <span class="o">+</span> <span class="s2">&quot;		&amp; &quot;</span>
+        <span class="n">ret</span> <span class="o">+=</span> <span class="nb">str</span><span class="p">(</span><span class="n">fts</span><span class="o">.</span><span class="n">order</span><span class="p">)</span> <span class="o">+</span> <span class="s2">&quot;		&amp; &quot;</span>
+        <span class="n">ret</span> <span class="o">+=</span> <span class="nb">str</span><span class="p">(</span><span class="n">_crps1</span><span class="p">)</span> <span class="o">+</span> <span class="s2">&quot;		&amp; &quot;</span>
+        <span class="n">ret</span> <span class="o">+=</span> <span class="nb">str</span><span class="p">(</span><span class="n">_crps2</span><span class="p">)</span> <span class="o">+</span> <span class="s2">&quot;	</span><span class="se">\\\\</span><span class="s2"> </span><span class="se">\n</span><span class="s2">&quot;</span>
+    <span class="nb">print</span><span class="p">(</span><span class="n">ret</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="plot_point"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.benchmarks.plot_point">[docs]</a><span class="k">def</span> <span class="nf">plot_point</span><span class="p">(</span><span class="n">axis</span><span class="p">,</span> <span class="n">points</span><span class="p">,</span> <span class="n">order</span><span class="p">,</span> <span class="n">label</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;red&#39;</span><span class="p">,</span> <span class="n">ls</span><span class="o">=</span><span class="s1">&#39;-&#39;</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mi">1</span><span class="p">):</span>
+    <span class="n">mi</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="n">points</span><span class="p">)</span> <span class="o">*</span> <span class="mf">0.95</span>
+    <span class="n">ma</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">points</span><span class="p">)</span> <span class="o">*</span> <span class="mf">1.05</span>
+    <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">order</span><span class="p">):</span>
+        <span class="n">points</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
+    <span class="n">axis</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">points</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="n">color</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="n">label</span><span class="p">,</span> <span class="n">ls</span><span class="o">=</span><span class="n">ls</span><span class="p">,</span><span class="n">linewidth</span><span class="o">=</span><span class="n">linewidth</span><span class="p">)</span>
+    <span class="k">return</span> <span class="p">[</span><span class="n">mi</span><span class="p">,</span> <span class="n">ma</span><span class="p">]</span></div>
+
+
+
+<div class="viewcode-block" id="plot_compared_series"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.benchmarks.plot_compared_series">[docs]</a><span class="k">def</span> <span class="nf">plot_compared_series</span><span class="p">(</span><span class="n">original</span><span class="p">,</span> <span class="n">models</span><span class="p">,</span> <span class="n">colors</span><span class="p">,</span> <span class="n">typeonlegend</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">save</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">file</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">tam</span><span class="o">=</span><span class="p">[</span><span class="mi">20</span><span class="p">,</span> <span class="mi">5</span><span class="p">],</span>
+                         <span class="n">points</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">intervals</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mf">1.5</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Plot the forecasts of several one step ahead models, by point or by interval</span>
+
+<span class="sd">    :param original: Original time series data (list)</span>
+<span class="sd">    :param models: List of models to compare</span>
+<span class="sd">    :param colors: List of models colors</span>
+<span class="sd">    :param typeonlegend: Add the type of forecast (point / interval) on legend</span>
+<span class="sd">    :param save: Save the picture on file</span>
+<span class="sd">    :param file: Filename to save the picture</span>
+<span class="sd">    :param tam: Size of the picture</span>
+<span class="sd">    :param points: True to plot the point forecasts, False otherwise</span>
+<span class="sd">    :param intervals: True to plot the interval forecasts, False otherwise</span>
+<span class="sd">    :param linewidth:</span>
+<span class="sd">    :return:</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="n">tam</span><span class="p">)</span>
+    <span class="n">ax</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">add_subplot</span><span class="p">(</span><span class="mi">111</span><span class="p">)</span>
+
+    <span class="n">mi</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="n">ma</span> <span class="o">=</span> <span class="p">[]</span>
+
+    <span class="n">legends</span> <span class="o">=</span> <span class="p">[]</span>
+
+    <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">original</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;black&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">&quot;Original&quot;</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="n">linewidth</span><span class="o">*</span><span class="mf">1.5</span><span class="p">)</span>
+
+    <span class="k">for</span> <span class="n">count</span><span class="p">,</span> <span class="n">fts</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">models</span><span class="p">,</span> <span class="n">start</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
+        <span class="k">try</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">fts</span><span class="o">.</span><span class="n">has_point_forecasting</span> <span class="ow">and</span> <span class="n">points</span><span class="p">:</span>
+                <span class="n">forecasts</span> <span class="o">=</span> <span class="n">fts</span><span class="o">.</span><span class="n">forecast</span><span class="p">(</span><span class="n">original</span><span class="p">)</span>
+                <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">forecasts</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span>
+                    <span class="n">forecasts</span> <span class="o">=</span> <span class="n">forecasts</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
+                <span class="n">mi</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">min</span><span class="p">(</span><span class="n">forecasts</span><span class="p">)</span> <span class="o">*</span> <span class="mf">0.95</span><span class="p">)</span>
+                <span class="n">ma</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">max</span><span class="p">(</span><span class="n">forecasts</span><span class="p">)</span> <span class="o">*</span> <span class="mf">1.05</span><span class="p">)</span>
+                <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">fts</span><span class="o">.</span><span class="n">order</span><span class="p">):</span>
+                    <span class="n">forecasts</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
+                <span class="n">lbl</span> <span class="o">=</span> <span class="n">fts</span><span class="o">.</span><span class="n">shortname</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">fts</span><span class="o">.</span><span class="n">order</span> <span class="k">if</span> <span class="n">fts</span><span class="o">.</span><span class="n">is_high_order</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">fts</span><span class="o">.</span><span class="n">benchmark_only</span> <span class="k">else</span> <span class="s2">&quot;&quot;</span><span class="p">)</span>
+                <span class="k">if</span> <span class="n">typeonlegend</span><span class="p">:</span> <span class="n">lbl</span> <span class="o">+=</span> <span class="s2">&quot; (Point)&quot;</span>
+                <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">forecasts</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="n">colors</span><span class="p">[</span><span class="n">count</span><span class="p">],</span> <span class="n">label</span><span class="o">=</span><span class="n">lbl</span><span class="p">,</span> <span class="n">ls</span><span class="o">=</span><span class="s2">&quot;-&quot;</span><span class="p">,</span><span class="n">linewidth</span><span class="o">=</span><span class="n">linewidth</span><span class="p">)</span>
+
+            <span class="k">if</span> <span class="n">fts</span><span class="o">.</span><span class="n">has_interval_forecasting</span> <span class="ow">and</span> <span class="n">intervals</span><span class="p">:</span>
+                <span class="n">forecasts</span> <span class="o">=</span> <span class="n">fts</span><span class="o">.</span><span class="n">forecast_interval</span><span class="p">(</span><span class="n">original</span><span class="p">)</span>
+                <span class="n">lbl</span> <span class="o">=</span> <span class="n">fts</span><span class="o">.</span><span class="n">shortname</span> <span class="o">+</span> <span class="s2">&quot; &quot;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">fts</span><span class="o">.</span><span class="n">order</span> <span class="k">if</span> <span class="n">fts</span><span class="o">.</span><span class="n">is_high_order</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">fts</span><span class="o">.</span><span class="n">benchmark_only</span> <span class="k">else</span> <span class="s2">&quot;&quot;</span><span class="p">)</span>
+                <span class="k">if</span> <span class="ow">not</span> <span class="n">points</span> <span class="ow">and</span> <span class="n">intervals</span><span class="p">:</span>
+                    <span class="n">ls</span> <span class="o">=</span> <span class="s2">&quot;-&quot;</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">ls</span> <span class="o">=</span> <span class="s2">&quot;--&quot;</span>
+                <span class="n">tmpmi</span><span class="p">,</span> <span class="n">tmpma</span> <span class="o">=</span> <span class="n">Util</span><span class="o">.</span><span class="n">plot_interval</span><span class="p">(</span><span class="n">ax</span><span class="p">,</span> <span class="n">forecasts</span><span class="p">,</span> <span class="n">fts</span><span class="o">.</span><span class="n">order</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="n">lbl</span><span class="p">,</span> <span class="n">typeonlegend</span><span class="o">=</span><span class="n">typeonlegend</span><span class="p">,</span>
+                                             <span class="n">color</span><span class="o">=</span><span class="n">colors</span><span class="p">[</span><span class="n">count</span><span class="p">],</span> <span class="n">ls</span><span class="o">=</span><span class="n">ls</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="n">linewidth</span><span class="p">)</span>
+                <span class="n">mi</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">tmpmi</span><span class="p">)</span>
+                <span class="n">ma</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">tmpma</span><span class="p">)</span>
+        <span class="k">except</span> <span class="ne">ValueError</span> <span class="k">as</span> <span class="n">ex</span><span class="p">:</span>
+            <span class="nb">print</span><span class="p">(</span><span class="n">fts</span><span class="o">.</span><span class="n">shortname</span><span class="p">)</span>
+
+        <span class="n">handles0</span><span class="p">,</span> <span class="n">labels0</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">get_legend_handles_labels</span><span class="p">()</span>
+        <span class="n">lgd</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">handles0</span><span class="p">,</span> <span class="n">labels0</span><span class="p">,</span> <span class="n">loc</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">bbox_to_anchor</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>
+        <span class="n">legends</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">lgd</span><span class="p">)</span>
+
+    <span class="c1"># ax.set_title(fts.name)</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">([</span><span class="nb">min</span><span class="p">(</span><span class="n">mi</span><span class="p">),</span> <span class="nb">max</span><span class="p">(</span><span class="n">ma</span><span class="p">)])</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;F(T)&#39;</span><span class="p">)</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;T&#39;</span><span class="p">)</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">original</span><span class="p">)])</span></div>
+
+    <span class="c1">#Util.show_and_save_image(fig, file, save, lgd=legends)</span>
+
+
+<div class="viewcode-block" id="plotCompared"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.benchmarks.plotCompared">[docs]</a><span class="k">def</span> <span class="nf">plotCompared</span><span class="p">(</span><span class="n">original</span><span class="p">,</span> <span class="n">forecasts</span><span class="p">,</span> <span class="n">labels</span><span class="p">,</span> <span class="n">title</span><span class="p">):</span>
+    <span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">[</span><span class="mi">13</span><span class="p">,</span> <span class="mi">6</span><span class="p">])</span>
+    <span class="n">ax</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">add_subplot</span><span class="p">(</span><span class="mi">111</span><span class="p">)</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">original</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;k&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">&quot;Original&quot;</span><span class="p">)</span>
+    <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">forecasts</span><span class="p">)):</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">forecasts</span><span class="p">[</span><span class="n">c</span><span class="p">],</span> <span class="n">label</span><span class="o">=</span><span class="n">labels</span><span class="p">[</span><span class="n">c</span><span class="p">])</span>
+    <span class="n">handles0</span><span class="p">,</span> <span class="n">labels0</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">get_legend_handles_labels</span><span class="p">()</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">handles0</span><span class="p">,</span> <span class="n">labels0</span><span class="p">)</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="n">title</span><span class="p">)</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;F(T)&#39;</span><span class="p">)</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;T&#39;</span><span class="p">)</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">original</span><span class="p">)])</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">([</span><span class="nb">min</span><span class="p">(</span><span class="n">original</span><span class="p">),</span> <span class="nb">max</span><span class="p">(</span><span class="n">original</span><span class="p">)])</span></div>
+
+
+<div class="viewcode-block" id="SelecaoSimples_MenorRMSE"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.benchmarks.SelecaoSimples_MenorRMSE">[docs]</a><span class="k">def</span> <span class="nf">SelecaoSimples_MenorRMSE</span><span class="p">(</span><span class="n">original</span><span class="p">,</span> <span class="n">parameters</span><span class="p">,</span> <span class="n">modelo</span><span class="p">):</span>
+    <span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="n">errors</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="n">forecasted_best</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Série Original&quot;</span><span class="p">)</span>
+    <span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">[</span><span class="mi">20</span><span class="p">,</span> <span class="mi">12</span><span class="p">])</span>
+    <span class="n">fig</span><span class="o">.</span><span class="n">suptitle</span><span class="p">(</span><span class="s2">&quot;Comparação de modelos &quot;</span><span class="p">)</span>
+    <span class="n">ax0</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">add_axes</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">,</span> <span class="mf">0.65</span><span class="p">,</span> <span class="mf">0.45</span><span class="p">])</span>  <span class="c1"># left, bottom, width, height</span>
+    <span class="n">ax0</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">original</span><span class="p">)])</span>
+    <span class="n">ax0</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">([</span><span class="nb">min</span><span class="p">(</span><span class="n">original</span><span class="p">),</span> <span class="nb">max</span><span class="p">(</span><span class="n">original</span><span class="p">)])</span>
+    <span class="n">ax0</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">&#39;Série Temporal&#39;</span><span class="p">)</span>
+    <span class="n">ax0</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;F(T)&#39;</span><span class="p">)</span>
+    <span class="n">ax0</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;T&#39;</span><span class="p">)</span>
+    <span class="n">ax0</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">original</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">&quot;Original&quot;</span><span class="p">)</span>
+    <span class="n">min_rmse</span> <span class="o">=</span> <span class="mf">100000.0</span>
+    <span class="n">best</span> <span class="o">=</span> <span class="kc">None</span>
+    <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">parameters</span><span class="p">:</span>
+        <span class="n">sets</span> <span class="o">=</span> <span class="n">Grid</span><span class="o">.</span><span class="n">GridPartitioner</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">original</span><span class="p">,</span> <span class="n">npart</span><span class="o">=</span><span class="n">p</span><span class="p">)</span><span class="o">.</span><span class="n">sets</span>
+        <span class="n">fts</span> <span class="o">=</span> <span class="n">modelo</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">p</span><span class="p">)</span> <span class="o">+</span> <span class="s2">&quot; particoes&quot;</span><span class="p">)</span>
+        <span class="n">fts</span><span class="o">.</span><span class="n">train</span><span class="p">(</span><span class="n">original</span><span class="p">,</span> <span class="n">sets</span><span class="o">=</span><span class="n">sets</span><span class="p">)</span>
+        <span class="c1"># print(original)</span>
+        <span class="n">forecasted</span> <span class="o">=</span> <span class="n">fts</span><span class="o">.</span><span class="n">forecast</span><span class="p">(</span><span class="n">original</span><span class="p">)</span>
+        <span class="n">forecasted</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">original</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
+        <span class="c1"># print(forecasted)</span>
+        <span class="n">ax0</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">forecasted</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="n">fts</span><span class="o">.</span><span class="n">name</span><span class="p">)</span>
+        <span class="n">error</span> <span class="o">=</span> <span class="n">Measures</span><span class="o">.</span><span class="n">rmse</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">forecasted</span><span class="p">),</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">original</span><span class="p">))</span>
+        <span class="nb">print</span><span class="p">(</span><span class="n">p</span><span class="p">,</span> <span class="n">error</span><span class="p">)</span>
+        <span class="n">errors</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">error</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">error</span> <span class="o">&lt;</span> <span class="n">min_rmse</span><span class="p">:</span>
+            <span class="n">min_rmse</span> <span class="o">=</span> <span class="n">error</span>
+            <span class="n">best</span> <span class="o">=</span> <span class="n">fts</span>
+            <span class="n">forecasted_best</span> <span class="o">=</span> <span class="n">forecasted</span>
+    <span class="n">handles0</span><span class="p">,</span> <span class="n">labels0</span> <span class="o">=</span> <span class="n">ax0</span><span class="o">.</span><span class="n">get_legend_handles_labels</span><span class="p">()</span>
+    <span class="n">ax0</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">handles0</span><span class="p">,</span> <span class="n">labels0</span><span class="p">)</span>
+    <span class="n">ax1</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">add_axes</span><span class="p">([</span><span class="mf">0.7</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">,</span> <span class="mf">0.3</span><span class="p">,</span> <span class="mf">0.45</span><span class="p">])</span>  <span class="c1"># left, bottom, width, height</span>
+    <span class="n">ax1</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">&#39;Comparação dos Erros Quadráticos Médios&#39;</span><span class="p">)</span>
+    <span class="n">ax1</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;RMSE&#39;</span><span class="p">)</span>
+    <span class="n">ax1</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;Quantidade de Partições&#39;</span><span class="p">)</span>
+    <span class="n">ax1</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">([</span><span class="nb">min</span><span class="p">(</span><span class="n">parameters</span><span class="p">),</span> <span class="nb">max</span><span class="p">(</span><span class="n">parameters</span><span class="p">)])</span>
+    <span class="n">ax1</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">parameters</span><span class="p">,</span> <span class="n">errors</span><span class="p">)</span>
+    <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">best</span><span class="p">)</span>
+    <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">forecasted_best</span><span class="p">)</span>
+    <span class="c1"># Modelo diferencial</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">Série Diferencial&quot;</span><span class="p">)</span>
+    <span class="n">difffts</span> <span class="o">=</span> <span class="n">Transformations</span><span class="o">.</span><span class="n">differential</span><span class="p">(</span><span class="n">original</span><span class="p">)</span>
+    <span class="n">errors</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="n">forecastedd_best</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="n">ax2</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">add_axes</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mf">0.65</span><span class="p">,</span> <span class="mf">0.45</span><span class="p">])</span>  <span class="c1"># left, bottom, width, height</span>
+    <span class="n">ax2</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">difffts</span><span class="p">)])</span>
+    <span class="n">ax2</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">([</span><span class="nb">min</span><span class="p">(</span><span class="n">difffts</span><span class="p">),</span> <span class="nb">max</span><span class="p">(</span><span class="n">difffts</span><span class="p">)])</span>
+    <span class="n">ax2</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">&#39;Série Temporal&#39;</span><span class="p">)</span>
+    <span class="n">ax2</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;F(T)&#39;</span><span class="p">)</span>
+    <span class="n">ax2</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;T&#39;</span><span class="p">)</span>
+    <span class="n">ax2</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">difffts</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">&quot;Original&quot;</span><span class="p">)</span>
+    <span class="n">min_rmse</span> <span class="o">=</span> <span class="mf">100000.0</span>
+    <span class="n">bestd</span> <span class="o">=</span> <span class="kc">None</span>
+    <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">parameters</span><span class="p">:</span>
+        <span class="n">sets</span> <span class="o">=</span> <span class="n">Grid</span><span class="o">.</span><span class="n">GridPartitioner</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">difffts</span><span class="p">,</span> <span class="n">npart</span><span class="o">=</span><span class="n">p</span><span class="p">)</span>
+        <span class="n">fts</span> <span class="o">=</span> <span class="n">modelo</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">p</span><span class="p">)</span> <span class="o">+</span> <span class="s2">&quot; particoes&quot;</span><span class="p">)</span>
+        <span class="n">fts</span><span class="o">.</span><span class="n">train</span><span class="p">(</span><span class="n">difffts</span><span class="p">,</span> <span class="n">sets</span><span class="o">=</span><span class="n">sets</span><span class="p">)</span>
+        <span class="n">forecasted</span> <span class="o">=</span> <span class="n">fts</span><span class="o">.</span><span class="n">forecast</span><span class="p">(</span><span class="n">difffts</span><span class="p">)</span>
+        <span class="n">forecasted</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">difffts</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
+        <span class="n">ax2</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">forecasted</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="n">fts</span><span class="o">.</span><span class="n">name</span><span class="p">)</span>
+        <span class="n">error</span> <span class="o">=</span> <span class="n">Measures</span><span class="o">.</span><span class="n">rmse</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">forecasted</span><span class="p">),</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">difffts</span><span class="p">))</span>
+        <span class="nb">print</span><span class="p">(</span><span class="n">p</span><span class="p">,</span> <span class="n">error</span><span class="p">)</span>
+        <span class="n">errors</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">error</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">error</span> <span class="o">&lt;</span> <span class="n">min_rmse</span><span class="p">:</span>
+            <span class="n">min_rmse</span> <span class="o">=</span> <span class="n">error</span>
+            <span class="n">bestd</span> <span class="o">=</span> <span class="n">fts</span>
+            <span class="n">forecastedd_best</span> <span class="o">=</span> <span class="n">forecasted</span>
+    <span class="n">handles0</span><span class="p">,</span> <span class="n">labels0</span> <span class="o">=</span> <span class="n">ax2</span><span class="o">.</span><span class="n">get_legend_handles_labels</span><span class="p">()</span>
+    <span class="n">ax2</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">handles0</span><span class="p">,</span> <span class="n">labels0</span><span class="p">)</span>
+    <span class="n">ax3</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">add_axes</span><span class="p">([</span><span class="mf">0.7</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mf">0.3</span><span class="p">,</span> <span class="mf">0.45</span><span class="p">])</span>  <span class="c1"># left, bottom, width, height</span>
+    <span class="n">ax3</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">&#39;Comparação dos Erros Quadráticos Médios&#39;</span><span class="p">)</span>
+    <span class="n">ax3</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;RMSE&#39;</span><span class="p">)</span>
+    <span class="n">ax3</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;Quantidade de Partições&#39;</span><span class="p">)</span>
+    <span class="n">ax3</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">([</span><span class="nb">min</span><span class="p">(</span><span class="n">parameters</span><span class="p">),</span> <span class="nb">max</span><span class="p">(</span><span class="n">parameters</span><span class="p">)])</span>
+    <span class="n">ax3</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">parameters</span><span class="p">,</span> <span class="n">errors</span><span class="p">)</span>
+    <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">bestd</span><span class="p">)</span>
+    <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">forecastedd_best</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">ret</span></div>
+
+
+<div class="viewcode-block" id="compareModelsPlot"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.benchmarks.compareModelsPlot">[docs]</a><span class="k">def</span> <span class="nf">compareModelsPlot</span><span class="p">(</span><span class="n">original</span><span class="p">,</span> <span class="n">models_fo</span><span class="p">,</span> <span class="n">models_ho</span><span class="p">):</span>
+    <span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">[</span><span class="mi">13</span><span class="p">,</span> <span class="mi">6</span><span class="p">])</span>
+    <span class="n">fig</span><span class="o">.</span><span class="n">suptitle</span><span class="p">(</span><span class="s2">&quot;Comparação de modelos &quot;</span><span class="p">)</span>
+    <span class="n">ax0</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">add_axes</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">])</span>  <span class="c1"># left, bottom, width, height</span>
+    <span class="n">rows</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="k">for</span> <span class="n">model</span> <span class="ow">in</span> <span class="n">models_fo</span><span class="p">:</span>
+        <span class="n">fts</span> <span class="o">=</span> <span class="n">model</span><span class="p">[</span><span class="s2">&quot;model&quot;</span><span class="p">]</span>
+        <span class="n">ax0</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">model</span><span class="p">[</span><span class="s2">&quot;forecasted&quot;</span><span class="p">],</span> <span class="n">label</span><span class="o">=</span><span class="n">model</span><span class="p">[</span><span class="s2">&quot;name&quot;</span><span class="p">])</span>
+    <span class="k">for</span> <span class="n">model</span> <span class="ow">in</span> <span class="n">models_ho</span><span class="p">:</span>
+        <span class="n">fts</span> <span class="o">=</span> <span class="n">model</span><span class="p">[</span><span class="s2">&quot;model&quot;</span><span class="p">]</span>
+        <span class="n">ax0</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">model</span><span class="p">[</span><span class="s2">&quot;forecasted&quot;</span><span class="p">],</span> <span class="n">label</span><span class="o">=</span><span class="n">model</span><span class="p">[</span><span class="s2">&quot;name&quot;</span><span class="p">])</span>
+    <span class="n">handles0</span><span class="p">,</span> <span class="n">labels0</span> <span class="o">=</span> <span class="n">ax0</span><span class="o">.</span><span class="n">get_legend_handles_labels</span><span class="p">()</span>
+    <span class="n">ax0</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">handles0</span><span class="p">,</span> <span class="n">labels0</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="compareModelsTable"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.benchmarks.compareModelsTable">[docs]</a><span class="k">def</span> <span class="nf">compareModelsTable</span><span class="p">(</span><span class="n">original</span><span class="p">,</span> <span class="n">models_fo</span><span class="p">,</span> <span class="n">models_ho</span><span class="p">):</span>
+    <span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">[</span><span class="mi">12</span><span class="p">,</span> <span class="mi">4</span><span class="p">])</span>
+    <span class="n">fig</span><span class="o">.</span><span class="n">suptitle</span><span class="p">(</span><span class="s2">&quot;Comparação de modelos &quot;</span><span class="p">)</span>
+    <span class="n">columns</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;Modelo&#39;</span><span class="p">,</span> <span class="s1">&#39;Ordem&#39;</span><span class="p">,</span> <span class="s1">&#39;Partições&#39;</span><span class="p">,</span> <span class="s1">&#39;RMSE&#39;</span><span class="p">,</span> <span class="s1">&#39;MAPE (%)&#39;</span><span class="p">]</span>
+    <span class="n">rows</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="k">for</span> <span class="n">model</span> <span class="ow">in</span> <span class="n">models_fo</span><span class="p">:</span>
+        <span class="n">fts</span> <span class="o">=</span> <span class="n">model</span><span class="p">[</span><span class="s2">&quot;model&quot;</span><span class="p">]</span>
+        <span class="n">error_r</span> <span class="o">=</span> <span class="n">Measures</span><span class="o">.</span><span class="n">rmse</span><span class="p">(</span><span class="n">model</span><span class="p">[</span><span class="s2">&quot;forecasted&quot;</span><span class="p">],</span> <span class="n">original</span><span class="p">)</span>
+        <span class="n">error_m</span> <span class="o">=</span> <span class="nb">round</span><span class="p">(</span><span class="n">Measures</span><span class="o">.</span><span class="n">mape</span><span class="p">(</span><span class="n">model</span><span class="p">[</span><span class="s2">&quot;forecasted&quot;</span><span class="p">],</span> <span class="n">original</span><span class="p">)</span> <span class="o">*</span> <span class="mi">100</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
+        <span class="n">rows</span><span class="o">.</span><span class="n">append</span><span class="p">([</span><span class="n">model</span><span class="p">[</span><span class="s2">&quot;name&quot;</span><span class="p">],</span> <span class="n">fts</span><span class="o">.</span><span class="n">order</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">fts</span><span class="o">.</span><span class="n">sets</span><span class="p">),</span> <span class="n">error_r</span><span class="p">,</span> <span class="n">error_m</span><span class="p">])</span>
+    <span class="k">for</span> <span class="n">model</span> <span class="ow">in</span> <span class="n">models_ho</span><span class="p">:</span>
+        <span class="n">fts</span> <span class="o">=</span> <span class="n">model</span><span class="p">[</span><span class="s2">&quot;model&quot;</span><span class="p">]</span>
+        <span class="n">error_r</span> <span class="o">=</span> <span class="n">Measures</span><span class="o">.</span><span class="n">rmse</span><span class="p">(</span><span class="n">model</span><span class="p">[</span><span class="s2">&quot;forecasted&quot;</span><span class="p">][</span><span class="n">fts</span><span class="o">.</span><span class="n">order</span><span class="p">:],</span> <span class="n">original</span><span class="p">[</span><span class="n">fts</span><span class="o">.</span><span class="n">order</span><span class="p">:])</span>
+        <span class="n">error_m</span> <span class="o">=</span> <span class="nb">round</span><span class="p">(</span><span class="n">Measures</span><span class="o">.</span><span class="n">mape</span><span class="p">(</span><span class="n">model</span><span class="p">[</span><span class="s2">&quot;forecasted&quot;</span><span class="p">][</span><span class="n">fts</span><span class="o">.</span><span class="n">order</span><span class="p">:],</span> <span class="n">original</span><span class="p">[</span><span class="n">fts</span><span class="o">.</span><span class="n">order</span><span class="p">:])</span> <span class="o">*</span> <span class="mi">100</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
+        <span class="n">rows</span><span class="o">.</span><span class="n">append</span><span class="p">([</span><span class="n">model</span><span class="p">[</span><span class="s2">&quot;name&quot;</span><span class="p">],</span> <span class="n">fts</span><span class="o">.</span><span class="n">order</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">fts</span><span class="o">.</span><span class="n">sets</span><span class="p">),</span> <span class="n">error_r</span><span class="p">,</span> <span class="n">error_m</span><span class="p">])</span>
+    <span class="n">ax1</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">add_axes</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">])</span>  <span class="c1"># left, bottom, width, height</span>
+    <span class="n">ax1</span><span class="o">.</span><span class="n">set_xticks</span><span class="p">([])</span>
+    <span class="n">ax1</span><span class="o">.</span><span class="n">set_yticks</span><span class="p">([])</span>
+    <span class="n">ax1</span><span class="o">.</span><span class="n">table</span><span class="p">(</span><span class="n">cellText</span><span class="o">=</span><span class="n">rows</span><span class="p">,</span>
+              <span class="n">colLabels</span><span class="o">=</span><span class="n">columns</span><span class="p">,</span>
+              <span class="n">cellLoc</span><span class="o">=</span><span class="s1">&#39;center&#39;</span><span class="p">,</span>
+              <span class="n">bbox</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">])</span>
+    <span class="n">sup</span> <span class="o">=</span> <span class="s2">&quot;</span><span class="se">\\</span><span class="s2">begin</span><span class="si">{tabular}</span><span class="s2">{&quot;</span>
+    <span class="n">header</span> <span class="o">=</span> <span class="s2">&quot;&quot;</span>
+    <span class="n">body</span> <span class="o">=</span> <span class="s2">&quot;&quot;</span>
+    <span class="n">footer</span> <span class="o">=</span> <span class="s2">&quot;&quot;</span>
+
+    <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="n">columns</span><span class="p">:</span>
+        <span class="n">sup</span> <span class="o">=</span> <span class="n">sup</span> <span class="o">+</span> <span class="s2">&quot;|c&quot;</span>
+        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">header</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="n">header</span> <span class="o">=</span> <span class="n">header</span> <span class="o">+</span> <span class="s2">&quot; &amp; &quot;</span>
+        <span class="n">header</span> <span class="o">=</span> <span class="n">header</span> <span class="o">+</span> <span class="s2">&quot;</span><span class="se">\\</span><span class="s2">textbf{&quot;</span> <span class="o">+</span> <span class="n">c</span> <span class="o">+</span> <span class="s2">&quot;} &quot;</span>
+    <span class="n">sup</span> <span class="o">=</span> <span class="n">sup</span> <span class="o">+</span> <span class="s2">&quot;|} </span><span class="se">\\</span><span class="s2">hline</span><span class="se">\n</span><span class="s2">&quot;</span>
+    <span class="n">header</span> <span class="o">=</span> <span class="n">header</span> <span class="o">+</span> <span class="s2">&quot;</span><span class="se">\\\\</span><span class="s2"> </span><span class="se">\\</span><span class="s2">hline </span><span class="se">\n</span><span class="s2">&quot;</span>
+
+    <span class="k">for</span> <span class="n">r</span> <span class="ow">in</span> <span class="n">rows</span><span class="p">:</span>
+        <span class="n">lin</span> <span class="o">=</span> <span class="s2">&quot;&quot;</span>
+        <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="n">r</span><span class="p">:</span>
+            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">lin</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
+                <span class="n">lin</span> <span class="o">=</span> <span class="n">lin</span> <span class="o">+</span> <span class="s2">&quot; &amp; &quot;</span>
+            <span class="n">lin</span> <span class="o">=</span> <span class="n">lin</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">c</span><span class="p">)</span>
+
+        <span class="n">body</span> <span class="o">=</span> <span class="n">body</span> <span class="o">+</span> <span class="n">lin</span> <span class="o">+</span> <span class="s2">&quot;</span><span class="se">\\\\</span><span class="s2"> </span><span class="se">\\</span><span class="s2">hline </span><span class="se">\n</span><span class="s2">&quot;</span>
+
+    <span class="k">return</span> <span class="n">sup</span> <span class="o">+</span> <span class="n">header</span> <span class="o">+</span> <span class="n">body</span> <span class="o">+</span> <span class="s2">&quot;</span><span class="se">\\</span><span class="s2">end</span><span class="si">{tabular}</span><span class="s2">&quot;</span></div>
+
+
+<div class="viewcode-block" id="simpleSearch_RMSE"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.benchmarks.simpleSearch_RMSE">[docs]</a><span class="k">def</span> <span class="nf">simpleSearch_RMSE</span><span class="p">(</span><span class="n">train</span><span class="p">,</span> <span class="n">test</span><span class="p">,</span> <span class="n">model</span><span class="p">,</span> <span class="n">partitions</span><span class="p">,</span> <span class="n">orders</span><span class="p">,</span> <span class="n">save</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">file</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">tam</span><span class="o">=</span><span class="p">[</span><span class="mi">10</span><span class="p">,</span> <span class="mi">15</span><span class="p">],</span>
+                      <span class="n">plotforecasts</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">elev</span><span class="o">=</span><span class="mi">30</span><span class="p">,</span> <span class="n">azim</span><span class="o">=</span><span class="mi">144</span><span class="p">,</span> <span class="n">intervals</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span><span class="n">parameters</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+                      <span class="n">partitioner</span><span class="o">=</span><span class="n">Grid</span><span class="o">.</span><span class="n">GridPartitioner</span><span class="p">,</span><span class="n">transformation</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span><span class="n">indexer</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+    <span class="n">_3d</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">orders</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">1</span>
+    <span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="k">if</span> <span class="n">_3d</span><span class="p">:</span>
+        <span class="n">errors</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mi">0</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">partitions</span><span class="p">))]</span> <span class="k">for</span> <span class="n">kk</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">orders</span><span class="p">))])</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">errors</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="n">forecasted_best</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="n">tam</span><span class="p">)</span>
+    <span class="c1"># fig.suptitle(&quot;Comparação de modelos &quot;)</span>
+    <span class="k">if</span> <span class="n">plotforecasts</span><span class="p">:</span>
+        <span class="n">ax0</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">add_axes</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mf">0.4</span><span class="p">,</span> <span class="mf">0.9</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">])</span>  <span class="c1"># left, bottom, width, height</span>
+        <span class="n">ax0</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">train</span><span class="p">)])</span>
+        <span class="n">ax0</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">([</span><span class="nb">min</span><span class="p">(</span><span class="n">train</span><span class="p">)</span> <span class="o">*</span> <span class="mf">0.9</span><span class="p">,</span> <span class="nb">max</span><span class="p">(</span><span class="n">train</span><span class="p">)</span> <span class="o">*</span> <span class="mf">1.1</span><span class="p">])</span>
+        <span class="n">ax0</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">&#39;Forecasts&#39;</span><span class="p">)</span>
+        <span class="n">ax0</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;F(T)&#39;</span><span class="p">)</span>
+        <span class="n">ax0</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;T&#39;</span><span class="p">)</span>
+    <span class="n">min_rmse</span> <span class="o">=</span> <span class="mf">1000000.0</span>
+    <span class="n">best</span> <span class="o">=</span> <span class="kc">None</span>
+
+    <span class="k">for</span> <span class="n">pc</span><span class="p">,</span> <span class="n">p</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">partitions</span><span class="p">,</span> <span class="n">start</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
+
+        <span class="n">sets</span> <span class="o">=</span> <span class="n">partitioner</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">train</span><span class="p">,</span> <span class="n">npart</span><span class="o">=</span><span class="n">p</span><span class="p">,</span> <span class="n">transformation</span><span class="o">=</span><span class="n">transformation</span><span class="p">)</span><span class="o">.</span><span class="n">sets</span>
+        <span class="k">for</span> <span class="n">oc</span><span class="p">,</span> <span class="n">o</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">orders</span><span class="p">,</span> <span class="n">start</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
+            <span class="n">fts</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="s2">&quot;q = &quot;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">p</span><span class="p">)</span> <span class="o">+</span> <span class="s2">&quot; n = &quot;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">o</span><span class="p">))</span>
+            <span class="n">fts</span><span class="o">.</span><span class="n">append_transformation</span><span class="p">(</span><span class="n">transformation</span><span class="p">)</span>
+            <span class="n">fts</span><span class="o">.</span><span class="n">train</span><span class="p">(</span><span class="n">train</span><span class="p">,</span> <span class="n">sets</span><span class="o">=</span><span class="n">sets</span><span class="p">,</span> <span class="n">order</span><span class="o">=</span><span class="n">o</span><span class="p">,</span> <span class="n">parameters</span><span class="o">=</span><span class="n">parameters</span><span class="p">)</span>
+            <span class="k">if</span> <span class="ow">not</span> <span class="n">intervals</span><span class="p">:</span>
+                <span class="n">forecasted</span> <span class="o">=</span> <span class="n">fts</span><span class="o">.</span><span class="n">forecast</span><span class="p">(</span><span class="n">test</span><span class="p">)</span>
+                <span class="k">if</span> <span class="ow">not</span> <span class="n">fts</span><span class="o">.</span><span class="n">has_seasonality</span><span class="p">:</span>
+                    <span class="n">error</span> <span class="o">=</span> <span class="n">Measures</span><span class="o">.</span><span class="n">rmse</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">test</span><span class="p">[</span><span class="n">o</span><span class="p">:]),</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">forecasted</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">]))</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">error</span> <span class="o">=</span> <span class="n">Measures</span><span class="o">.</span><span class="n">rmse</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">test</span><span class="p">[</span><span class="n">o</span><span class="p">:]),</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">forecasted</span><span class="p">))</span>
+                <span class="k">for</span> <span class="n">kk</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">o</span><span class="p">):</span>
+                    <span class="n">forecasted</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
+                <span class="k">if</span> <span class="n">plotforecasts</span><span class="p">:</span> <span class="n">ax0</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">forecasted</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="n">fts</span><span class="o">.</span><span class="n">name</span><span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">forecasted</span> <span class="o">=</span> <span class="n">fts</span><span class="o">.</span><span class="n">forecast_interval</span><span class="p">(</span><span class="n">test</span><span class="p">)</span>
+                <span class="n">error</span> <span class="o">=</span> <span class="mf">1.0</span> <span class="o">-</span> <span class="n">Measures</span><span class="o">.</span><span class="n">rmse_interval</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">test</span><span class="p">[</span><span class="n">o</span><span class="p">:]),</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">forecasted</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">]))</span>
+            <span class="k">if</span> <span class="n">_3d</span><span class="p">:</span>
+                <span class="n">errors</span><span class="p">[</span><span class="n">oc</span><span class="p">,</span> <span class="n">pc</span><span class="p">]</span> <span class="o">=</span> <span class="n">error</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">errors</span><span class="o">.</span><span class="n">append</span><span class="p">(</span> <span class="n">error</span> <span class="p">)</span>
+            <span class="k">if</span> <span class="n">error</span> <span class="o">&lt;</span> <span class="n">min_rmse</span><span class="p">:</span>
+                <span class="n">min_rmse</span> <span class="o">=</span> <span class="n">error</span>
+                <span class="n">best</span> <span class="o">=</span> <span class="n">fts</span>
+                <span class="n">forecasted_best</span> <span class="o">=</span> <span class="n">forecasted</span>
+
+    <span class="c1"># print(min_rmse)</span>
+    <span class="k">if</span> <span class="n">plotforecasts</span><span class="p">:</span>
+        <span class="c1"># handles0, labels0 = ax0.get_legend_handles_labels()</span>
+        <span class="c1"># ax0.legend(handles0, labels0)</span>
+        <span class="n">ax0</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">test</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">&quot;Original&quot;</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mf">3.0</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s2">&quot;black&quot;</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">_3d</span><span class="p">:</span> <span class="n">ax1</span> <span class="o">=</span> <span class="n">Axes3D</span><span class="p">(</span><span class="n">fig</span><span class="p">,</span> <span class="n">rect</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mf">0.9</span><span class="p">,</span> <span class="mf">0.9</span><span class="p">],</span> <span class="n">elev</span><span class="o">=</span><span class="n">elev</span><span class="p">,</span> <span class="n">azim</span><span class="o">=</span><span class="n">azim</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">_3d</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">plotforecasts</span><span class="p">:</span>
+        <span class="n">ax1</span> <span class="o">=</span> <span class="n">Axes3D</span><span class="p">(</span><span class="n">fig</span><span class="p">,</span> <span class="n">rect</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mf">0.9</span><span class="p">,</span> <span class="mf">0.9</span><span class="p">],</span> <span class="n">elev</span><span class="o">=</span><span class="n">elev</span><span class="p">,</span> <span class="n">azim</span><span class="o">=</span><span class="n">azim</span><span class="p">)</span>
+        <span class="n">ax1</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">&#39;Error Surface&#39;</span><span class="p">)</span>
+        <span class="n">ax1</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;Model order&#39;</span><span class="p">)</span>
+        <span class="n">ax1</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;Number of partitions&#39;</span><span class="p">)</span>
+        <span class="n">ax1</span><span class="o">.</span><span class="n">set_zlabel</span><span class="p">(</span><span class="s1">&#39;RMSE&#39;</span><span class="p">)</span>
+        <span class="n">X</span><span class="p">,</span> <span class="n">Y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">meshgrid</span><span class="p">(</span><span class="n">partitions</span><span class="p">,</span> <span class="n">orders</span><span class="p">)</span>
+        <span class="n">surf</span> <span class="o">=</span> <span class="n">ax1</span><span class="o">.</span><span class="n">plot_surface</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">errors</span><span class="p">,</span> <span class="n">rstride</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">cstride</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">antialiased</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">ax1</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">add_axes</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mf">0.9</span><span class="p">,</span> <span class="mf">0.9</span><span class="p">])</span>
+        <span class="n">ax1</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">&#39;Error Curve&#39;</span><span class="p">)</span>
+        <span class="n">ax1</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;Number of partitions&#39;</span><span class="p">)</span>
+        <span class="n">ax1</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;RMSE&#39;</span><span class="p">)</span>
+        <span class="n">ax1</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">partitions</span><span class="p">,</span> <span class="n">errors</span><span class="p">)</span>
+    <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">best</span><span class="p">)</span>
+    <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">forecasted_best</span><span class="p">)</span>
+    <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">min_rmse</span><span class="p">)</span>
+
+    <span class="c1"># plt.tight_layout()</span>
+
+    <span class="n">cUtil</span><span class="o">.</span><span class="n">show_and_save_image</span><span class="p">(</span><span class="n">fig</span><span class="p">,</span> <span class="n">file</span><span class="p">,</span> <span class="n">save</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">ret</span></div>
+
+
+
+<div class="viewcode-block" id="pftsExploreOrderAndPartitions"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.benchmarks.pftsExploreOrderAndPartitions">[docs]</a><span class="k">def</span> <span class="nf">pftsExploreOrderAndPartitions</span><span class="p">(</span><span class="n">data</span><span class="p">,</span><span class="n">save</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">file</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+    <span class="n">fig</span><span class="p">,</span> <span class="n">axes</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">nrows</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span> <span class="n">ncols</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">[</span><span class="mi">6</span><span class="p">,</span> <span class="mi">8</span><span class="p">])</span>
+    <span class="n">data_fs1</span> <span class="o">=</span> <span class="n">Grid</span><span class="o">.</span><span class="n">GridPartitioner</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">data</span><span class="p">,</span> <span class="n">npart</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span><span class="o">.</span><span class="n">sets</span>
+    <span class="n">mi</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="n">ma</span> <span class="o">=</span> <span class="p">[]</span>
+
+    <span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">&#39;Point Forecasts by Order&#39;</span><span class="p">)</span>
+    <span class="n">axes</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">&#39;Interval Forecasts by Order&#39;</span><span class="p">)</span>
+
+    <span class="k">for</span> <span class="n">order</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">6</span><span class="p">):</span>
+        <span class="n">fts</span> <span class="o">=</span> <span class="n">pwfts</span><span class="o">.</span><span class="n">ProbabilisticWeightedFTS</span><span class="p">(</span><span class="s2">&quot;&quot;</span><span class="p">)</span>
+        <span class="n">fts</span><span class="o">.</span><span class="n">shortname</span> <span class="o">=</span> <span class="s2">&quot;n = &quot;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">order</span><span class="p">)</span>
+        <span class="n">fts</span><span class="o">.</span><span class="n">train</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">sets</span><span class="o">=</span><span class="n">data_fs1</span><span class="o">.</span><span class="n">sets</span><span class="p">,</span> <span class="n">order</span><span class="o">=</span><span class="n">order</span><span class="p">)</span>
+        <span class="n">point_forecasts</span> <span class="o">=</span> <span class="n">fts</span><span class="o">.</span><span class="n">forecast</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+        <span class="n">interval_forecasts</span> <span class="o">=</span> <span class="n">fts</span><span class="o">.</span><span class="n">forecast_interval</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+        <span class="n">lower</span> <span class="o">=</span> <span class="p">[</span><span class="n">kk</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">kk</span> <span class="ow">in</span> <span class="n">interval_forecasts</span><span class="p">]</span>
+        <span class="n">upper</span> <span class="o">=</span> <span class="p">[</span><span class="n">kk</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">kk</span> <span class="ow">in</span> <span class="n">interval_forecasts</span><span class="p">]</span>
+        <span class="n">mi</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">min</span><span class="p">(</span><span class="n">lower</span><span class="p">)</span> <span class="o">*</span> <span class="mf">0.95</span><span class="p">)</span>
+        <span class="n">ma</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">max</span><span class="p">(</span><span class="n">upper</span><span class="p">)</span> <span class="o">*</span> <span class="mf">1.05</span><span class="p">)</span>
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">order</span><span class="p">):</span>
+            <span class="n">point_forecasts</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
+            <span class="n">lower</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
+            <span class="n">upper</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
+        <span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">point_forecasts</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="n">fts</span><span class="o">.</span><span class="n">shortname</span><span class="p">)</span>
+        <span class="n">axes</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">lower</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="n">fts</span><span class="o">.</span><span class="n">shortname</span><span class="p">)</span>
+        <span class="n">axes</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">upper</span><span class="p">)</span>
+
+    <span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">&#39;Point Forecasts by Number of Partitions&#39;</span><span class="p">)</span>
+    <span class="n">axes</span><span class="p">[</span><span class="mi">3</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">&#39;Interval Forecasts by Number of Partitions&#39;</span><span class="p">)</span>
+
+    <span class="k">for</span> <span class="n">partitions</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">11</span><span class="p">):</span>
+        <span class="n">data_fs</span> <span class="o">=</span> <span class="n">Grid</span><span class="o">.</span><span class="n">GridPartitioner</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">data</span><span class="p">,</span> <span class="n">npart</span><span class="o">=</span><span class="n">partitions</span><span class="p">)</span><span class="o">.</span><span class="n">sets</span>
+        <span class="n">fts</span> <span class="o">=</span> <span class="n">pwfts</span><span class="o">.</span><span class="n">ProbabilisticWeightedFTS</span><span class="p">(</span><span class="s2">&quot;&quot;</span><span class="p">)</span>
+        <span class="n">fts</span><span class="o">.</span><span class="n">shortname</span> <span class="o">=</span> <span class="s2">&quot;q = &quot;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">partitions</span><span class="p">)</span>
+        <span class="n">fts</span><span class="o">.</span><span class="n">train</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">sets</span><span class="o">=</span><span class="n">data_fs</span><span class="o">.</span><span class="n">sets</span><span class="p">,</span> <span class="n">order</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+        <span class="n">point_forecasts</span> <span class="o">=</span> <span class="n">fts</span><span class="o">.</span><span class="n">forecast</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+        <span class="n">interval_forecasts</span> <span class="o">=</span> <span class="n">fts</span><span class="o">.</span><span class="n">forecast_interval</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+        <span class="n">lower</span> <span class="o">=</span> <span class="p">[</span><span class="n">kk</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">kk</span> <span class="ow">in</span> <span class="n">interval_forecasts</span><span class="p">]</span>
+        <span class="n">upper</span> <span class="o">=</span> <span class="p">[</span><span class="n">kk</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">kk</span> <span class="ow">in</span> <span class="n">interval_forecasts</span><span class="p">]</span>
+        <span class="n">mi</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">min</span><span class="p">(</span><span class="n">lower</span><span class="p">)</span> <span class="o">*</span> <span class="mf">0.95</span><span class="p">)</span>
+        <span class="n">ma</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">max</span><span class="p">(</span><span class="n">upper</span><span class="p">)</span> <span class="o">*</span> <span class="mf">1.05</span><span class="p">)</span>
+        <span class="n">point_forecasts</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
+        <span class="n">lower</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
+        <span class="n">upper</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
+        <span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">point_forecasts</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="n">fts</span><span class="o">.</span><span class="n">shortname</span><span class="p">)</span>
+        <span class="n">axes</span><span class="p">[</span><span class="mi">3</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">lower</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="n">fts</span><span class="o">.</span><span class="n">shortname</span><span class="p">)</span>
+        <span class="n">axes</span><span class="p">[</span><span class="mi">3</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">upper</span><span class="p">)</span>
+
+    <span class="k">for</span> <span class="n">ax</span> <span class="ow">in</span> <span class="n">axes</span><span class="p">:</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;F(T)&#39;</span><span class="p">)</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;T&#39;</span><span class="p">)</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">&quot;Original&quot;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s2">&quot;black&quot;</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mf">1.5</span><span class="p">)</span>
+        <span class="n">handles</span><span class="p">,</span> <span class="n">labels</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">get_legend_handles_labels</span><span class="p">()</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">handles</span><span class="p">,</span> <span class="n">labels</span><span class="p">,</span> <span class="n">loc</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">bbox_to_anchor</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">([</span><span class="nb">min</span><span class="p">(</span><span class="n">mi</span><span class="p">),</span> <span class="nb">max</span><span class="p">(</span><span class="n">ma</span><span class="p">)])</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">)])</span>
+
+    <span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
+
+    <span class="n">cUtil</span><span class="o">.</span><span class="n">show_and_save_image</span><span class="p">(</span><span class="n">fig</span><span class="p">,</span> <span class="n">file</span><span class="p">,</span> <span class="n">save</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="train_test_time"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.benchmarks.train_test_time">[docs]</a><span class="k">def</span> <span class="nf">train_test_time</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">windowsize</span><span class="p">,</span> <span class="n">train</span><span class="o">=</span><span class="mf">0.8</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+    <span class="kn">import</span> <span class="nn">time</span>
+
+    <span class="n">tag</span> <span class="o">=</span> <span class="n">__pop</span><span class="p">(</span><span class="s1">&#39;tag&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">)</span>
+    <span class="n">steps</span> <span class="o">=</span> <span class="n">__pop</span><span class="p">(</span><span class="s1">&#39;steps&#39;</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">)</span>
+    <span class="n">dataset</span> <span class="o">=</span> <span class="n">__pop</span><span class="p">(</span><span class="s1">&#39;dataset&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">)</span>
+
+    <span class="n">partitions</span> <span class="o">=</span> <span class="n">__pop</span><span class="p">(</span><span class="s1">&#39;partitions&#39;</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">)</span>
+
+    <span class="n">fts_methods</span> <span class="o">=</span> <span class="n">__pop</span><span class="p">(</span><span class="s1">&#39;methods&#39;</span><span class="p">,</span> <span class="p">[],</span> <span class="n">kwargs</span><span class="p">)</span>
+
+    <span class="n">file</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;file&#39;</span><span class="p">,</span> <span class="s2">&quot;benchmarks.db&quot;</span><span class="p">)</span>
+
+    <span class="n">inc</span> <span class="o">=</span> <span class="n">__pop</span><span class="p">(</span><span class="s2">&quot;inc&quot;</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">)</span>
+
+    <span class="n">conn</span> <span class="o">=</span> <span class="n">bUtil</span><span class="o">.</span><span class="n">open_benchmark_db</span><span class="p">(</span><span class="n">file</span><span class="p">)</span>
+
+    <span class="k">for</span> <span class="n">ct</span><span class="p">,</span> <span class="n">train</span><span class="p">,</span> <span class="n">test</span> <span class="ow">in</span> <span class="n">cUtil</span><span class="o">.</span><span class="n">sliding_window</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">windowsize</span><span class="p">,</span> <span class="n">train</span><span class="p">,</span> <span class="n">inc</span><span class="o">=</span><span class="n">inc</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="n">partitioner</span> <span class="o">=</span> <span class="n">Grid</span><span class="o">.</span><span class="n">GridPartitioner</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">train</span><span class="p">,</span> <span class="n">npart</span><span class="o">=</span><span class="n">partitions</span><span class="p">)</span>
+        <span class="k">for</span> <span class="nb">id</span><span class="p">,</span> <span class="n">fts_method</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">fts_methods</span><span class="p">):</span>
+            <span class="nb">print</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">fts_method</span><span class="p">,</span> <span class="n">ct</span><span class="p">)</span>
+            <span class="n">times</span> <span class="o">=</span> <span class="p">[]</span>
+            <span class="n">model</span> <span class="o">=</span> <span class="n">fts_method</span><span class="p">(</span><span class="n">partitioner</span> <span class="o">=</span> <span class="n">partitioner</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+            <span class="n">_start</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
+            <span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">train</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+            <span class="n">_end</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
+            <span class="n">times</span><span class="o">.</span><span class="n">append</span><span class="p">(</span> <span class="n">_end</span> <span class="o">-</span> <span class="n">_start</span> <span class="p">)</span>
+            <span class="n">_start</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
+            <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">train</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+            <span class="n">_end</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
+            <span class="n">times</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">_end</span> <span class="o">-</span> <span class="n">_start</span><span class="p">)</span>
+            <span class="k">for</span> <span class="n">ct</span><span class="p">,</span> <span class="n">method</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">([</span><span class="s1">&#39;train&#39;</span><span class="p">,</span><span class="s1">&#39;test&#39;</span><span class="p">]):</span>
+                <span class="n">job</span> <span class="o">=</span> <span class="p">{</span>
+                    <span class="s1">&#39;steps&#39;</span><span class="p">:</span> <span class="n">steps</span><span class="p">,</span> <span class="s1">&#39;method&#39;</span><span class="p">:</span> <span class="n">method</span><span class="p">,</span> <span class="s1">&#39;time&#39;</span><span class="p">:</span> <span class="n">times</span><span class="p">[</span><span class="n">ct</span><span class="p">],</span>
+                    <span class="s1">&#39;model&#39;</span><span class="p">:</span> <span class="n">model</span><span class="o">.</span><span class="n">shortname</span><span class="p">,</span> <span class="s1">&#39;transformation&#39;</span><span class="p">:</span> <span class="kc">None</span><span class="p">,</span>
+                    <span class="s1">&#39;order&#39;</span><span class="p">:</span> <span class="n">model</span><span class="o">.</span><span class="n">order</span><span class="p">,</span> <span class="s1">&#39;partitioner&#39;</span><span class="p">:</span> <span class="n">partitioner</span><span class="o">.</span><span class="n">name</span><span class="p">,</span>
+                    <span class="s1">&#39;partitions&#39;</span><span class="p">:</span> <span class="n">partitions</span><span class="p">,</span> <span class="s1">&#39;size&#39;</span><span class="p">:</span> <span class="nb">len</span><span class="p">(</span><span class="n">model</span><span class="p">)</span>
+                <span class="p">}</span>
+
+                <span class="n">data</span> <span class="o">=</span> <span class="n">bUtil</span><span class="o">.</span><span class="n">process_common_data2</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">tag</span><span class="p">,</span> <span class="s1">&#39;train&#39;</span><span class="p">,</span> <span class="n">job</span><span class="p">)</span>
+                <span class="n">common_process_time_jobs</span><span class="p">(</span><span class="n">conn</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">job</span><span class="p">)</span>
+
+
+    <span class="n">conn</span><span class="o">.</span><span class="n">close</span><span class="p">()</span></div>
+
+
+<div class="viewcode-block" id="distributed_model_train_test_time"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.benchmarks.distributed_model_train_test_time">[docs]</a><span class="k">def</span> <span class="nf">distributed_model_train_test_time</span><span class="p">(</span><span class="n">models</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">windowsize</span><span class="p">,</span> <span class="n">train</span><span class="o">=</span><span class="mf">0.8</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Assess the train and test times for a given list of configured models and save the results on a database.</span>
+
+<span class="sd">    :param models: A list of FTS models already configured, but not yet trained,</span>
+<span class="sd">    :param data: time series data, including train and test data</span>
+<span class="sd">    :param windowsize: Train/test data windows</span>
+<span class="sd">    :param train: Percent of data window that will be used to train the models</span>
+<span class="sd">    :param kwargs:</span>
+<span class="sd">    :return:</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="kn">import</span> <span class="nn">time</span>
+
+    <span class="n">tag</span> <span class="o">=</span> <span class="n">__pop</span><span class="p">(</span><span class="s1">&#39;tag&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">)</span>
+    <span class="n">num_batches</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;num_batches&#39;</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+    <span class="n">dataset</span> <span class="o">=</span> <span class="n">__pop</span><span class="p">(</span><span class="s1">&#39;dataset&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">)</span>
+
+    <span class="n">file</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;file&#39;</span><span class="p">,</span> <span class="s2">&quot;benchmarks.db&quot;</span><span class="p">)</span>
+
+    <span class="n">inc</span> <span class="o">=</span> <span class="n">__pop</span><span class="p">(</span><span class="s2">&quot;inc&quot;</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">)</span>
+
+    <span class="n">conn</span> <span class="o">=</span> <span class="n">bUtil</span><span class="o">.</span><span class="n">open_benchmark_db</span><span class="p">(</span><span class="n">file</span><span class="p">)</span>
+
+    <span class="k">for</span> <span class="n">ct</span><span class="p">,</span> <span class="n">train</span><span class="p">,</span> <span class="n">test</span> <span class="ow">in</span> <span class="n">cUtil</span><span class="o">.</span><span class="n">sliding_window</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">windowsize</span><span class="p">,</span> <span class="n">train</span><span class="p">,</span> <span class="n">inc</span><span class="o">=</span><span class="n">inc</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="k">for</span> <span class="nb">id</span><span class="p">,</span> <span class="n">model</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">models</span><span class="p">):</span>
+            <span class="nb">print</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">model</span><span class="p">,</span> <span class="n">ct</span><span class="p">)</span>
+
+            <span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">train</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+
+            <span class="k">for</span> <span class="n">time</span> <span class="ow">in</span> <span class="n">model</span><span class="o">.</span><span class="vm">__dict__</span><span class="p">[</span><span class="s1">&#39;training_time&#39;</span><span class="p">]:</span>
+                <span class="n">job</span> <span class="o">=</span> <span class="p">{</span>
+                    <span class="s1">&#39;steps&#39;</span><span class="p">:</span> <span class="n">num_batches</span><span class="p">,</span> <span class="s1">&#39;method&#39;</span><span class="p">:</span> <span class="s1">&#39;train&#39;</span><span class="p">,</span> <span class="s1">&#39;time&#39;</span><span class="p">:</span> <span class="n">time</span><span class="p">,</span>
+                    <span class="s1">&#39;model&#39;</span><span class="p">:</span> <span class="n">model</span><span class="o">.</span><span class="n">shortname</span><span class="p">,</span> <span class="s1">&#39;transformation&#39;</span><span class="p">:</span> <span class="kc">None</span><span class="p">,</span>
+                    <span class="s1">&#39;order&#39;</span><span class="p">:</span> <span class="n">model</span><span class="o">.</span><span class="n">order</span><span class="p">,</span> <span class="s1">&#39;partitioner&#39;</span><span class="p">:</span> <span class="kc">None</span><span class="p">,</span>
+                    <span class="s1">&#39;partitions&#39;</span><span class="p">:</span> <span class="kc">None</span><span class="p">,</span> <span class="s1">&#39;size&#39;</span><span class="p">:</span> <span class="nb">len</span><span class="p">(</span><span class="n">model</span><span class="p">)</span>
+                <span class="p">}</span>
+                <span class="n">data</span> <span class="o">=</span> <span class="n">bUtil</span><span class="o">.</span><span class="n">process_common_data2</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">tag</span><span class="p">,</span> <span class="s1">&#39;train&#39;</span><span class="p">,</span> <span class="n">job</span><span class="p">)</span>
+                <span class="n">common_process_time_jobs</span><span class="p">(</span><span class="n">conn</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">job</span><span class="p">)</span>
+
+            <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">train</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+
+            <span class="k">for</span> <span class="n">time</span> <span class="ow">in</span> <span class="n">model</span><span class="o">.</span><span class="vm">__dict__</span><span class="p">[</span><span class="s1">&#39;forecasting_time&#39;</span><span class="p">]:</span>
+                <span class="n">job</span> <span class="o">=</span> <span class="p">{</span>
+                    <span class="s1">&#39;steps&#39;</span><span class="p">:</span> <span class="n">num_batches</span><span class="p">,</span> <span class="s1">&#39;method&#39;</span><span class="p">:</span> <span class="s1">&#39;test&#39;</span><span class="p">,</span> <span class="s1">&#39;time&#39;</span><span class="p">:</span> <span class="n">time</span><span class="p">,</span>
+                    <span class="s1">&#39;model&#39;</span><span class="p">:</span> <span class="n">model</span><span class="o">.</span><span class="n">shortname</span><span class="p">,</span> <span class="s1">&#39;transformation&#39;</span><span class="p">:</span> <span class="kc">None</span><span class="p">,</span>
+                    <span class="s1">&#39;order&#39;</span><span class="p">:</span> <span class="n">model</span><span class="o">.</span><span class="n">order</span><span class="p">,</span> <span class="s1">&#39;partitioner&#39;</span><span class="p">:</span> <span class="kc">None</span><span class="p">,</span>
+                    <span class="s1">&#39;partitions&#39;</span><span class="p">:</span> <span class="kc">None</span><span class="p">,</span> <span class="s1">&#39;size&#39;</span><span class="p">:</span> <span class="nb">len</span><span class="p">(</span><span class="n">model</span><span class="p">)</span>
+                <span class="p">}</span>
+
+                <span class="n">data</span> <span class="o">=</span> <span class="n">bUtil</span><span class="o">.</span><span class="n">process_common_data2</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">tag</span><span class="p">,</span> <span class="s1">&#39;test&#39;</span><span class="p">,</span> <span class="n">job</span><span class="p">)</span>
+                <span class="n">common_process_time_jobs</span><span class="p">(</span><span class="n">conn</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">job</span><span class="p">)</span>
+
+    <span class="n">conn</span><span class="o">.</span><span class="n">close</span><span class="p">()</span></div>
+
+
+</pre></div>
+
+            <div class="clearer"></div>
+          </div>
+        </div>
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+        <li class="nav-item nav-item-0"><a href="../../../index.html">pyFTS 1.6 documentation</a> &#187;</li>
+          <li class="nav-item nav-item-1"><a href="../../index.html" accesskey="U">Module code</a> &#187;</li>
+        <li class="nav-item nav-item-this"><a href="">pyFTS.benchmarks.knn</a></li> 
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+            
+  <h1>Source code for pyFTS.benchmarks.knn</h1><div class="highlight"><pre>
+<span></span><span class="ch">#!/usr/bin/python</span>
+<span class="c1"># -*- coding: utf8 -*-</span>
+
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">from</span> <span class="nn">statsmodels.tsa.tsatools</span> <span class="kn">import</span> <span class="n">lagmat</span>
+<span class="kn">from</span> <span class="nn">pyFTS.common</span> <span class="kn">import</span> <span class="n">fts</span>
+<span class="kn">from</span> <span class="nn">pyFTS.probabilistic</span> <span class="kn">import</span> <span class="n">ProbabilityDistribution</span>
+<span class="kn">from</span> <span class="nn">sklearn.neighbors</span> <span class="kn">import</span> <span class="n">KDTree</span>
+<span class="kn">from</span> <span class="nn">itertools</span> <span class="kn">import</span> <span class="n">product</span>
+<span class="kn">from</span> <span class="nn">pyFTS.models.ensemble.ensemble</span> <span class="kn">import</span> <span class="n">sampler</span>
+
+<div class="viewcode-block" id="KNearestNeighbors"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.knn.KNearestNeighbors">[docs]</a><span class="k">class</span> <span class="nc">KNearestNeighbors</span><span class="p">(</span><span class="n">fts</span><span class="o">.</span><span class="n">FTS</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    A façade for sklearn.neighbors</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">(</span><span class="n">KNearestNeighbors</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="s2">&quot;kNN&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">shortname</span> <span class="o">=</span> <span class="s2">&quot;kNN&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">detail</span> <span class="o">=</span> <span class="s2">&quot;K-Nearest Neighbors&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">uod_clip</span> <span class="o">=</span> <span class="kc">False</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">is_high_order</span> <span class="o">=</span> <span class="kc">True</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">has_point_forecasting</span> <span class="o">=</span> <span class="kc">True</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">has_interval_forecasting</span> <span class="o">=</span> <span class="kc">True</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">has_probability_forecasting</span> <span class="o">=</span> <span class="kc">True</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">benchmark_only</span> <span class="o">=</span> <span class="kc">True</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">min_order</span> <span class="o">=</span> <span class="mi">1</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">alpha</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;alpha&quot;</span><span class="p">,</span> <span class="mf">0.05</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">lag</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">k</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;k&quot;</span><span class="p">,</span> <span class="mi">30</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">uod</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">kdtree</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">values</span> <span class="o">=</span> <span class="kc">None</span>
+
+    <span class="k">def</span> <span class="nf">_prepare_x</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">):</span>
+        <span class="n">l</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+        <span class="n">X</span> <span class="o">=</span> <span class="p">[]</span>
+
+        <span class="k">if</span> <span class="n">l</span> <span class="o">==</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">:</span>
+            <span class="n">l</span> <span class="o">+=</span> <span class="mi">1</span>
+
+        <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">,</span> <span class="n">l</span><span class="p">):</span>
+            <span class="n">X</span><span class="o">.</span><span class="n">append</span><span class="p">([</span><span class="n">data</span><span class="p">[</span><span class="n">t</span> <span class="o">-</span> <span class="n">k</span> <span class="o">-</span> <span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">)])</span>
+
+        <span class="k">return</span> <span class="n">X</span>
+
+    <span class="k">def</span> <span class="nf">_prepare_xy</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">):</span>
+        <span class="n">l</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+        <span class="n">X</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="n">Y</span> <span class="o">=</span> <span class="p">[]</span>
+
+        <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">,</span> <span class="n">l</span><span class="p">):</span>
+            <span class="n">X</span><span class="o">.</span><span class="n">append</span><span class="p">([</span><span class="n">data</span><span class="p">[</span><span class="n">t</span> <span class="o">-</span> <span class="n">k</span> <span class="o">-</span> <span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">)])</span>
+            <span class="n">Y</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="n">t</span><span class="p">])</span>
+
+        <span class="k">return</span> <span class="p">(</span><span class="n">X</span><span class="p">,</span><span class="n">Y</span><span class="p">)</span>
+
+<div class="viewcode-block" id="KNearestNeighbors.train"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.knn.KNearestNeighbors.train">[docs]</a>    <span class="k">def</span> <span class="nf">train</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="n">X</span><span class="p">,</span><span class="n">Y</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_prepare_xy</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">kdtree</span> <span class="o">=</span> <span class="n">KDTree</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">X</span><span class="p">))</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">values</span> <span class="o">=</span> <span class="n">Y</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">shortname</span> <span class="o">=</span> <span class="s2">&quot;kNN(</span><span class="si">{}</span><span class="s2">)-</span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">alpha</span><span class="p">)</span></div>
+
+<div class="viewcode-block" id="KNearestNeighbors.knn"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.knn.KNearestNeighbors.knn">[docs]</a>    <span class="k">def</span> <span class="nf">knn</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sample</span><span class="p">):</span>
+        <span class="n">X</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_prepare_x</span><span class="p">(</span><span class="n">sample</span><span class="p">)</span>
+        <span class="n">_</span><span class="p">,</span> <span class="n">ix</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">kdtree</span><span class="o">.</span><span class="n">query</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">X</span><span class="p">),</span> <span class="bp">self</span><span class="o">.</span><span class="n">k</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">values</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">ix</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span> <span class="p">]</span></div>
+
+<div class="viewcode-block" id="KNearestNeighbors.forecast"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.knn.KNearestNeighbors.forecast">[docs]</a>    <span class="k">def</span> <span class="nf">forecast</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="n">l</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+        <span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">,</span> <span class="n">l</span><span class="o">+</span><span class="p">(</span><span class="mi">1</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span> <span class="o">==</span> <span class="n">l</span> <span class="k">else</span> <span class="mi">0</span><span class="p">)):</span>
+
+            <span class="n">sample</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="n">k</span><span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span> <span class="p">:</span> <span class="n">k</span><span class="p">]</span>
+
+            <span class="n">forecasts</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">knn</span><span class="p">(</span><span class="n">sample</span><span class="p">)</span>
+
+            <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">nanmean</span><span class="p">(</span><span class="n">forecasts</span><span class="p">))</span>
+
+        <span class="k">return</span> <span class="n">ret</span></div>
+
+<div class="viewcode-block" id="KNearestNeighbors.forecast_interval"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.knn.KNearestNeighbors.forecast_interval">[docs]</a>    <span class="k">def</span> <span class="nf">forecast_interval</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+
+        <span class="n">alpha</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;alpha&#39;</span><span class="p">,</span><span class="bp">self</span><span class="o">.</span><span class="n">alpha</span><span class="p">)</span>
+
+        <span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">)):</span>
+
+            <span class="n">sample</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="n">k</span><span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span> <span class="p">:</span> <span class="n">k</span><span class="p">]</span>
+
+            <span class="n">forecasts</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">knn</span><span class="p">(</span><span class="n">sample</span><span class="p">)</span>
+
+            <span class="n">i</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">percentile</span><span class="p">(</span><span class="n">forecasts</span><span class="p">,</span> <span class="p">[</span><span class="n">alpha</span><span class="o">*</span><span class="mi">100</span><span class="p">,</span> <span class="p">(</span><span class="mi">1</span><span class="o">-</span><span class="n">alpha</span><span class="p">)</span><span class="o">*</span><span class="mi">100</span><span class="p">])</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
+            <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">i</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">ret</span></div>
+
+<div class="viewcode-block" id="KNearestNeighbors.forecast_ahead_interval"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.knn.KNearestNeighbors.forecast_ahead_interval">[docs]</a>    <span class="k">def</span> <span class="nf">forecast_ahead_interval</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">steps</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="n">alpha</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;alpha&#39;</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">alpha</span><span class="p">)</span>
+
+        <span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
+
+        <span class="n">start</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;start&#39;</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">)</span>
+
+        <span class="n">sample</span> <span class="o">=</span> <span class="p">[[</span><span class="n">k</span><span class="p">]</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">data</span><span class="p">[</span><span class="n">start</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">:</span> <span class="n">start</span><span class="p">]]</span>
+
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">,</span> <span class="n">steps</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">):</span>
+            <span class="n">forecasts</span> <span class="o">=</span> <span class="p">[]</span>
+
+            <span class="n">lags</span> <span class="o">=</span> <span class="p">[</span><span class="n">sample</span><span class="p">[</span><span class="n">k</span> <span class="o">-</span> <span class="n">i</span> <span class="o">-</span> <span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">)]</span>
+
+            <span class="c1"># Trace the possible paths</span>
+            <span class="k">for</span> <span class="n">path</span> <span class="ow">in</span> <span class="n">product</span><span class="p">(</span><span class="o">*</span><span class="n">lags</span><span class="p">):</span>
+                <span class="n">forecasts</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">knn</span><span class="p">(</span><span class="n">path</span><span class="p">))</span>
+
+            <span class="n">sample</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">sampler</span><span class="p">(</span><span class="n">forecasts</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="o">.</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">),</span> <span class="n">bounds</span><span class="o">=</span><span class="kc">True</span><span class="p">))</span>
+
+            <span class="n">interval</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">percentile</span><span class="p">(</span><span class="n">forecasts</span><span class="p">,</span> <span class="p">[</span><span class="n">alpha</span><span class="o">*</span><span class="mi">100</span><span class="p">,</span> <span class="p">(</span><span class="mi">1</span><span class="o">-</span><span class="n">alpha</span><span class="p">)</span><span class="o">*</span><span class="mi">100</span><span class="p">])</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
+
+            <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">interval</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">ret</span></div>
+
+<div class="viewcode-block" id="KNearestNeighbors.forecast_distribution"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.knn.KNearestNeighbors.forecast_distribution">[docs]</a>    <span class="k">def</span> <span class="nf">forecast_distribution</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
+
+        <span class="n">smooth</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;smooth&quot;</span><span class="p">,</span> <span class="s2">&quot;histogram&quot;</span><span class="p">)</span>
+
+        <span class="n">uod</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_UoD</span><span class="p">()</span>
+
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">)):</span>
+
+            <span class="n">sample</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="n">k</span><span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span> <span class="p">:</span> <span class="n">k</span><span class="p">]</span>
+
+            <span class="n">forecasts</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">knn</span><span class="p">(</span><span class="n">sample</span><span class="p">)</span>
+
+            <span class="n">dist</span> <span class="o">=</span> <span class="n">ProbabilityDistribution</span><span class="o">.</span><span class="n">ProbabilityDistribution</span><span class="p">(</span><span class="n">smooth</span><span class="p">,</span> <span class="n">uod</span><span class="o">=</span><span class="n">uod</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="n">forecasts</span><span class="p">,</span>
+                                                                   <span class="n">name</span><span class="o">=</span><span class="s2">&quot;&quot;</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+            <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">dist</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">ret</span></div>
+
+<div class="viewcode-block" id="KNearestNeighbors.forecast_ahead_distribution"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.knn.KNearestNeighbors.forecast_ahead_distribution">[docs]</a>    <span class="k">def</span> <span class="nf">forecast_ahead_distribution</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">steps</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="n">smooth</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;smooth&quot;</span><span class="p">,</span> <span class="s2">&quot;histogram&quot;</span><span class="p">)</span>
+
+        <span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
+
+        <span class="n">start</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;start&#39;</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">)</span>
+
+        <span class="n">uod</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_UoD</span><span class="p">()</span>
+
+        <span class="n">sample</span> <span class="o">=</span> <span class="p">[[</span><span class="n">k</span><span class="p">]</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">data</span><span class="p">[</span><span class="n">start</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">:</span> <span class="n">start</span><span class="p">]]</span>
+
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">,</span> <span class="n">steps</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">):</span>
+            <span class="n">forecasts</span> <span class="o">=</span> <span class="p">[]</span>
+
+            <span class="n">lags</span> <span class="o">=</span> <span class="p">[</span><span class="n">sample</span><span class="p">[</span><span class="n">k</span> <span class="o">-</span> <span class="n">i</span> <span class="o">-</span> <span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">)]</span>
+
+            <span class="c1"># Trace the possible paths</span>
+            <span class="k">for</span> <span class="n">path</span> <span class="ow">in</span> <span class="n">product</span><span class="p">(</span><span class="o">*</span><span class="n">lags</span><span class="p">):</span>
+                <span class="n">forecasts</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">knn</span><span class="p">(</span><span class="n">path</span><span class="p">))</span>
+
+            <span class="n">dist</span> <span class="o">=</span> <span class="n">ProbabilityDistribution</span><span class="o">.</span><span class="n">ProbabilityDistribution</span><span class="p">(</span><span class="n">smooth</span><span class="p">,</span> <span class="n">uod</span><span class="o">=</span><span class="n">uod</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="n">forecasts</span><span class="p">,</span>
+                                                                   <span class="n">name</span><span class="o">=</span><span class="s2">&quot;&quot;</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+            <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">dist</span><span class="p">)</span>
+
+            <span class="n">sample</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">sampler</span><span class="p">(</span><span class="n">forecasts</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="o">.</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">),</span> <span class="n">bounds</span><span class="o">=</span><span class="kc">True</span><span class="p">))</span>
+
+        <span class="k">return</span> <span class="n">ret</span></div></div>
+
+
+</pre></div>
+
+            <div class="clearer"></div>
+          </div>
+        </div>
+      </div>
+      <div class="sphinxsidebar" role="navigation" aria-label="main navigation">
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+<script>$('#searchbox').show(0);</script>
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+      <ul>
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+             >index</a></li>
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+          <a href="../../../py-modindex.html" title="Python Module Index"
+             >modules</a> |</li>
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+        <li class="nav-item nav-item-this"><a href="">pyFTS.benchmarks.knn</a></li> 
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+        &#169; Copyright 2018, Machine Intelligence and Data Science Laboratory - UFMG - Brazil.
+      Created using <a href="https://www.sphinx-doc.org/">Sphinx</a> 3.1.2.
+    </div>
+  </body>
+</html>
\ No newline at end of file
diff --git a/docs/build/html/_modules/pyFTS/benchmarks/naive.html b/docs/build/html/_modules/pyFTS/benchmarks/naive.html
new file mode 100644
index 0000000..0547925
--- /dev/null
+++ b/docs/build/html/_modules/pyFTS/benchmarks/naive.html
@@ -0,0 +1,118 @@
+
+
+<!doctype html>
+
+<html>
+  <head>
+    <meta charset="utf-8" />
+    <meta name="viewport" content="width=device-width, initial-scale=1.0"><script type="text/javascript">
+
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+    <title>pyFTS.benchmarks.naive &#8212; pyFTS 1.6 documentation</title>
+    <link rel="stylesheet" href="../../../_static/bizstyle.css" type="text/css" />
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+          <li class="nav-item nav-item-1"><a href="../../index.html" accesskey="U">Module code</a> &#187;</li>
+        <li class="nav-item nav-item-this"><a href="">pyFTS.benchmarks.naive</a></li> 
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+
+    <div class="document">
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+        <div class="bodywrapper">
+          <div class="body" role="main">
+            
+  <h1>Source code for pyFTS.benchmarks.naive</h1><div class="highlight"><pre>
+<span></span><span class="ch">#!/usr/bin/python</span>
+<span class="c1"># -*- coding: utf8 -*-</span>
+
+<span class="kn">from</span> <span class="nn">pyFTS.common</span> <span class="kn">import</span> <span class="n">fts</span>
+
+
+<div class="viewcode-block" id="Naive"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.naive.Naive">[docs]</a><span class="k">class</span> <span class="nc">Naive</span><span class="p">(</span><span class="n">fts</span><span class="o">.</span><span class="n">FTS</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;Naïve Forecasting method&quot;&quot;&quot;</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">(</span><span class="n">Naive</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">order</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s2">&quot;Naive&quot;</span><span class="p">,</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="s2">&quot;Naïve Model&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">detail</span> <span class="o">=</span> <span class="s2">&quot;Naïve Model&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">benchmark_only</span> <span class="o">=</span> <span class="kc">True</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">is_high_order</span> <span class="o">=</span> <span class="kc">False</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">uod_clip</span> <span class="o">=</span> <span class="kc">False</span>
+
+<div class="viewcode-block" id="Naive.forecast"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.naive.Naive.forecast">[docs]</a>    <span class="k">def</span> <span class="nf">forecast</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="k">return</span> <span class="n">data</span></div></div>
+
+</pre></div>
+
+            <div class="clearer"></div>
+          </div>
+        </div>
+      </div>
+      <div class="sphinxsidebar" role="navigation" aria-label="main navigation">
+        <div class="sphinxsidebarwrapper">
+<div id="searchbox" style="display: none" role="search">
+  <h3 id="searchlabel">Quick search</h3>
+    <div class="searchformwrapper">
+    <form class="search" action="../../../search.html" method="get">
+      <input type="text" name="q" aria-labelledby="searchlabel" />
+      <input type="submit" value="Go" />
+    </form>
+    </div>
+</div>
+<script>$('#searchbox').show(0);</script>
+        </div>
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+      <div class="clearer"></div>
+    </div>
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+      <h3>Navigation</h3>
+      <ul>
+        <li class="right" style="margin-right: 10px">
+          <a href="../../../genindex.html" title="General Index"
+             >index</a></li>
+        <li class="right" >
+          <a href="../../../py-modindex.html" title="Python Module Index"
+             >modules</a> |</li>
+        <li class="nav-item nav-item-0"><a href="../../../index.html">pyFTS 1.6 documentation</a> &#187;</li>
+          <li class="nav-item nav-item-1"><a href="../../index.html" >Module code</a> &#187;</li>
+        <li class="nav-item nav-item-this"><a href="">pyFTS.benchmarks.naive</a></li> 
+      </ul>
+    </div>
+    <div class="footer" role="contentinfo">
+        &#169; Copyright 2018, Machine Intelligence and Data Science Laboratory - UFMG - Brazil.
+      Created using <a href="https://www.sphinx-doc.org/">Sphinx</a> 3.1.2.
+    </div>
+  </body>
+</html>
\ No newline at end of file
diff --git a/docs/build/html/_modules/pyFTS/benchmarks/quantreg.html b/docs/build/html/_modules/pyFTS/benchmarks/quantreg.html
new file mode 100644
index 0000000..f50902a
--- /dev/null
+++ b/docs/build/html/_modules/pyFTS/benchmarks/quantreg.html
@@ -0,0 +1,267 @@
+
+
+<!doctype html>
+
+<html>
+  <head>
+    <meta charset="utf-8" />
+    <meta name="viewport" content="width=device-width, initial-scale=1.0"><script type="text/javascript">
+
+      var _gaq = _gaq || [];
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+    <title>pyFTS.benchmarks.quantreg &#8212; pyFTS 1.6 documentation</title>
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+  <h1>Source code for pyFTS.benchmarks.quantreg</h1><div class="highlight"><pre>
+<span></span><span class="ch">#!/usr/bin/python</span>
+<span class="c1"># -*- coding: utf8 -*-</span>
+
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+<span class="kn">from</span> <span class="nn">statsmodels.regression.quantile_regression</span> <span class="kn">import</span> <span class="n">QuantReg</span>
+<span class="kn">from</span> <span class="nn">statsmodels.tsa.tsatools</span> <span class="kn">import</span> <span class="n">lagmat</span>
+<span class="kn">from</span> <span class="nn">pyFTS.common</span> <span class="kn">import</span> <span class="n">SortedCollection</span><span class="p">,</span> <span class="n">fts</span>
+<span class="kn">from</span> <span class="nn">pyFTS.probabilistic</span> <span class="kn">import</span> <span class="n">ProbabilityDistribution</span>
+
+
+<div class="viewcode-block" id="QuantileRegression"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.quantreg.QuantileRegression">[docs]</a><span class="k">class</span> <span class="nc">QuantileRegression</span><span class="p">(</span><span class="n">fts</span><span class="o">.</span><span class="n">FTS</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;Façade for statsmodels.regression.quantile_regression&quot;&quot;&quot;</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">(</span><span class="n">QuantileRegression</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="s2">&quot;QAR&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">detail</span> <span class="o">=</span> <span class="s2">&quot;Quantile Regression&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">is_high_order</span> <span class="o">=</span> <span class="kc">True</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">has_point_forecasting</span> <span class="o">=</span> <span class="kc">True</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">has_interval_forecasting</span> <span class="o">=</span> <span class="kc">True</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">has_probability_forecasting</span> <span class="o">=</span> <span class="kc">True</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">uod_clip</span> <span class="o">=</span> <span class="kc">False</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">benchmark_only</span> <span class="o">=</span> <span class="kc">True</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">min_order</span> <span class="o">=</span> <span class="mi">1</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">alpha</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;alpha&quot;</span><span class="p">,</span> <span class="mf">0.05</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">dist</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;dist&quot;</span><span class="p">,</span> <span class="kc">False</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">upper_qt</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">mean_qt</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">lower_qt</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">dist_qt</span> <span class="o">=</span> <span class="kc">None</span>
+
+<div class="viewcode-block" id="QuantileRegression.train"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.quantreg.QuantileRegression.train">[docs]</a>    <span class="k">def</span> <span class="nf">train</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">indexer</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">):</span>
+            <span class="n">data</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">indexer</span><span class="o">.</span><span class="n">get_data</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+
+        <span class="n">lagdata</span><span class="p">,</span> <span class="n">ndata</span> <span class="o">=</span> <span class="n">lagmat</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">maxlag</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">,</span> <span class="n">trim</span><span class="o">=</span><span class="s2">&quot;both&quot;</span><span class="p">,</span> <span class="n">original</span><span class="o">=</span><span class="s1">&#39;sep&#39;</span><span class="p">)</span>
+
+        <span class="n">mqt</span> <span class="o">=</span> <span class="n">QuantReg</span><span class="p">(</span><span class="n">ndata</span><span class="p">,</span> <span class="n">lagdata</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="mf">0.5</span><span class="p">)</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">alpha</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">uqt</span> <span class="o">=</span> <span class="n">QuantReg</span><span class="p">(</span><span class="n">ndata</span><span class="p">,</span> <span class="n">lagdata</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">alpha</span><span class="p">)</span>
+            <span class="n">lqt</span> <span class="o">=</span> <span class="n">QuantReg</span><span class="p">(</span><span class="n">ndata</span><span class="p">,</span> <span class="n">lagdata</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">alpha</span><span class="p">)</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">mean_qt</span> <span class="o">=</span> <span class="p">[</span><span class="n">k</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">mqt</span><span class="o">.</span><span class="n">params</span><span class="p">]</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">alpha</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">upper_qt</span> <span class="o">=</span> <span class="p">[</span><span class="n">k</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">uqt</span><span class="o">.</span><span class="n">params</span><span class="p">]</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">lower_qt</span> <span class="o">=</span> <span class="p">[</span><span class="n">k</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">lqt</span><span class="o">.</span><span class="n">params</span><span class="p">]</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">dist</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">dist_qt</span> <span class="o">=</span> <span class="p">[]</span>
+            <span class="k">for</span> <span class="n">alpha</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mf">0.05</span><span class="p">,</span><span class="mf">0.5</span><span class="p">,</span><span class="mf">0.05</span><span class="p">):</span>
+                <span class="n">lqt</span> <span class="o">=</span> <span class="n">QuantReg</span><span class="p">(</span><span class="n">ndata</span><span class="p">,</span> <span class="n">lagdata</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">alpha</span><span class="p">)</span>
+                <span class="n">uqt</span> <span class="o">=</span> <span class="n">QuantReg</span><span class="p">(</span><span class="n">ndata</span><span class="p">,</span> <span class="n">lagdata</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">alpha</span><span class="p">)</span>
+                <span class="n">lo_qt</span> <span class="o">=</span> <span class="p">[</span><span class="n">k</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">lqt</span><span class="o">.</span><span class="n">params</span><span class="p">]</span>
+                <span class="n">up_qt</span> <span class="o">=</span> <span class="p">[</span><span class="n">k</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">uqt</span><span class="o">.</span><span class="n">params</span><span class="p">]</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">dist_qt</span><span class="o">.</span><span class="n">append</span><span class="p">([</span><span class="n">lo_qt</span><span class="p">,</span> <span class="n">up_qt</span><span class="p">])</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">shortname</span> <span class="o">=</span> <span class="s2">&quot;QAR(</span><span class="si">{}</span><span class="s2">)-</span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">,</span><span class="bp">self</span><span class="o">.</span><span class="n">alpha</span><span class="p">)</span></div>
+
+<div class="viewcode-block" id="QuantileRegression.linearmodel"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.quantreg.QuantileRegression.linearmodel">[docs]</a>    <span class="k">def</span> <span class="nf">linearmodel</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span><span class="n">data</span><span class="p">,</span><span class="n">params</span><span class="p">):</span>
+        <span class="c1">#return params[0] + sum([ data[k] * params[k+1] for k in np.arange(0, self.order) ])</span>
+        <span class="k">return</span> <span class="nb">sum</span><span class="p">([</span><span class="n">data</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">*</span> <span class="n">params</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">)])</span></div>
+
+<div class="viewcode-block" id="QuantileRegression.point_to_interval"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.quantreg.QuantileRegression.point_to_interval">[docs]</a>    <span class="k">def</span> <span class="nf">point_to_interval</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">lo_params</span><span class="p">,</span> <span class="n">up_params</span><span class="p">):</span>
+        <span class="n">lo</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">linearmodel</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">lo_params</span><span class="p">)</span>
+        <span class="n">up</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">linearmodel</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">up_params</span><span class="p">)</span>
+        <span class="k">return</span> <span class="p">[</span><span class="n">lo</span><span class="p">,</span> <span class="n">up</span><span class="p">]</span></div>
+
+<div class="viewcode-block" id="QuantileRegression.interval_to_interval"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.quantreg.QuantileRegression.interval_to_interval">[docs]</a>    <span class="k">def</span> <span class="nf">interval_to_interval</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">lo_params</span><span class="p">,</span> <span class="n">up_params</span><span class="p">):</span>
+        <span class="n">lo</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">linearmodel</span><span class="p">([</span><span class="n">k</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">data</span><span class="p">],</span> <span class="n">lo_params</span><span class="p">)</span>
+        <span class="n">up</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">linearmodel</span><span class="p">([</span><span class="n">k</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">data</span><span class="p">],</span> <span class="n">up_params</span><span class="p">)</span>
+        <span class="k">return</span> <span class="p">[</span><span class="n">lo</span><span class="p">,</span> <span class="n">up</span><span class="p">]</span></div>
+
+<div class="viewcode-block" id="QuantileRegression.forecast"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.quantreg.QuantileRegression.forecast">[docs]</a>    <span class="k">def</span> <span class="nf">forecast</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ndata</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+
+        <span class="n">l</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">ndata</span><span class="p">)</span>
+
+        <span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
+
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">,</span> <span class="n">l</span><span class="o">+</span><span class="mi">1</span><span class="p">):</span>   <span class="c1">#+1 to forecast one step ahead given all available lags</span>
+            <span class="n">sample</span> <span class="o">=</span> <span class="n">ndata</span><span class="p">[</span><span class="n">k</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span> <span class="p">:</span> <span class="n">k</span><span class="p">]</span>
+
+            <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">linearmodel</span><span class="p">(</span><span class="n">sample</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">mean_qt</span><span class="p">))</span>
+
+        <span class="k">return</span> <span class="n">ret</span></div>
+
+<div class="viewcode-block" id="QuantileRegression.forecast_interval"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.quantreg.QuantileRegression.forecast_interval">[docs]</a>    <span class="k">def</span> <span class="nf">forecast_interval</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ndata</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+
+        <span class="n">l</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">ndata</span><span class="p">)</span>
+
+        <span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
+
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span> <span class="p">,</span> <span class="n">l</span><span class="p">):</span>
+            <span class="n">sample</span> <span class="o">=</span> <span class="n">ndata</span><span class="p">[</span><span class="n">k</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">:</span> <span class="n">k</span><span class="p">]</span>
+            <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">point_to_interval</span><span class="p">(</span><span class="n">sample</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">lower_qt</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">upper_qt</span><span class="p">))</span>
+
+        <span class="k">return</span> <span class="n">ret</span></div>
+
+<div class="viewcode-block" id="QuantileRegression.forecast_ahead_interval"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.quantreg.QuantileRegression.forecast_ahead_interval">[docs]</a>    <span class="k">def</span> <span class="nf">forecast_ahead_interval</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ndata</span><span class="p">,</span> <span class="n">steps</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+
+        <span class="n">smoothing</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;smoothing&quot;</span><span class="p">,</span> <span class="mf">0.01</span><span class="p">)</span>
+
+        <span class="n">l</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">ndata</span><span class="p">)</span>
+
+        <span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
+
+        <span class="n">nmeans</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">forecast_ahead</span><span class="p">(</span><span class="n">ndata</span><span class="p">,</span> <span class="n">steps</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">):</span>
+            <span class="n">nmeans</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="n">k</span><span class="p">,</span><span class="n">ndata</span><span class="p">[</span><span class="n">k</span><span class="p">])</span>
+
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">,</span> <span class="n">steps</span><span class="o">+</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">):</span>
+            <span class="n">intl</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">point_to_interval</span><span class="p">(</span><span class="n">nmeans</span><span class="p">[</span><span class="n">k</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">:</span> <span class="n">k</span><span class="p">],</span> <span class="bp">self</span><span class="o">.</span><span class="n">lower_qt</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">upper_qt</span><span class="p">)</span>
+
+            <span class="n">tmpk</span> <span class="o">=</span> <span class="n">k</span><span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span>
+
+            <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">([</span><span class="n">intl</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">*</span><span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="p">(</span><span class="n">tmpk</span><span class="o">*</span><span class="n">smoothing</span><span class="p">)),</span> <span class="n">intl</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">*</span><span class="p">(</span><span class="mi">1</span> <span class="o">+</span> <span class="p">(</span><span class="n">tmpk</span><span class="o">*</span><span class="n">smoothing</span><span class="p">))])</span>
+
+        <span class="k">return</span> <span class="n">ret</span><span class="p">[</span><span class="o">-</span><span class="n">steps</span><span class="p">:]</span></div>
+
+<div class="viewcode-block" id="QuantileRegression.forecast_distribution"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.quantreg.QuantileRegression.forecast_distribution">[docs]</a>    <span class="k">def</span> <span class="nf">forecast_distribution</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ndata</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+
+        <span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
+
+        <span class="n">l</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">ndata</span><span class="p">)</span>
+
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">,</span> <span class="n">l</span> <span class="o">+</span> <span class="mi">1</span><span class="p">):</span>
+            <span class="n">dist</span> <span class="o">=</span> <span class="n">ProbabilityDistribution</span><span class="o">.</span><span class="n">ProbabilityDistribution</span><span class="p">(</span><span class="nb">type</span><span class="o">=</span><span class="s2">&quot;histogram&quot;</span><span class="p">,</span>
+                                                                   <span class="n">uod</span><span class="o">=</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">original_min</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">original_max</span><span class="p">])</span>
+            <span class="n">intervals</span> <span class="o">=</span> <span class="p">[]</span>
+            <span class="k">for</span> <span class="n">qt</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">dist_qt</span><span class="p">:</span>
+                <span class="n">sample</span> <span class="o">=</span> <span class="n">ndata</span><span class="p">[</span><span class="n">k</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">:</span> <span class="n">k</span><span class="p">]</span>
+                <span class="n">intl</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">point_to_interval</span><span class="p">(</span><span class="n">sample</span><span class="p">,</span> <span class="n">qt</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">qt</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
+                <span class="n">intervals</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">intl</span><span class="p">)</span>
+
+            <span class="n">dist</span><span class="o">.</span><span class="n">append_interval</span><span class="p">(</span><span class="n">intervals</span><span class="p">)</span>
+
+            <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">dist</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">ret</span></div>
+
+<div class="viewcode-block" id="QuantileRegression.forecast_ahead_distribution"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.quantreg.QuantileRegression.forecast_ahead_distribution">[docs]</a>    <span class="k">def</span> <span class="nf">forecast_ahead_distribution</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ndata</span><span class="p">,</span> <span class="n">steps</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="n">smoothing</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;smoothing&quot;</span><span class="p">,</span> <span class="mf">0.01</span><span class="p">)</span>
+
+        <span class="n">l</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">ndata</span><span class="p">)</span>
+
+        <span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
+
+        <span class="n">nmeans</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">forecast_ahead</span><span class="p">(</span><span class="n">ndata</span><span class="p">,</span> <span class="n">steps</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">):</span>
+            <span class="n">nmeans</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="n">ndata</span><span class="p">[</span><span class="n">k</span><span class="p">])</span>
+
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">,</span> <span class="n">steps</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">):</span>
+            <span class="n">dist</span> <span class="o">=</span> <span class="n">ProbabilityDistribution</span><span class="o">.</span><span class="n">ProbabilityDistribution</span><span class="p">(</span><span class="nb">type</span><span class="o">=</span><span class="s2">&quot;histogram&quot;</span><span class="p">,</span>
+                                                                   <span class="n">uod</span><span class="o">=</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">original_min</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">original_max</span><span class="p">])</span>
+
+            <span class="n">intervals</span> <span class="o">=</span> <span class="p">[[</span><span class="n">nmeans</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">],</span> <span class="n">nmeans</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">]]]</span>
+            <span class="k">for</span> <span class="n">qt</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">dist_qt</span><span class="p">:</span>
+                <span class="n">intl1</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">point_to_interval</span><span class="p">(</span><span class="n">nmeans</span><span class="p">[</span><span class="n">k</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">:</span> <span class="n">k</span><span class="p">],</span> <span class="n">qt</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">qt</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
+                <span class="n">tmpk</span> <span class="o">=</span> <span class="n">k</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span>
+                <span class="n">intl2</span> <span class="o">=</span> <span class="p">[</span><span class="n">intl1</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="p">(</span><span class="n">tmpk</span> <span class="o">*</span> <span class="n">smoothing</span><span class="p">)),</span> <span class="n">intl1</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="p">(</span><span class="mi">1</span> <span class="o">+</span> <span class="p">(</span><span class="n">tmpk</span> <span class="o">*</span> <span class="n">smoothing</span><span class="p">))]</span>
+                <span class="n">intervals</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">intl2</span><span class="p">)</span>
+            <span class="n">dist</span><span class="o">.</span><span class="n">append_interval</span><span class="p">(</span><span class="n">intervals</span><span class="p">)</span>
+
+            <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">dist</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">ret</span></div></div>
+
+</pre></div>
+
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+
+
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+            
+  <h1>Source code for pyFTS.common.Util</h1><div class="highlight"><pre>
+<span></span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">Common facilities for pyFTS</span>
+<span class="sd">&quot;&quot;&quot;</span>
+
+<span class="kn">import</span> <span class="nn">time</span>
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+<span class="kn">import</span> <span class="nn">matplotlib.cm</span> <span class="k">as</span> <span class="nn">cmx</span>
+<span class="kn">import</span> <span class="nn">matplotlib.colors</span> <span class="k">as</span> <span class="nn">pltcolors</span>
+<span class="kn">from</span> <span class="nn">pyFTS.probabilistic</span> <span class="kn">import</span> <span class="n">ProbabilityDistribution</span>
+<span class="kn">from</span> <span class="nn">pyFTS.common</span> <span class="kn">import</span> <span class="n">Transformations</span>
+
+
+<div class="viewcode-block" id="plot_compared_intervals_ahead"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.Util.plot_compared_intervals_ahead">[docs]</a><span class="k">def</span> <span class="nf">plot_compared_intervals_ahead</span><span class="p">(</span><span class="n">original</span><span class="p">,</span> <span class="n">models</span><span class="p">,</span> <span class="n">colors</span><span class="p">,</span> <span class="n">distributions</span><span class="p">,</span> <span class="n">time_from</span><span class="p">,</span> <span class="n">time_to</span><span class="p">,</span> <span class="n">intervals</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
+                               <span class="n">save</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">file</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">tam</span><span class="o">=</span><span class="p">[</span><span class="mi">20</span><span class="p">,</span> <span class="mi">5</span><span class="p">],</span> <span class="n">resolution</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+                               <span class="n">cmap</span><span class="o">=</span><span class="s1">&#39;Blues&#39;</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mf">1.5</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Plot the forecasts of several one step ahead models, by point or by interval</span>
+
+<span class="sd">    :param original: Original time series data (list)</span>
+<span class="sd">    :param models: List of models to compare</span>
+<span class="sd">    :param colors: List of models colors</span>
+<span class="sd">    :param distributions: True to plot a distribution</span>
+<span class="sd">    :param time_from: index of data poit to start the ahead forecasting</span>
+<span class="sd">    :param time_to: number of steps ahead to forecast</span>
+<span class="sd">    :param interpol: Fill space between distribution plots</span>
+<span class="sd">    :param save: Save the picture on file</span>
+<span class="sd">    :param file: Filename to save the picture</span>
+<span class="sd">    :param tam: Size of the picture</span>
+<span class="sd">    :param resolution:</span>
+<span class="sd">    :param cmap: Color map to be used on distribution plot</span>
+<span class="sd">    :param option: Distribution type to be passed for models</span>
+<span class="sd">    :return:</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="n">tam</span><span class="p">)</span>
+    <span class="n">ax</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">add_subplot</span><span class="p">(</span><span class="mi">111</span><span class="p">)</span>
+
+    <span class="n">cm</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">get_cmap</span><span class="p">(</span><span class="n">cmap</span><span class="p">)</span>
+    <span class="n">cNorm</span> <span class="o">=</span> <span class="n">pltcolors</span><span class="o">.</span><span class="n">Normalize</span><span class="p">(</span><span class="n">vmin</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">vmax</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+    <span class="n">scalarMap</span> <span class="o">=</span> <span class="n">cmx</span><span class="o">.</span><span class="n">ScalarMappable</span><span class="p">(</span><span class="n">norm</span><span class="o">=</span><span class="n">cNorm</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="n">cm</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="n">resolution</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> <span class="n">resolution</span> <span class="o">=</span> <span class="p">(</span><span class="nb">max</span><span class="p">(</span><span class="n">original</span><span class="p">)</span> <span class="o">-</span> <span class="nb">min</span><span class="p">(</span><span class="n">original</span><span class="p">))</span> <span class="o">/</span> <span class="mi">100</span>
+
+    <span class="n">mi</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="n">ma</span> <span class="o">=</span> <span class="p">[]</span>
+
+    <span class="k">for</span> <span class="n">count</span><span class="p">,</span> <span class="n">fts</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">models</span><span class="p">,</span> <span class="n">start</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
+        <span class="k">if</span> <span class="n">fts</span><span class="o">.</span><span class="n">has_probability_forecasting</span> <span class="ow">and</span> <span class="n">distributions</span><span class="p">[</span><span class="n">count</span><span class="p">]:</span>
+            <span class="n">density</span> <span class="o">=</span> <span class="n">fts</span><span class="o">.</span><span class="n">forecast_ahead_distribution</span><span class="p">(</span><span class="n">original</span><span class="p">[</span><span class="n">time_from</span> <span class="o">-</span> <span class="n">fts</span><span class="o">.</span><span class="n">order</span><span class="p">:</span><span class="n">time_from</span><span class="p">],</span> <span class="n">time_to</span><span class="p">,</span>
+                                                      <span class="n">resolution</span><span class="o">=</span><span class="n">resolution</span><span class="p">)</span>
+
+            <span class="c1">#plot_density_scatter(ax, cmap, density, fig, resolution, time_from, time_to)</span>
+            <span class="n">plot_density_rectange</span><span class="p">(</span><span class="n">ax</span><span class="p">,</span> <span class="n">cm</span><span class="p">,</span> <span class="n">density</span><span class="p">,</span> <span class="n">fig</span><span class="p">,</span> <span class="n">resolution</span><span class="p">,</span> <span class="n">time_from</span><span class="p">,</span> <span class="n">time_to</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">fts</span><span class="o">.</span><span class="n">has_interval_forecasting</span> <span class="ow">and</span> <span class="n">intervals</span><span class="p">:</span>
+            <span class="n">forecasts</span> <span class="o">=</span> <span class="n">fts</span><span class="o">.</span><span class="n">forecast_ahead_interval</span><span class="p">(</span><span class="n">original</span><span class="p">[</span><span class="n">time_from</span> <span class="o">-</span> <span class="n">fts</span><span class="o">.</span><span class="n">order</span><span class="p">:</span><span class="n">time_from</span><span class="p">],</span> <span class="n">time_to</span><span class="p">)</span>
+            <span class="n">lower</span> <span class="o">=</span> <span class="p">[</span><span class="n">kk</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">kk</span> <span class="ow">in</span> <span class="n">forecasts</span><span class="p">]</span>
+            <span class="n">upper</span> <span class="o">=</span> <span class="p">[</span><span class="n">kk</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">kk</span> <span class="ow">in</span> <span class="n">forecasts</span><span class="p">]</span>
+            <span class="n">mi</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">min</span><span class="p">(</span><span class="n">lower</span><span class="p">))</span>
+            <span class="n">ma</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">max</span><span class="p">(</span><span class="n">upper</span><span class="p">))</span>
+            <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">time_from</span> <span class="o">-</span> <span class="n">fts</span><span class="o">.</span><span class="n">order</span><span class="p">):</span>
+                <span class="n">lower</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
+                <span class="n">upper</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
+            <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">lower</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="n">colors</span><span class="p">[</span><span class="n">count</span><span class="p">],</span> <span class="n">label</span><span class="o">=</span><span class="n">fts</span><span class="o">.</span><span class="n">shortname</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="n">linewidth</span><span class="p">)</span>
+            <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">upper</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="n">colors</span><span class="p">[</span><span class="n">count</span><span class="p">],</span> <span class="n">linewidth</span><span class="o">=</span><span class="n">linewidth</span><span class="o">*</span><span class="mf">1.5</span><span class="p">)</span>
+
+    <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">original</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;black&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">&quot;Original&quot;</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="n">linewidth</span><span class="o">*</span><span class="mf">1.5</span><span class="p">)</span>
+    <span class="n">handles0</span><span class="p">,</span> <span class="n">labels0</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">get_legend_handles_labels</span><span class="p">()</span>
+    <span class="k">if</span> <span class="kc">True</span> <span class="ow">in</span> <span class="n">distributions</span><span class="p">:</span>
+        <span class="n">lgd</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">handles0</span><span class="p">,</span> <span class="n">labels0</span><span class="p">,</span> <span class="n">loc</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">lgd</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">handles0</span><span class="p">,</span> <span class="n">labels0</span><span class="p">,</span> <span class="n">loc</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">bbox_to_anchor</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>
+    <span class="n">_mi</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="n">mi</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">_mi</span> <span class="o">&lt;</span> <span class="mi">0</span><span class="p">:</span>
+        <span class="n">_mi</span> <span class="o">*=</span> <span class="mf">1.1</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">_mi</span> <span class="o">*=</span> <span class="mf">0.9</span>
+    <span class="n">_ma</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">ma</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">_ma</span> <span class="o">&lt;</span> <span class="mi">0</span><span class="p">:</span>
+        <span class="n">_ma</span> <span class="o">*=</span> <span class="mf">0.9</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">_ma</span> <span class="o">*=</span> <span class="mf">1.1</span>
+
+    <span class="n">ax</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">([</span><span class="n">_mi</span><span class="p">,</span> <span class="n">_ma</span><span class="p">])</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;F(T)&#39;</span><span class="p">)</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;T&#39;</span><span class="p">)</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">original</span><span class="p">)])</span>
+
+    <span class="n">show_and_save_image</span><span class="p">(</span><span class="n">fig</span><span class="p">,</span> <span class="n">file</span><span class="p">,</span> <span class="n">save</span><span class="p">,</span> <span class="n">lgd</span><span class="o">=</span><span class="n">lgd</span><span class="p">)</span></div>
+
+
+
+<div class="viewcode-block" id="plot_density_rectange"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.Util.plot_density_rectange">[docs]</a><span class="k">def</span> <span class="nf">plot_density_rectange</span><span class="p">(</span><span class="n">ax</span><span class="p">,</span> <span class="n">cmap</span><span class="p">,</span> <span class="n">density</span><span class="p">,</span> <span class="n">fig</span><span class="p">,</span> <span class="n">resolution</span><span class="p">,</span> <span class="n">time_from</span><span class="p">,</span> <span class="n">time_to</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Auxiliar function to plot_compared_intervals_ahead</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="kn">from</span> <span class="nn">matplotlib.patches</span> <span class="kn">import</span> <span class="n">Rectangle</span>
+    <span class="kn">from</span> <span class="nn">matplotlib.collections</span> <span class="kn">import</span> <span class="n">PatchCollection</span>
+    <span class="n">patches</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="n">colors</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">density</span><span class="o">.</span><span class="n">index</span><span class="p">:</span>
+        <span class="k">for</span> <span class="n">y</span> <span class="ow">in</span> <span class="n">density</span><span class="o">.</span><span class="n">columns</span><span class="p">:</span>
+            <span class="n">s</span> <span class="o">=</span> <span class="n">Rectangle</span><span class="p">((</span><span class="n">time_from</span> <span class="o">+</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">),</span> <span class="mi">1</span><span class="p">,</span> <span class="n">resolution</span><span class="p">,</span> <span class="n">fill</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">lw</span> <span class="o">=</span> <span class="mi">0</span><span class="p">)</span>
+            <span class="n">patches</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">s</span><span class="p">)</span>
+            <span class="n">colors</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">density</span><span class="p">[</span><span class="n">y</span><span class="p">][</span><span class="n">x</span><span class="p">]</span><span class="o">*</span><span class="mi">5</span><span class="p">)</span>
+    <span class="n">pc</span> <span class="o">=</span> <span class="n">PatchCollection</span><span class="p">(</span><span class="n">patches</span><span class="o">=</span><span class="n">patches</span><span class="p">,</span> <span class="n">match_original</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+    <span class="n">pc</span><span class="o">.</span><span class="n">set_clim</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">])</span>
+    <span class="n">pc</span><span class="o">.</span><span class="n">set_cmap</span><span class="p">(</span><span class="n">cmap</span><span class="p">)</span>
+    <span class="n">pc</span><span class="o">.</span><span class="n">set_array</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">colors</span><span class="p">))</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">add_collection</span><span class="p">(</span><span class="n">pc</span><span class="p">)</span>
+    <span class="n">cb</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">colorbar</span><span class="p">(</span><span class="n">pc</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">)</span>
+    <span class="n">cb</span><span class="o">.</span><span class="n">set_label</span><span class="p">(</span><span class="s1">&#39;Density&#39;</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="plot_probability_distributions"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.Util.plot_probability_distributions">[docs]</a><span class="k">def</span> <span class="nf">plot_probability_distributions</span><span class="p">(</span><span class="n">pmfs</span><span class="p">,</span> <span class="n">lcolors</span><span class="p">,</span> <span class="n">tam</span><span class="o">=</span><span class="p">[</span><span class="mi">15</span><span class="p">,</span> <span class="mi">7</span><span class="p">]):</span>
+    <span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="n">tam</span><span class="p">)</span>
+    <span class="n">ax</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">add_subplot</span><span class="p">(</span><span class="mi">111</span><span class="p">)</span>
+
+    <span class="k">for</span> <span class="n">k</span><span class="p">,</span><span class="n">m</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">pmfs</span><span class="p">,</span><span class="n">start</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
+        <span class="n">m</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">ax</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="n">lcolors</span><span class="p">[</span><span class="n">k</span><span class="p">])</span>
+
+    <span class="n">handles0</span><span class="p">,</span> <span class="n">labels0</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">get_legend_handles_labels</span><span class="p">()</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">handles0</span><span class="p">,</span> <span class="n">labels0</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="plot_distribution"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.Util.plot_distribution">[docs]</a><span class="k">def</span> <span class="nf">plot_distribution</span><span class="p">(</span><span class="n">ax</span><span class="p">,</span> <span class="n">cmap</span><span class="p">,</span> <span class="n">probabilitydist</span><span class="p">,</span> <span class="n">fig</span><span class="p">,</span> <span class="n">time_from</span><span class="p">,</span> <span class="n">reference_data</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Plot forecasted ProbabilityDistribution objects on a matplotlib axis</span>
+
+<span class="sd">    :param ax: matplotlib axis</span>
+<span class="sd">    :param cmap: matplotlib colormap name</span>
+<span class="sd">    :param probabilitydist: list of ProbabilityDistribution objects</span>
+<span class="sd">    :param fig: matplotlib figure</span>
+<span class="sd">    :param time_from: starting time (on x axis) to begin the plots</span>
+<span class="sd">    :param reference_data:</span>
+<span class="sd">    :return:</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="kn">from</span> <span class="nn">matplotlib.patches</span> <span class="kn">import</span> <span class="n">Rectangle</span>
+    <span class="kn">from</span> <span class="nn">matplotlib.collections</span> <span class="kn">import</span> <span class="n">PatchCollection</span>
+    <span class="n">patches</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="n">colors</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="k">for</span> <span class="n">ct</span><span class="p">,</span> <span class="n">dt</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">probabilitydist</span><span class="p">):</span>
+        <span class="n">disp</span> <span class="o">=</span> <span class="mf">0.0</span>
+        <span class="k">if</span> <span class="n">reference_data</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">disp</span> <span class="o">=</span> <span class="n">reference_data</span><span class="p">[</span><span class="n">time_from</span><span class="o">+</span><span class="n">ct</span><span class="p">]</span>
+
+        <span class="k">for</span> <span class="n">y</span> <span class="ow">in</span> <span class="n">dt</span><span class="o">.</span><span class="n">bins</span><span class="p">:</span>
+            <span class="n">s</span> <span class="o">=</span> <span class="n">Rectangle</span><span class="p">((</span><span class="n">time_from</span><span class="o">+</span><span class="n">ct</span><span class="p">,</span> <span class="n">y</span><span class="o">+</span><span class="n">disp</span><span class="p">),</span> <span class="mi">1</span><span class="p">,</span> <span class="n">dt</span><span class="o">.</span><span class="n">resolution</span><span class="p">,</span> <span class="n">fill</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">lw</span> <span class="o">=</span> <span class="mi">0</span><span class="p">)</span>
+            <span class="n">patches</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">s</span><span class="p">)</span>
+            <span class="n">colors</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">dt</span><span class="o">.</span><span class="n">density</span><span class="p">(</span><span class="n">y</span><span class="p">))</span>
+    <span class="n">scale</span> <span class="o">=</span> <span class="n">Transformations</span><span class="o">.</span><span class="n">Scale</span><span class="p">()</span>
+    <span class="n">colors</span> <span class="o">=</span> <span class="n">scale</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">colors</span><span class="p">)</span>
+    <span class="n">pc</span> <span class="o">=</span> <span class="n">PatchCollection</span><span class="p">(</span><span class="n">patches</span><span class="o">=</span><span class="n">patches</span><span class="p">,</span> <span class="n">match_original</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+    <span class="n">pc</span><span class="o">.</span><span class="n">set_clim</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">])</span>
+    <span class="n">pc</span><span class="o">.</span><span class="n">set_cmap</span><span class="p">(</span><span class="n">cmap</span><span class="p">)</span>
+    <span class="n">pc</span><span class="o">.</span><span class="n">set_array</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">colors</span><span class="p">))</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">add_collection</span><span class="p">(</span><span class="n">pc</span><span class="p">)</span>
+    <span class="n">cb</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">colorbar</span><span class="p">(</span><span class="n">pc</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">)</span>
+    <span class="n">cb</span><span class="o">.</span><span class="n">set_label</span><span class="p">(</span><span class="s1">&#39;Density&#39;</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="plot_distribution2"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.Util.plot_distribution2">[docs]</a><span class="k">def</span> <span class="nf">plot_distribution2</span><span class="p">(</span><span class="n">probabilitydist</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Plot distributions in y-axis over the time (x-axis)</span>
+
+<span class="sd">    :param probabilitydist: the forecasted probability distributions to plot</span>
+<span class="sd">    :param data: the original test sample</span>
+<span class="sd">    :keyword start_at: the time index (inside of data) to start to plot the probability distributions</span>
+<span class="sd">    :keyword ax: a matplotlib axis. If no value was provided a new axis is created.</span>
+<span class="sd">    :keyword cmap: a matplotlib colormap name, the default value is &#39;Blues&#39;</span>
+<span class="sd">    :keyword quantiles: the list of quantiles intervals to plot, e. g. [.05, .25, .75, .95]</span>
+<span class="sd">    :keyword median: a boolean value indicating if the median value will be plot.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="kn">import</span> <span class="nn">matplotlib.colorbar</span> <span class="k">as</span> <span class="nn">cbar</span>
+    <span class="kn">import</span> <span class="nn">matplotlib.cm</span> <span class="k">as</span> <span class="nn">cm</span>
+
+    <span class="n">ax</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;ax&#39;</span><span class="p">,</span><span class="kc">None</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">ax</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">nrows</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">ncols</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">[</span><span class="mi">15</span><span class="p">,</span> <span class="mi">5</span><span class="p">])</span>
+
+    <span class="n">l</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">probabilitydist</span><span class="p">)</span>
+
+    <span class="n">cmap</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;cmap&#39;</span><span class="p">,</span><span class="s1">&#39;Blues&#39;</span><span class="p">)</span>
+    <span class="n">cmap</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">get_cmap</span><span class="p">(</span><span class="n">cmap</span><span class="p">)</span>
+
+    <span class="n">start_at</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;start_at&#39;</span><span class="p">,</span><span class="mi">0</span><span class="p">)</span>
+
+    <span class="n">x</span> <span class="o">=</span> <span class="p">[</span><span class="n">k</span> <span class="o">+</span> <span class="n">start_at</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">l</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)]</span>
+
+    <span class="n">qt</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;quantiles&#39;</span><span class="p">,</span><span class="kc">None</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="n">qt</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="n">qt</span> <span class="o">=</span> <span class="p">[</span><span class="nb">round</span><span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="o">.</span><span class="mi">05</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="o">.</span><span class="mi">05</span><span class="p">)]</span>
+        <span class="n">qt</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="o">.</span><span class="mi">01</span><span class="p">)</span>
+        <span class="n">qt</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="o">.</span><span class="mi">99</span><span class="p">)</span>
+
+    <span class="n">lq</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">qt</span><span class="p">)</span>
+
+    <span class="n">normal</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">Normalize</span><span class="p">(</span><span class="nb">min</span><span class="p">(</span><span class="n">qt</span><span class="p">),</span> <span class="nb">max</span><span class="p">(</span><span class="n">qt</span><span class="p">))</span>
+    <span class="n">scalarMap</span> <span class="o">=</span> <span class="n">cm</span><span class="o">.</span><span class="n">ScalarMappable</span><span class="p">(</span><span class="n">norm</span><span class="o">=</span><span class="n">normal</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="n">cmap</span><span class="p">)</span>
+
+    <span class="k">for</span> <span class="n">ct</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="nb">int</span><span class="p">(</span><span class="n">lq</span> <span class="o">/</span> <span class="mi">2</span><span class="p">)</span> <span class="o">+</span> <span class="mi">1</span><span class="p">):</span>
+        <span class="k">try</span><span class="p">:</span>
+            <span class="n">y</span> <span class="o">=</span> <span class="p">[[</span><span class="n">data</span><span class="p">[</span><span class="n">start_at</span><span class="p">],</span> <span class="n">data</span><span class="p">[</span><span class="n">start_at</span><span class="p">]]]</span>
+            <span class="k">for</span> <span class="n">pd</span> <span class="ow">in</span> <span class="n">probabilitydist</span><span class="p">:</span>
+                <span class="n">qts</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">quantile</span><span class="p">([</span><span class="n">qt</span><span class="p">[</span><span class="n">ct</span> <span class="o">-</span> <span class="mi">1</span><span class="p">],</span> <span class="n">qt</span><span class="p">[</span><span class="o">-</span><span class="n">ct</span><span class="p">]])</span>
+                <span class="n">y</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">qts</span><span class="p">)</span>
+
+            <span class="n">ax</span><span class="o">.</span><span class="n">fill_between</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="p">[</span><span class="n">k</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">y</span><span class="p">],</span> <span class="p">[</span><span class="n">k</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">y</span><span class="p">],</span>
+                            <span class="n">facecolor</span><span class="o">=</span><span class="n">scalarMap</span><span class="o">.</span><span class="n">to_rgba</span><span class="p">(</span><span class="n">ct</span> <span class="o">/</span> <span class="n">lq</span><span class="p">))</span>
+        <span class="k">except</span><span class="p">:</span>
+            <span class="k">pass</span>
+
+    <span class="k">if</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;median&#39;</span><span class="p">,</span><span class="kc">True</span><span class="p">):</span>
+        <span class="n">y</span> <span class="o">=</span> <span class="p">[</span><span class="n">data</span><span class="p">[</span><span class="n">start_at</span><span class="p">]]</span>
+        <span class="k">for</span> <span class="n">pd</span> <span class="ow">in</span> <span class="n">probabilitydist</span><span class="p">:</span>
+            <span class="n">qts</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">quantile</span><span class="p">([</span><span class="o">.</span><span class="mi">5</span><span class="p">])</span>
+            <span class="n">y</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">qts</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
+
+        <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;red&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Median&#39;</span><span class="p">)</span>
+
+    <span class="n">cax</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">cbar</span><span class="o">.</span><span class="n">make_axes</span><span class="p">(</span><span class="n">ax</span><span class="p">)</span>
+    <span class="n">cb</span> <span class="o">=</span> <span class="n">cbar</span><span class="o">.</span><span class="n">ColorbarBase</span><span class="p">(</span><span class="n">cax</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="n">cmap</span><span class="p">,</span> <span class="n">norm</span><span class="o">=</span><span class="n">normal</span><span class="p">)</span>
+    <span class="n">cb</span><span class="o">.</span><span class="n">set_label</span><span class="p">(</span><span class="s1">&#39;Density&#39;</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="plot_distribution_tiled"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.Util.plot_distribution_tiled">[docs]</a><span class="k">def</span> <span class="nf">plot_distribution_tiled</span><span class="p">(</span><span class="n">distributions</span><span class="p">,</span><span class="n">data</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span><span class="n">rows</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span><span class="n">cols</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span><span class="n">index</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span><span class="n">axis</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span><span class="n">size</span><span class="o">=</span><span class="p">[</span><span class="mi">10</span><span class="p">,</span><span class="mi">20</span><span class="p">]):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Plot one distribution individually in each axis, with probability in y-axis and UoD on x-axis</span>
+
+<span class="sd">    :param distributions:</span>
+<span class="sd">    :param data:</span>
+<span class="sd">    :param rows:</span>
+<span class="sd">    :param cols:</span>
+<span class="sd">    :param index:</span>
+<span class="sd">    :param axis:</span>
+<span class="sd">    :param size:</span>
+<span class="sd">    :return:</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="k">if</span> <span class="n">axis</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="n">fig</span><span class="p">,</span> <span class="n">axis</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">nrows</span><span class="o">=</span><span class="n">rows</span><span class="p">,</span> <span class="n">ncols</span><span class="o">=</span><span class="n">cols</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="n">size</span><span class="p">)</span>
+
+    <span class="k">for</span> <span class="n">ct</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">rows</span><span class="o">*</span><span class="n">cols</span><span class="p">):</span>
+        <span class="n">col</span> <span class="o">=</span> <span class="n">ct</span> <span class="o">%</span> <span class="n">cols</span>
+        <span class="n">row</span> <span class="o">=</span> <span class="n">ct</span> <span class="o">//</span> <span class="n">cols</span>
+        <span class="k">if</span> <span class="n">index</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">ix</span> <span class="o">=</span> <span class="n">ct</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">ix</span> <span class="o">=</span><span class="n">index</span><span class="p">[</span><span class="n">ct</span><span class="p">]</span>
+        <span class="n">forecast</span> <span class="o">=</span> <span class="n">distributions</span><span class="p">[</span><span class="n">ix</span><span class="p">]</span>
+        <span class="n">forecast</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="n">axis</span><span class="p">[</span><span class="n">row</span><span class="p">][</span><span class="n">col</span><span class="p">])</span>
+        <span class="k">if</span> <span class="n">data</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">axis</span><span class="p">[</span><span class="n">row</span><span class="p">][</span><span class="n">col</span><span class="p">]</span><span class="o">.</span><span class="n">axvline</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="n">ix</span><span class="p">])</span>
+        <span class="n">axis</span><span class="p">[</span><span class="n">row</span><span class="p">][</span><span class="n">col</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">&#39;t+</span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">ix</span><span class="p">))</span>
+        <span class="n">axis</span><span class="p">[</span><span class="n">row</span><span class="p">][</span><span class="n">col</span><span class="p">]</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="kc">None</span><span class="p">)</span>
+
+    <span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span></div>
+
+
+<div class="viewcode-block" id="plot_interval"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.Util.plot_interval">[docs]</a><span class="k">def</span> <span class="nf">plot_interval</span><span class="p">(</span><span class="n">axis</span><span class="p">,</span> <span class="n">intervals</span><span class="p">,</span> <span class="n">order</span><span class="p">,</span> <span class="n">label</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;red&#39;</span><span class="p">,</span> <span class="n">typeonlegend</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">ls</span><span class="o">=</span><span class="s1">&#39;-&#39;</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mi">1</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Plot forecasted intervals on matplotlib</span>
+
+<span class="sd">    :param axis: matplotlib axis</span>
+<span class="sd">    :param intervals: list of forecasted intervals</span>
+<span class="sd">    :param order: order of the model that create the forecasts</span>
+<span class="sd">    :param label: figure label</span>
+<span class="sd">    :param color: matplotlib color name</span>
+<span class="sd">    :param typeonlegend:</span>
+<span class="sd">    :param ls: matplotlib line style</span>
+<span class="sd">    :param linewidth: matplotlib width</span>
+<span class="sd">    :return:</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">lower</span> <span class="o">=</span> <span class="p">[</span><span class="n">kk</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">kk</span> <span class="ow">in</span> <span class="n">intervals</span><span class="p">]</span>
+    <span class="n">upper</span> <span class="o">=</span> <span class="p">[</span><span class="n">kk</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">kk</span> <span class="ow">in</span> <span class="n">intervals</span><span class="p">]</span>
+    <span class="n">mi</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="n">lower</span><span class="p">)</span> <span class="o">*</span> <span class="mf">0.95</span>
+    <span class="n">ma</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">upper</span><span class="p">)</span> <span class="o">*</span> <span class="mf">1.05</span>
+    <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">order</span><span class="o">+</span><span class="mi">1</span><span class="p">):</span>
+        <span class="n">lower</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
+        <span class="n">upper</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">typeonlegend</span><span class="p">:</span> <span class="n">label</span> <span class="o">+=</span> <span class="s2">&quot; (Interval)&quot;</span>
+    <span class="n">axis</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">lower</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="n">color</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="n">label</span><span class="p">,</span> <span class="n">ls</span><span class="o">=</span><span class="n">ls</span><span class="p">,</span><span class="n">linewidth</span><span class="o">=</span><span class="n">linewidth</span><span class="p">)</span>
+    <span class="n">axis</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">upper</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="n">color</span><span class="p">,</span> <span class="n">ls</span><span class="o">=</span><span class="n">ls</span><span class="p">,</span><span class="n">linewidth</span><span class="o">=</span><span class="n">linewidth</span><span class="p">)</span>
+    <span class="k">return</span> <span class="p">[</span><span class="n">mi</span><span class="p">,</span> <span class="n">ma</span><span class="p">]</span></div>
+
+
+<div class="viewcode-block" id="plot_interval2"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.Util.plot_interval2">[docs]</a><span class="k">def</span> <span class="nf">plot_interval2</span><span class="p">(</span><span class="n">intervals</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Plot forecasted intervals on matplotlib</span>
+
+<span class="sd">    :param intervals: list of forecasted intervals</span>
+<span class="sd">    :param data: the original test sample</span>
+<span class="sd">    :keyword start_at: the time index (inside of data) to start to plot the intervals</span>
+<span class="sd">    :keyword label: figure label</span>
+<span class="sd">    :keyword color: matplotlib color name</span>
+<span class="sd">    :keyword typeonlegend:</span>
+<span class="sd">    :keyword ls: matplotlib line style</span>
+<span class="sd">    :keyword linewidth: matplotlib width</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">l</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">intervals</span><span class="p">)</span>
+
+    <span class="n">nintervals</span> <span class="o">=</span> <span class="n">intervals</span>
+
+    <span class="n">start_at</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;start_at&#39;</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+
+    <span class="n">ax</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;ax&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">ax</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">nrows</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">ncols</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">[</span><span class="mi">15</span><span class="p">,</span> <span class="mi">5</span><span class="p">])</span>
+
+    <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">start_at</span><span class="p">):</span>
+        <span class="n">nintervals</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="p">[</span><span class="kc">None</span><span class="p">,</span><span class="kc">None</span><span class="p">])</span>
+
+    <span class="n">nintervals</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="n">start_at</span><span class="p">,</span> <span class="p">[</span><span class="n">data</span><span class="p">[</span><span class="n">start_at</span><span class="p">],</span> <span class="n">data</span><span class="p">[</span><span class="n">start_at</span><span class="p">]])</span>
+
+    <span class="n">lower</span> <span class="o">=</span> <span class="p">[</span><span class="n">kk</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">kk</span> <span class="ow">in</span> <span class="n">nintervals</span><span class="p">]</span>
+    <span class="n">upper</span> <span class="o">=</span> <span class="p">[</span><span class="n">kk</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">kk</span> <span class="ow">in</span> <span class="n">nintervals</span><span class="p">]</span>
+
+    <span class="n">typeonlegend</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;typeonlegend&#39;</span><span class="p">,</span> <span class="kc">False</span><span class="p">)</span>
+    <span class="n">color</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;color&#39;</span><span class="p">,</span> <span class="s1">&#39;red&#39;</span><span class="p">)</span>
+    <span class="n">label</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;label&#39;</span><span class="p">,</span><span class="s1">&#39;&#39;</span><span class="p">)</span>
+    <span class="n">linewidth</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;linewidth&#39;</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+
+    <span class="n">ls</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;ls&#39;</span><span class="p">,</span><span class="s1">&#39;-&#39;</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="n">typeonlegend</span><span class="p">:</span> <span class="n">label</span> <span class="o">+=</span> <span class="s2">&quot; (Interval)&quot;</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">lower</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="n">color</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="n">label</span><span class="p">,</span> <span class="n">ls</span><span class="o">=</span><span class="n">ls</span><span class="p">,</span><span class="n">linewidth</span><span class="o">=</span><span class="n">linewidth</span><span class="p">)</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">upper</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="n">color</span><span class="p">,</span> <span class="n">ls</span><span class="o">=</span><span class="n">ls</span><span class="p">,</span><span class="n">linewidth</span><span class="o">=</span><span class="n">linewidth</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="plot_rules"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.Util.plot_rules">[docs]</a><span class="k">def</span> <span class="nf">plot_rules</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="p">[</span><span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">],</span> <span class="n">axis</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">rules_by_axis</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="mi">1</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Plot the FLRG rules of a FTS model on a matplotlib axis</span>
+
+<span class="sd">    :param model: FTS model</span>
+<span class="sd">    :param size: figure size</span>
+<span class="sd">    :param axis: matplotlib axis</span>
+<span class="sd">    :param rules_by_axis: number of rules plotted by column</span>
+<span class="sd">    :param columns: number of columns</span>
+<span class="sd">    :return:</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">if</span> <span class="n">axis</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">rules_by_axis</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="n">rows</span> <span class="o">=</span> <span class="mi">1</span>
+    <span class="k">elif</span> <span class="n">axis</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">rules_by_axis</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="n">rows</span> <span class="o">=</span> <span class="p">(((</span><span class="nb">len</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">flrgs</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span><span class="o">//</span><span class="n">rules_by_axis</span><span class="p">))</span> <span class="o">//</span> <span class="n">columns</span><span class="p">)</span><span class="o">+</span><span class="mi">1</span>
+
+    <span class="n">fig</span><span class="p">,</span> <span class="n">axis</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">nrows</span><span class="o">=</span><span class="n">rows</span><span class="p">,</span> <span class="n">ncols</span><span class="o">=</span><span class="n">columns</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="n">size</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="n">rules_by_axis</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="n">draw_sets_on_axis</span><span class="p">(</span><span class="n">axis</span><span class="p">,</span> <span class="n">model</span><span class="p">,</span> <span class="n">size</span><span class="p">)</span>
+
+    <span class="n">_lhs</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">ordered_sets</span> <span class="k">if</span> <span class="ow">not</span> <span class="n">model</span><span class="o">.</span><span class="n">is_high_order</span> <span class="k">else</span> <span class="n">model</span><span class="o">.</span><span class="n">flrgs</span><span class="o">.</span><span class="n">keys</span><span class="p">()</span>
+
+    <span class="k">for</span> <span class="n">ct</span><span class="p">,</span> <span class="n">key</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">_lhs</span><span class="p">):</span>
+
+        <span class="n">xticks</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="n">xtickslabels</span> <span class="o">=</span> <span class="p">[]</span>
+
+        <span class="k">if</span> <span class="n">rules_by_axis</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">ax</span> <span class="o">=</span> <span class="n">axis</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">colcount</span> <span class="o">=</span> <span class="p">(</span><span class="n">ct</span> <span class="o">//</span> <span class="n">rules_by_axis</span><span class="p">)</span> <span class="o">%</span> <span class="n">columns</span>
+            <span class="n">rowcount</span> <span class="o">=</span> <span class="p">(</span><span class="n">ct</span> <span class="o">//</span> <span class="n">rules_by_axis</span><span class="p">)</span> <span class="o">//</span> <span class="n">columns</span>
+
+            <span class="k">if</span> <span class="n">rows</span> <span class="o">&gt;</span> <span class="mi">1</span> <span class="ow">and</span> <span class="n">columns</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
+                <span class="n">ax</span> <span class="o">=</span> <span class="n">axis</span><span class="p">[</span><span class="n">rowcount</span><span class="p">,</span> <span class="n">colcount</span><span class="p">]</span>
+            <span class="k">elif</span> <span class="n">columns</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
+                <span class="n">ax</span> <span class="o">=</span> <span class="n">axis</span><span class="p">[</span><span class="n">rowcount</span><span class="p">]</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">ax</span> <span class="o">=</span> <span class="n">axis</span>
+
+            <span class="k">if</span> <span class="n">ct</span> <span class="o">%</span> <span class="n">rules_by_axis</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+                <span class="n">draw_sets_on_axis</span><span class="p">(</span><span class="n">ax</span><span class="p">,</span> <span class="n">model</span><span class="p">,</span> <span class="n">size</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="ow">not</span> <span class="n">model</span><span class="o">.</span><span class="n">is_high_order</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">model</span><span class="o">.</span><span class="n">flrgs</span><span class="p">:</span>
+                <span class="n">x</span> <span class="o">=</span> <span class="p">(</span><span class="n">ct</span> <span class="o">%</span> <span class="n">rules_by_axis</span><span class="p">)</span> <span class="o">+</span> <span class="mi">1</span>
+                <span class="n">flrg</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">key</span><span class="p">]</span>
+                <span class="n">y</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="n">key</span><span class="p">]</span><span class="o">.</span><span class="n">centroid</span>
+                <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">([</span><span class="n">x</span><span class="p">],[</span><span class="n">y</span><span class="p">],</span><span class="s1">&#39;o&#39;</span><span class="p">)</span>
+                <span class="n">xticks</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+                <span class="n">xtickslabels</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">key</span><span class="p">)</span>
+                <span class="k">for</span> <span class="n">rhs</span> <span class="ow">in</span> <span class="n">flrg</span><span class="o">.</span><span class="n">RHS</span><span class="p">:</span>
+                    <span class="n">dest</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="n">rhs</span><span class="p">]</span><span class="o">.</span><span class="n">centroid</span>
+                    <span class="n">ax</span><span class="o">.</span><span class="n">arrow</span><span class="p">(</span><span class="n">x</span><span class="o">+.</span><span class="mi">1</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="mf">0.8</span><span class="p">,</span> <span class="n">dest</span> <span class="o">-</span> <span class="n">y</span><span class="p">,</span> <span class="c1">#length_includes_head=True,</span>
+                               <span class="n">head_width</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">head_length</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="s1">&#39;full&#39;</span><span class="p">,</span> <span class="n">overhang</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
+                               <span class="n">fc</span><span class="o">=</span><span class="s1">&#39;k&#39;</span><span class="p">,</span> <span class="n">ec</span><span class="o">=</span><span class="s1">&#39;k&#39;</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">flrg</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">key</span><span class="p">]</span>
+            <span class="n">x</span> <span class="o">=</span> <span class="p">(</span><span class="n">ct</span><span class="o">%</span><span class="n">rules_by_axis</span><span class="p">)</span><span class="o">*</span><span class="n">model</span><span class="o">.</span><span class="n">order</span> <span class="o">+</span> <span class="mi">1</span>
+            <span class="k">for</span> <span class="n">ct2</span><span class="p">,</span> <span class="n">lhs</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">flrg</span><span class="o">.</span><span class="n">LHS</span><span class="p">):</span>
+                <span class="n">y</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="n">lhs</span><span class="p">]</span><span class="o">.</span><span class="n">centroid</span>
+                <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">([</span><span class="n">x</span><span class="o">+</span><span class="n">ct2</span><span class="p">],</span> <span class="p">[</span><span class="n">y</span><span class="p">],</span> <span class="s1">&#39;o&#39;</span><span class="p">)</span>
+                <span class="n">xticks</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">x</span><span class="o">+</span><span class="n">ct2</span><span class="p">)</span>
+                <span class="n">xtickslabels</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">lhs</span><span class="p">)</span>
+            <span class="k">for</span> <span class="n">ct2</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">model</span><span class="o">.</span><span class="n">order</span><span class="p">):</span>
+                <span class="n">fs1</span> <span class="o">=</span> <span class="n">flrg</span><span class="o">.</span><span class="n">LHS</span><span class="p">[</span><span class="n">ct2</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
+                <span class="n">fs2</span> <span class="o">=</span> <span class="n">flrg</span><span class="o">.</span><span class="n">LHS</span><span class="p">[</span><span class="n">ct2</span><span class="p">]</span>
+                <span class="n">y</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="n">fs1</span><span class="p">]</span><span class="o">.</span><span class="n">centroid</span>
+                <span class="n">dest</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="n">fs2</span><span class="p">]</span><span class="o">.</span><span class="n">centroid</span>
+                <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">([</span><span class="n">x</span><span class="o">+</span><span class="n">ct2</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="n">x</span><span class="o">+</span><span class="n">ct2</span><span class="p">],</span> <span class="p">[</span><span class="n">y</span><span class="p">,</span><span class="n">dest</span><span class="p">],</span><span class="s1">&#39;-&#39;</span><span class="p">)</span>
+
+            <span class="n">y</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="n">flrg</span><span class="o">.</span><span class="n">LHS</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]]</span><span class="o">.</span><span class="n">centroid</span>
+            <span class="k">for</span> <span class="n">rhs</span> <span class="ow">in</span> <span class="n">flrg</span><span class="o">.</span><span class="n">RHS</span><span class="p">:</span>
+                <span class="n">dest</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="n">rhs</span><span class="p">]</span><span class="o">.</span><span class="n">centroid</span>
+                <span class="n">ax</span><span class="o">.</span><span class="n">arrow</span><span class="p">(</span><span class="n">x</span> <span class="o">+</span> <span class="n">model</span><span class="o">.</span><span class="n">order</span> <span class="o">-</span><span class="mi">1</span> <span class="o">+</span> <span class="o">.</span><span class="mi">1</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="mf">0.8</span><span class="p">,</span> <span class="n">dest</span> <span class="o">-</span> <span class="n">y</span><span class="p">,</span>  <span class="c1"># length_includes_head=True,</span>
+                           <span class="n">head_width</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">head_length</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="s1">&#39;full&#39;</span><span class="p">,</span> <span class="n">overhang</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
+                           <span class="n">fc</span><span class="o">=</span><span class="s1">&#39;k&#39;</span><span class="p">,</span> <span class="n">ec</span><span class="o">=</span><span class="s1">&#39;k&#39;</span><span class="p">)</span>
+
+
+        <span class="n">ax</span><span class="o">.</span><span class="n">set_xticks</span><span class="p">(</span><span class="n">xticks</span><span class="p">)</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">set_xticklabels</span><span class="p">(</span><span class="n">xtickslabels</span><span class="p">)</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span><span class="n">rules_by_axis</span><span class="o">*</span><span class="n">model</span><span class="o">.</span><span class="n">order</span><span class="o">+</span><span class="mi">1</span><span class="p">])</span>
+
+    <span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span></div>
+
+
+<div class="viewcode-block" id="draw_sets_on_axis"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.Util.draw_sets_on_axis">[docs]</a><span class="k">def</span> <span class="nf">draw_sets_on_axis</span><span class="p">(</span><span class="n">axis</span><span class="p">,</span> <span class="n">model</span><span class="p">,</span> <span class="n">size</span><span class="p">):</span>
+    <span class="k">if</span> <span class="n">axis</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="n">fig</span><span class="p">,</span> <span class="n">axis</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">nrows</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">ncols</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="n">size</span><span class="p">)</span>
+    <span class="k">for</span> <span class="n">ct</span><span class="p">,</span> <span class="n">key</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">ordered_sets</span><span class="p">):</span>
+        <span class="n">fs</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="n">key</span><span class="p">]</span>
+        <span class="n">axis</span><span class="o">.</span><span class="n">plot</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">fs</span><span class="o">.</span><span class="n">parameters</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="n">fs</span><span class="o">.</span><span class="n">name</span><span class="p">)</span>
+        <span class="n">axis</span><span class="o">.</span><span class="n">axhline</span><span class="p">(</span><span class="n">fs</span><span class="o">.</span><span class="n">centroid</span><span class="p">,</span> <span class="n">c</span><span class="o">=</span><span class="s2">&quot;lightgray&quot;</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
+    <span class="n">axis</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">ordered_sets</span><span class="p">)])</span>
+    <span class="n">axis</span><span class="o">.</span><span class="n">set_xticks</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">ordered_sets</span><span class="p">)))</span>
+    <span class="n">tmp</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;&#39;</span><span class="p">]</span>
+    <span class="n">tmp</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">ordered_sets</span><span class="p">)</span>
+    <span class="n">axis</span><span class="o">.</span><span class="n">set_xticklabels</span><span class="p">(</span><span class="n">tmp</span><span class="p">)</span>
+    <span class="n">axis</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">([</span><span class="n">model</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">min</span><span class="p">,</span> <span class="n">model</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">max</span><span class="p">])</span>
+    <span class="n">axis</span><span class="o">.</span><span class="n">set_yticks</span><span class="p">([</span><span class="n">model</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="n">k</span><span class="p">]</span><span class="o">.</span><span class="n">centroid</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">model</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">ordered_sets</span><span class="p">])</span>
+    <span class="n">axis</span><span class="o">.</span><span class="n">set_yticklabels</span><span class="p">([</span><span class="nb">str</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="n">k</span><span class="p">]</span><span class="o">.</span><span class="n">centroid</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span> <span class="o">+</span> <span class="s2">&quot; - &quot;</span> <span class="o">+</span> <span class="n">k</span>
+                          <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">model</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">ordered_sets</span><span class="p">])</span></div>
+
+
+<span class="n">current_milli_time</span> <span class="o">=</span> <span class="k">lambda</span><span class="p">:</span> <span class="nb">int</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span> <span class="o">*</span> <span class="mi">1000</span><span class="p">))</span>
+
+
+<div class="viewcode-block" id="uniquefilename"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.Util.uniquefilename">[docs]</a><span class="k">def</span> <span class="nf">uniquefilename</span><span class="p">(</span><span class="n">name</span><span class="p">):</span>
+    <span class="k">if</span> <span class="s1">&#39;.&#39;</span> <span class="ow">in</span> <span class="n">name</span><span class="p">:</span>
+        <span class="n">tmp</span> <span class="o">=</span> <span class="n">name</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;.&#39;</span><span class="p">)</span>
+        <span class="k">return</span>  <span class="n">tmp</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">current_milli_time</span><span class="p">())</span> <span class="o">+</span> <span class="s1">&#39;.&#39;</span> <span class="o">+</span> <span class="n">tmp</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">return</span> <span class="n">name</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">current_milli_time</span><span class="p">())</span></div>
+
+
+<div class="viewcode-block" id="show_and_save_image"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.Util.show_and_save_image">[docs]</a><span class="k">def</span> <span class="nf">show_and_save_image</span><span class="p">(</span><span class="n">fig</span><span class="p">,</span> <span class="n">file</span><span class="p">,</span> <span class="n">flag</span><span class="p">,</span> <span class="n">lgd</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Show and image and save on file</span>
+
+<span class="sd">    :param fig: Matplotlib Figure object</span>
+<span class="sd">    :param file: filename to save the picture</span>
+<span class="sd">    :param flag: if True the image will be saved</span>
+<span class="sd">    :param lgd: legend</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+    <span class="k">if</span> <span class="n">flag</span><span class="p">:</span>
+        <span class="k">if</span> <span class="n">lgd</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">fig</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="n">file</span><span class="p">,</span> <span class="n">additional_artists</span><span class="o">=</span><span class="n">lgd</span><span class="p">,</span><span class="n">bbox_inches</span><span class="o">=</span><span class="s1">&#39;tight&#39;</span><span class="p">)</span>  <span class="c1">#bbox_extra_artists=(lgd,), )</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">fig</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="n">file</span><span class="p">)</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">close</span><span class="p">(</span><span class="n">fig</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="enumerate2"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.Util.enumerate2">[docs]</a><span class="k">def</span> <span class="nf">enumerate2</span><span class="p">(</span><span class="n">xs</span><span class="p">,</span> <span class="n">start</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">step</span><span class="o">=</span><span class="mi">1</span><span class="p">):</span>
+    <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">xs</span><span class="p">:</span>
+        <span class="k">yield</span> <span class="p">(</span><span class="n">start</span><span class="p">,</span> <span class="n">x</span><span class="p">)</span>
+        <span class="n">start</span> <span class="o">+=</span> <span class="n">step</span></div>
+
+
+<div class="viewcode-block" id="sliding_window"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.Util.sliding_window">[docs]</a><span class="k">def</span> <span class="nf">sliding_window</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">windowsize</span><span class="p">,</span> <span class="n">train</span><span class="o">=</span><span class="mf">0.8</span><span class="p">,</span> <span class="n">inc</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Sliding window method of cross validation for time series</span>
+
+<span class="sd">    :param data: the entire dataset</span>
+<span class="sd">    :param windowsize: window size</span>
+<span class="sd">    :param train: percentual of the window size will be used for training the models</span>
+<span class="sd">    :param inc: percentual of data used for slide the window</span>
+<span class="sd">    :return: window count, training set, test set</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">multivariate</span> <span class="o">=</span> <span class="kc">True</span> <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">)</span> <span class="k">else</span> <span class="kc">False</span>
+
+    <span class="n">l</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">)</span> <span class="k">if</span> <span class="ow">not</span> <span class="n">multivariate</span> <span class="k">else</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">index</span><span class="p">)</span>
+    <span class="n">ttrain</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">windowsize</span> <span class="o">*</span> <span class="n">train</span><span class="p">,</span> <span class="mi">0</span><span class="p">))</span>
+    <span class="n">ic</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">windowsize</span> <span class="o">*</span> <span class="n">inc</span><span class="p">,</span> <span class="mi">0</span><span class="p">))</span>
+
+    <span class="n">progressbar</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;progress&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
+
+    <span class="n">rng</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="n">l</span><span class="o">-</span><span class="n">windowsize</span><span class="o">+</span><span class="n">ic</span><span class="p">,</span><span class="n">ic</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="n">progressbar</span><span class="p">:</span>
+        <span class="kn">from</span> <span class="nn">tqdm</span> <span class="kn">import</span> <span class="n">tqdm</span>
+        <span class="n">rng</span> <span class="o">=</span> <span class="n">tqdm</span><span class="p">(</span><span class="n">rng</span><span class="p">)</span>
+
+    <span class="k">for</span> <span class="n">count</span> <span class="ow">in</span> <span class="n">rng</span><span class="p">:</span>
+        <span class="k">if</span> <span class="n">count</span> <span class="o">+</span> <span class="n">windowsize</span> <span class="o">&gt;</span> <span class="n">l</span><span class="p">:</span>
+            <span class="n">_end</span> <span class="o">=</span> <span class="n">l</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">_end</span> <span class="o">=</span> <span class="n">count</span> <span class="o">+</span> <span class="n">windowsize</span>
+        <span class="k">if</span> <span class="n">multivariate</span><span class="p">:</span>
+            <span class="k">yield</span> <span class="p">(</span><span class="n">count</span><span class="p">,</span> <span class="n">data</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="n">count</span><span class="p">:</span> <span class="n">count</span> <span class="o">+</span> <span class="n">ttrain</span><span class="p">],</span> <span class="n">data</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="n">count</span> <span class="o">+</span> <span class="n">ttrain</span><span class="p">:</span> <span class="n">_end</span><span class="p">])</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">yield</span> <span class="p">(</span><span class="n">count</span><span class="p">,</span>  <span class="n">data</span><span class="p">[</span><span class="n">count</span> <span class="p">:</span> <span class="n">count</span> <span class="o">+</span> <span class="n">ttrain</span><span class="p">],</span> <span class="n">data</span><span class="p">[</span><span class="n">count</span> <span class="o">+</span> <span class="n">ttrain</span> <span class="p">:</span> <span class="n">_end</span><span class="p">]</span>  <span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="persist_obj"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.Util.persist_obj">[docs]</a><span class="k">def</span> <span class="nf">persist_obj</span><span class="p">(</span><span class="n">obj</span><span class="p">,</span> <span class="n">file</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Persist an object on filesystem. This function depends on Dill package</span>
+
+<span class="sd">    :param obj: object on memory</span>
+<span class="sd">    :param file: file name to store the object</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="kn">import</span> <span class="nn">dill</span>
+    <span class="k">try</span><span class="p">:</span>
+        <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">file</span><span class="p">,</span> <span class="s1">&#39;wb&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">_file</span><span class="p">:</span>
+            <span class="n">dill</span><span class="o">.</span><span class="n">dump</span><span class="p">(</span><span class="n">obj</span><span class="p">,</span> <span class="n">_file</span><span class="p">)</span>
+    <span class="k">except</span> <span class="ne">Exception</span> <span class="k">as</span> <span class="n">ex</span><span class="p">:</span>
+        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;File </span><span class="si">{}</span><span class="s2"> could not be saved due exception </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">file</span><span class="p">,</span> <span class="n">ex</span><span class="p">))</span></div>
+
+
+<div class="viewcode-block" id="load_obj"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.Util.load_obj">[docs]</a><span class="k">def</span> <span class="nf">load_obj</span><span class="p">(</span><span class="n">file</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Load to memory an object stored filesystem. This function depends on Dill package</span>
+
+<span class="sd">    :param file: file name where the object is stored</span>
+<span class="sd">    :return: object</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="kn">import</span> <span class="nn">dill</span>
+    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">file</span><span class="p">,</span> <span class="s1">&#39;rb&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">_file</span><span class="p">:</span>
+        <span class="n">obj</span> <span class="o">=</span> <span class="n">dill</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">_file</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">obj</span></div>
+
+
+<div class="viewcode-block" id="persist_env"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.Util.persist_env">[docs]</a><span class="k">def</span> <span class="nf">persist_env</span><span class="p">(</span><span class="n">file</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Persist an entire environment on file. This function depends on Dill package</span>
+
+<span class="sd">    :param file: file name to store the environment</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="kn">import</span> <span class="nn">dill</span>
+    <span class="n">dill</span><span class="o">.</span><span class="n">dump_session</span><span class="p">(</span><span class="n">file</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="load_env"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.Util.load_env">[docs]</a><span class="k">def</span> <span class="nf">load_env</span><span class="p">(</span><span class="n">file</span><span class="p">):</span>
+    <span class="kn">import</span> <span class="nn">dill</span>
+    <span class="n">dill</span><span class="o">.</span><span class="n">load_session</span><span class="p">(</span><span class="n">file</span><span class="p">)</span></div>
+
+
+
+</pre></div>
+
+            <div class="clearer"></div>
+          </div>
+        </div>
+      </div>
+      <div class="sphinxsidebar" role="navigation" aria-label="main navigation">
+        <div class="sphinxsidebarwrapper">
+<div id="searchbox" style="display: none" role="search">
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+<script>$('#searchbox').show(0);</script>
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+             >index</a></li>
+        <li class="right" >
+          <a href="../../../py-modindex.html" title="Python Module Index"
+             >modules</a> |</li>
+        <li class="nav-item nav-item-0"><a href="../../../index.html">pyFTS 1.6 documentation</a> &#187;</li>
+          <li class="nav-item nav-item-1"><a href="../../index.html" >Module code</a> &#187;</li>
+        <li class="nav-item nav-item-this"><a href="">pyFTS.common.Util</a></li> 
+      </ul>
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+      Created using <a href="https://www.sphinx-doc.org/">Sphinx</a> 3.1.2.
+    </div>
+  </body>
+</html>
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diff --git a/docs/build/html/_modules/pyFTS/common/fts.html b/docs/build/html/_modules/pyFTS/common/fts.html
new file mode 100644
index 0000000..6c62349
--- /dev/null
+++ b/docs/build/html/_modules/pyFTS/common/fts.html
@@ -0,0 +1,750 @@
+
+
+<!doctype html>
+
+<html>
+  <head>
+    <meta charset="utf-8" />
+    <meta name="viewport" content="width=device-width, initial-scale=1.0"><script type="text/javascript">
+
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+    <title>pyFTS.common.fts &#8212; pyFTS 1.6 documentation</title>
+    <link rel="stylesheet" href="../../../_static/bizstyle.css" type="text/css" />
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+      <h3>Navigation</h3>
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+          <li class="nav-item nav-item-1"><a href="../../index.html" accesskey="U">Module code</a> &#187;</li>
+        <li class="nav-item nav-item-this"><a href="">pyFTS.common.fts</a></li> 
+      </ul>
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+
+    <div class="document">
+      <div class="documentwrapper">
+        <div class="bodywrapper">
+          <div class="body" role="main">
+            
+  <h1>Source code for pyFTS.common.fts</h1><div class="highlight"><pre>
+<span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+<span class="kn">from</span> <span class="nn">pyFTS.common</span> <span class="kn">import</span> <span class="n">FuzzySet</span><span class="p">,</span> <span class="n">SortedCollection</span><span class="p">,</span> <span class="n">tree</span><span class="p">,</span> <span class="n">Util</span>
+
+
+<div class="viewcode-block" id="FTS"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.fts.FTS">[docs]</a><span class="k">class</span> <span class="nc">FTS</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Fuzzy Time Series object model</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Create a Fuzzy Time Series model</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span> <span class="o">=</span> <span class="p">{}</span>
+        <span class="sd">&quot;&quot;&quot;The list of Fuzzy Logical Relationship Groups - FLRG&quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">order</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;order&#39;</span><span class="p">,</span><span class="mi">1</span><span class="p">)</span>
+        <span class="sd">&quot;&quot;&quot;A integer with the model order (number of past lags are used on forecasting)&quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">shortname</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;name&#39;</span><span class="p">,</span><span class="s2">&quot;&quot;</span><span class="p">)</span>
+        <span class="sd">&quot;&quot;&quot;A string with a short name or alias for the model&quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;name&#39;</span><span class="p">,</span><span class="s2">&quot;&quot;</span><span class="p">)</span>
+        <span class="sd">&quot;&quot;&quot;A string with the model name&quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">detail</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;name&#39;</span><span class="p">,</span><span class="s2">&quot;&quot;</span><span class="p">)</span>
+        <span class="sd">&quot;&quot;&quot;A string with the model detailed information&quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">is_wrapper</span> <span class="o">=</span> <span class="kc">False</span>
+        <span class="sd">&quot;&quot;&quot;Indicates that this model is a wrapper for other(s) method(s)&quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">is_high_order</span> <span class="o">=</span> <span class="kc">False</span>
+        <span class="sd">&quot;&quot;&quot;A boolean value indicating if the model support orders greater than 1, default: False&quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">min_order</span> <span class="o">=</span> <span class="mi">1</span>
+        <span class="sd">&quot;&quot;&quot;In high order models, this integer value indicates the minimal order supported for the model, default: 1&quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">has_seasonality</span> <span class="o">=</span> <span class="kc">False</span>
+        <span class="sd">&quot;&quot;&quot;A boolean value indicating if the model supports seasonal indexers, default: False&quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">has_point_forecasting</span> <span class="o">=</span> <span class="kc">True</span>
+        <span class="sd">&quot;&quot;&quot;A boolean value indicating if the model supports point forecasting, default: True&quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">has_interval_forecasting</span> <span class="o">=</span> <span class="kc">False</span>
+        <span class="sd">&quot;&quot;&quot;A boolean value indicating if the model supports interval forecasting, default: False&quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">has_probability_forecasting</span> <span class="o">=</span> <span class="kc">False</span>
+        <span class="sd">&quot;&quot;&quot;A boolean value indicating if the model support probabilistic forecasting, default: False&quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">is_multivariate</span> <span class="o">=</span> <span class="kc">False</span>
+        <span class="sd">&quot;&quot;&quot;A boolean value indicating if the model support multivariate time series (Pandas DataFrame), default: False&quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">is_clustered</span> <span class="o">=</span> <span class="kc">False</span>
+        <span class="sd">&quot;&quot;&quot;A boolean value indicating if the model support multivariate time series (Pandas DataFrame), but works like </span>
+<span class="sd">        a monovariate method, default: False&quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">dump</span> <span class="o">=</span> <span class="kc">False</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">transformations</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="sd">&quot;&quot;&quot;A list with the data transformations (common.Transformations) applied on model pre and post processing, default: []&quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">transformations_param</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="sd">&quot;&quot;&quot;A list with the specific parameters for each data transformation&quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">original_max</span> <span class="o">=</span> <span class="mi">0</span>
+        <span class="sd">&quot;&quot;&quot;A float with the upper limit of the Universe of Discourse, the maximal value found on training data&quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">original_min</span> <span class="o">=</span> <span class="mi">0</span>
+        <span class="sd">&quot;&quot;&quot;A float with the lower limit of the Universe of Discourse, the minimal value found on training data&quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;partitioner&quot;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
+        <span class="sd">&quot;&quot;&quot;A pyFTS.partitioners.Partitioner object with the Universe of Discourse partitioner used on the model. This is a mandatory dependecy. &quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span> <span class="o">!=</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">sets</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">auto_update</span> <span class="o">=</span> <span class="kc">False</span>
+        <span class="sd">&quot;&quot;&quot;A boolean value indicating that model is incremental&quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">benchmark_only</span> <span class="o">=</span> <span class="kc">False</span>
+        <span class="sd">&quot;&quot;&quot;A boolean value indicating a façade for external (non-FTS) model used on benchmarks or ensembles.&quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">indexer</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;indexer&quot;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
+        <span class="sd">&quot;&quot;&quot;An pyFTS.models.seasonal.Indexer object for indexing the time series data&quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">uod_clip</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;uod_clip&quot;</span><span class="p">,</span> <span class="kc">True</span><span class="p">)</span>
+        <span class="sd">&quot;&quot;&quot;Flag indicating if the test data will be clipped inside the training Universe of Discourse&quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">alpha_cut</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;alpha_cut&quot;</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">)</span>
+        <span class="sd">&quot;&quot;&quot;A float with the minimal membership to be considered on fuzzyfication process&quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">lags</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;lags&quot;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
+        <span class="sd">&quot;&quot;&quot;The list of lag indexes for high order models&quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span>
+        <span class="sd">&quot;&quot;&quot;A integer indicating the largest lag used by the model. This value also indicates the minimum number of past lags </span>
+<span class="sd">        needed to forecast a single step ahead&quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">log</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">([],</span><span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;Datetime&quot;</span><span class="p">,</span><span class="s2">&quot;Operation&quot;</span><span class="p">,</span><span class="s2">&quot;Value&quot;</span><span class="p">])</span>
+        <span class="sd">&quot;&quot;&quot;&quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">is_time_variant</span> <span class="o">=</span> <span class="kc">False</span>
+        <span class="sd">&quot;&quot;&quot;A boolean value indicating if this model is time variant&quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">standard_horizon</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;standard_horizon&quot;</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+        <span class="sd">&quot;&quot;&quot;Standard forecasting horizon (Default: 1)&quot;&quot;&quot;</span>
+        
+
+<div class="viewcode-block" id="FTS.fuzzy"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.fts.FTS.fuzzy">[docs]</a>    <span class="k">def</span> <span class="nf">fuzzy</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">):</span>
+        <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Fuzzify a data point</span>
+
+<span class="sd">        :param data: data point</span>
+<span class="sd">        :return: maximum membership fuzzy set</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">best</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;fuzzyset&quot;</span><span class="p">:</span> <span class="s2">&quot;&quot;</span><span class="p">,</span> <span class="s2">&quot;membership&quot;</span><span class="p">:</span> <span class="mf">0.0</span><span class="p">}</span>
+
+        <span class="k">for</span> <span class="n">f</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">:</span>
+            <span class="n">fset</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="n">f</span><span class="p">]</span>
+            <span class="k">if</span> <span class="n">best</span><span class="p">[</span><span class="s2">&quot;membership&quot;</span><span class="p">]</span> <span class="o">&lt;=</span> <span class="n">fset</span><span class="o">.</span><span class="n">membership</span><span class="p">(</span><span class="n">data</span><span class="p">):</span>
+                <span class="n">best</span><span class="p">[</span><span class="s2">&quot;fuzzyset&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">fset</span><span class="o">.</span><span class="n">name</span>
+                <span class="n">best</span><span class="p">[</span><span class="s2">&quot;membership&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">fset</span><span class="o">.</span><span class="n">membership</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">best</span></div>
+
+<div class="viewcode-block" id="FTS.clip_uod"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.fts.FTS.clip_uod">[docs]</a>    <span class="k">def</span> <span class="nf">clip_uod</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ndata</span><span class="p">):</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">uod_clip</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">ndata</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">clip</span><span class="p">(</span><span class="n">ndata</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">min</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">max</span><span class="p">)</span>
+        <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">uod_clip</span><span class="p">:</span>
+            <span class="n">ndata</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">clip</span><span class="p">(</span><span class="n">ndata</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">original_min</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">original_max</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">ndata</span></div>
+
+<div class="viewcode-block" id="FTS.predict"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.fts.FTS.predict">[docs]</a>    <span class="k">def</span> <span class="nf">predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Forecast using trained model</span>
+
+<span class="sd">        :param data: time series with minimal length to the order of the model</span>
+
+<span class="sd">        :keyword type: the forecasting type, one of these values: point(default), interval, distribution or multivariate.</span>
+<span class="sd">        :keyword steps_ahead: The forecasting path H, i. e., tell the model to forecast from t+1 to t+H.</span>
+<span class="sd">        :keyword step_to: The forecasting step H, i. e., tell the model to forecast to t+H for each input sample </span>
+<span class="sd">        :keyword start_at: in the multi step forecasting, the index of the data where to start forecasting (default value: 0)</span>
+<span class="sd">        :keyword distributed: boolean, indicate if the forecasting procedure will be distributed in a dispy cluster (default value: False)</span>
+<span class="sd">        :keyword nodes: a list with the dispy cluster nodes addresses</span>
+<span class="sd">        :keyword explain: try to explain, step by step, the one-step-ahead point forecasting result given the input data. (default value: False)</span>
+<span class="sd">        :keyword generators: for multivariate methods on multi step ahead forecasting, generators is a dict where the keys</span>
+<span class="sd">                            are the dataframe columun names (except the target_variable) and the values are lambda functions that</span>
+<span class="sd">                            accept one value (the actual value of the variable) and return the next value or trained FTS</span>
+<span class="sd">                            models that accept the actual values and forecast new ones.</span>
+
+<span class="sd">        :return: a numpy array with the forecasted data</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="kn">import</span> <span class="nn">copy</span>
+
+        <span class="n">kw</span> <span class="o">=</span> <span class="n">copy</span><span class="o">.</span><span class="n">deepcopy</span><span class="p">(</span><span class="n">kwargs</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">is_multivariate</span><span class="p">:</span>
+            <span class="n">ndata</span> <span class="o">=</span> <span class="n">data</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">ndata</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">apply_transformations</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+
+        <span class="n">ndata</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">clip_uod</span><span class="p">(</span><span class="n">ndata</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="s1">&#39;distributed&#39;</span> <span class="ow">in</span> <span class="n">kw</span><span class="p">:</span>
+            <span class="n">distributed</span> <span class="o">=</span> <span class="n">kw</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s1">&#39;distributed&#39;</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">distributed</span> <span class="o">=</span> <span class="kc">False</span>
+
+        <span class="k">if</span> <span class="s1">&#39;type&#39;</span> <span class="ow">in</span> <span class="n">kw</span><span class="p">:</span>
+            <span class="nb">type</span> <span class="o">=</span> <span class="n">kw</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s2">&quot;type&quot;</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="nb">type</span> <span class="o">=</span> <span class="s1">&#39;point&#39;</span>
+
+        <span class="k">if</span> <span class="n">distributed</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">distributed</span> <span class="o">==</span> <span class="kc">False</span><span class="p">:</span>
+
+            <span class="n">steps_ahead</span> <span class="o">=</span> <span class="n">kw</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;steps_ahead&quot;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
+
+            <span class="n">step_to</span> <span class="o">=</span> <span class="n">kw</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;step_to&quot;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
+
+            <span class="k">if</span> <span class="p">(</span><span class="n">steps_ahead</span> <span class="o">==</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">step_to</span> <span class="o">==</span> <span class="kc">None</span><span class="p">)</span> <span class="ow">or</span> <span class="p">(</span><span class="n">steps_ahead</span> <span class="o">==</span> <span class="mi">1</span> <span class="ow">or</span> <span class="n">step_to</span> <span class="o">==</span><span class="mi">1</span><span class="p">):</span>
+                <span class="k">if</span> <span class="nb">type</span> <span class="o">==</span> <span class="s1">&#39;point&#39;</span><span class="p">:</span>
+                    <span class="n">ret</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">forecast</span><span class="p">(</span><span class="n">ndata</span><span class="p">,</span> <span class="o">**</span><span class="n">kw</span><span class="p">)</span>
+                <span class="k">elif</span> <span class="nb">type</span> <span class="o">==</span> <span class="s1">&#39;interval&#39;</span><span class="p">:</span>
+                    <span class="n">ret</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">forecast_interval</span><span class="p">(</span><span class="n">ndata</span><span class="p">,</span> <span class="o">**</span><span class="n">kw</span><span class="p">)</span>
+                <span class="k">elif</span> <span class="nb">type</span> <span class="o">==</span> <span class="s1">&#39;distribution&#39;</span><span class="p">:</span>
+                    <span class="n">ret</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">forecast_distribution</span><span class="p">(</span><span class="n">ndata</span><span class="p">,</span> <span class="o">**</span><span class="n">kw</span><span class="p">)</span>
+                <span class="k">elif</span> <span class="nb">type</span> <span class="o">==</span> <span class="s1">&#39;multivariate&#39;</span><span class="p">:</span>
+                    <span class="n">ret</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">forecast_multivariate</span><span class="p">(</span><span class="n">ndata</span><span class="p">,</span> <span class="o">**</span><span class="n">kw</span><span class="p">)</span>
+            <span class="k">elif</span> <span class="n">step_to</span> <span class="o">==</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">steps_ahead</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
+                <span class="k">if</span> <span class="nb">type</span> <span class="o">==</span> <span class="s1">&#39;point&#39;</span><span class="p">:</span>
+                    <span class="n">ret</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">forecast_ahead</span><span class="p">(</span><span class="n">ndata</span><span class="p">,</span> <span class="n">steps_ahead</span><span class="p">,</span> <span class="o">**</span><span class="n">kw</span><span class="p">)</span>
+                <span class="k">elif</span> <span class="nb">type</span> <span class="o">==</span> <span class="s1">&#39;interval&#39;</span><span class="p">:</span>
+                    <span class="n">ret</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">forecast_ahead_interval</span><span class="p">(</span><span class="n">ndata</span><span class="p">,</span> <span class="n">steps_ahead</span><span class="p">,</span> <span class="o">**</span><span class="n">kw</span><span class="p">)</span>
+                <span class="k">elif</span> <span class="nb">type</span> <span class="o">==</span> <span class="s1">&#39;distribution&#39;</span><span class="p">:</span>
+                    <span class="n">ret</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">forecast_ahead_distribution</span><span class="p">(</span><span class="n">ndata</span><span class="p">,</span> <span class="n">steps_ahead</span><span class="p">,</span> <span class="o">**</span><span class="n">kw</span><span class="p">)</span>
+                <span class="k">elif</span> <span class="nb">type</span> <span class="o">==</span> <span class="s1">&#39;multivariate&#39;</span><span class="p">:</span>
+                    <span class="n">ret</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">forecast_ahead_multivariate</span><span class="p">(</span><span class="n">ndata</span><span class="p">,</span> <span class="n">steps_ahead</span><span class="p">,</span> <span class="o">**</span><span class="n">kw</span><span class="p">)</span>
+            <span class="k">elif</span> <span class="n">step_to</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
+                <span class="k">if</span> <span class="nb">type</span> <span class="o">==</span> <span class="s1">&#39;point&#39;</span><span class="p">:</span>
+                    <span class="n">ret</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">forecast_step</span><span class="p">(</span><span class="n">ndata</span><span class="p">,</span> <span class="n">step_to</span><span class="p">,</span> <span class="o">**</span><span class="n">kw</span><span class="p">)</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="s1">&#39;This model only perform point step ahead forecasts!&#39;</span><span class="p">)</span>
+
+            <span class="k">if</span> <span class="ow">not</span> <span class="p">[</span><span class="s1">&#39;point&#39;</span><span class="p">,</span> <span class="s1">&#39;interval&#39;</span><span class="p">,</span> <span class="s1">&#39;distribution&#39;</span><span class="p">,</span> <span class="s1">&#39;multivariate&#39;</span><span class="p">]</span><span class="o">.</span><span class="fm">__contains__</span><span class="p">(</span><span class="nb">type</span><span class="p">):</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;The argument </span><span class="se">\&#39;</span><span class="s1">type</span><span class="se">\&#39;</span><span class="s1"> has an unknown value.&#39;</span><span class="p">)</span>
+
+        <span class="k">else</span><span class="p">:</span>
+
+            <span class="k">if</span> <span class="n">distributed</span> <span class="o">==</span> <span class="s1">&#39;dispy&#39;</span><span class="p">:</span>
+                <span class="kn">from</span> <span class="nn">pyFTS.distributed</span> <span class="kn">import</span> <span class="n">dispy</span>
+
+                <span class="n">nodes</span> <span class="o">=</span> <span class="n">kw</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s2">&quot;nodes&quot;</span><span class="p">,</span> <span class="p">[</span><span class="s1">&#39;127.0.0.1&#39;</span><span class="p">])</span>
+                <span class="n">num_batches</span> <span class="o">=</span> <span class="n">kw</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s1">&#39;num_batches&#39;</span><span class="p">,</span> <span class="mi">10</span><span class="p">)</span>
+
+                <span class="n">ret</span> <span class="o">=</span> <span class="n">dispy</span><span class="o">.</span><span class="n">distributed_predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">kw</span><span class="p">,</span> <span class="n">nodes</span><span class="p">,</span> <span class="n">ndata</span><span class="p">,</span> <span class="n">num_batches</span><span class="p">,</span> <span class="o">**</span><span class="n">kw</span><span class="p">)</span>
+
+            <span class="k">elif</span> <span class="n">distributed</span> <span class="o">==</span> <span class="s1">&#39;spark&#39;</span><span class="p">:</span>
+                <span class="kn">from</span> <span class="nn">pyFTS.distributed</span> <span class="kn">import</span> <span class="n">spark</span>
+
+                <span class="n">ret</span> <span class="o">=</span> <span class="n">spark</span><span class="o">.</span><span class="n">distributed_predict</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">ndata</span><span class="p">,</span> <span class="n">model</span><span class="o">=</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kw</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">is_multivariate</span><span class="p">:</span>
+            <span class="n">kw</span><span class="p">[</span><span class="s1">&#39;type&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="nb">type</span>
+            <span class="n">ret</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">apply_inverse_transformations</span><span class="p">(</span><span class="n">ret</span><span class="p">,</span> <span class="n">params</span><span class="o">=</span><span class="p">[</span><span class="n">data</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span> <span class="o">-</span> <span class="mi">1</span><span class="p">:]],</span> <span class="o">**</span><span class="n">kw</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="s1">&#39;statistics&#39;</span> <span class="ow">in</span> <span class="n">kw</span><span class="p">:</span>
+            <span class="n">kwargs</span><span class="p">[</span><span class="s1">&#39;statistics&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">kw</span><span class="p">[</span><span class="s1">&#39;statistics&#39;</span><span class="p">]</span>
+
+        <span class="k">return</span> <span class="n">ret</span></div>
+
+<div class="viewcode-block" id="FTS.forecast"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.fts.FTS.forecast">[docs]</a>    <span class="k">def</span> <span class="nf">forecast</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Point forecast one step ahead</span>
+
+<span class="sd">        :param data: time series data with the minimal length equal to the max_lag of the model</span>
+<span class="sd">        :param kwargs: model specific parameters</span>
+<span class="sd">        :return: a list with the forecasted values</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="s1">&#39;This model do not perform one step ahead point forecasts!&#39;</span><span class="p">)</span></div>
+
+<div class="viewcode-block" id="FTS.forecast_interval"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.fts.FTS.forecast_interval">[docs]</a>    <span class="k">def</span> <span class="nf">forecast_interval</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Interval forecast one step ahead</span>
+
+<span class="sd">        :param data: time series data with the minimal length equal to the max_lag of the model</span>
+<span class="sd">        :param kwargs: model specific parameters</span>
+<span class="sd">        :return: a list with the prediction intervals</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="s1">&#39;This model do not perform one step ahead interval forecasts!&#39;</span><span class="p">)</span></div>
+
+<div class="viewcode-block" id="FTS.forecast_distribution"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.fts.FTS.forecast_distribution">[docs]</a>    <span class="k">def</span> <span class="nf">forecast_distribution</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Probabilistic forecast one step ahead</span>
+
+<span class="sd">        :param data: time series data with the minimal length equal to the max_lag of the model</span>
+<span class="sd">        :param kwargs: model specific parameters</span>
+<span class="sd">        :return: a list with probabilistic.ProbabilityDistribution objects representing the forecasted Probability Distributions</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="s1">&#39;This model do not perform one step ahead distribution forecasts!&#39;</span><span class="p">)</span></div>
+
+<div class="viewcode-block" id="FTS.forecast_multivariate"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.fts.FTS.forecast_multivariate">[docs]</a>    <span class="k">def</span> <span class="nf">forecast_multivariate</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Multivariate forecast one step ahead</span>
+
+<span class="sd">        :param data: Pandas dataframe with one column for each variable and with the minimal length equal to the max_lag of the model</span>
+<span class="sd">        :param kwargs: model specific parameters</span>
+<span class="sd">        :return: a Pandas Dataframe object representing the forecasted values for each variable</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="s1">&#39;This model do not perform one step ahead multivariate forecasts!&#39;</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="FTS.forecast_ahead"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.fts.FTS.forecast_ahead">[docs]</a>    <span class="k">def</span> <span class="nf">forecast_ahead</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">steps</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Point forecast from 1 to H steps ahead, where H is given by the steps parameter</span>
+
+<span class="sd">        :param data: time series data with the minimal length equal to the max_lag of the model</span>
+<span class="sd">        :param steps: the number of steps ahead to forecast (default: 1)</span>
+<span class="sd">        :keyword start_at: in the multi step forecasting, the index of the data where to start forecasting (default: 0)</span>
+<span class="sd">        :return: a list with the forecasted values</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">)</span> <span class="o">&lt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">data</span>
+
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span>
+            <span class="n">data</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
+
+        <span class="n">start</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;start_at&#39;</span><span class="p">,</span><span class="mi">0</span><span class="p">)</span>
+
+        <span class="n">ret</span> <span class="o">=</span> <span class="n">data</span><span class="p">[:</span><span class="n">start</span><span class="o">+</span><span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">]</span>
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">start</span><span class="o">+</span><span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">,</span> <span class="n">steps</span><span class="o">+</span><span class="n">start</span><span class="o">+</span><span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">):</span>
+            <span class="n">tmp</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">forecast</span><span class="p">(</span><span class="n">ret</span><span class="p">[</span><span class="n">k</span><span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">:</span><span class="n">k</span><span class="p">],</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+
+            <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">tmp</span><span class="p">,(</span><span class="nb">list</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">)):</span>
+                <span class="n">tmp</span> <span class="o">=</span> <span class="n">tmp</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
+
+            <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">tmp</span><span class="p">)</span>
+            <span class="n">data</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">tmp</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">ret</span><span class="p">[</span><span class="o">-</span><span class="n">steps</span><span class="p">:]</span></div>
+
+<div class="viewcode-block" id="FTS.forecast_ahead_interval"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.fts.FTS.forecast_ahead_interval">[docs]</a>    <span class="k">def</span> <span class="nf">forecast_ahead_interval</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">steps</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Interval forecast from 1 to H steps ahead, where H is given by the steps parameter</span>
+
+<span class="sd">        :param data: time series data with the minimal length equal to the max_lag of the model</span>
+<span class="sd">        :param steps: the number of steps ahead to forecast</span>
+<span class="sd">        :keyword start_at: in the multi step forecasting, the index of the data where to start forecasting (default: 0)</span>
+<span class="sd">        :return: a list with the forecasted intervals</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="s1">&#39;This model do not perform multi step ahead interval forecasts!&#39;</span><span class="p">)</span></div>
+
+<div class="viewcode-block" id="FTS.forecast_ahead_distribution"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.fts.FTS.forecast_ahead_distribution">[docs]</a>    <span class="k">def</span> <span class="nf">forecast_ahead_distribution</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">steps</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Probabilistic forecast from 1 to H steps ahead, where H is given by the steps parameter</span>
+
+<span class="sd">        :param data: time series data with the minimal length equal to the max_lag of the model</span>
+<span class="sd">        :param steps: the number of steps ahead to forecast</span>
+<span class="sd">        :keyword start_at: in the multi step forecasting, the index of the data where to start forecasting (default: 0)</span>
+<span class="sd">        :return: a list with the forecasted Probability Distributions</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="s1">&#39;This model do not perform multi step ahead distribution forecasts!&#39;</span><span class="p">)</span></div>
+
+<div class="viewcode-block" id="FTS.forecast_ahead_multivariate"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.fts.FTS.forecast_ahead_multivariate">[docs]</a>    <span class="k">def</span> <span class="nf">forecast_ahead_multivariate</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">steps</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Multivariate forecast n step ahead</span>
+
+<span class="sd">        :param data: Pandas dataframe with one column for each variable and with the minimal length equal to the max_lag of the model</span>
+<span class="sd">        :param steps: the number of steps ahead to forecast</span>
+<span class="sd">        :keyword start_at: in the multi step forecasting, the index of the data where to start forecasting (default: 0)</span>
+<span class="sd">        :return: a Pandas Dataframe object representing the forecasted values for each variable</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="s1">&#39;This model do not perform one step ahead multivariate forecasts!&#39;</span><span class="p">)</span></div>
+
+<div class="viewcode-block" id="FTS.forecast_step"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.fts.FTS.forecast_step">[docs]</a>    <span class="k">def</span> <span class="nf">forecast_step</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">step</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Point forecast for H steps ahead, where H is given by the step parameter</span>
+
+<span class="sd">        :param data: time series data with the minimal length equal to the max_lag of the model</span>
+<span class="sd">        :param step: the forecasting horizon (default: 1)</span>
+<span class="sd">        :keyword start_at: in the multi step forecasting, the index of the data where to start forecasting (default: 0)</span>
+<span class="sd">        :return: a list with the forecasted values</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="n">l</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+
+        <span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
+
+        <span class="k">if</span> <span class="n">l</span> <span class="o">&lt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">data</span>
+
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span>
+            <span class="n">data</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
+
+        <span class="n">start</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;start_at&#39;</span><span class="p">,</span><span class="mi">0</span><span class="p">)</span>
+
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">start</span><span class="o">+</span><span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">,</span> <span class="n">l</span><span class="p">):</span>
+            <span class="n">sample</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="n">k</span><span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">:</span><span class="n">k</span><span class="p">]</span>
+            <span class="n">tmp</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">forecast_ahead</span><span class="p">(</span><span class="n">sample</span><span class="p">,</span> <span class="n">step</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+
+            <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">tmp</span><span class="p">,(</span><span class="nb">list</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">)):</span>
+                <span class="n">tmp</span> <span class="o">=</span> <span class="n">tmp</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
+
+            <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">tmp</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">ret</span></div>
+
+<div class="viewcode-block" id="FTS.train"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.fts.FTS.train">[docs]</a>    <span class="k">def</span> <span class="nf">train</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Method specific parameter fitting</span>
+
+<span class="sd">        :param data: training time series data</span>
+<span class="sd">        :param kwargs: Method specific parameters</span>
+
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">pass</span></div>
+
+<div class="viewcode-block" id="FTS.fit"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.fts.FTS.fit">[docs]</a>    <span class="k">def</span> <span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ndata</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Fit the model&#39;s parameters based on the training data.</span>
+
+<span class="sd">        :param ndata: training time series data</span>
+<span class="sd">        :param kwargs:</span>
+
+<span class="sd">        :keyword num_batches: split the training data in num_batches to save memory during the training process</span>
+<span class="sd">        :keyword save_model: save final model on disk</span>
+<span class="sd">        :keyword batch_save: save the model between each batch</span>
+<span class="sd">        :keyword file_path: path to save the model</span>
+<span class="sd">        :keyword distributed: boolean, indicate if the training procedure will be distributed in a dispy cluster</span>
+<span class="sd">        :keyword nodes: a list with the dispy cluster nodes addresses</span>
+
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="kn">import</span> <span class="nn">datetime</span><span class="o">,</span> <span class="nn">copy</span>
+
+        <span class="n">kw</span> <span class="o">=</span> <span class="n">copy</span><span class="o">.</span><span class="n">deepcopy</span><span class="p">(</span><span class="n">kwargs</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">is_multivariate</span><span class="p">:</span>
+            <span class="n">data</span> <span class="o">=</span> <span class="n">ndata</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">data</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">apply_transformations</span><span class="p">(</span><span class="n">ndata</span><span class="p">)</span>
+
+            <span class="bp">self</span><span class="o">.</span><span class="n">original_min</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nanmin</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">original_max</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nanmax</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="s1">&#39;partitioner&#39;</span> <span class="ow">in</span> <span class="n">kw</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span> <span class="o">=</span> <span class="n">kw</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s1">&#39;partitioner&#39;</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">is_multivariate</span> <span class="ow">and</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">is_wrapper</span> <span class="ow">and</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">benchmark_only</span><span class="p">:</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span><span class="s2">&quot;Fuzzy sets were not provided for the model. Use &#39;partitioner&#39; parameter. &quot;</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="s1">&#39;order&#39;</span> <span class="ow">in</span> <span class="n">kw</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">order</span> <span class="o">=</span> <span class="n">kw</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s1">&#39;order&#39;</span><span class="p">)</span>
+
+        <span class="n">dump</span> <span class="o">=</span> <span class="n">kw</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;dump&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
+
+        <span class="n">num_batches</span> <span class="o">=</span> <span class="n">kw</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s1">&#39;num_batches&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
+
+        <span class="n">save</span> <span class="o">=</span> <span class="n">kw</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;save_model&#39;</span><span class="p">,</span> <span class="kc">False</span><span class="p">)</span>  <span class="c1"># save model on disk</span>
+
+        <span class="n">batch_save</span> <span class="o">=</span> <span class="n">kw</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;batch_save&#39;</span><span class="p">,</span> <span class="kc">False</span><span class="p">)</span> <span class="c1">#save model between batches</span>
+
+        <span class="n">file_path</span> <span class="o">=</span> <span class="n">kw</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;file_path&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
+
+        <span class="n">distributed</span> <span class="o">=</span> <span class="n">kw</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s1">&#39;distributed&#39;</span><span class="p">,</span> <span class="kc">False</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">distributed</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">distributed</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">num_batches</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="n">num_batches</span> <span class="o">=</span> <span class="mi">10</span>
+
+            <span class="k">if</span> <span class="n">distributed</span> <span class="o">==</span> <span class="s1">&#39;dispy&#39;</span><span class="p">:</span>
+                <span class="kn">from</span> <span class="nn">pyFTS.distributed</span> <span class="kn">import</span> <span class="n">dispy</span>
+                <span class="n">nodes</span> <span class="o">=</span> <span class="n">kw</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s1">&#39;nodes&#39;</span><span class="p">,</span> <span class="kc">False</span><span class="p">)</span>
+                <span class="n">train_method</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;train_method&#39;</span><span class="p">,</span> <span class="n">dispy</span><span class="o">.</span><span class="n">simple_model_train</span><span class="p">)</span>
+                <span class="n">dispy</span><span class="o">.</span><span class="n">distributed_train</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">train_method</span><span class="p">,</span> <span class="n">nodes</span><span class="p">,</span> <span class="nb">type</span><span class="p">(</span><span class="bp">self</span><span class="p">),</span> <span class="n">data</span><span class="p">,</span> <span class="n">num_batches</span><span class="p">,</span> <span class="p">{},</span>
+                                       <span class="o">**</span><span class="n">kw</span><span class="p">)</span>
+            <span class="k">elif</span> <span class="n">distributed</span> <span class="o">==</span> <span class="s1">&#39;spark&#39;</span><span class="p">:</span>
+                <span class="kn">from</span> <span class="nn">pyFTS.distributed</span> <span class="kn">import</span> <span class="n">spark</span>
+                <span class="n">url</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;url&#39;</span><span class="p">,</span> <span class="s1">&#39;spark://127.0.0.1:7077&#39;</span><span class="p">)</span>
+                <span class="n">app</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;app&#39;</span><span class="p">,</span> <span class="s1">&#39;pyFTS&#39;</span><span class="p">)</span>
+
+                <span class="n">spark</span><span class="o">.</span><span class="n">distributed_train</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">url</span><span class="o">=</span><span class="n">url</span><span class="p">,</span> <span class="n">app</span><span class="o">=</span><span class="n">app</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+
+            <span class="k">if</span> <span class="n">dump</span> <span class="o">==</span> <span class="s1">&#39;time&#39;</span><span class="p">:</span>
+                <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;[{0: %H:%M:%S}] Start training&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">datetime</span><span class="o">.</span><span class="n">datetime</span><span class="o">.</span><span class="n">now</span><span class="p">()))</span>
+
+            <span class="k">if</span> <span class="n">num_batches</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">is_wrapper</span><span class="p">:</span>
+                <span class="n">n</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+                <span class="n">batch_size</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">n</span> <span class="o">/</span> <span class="n">num_batches</span><span class="p">)</span>
+                <span class="n">bcount</span> <span class="o">=</span> <span class="mi">1</span>
+
+                <span class="n">rng</span> <span class="o">=</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">,</span> <span class="n">n</span><span class="p">,</span> <span class="n">batch_size</span><span class="p">)</span>
+
+                <span class="k">if</span> <span class="n">dump</span> <span class="o">==</span> <span class="s1">&#39;tqdm&#39;</span><span class="p">:</span>
+                    <span class="kn">from</span> <span class="nn">tqdm</span> <span class="kn">import</span> <span class="n">tqdm</span>
+
+                    <span class="n">rng</span> <span class="o">=</span> <span class="n">tqdm</span><span class="p">(</span><span class="n">rng</span><span class="p">)</span>
+
+                <span class="k">for</span> <span class="n">ct</span> <span class="ow">in</span> <span class="n">rng</span><span class="p">:</span>
+                    <span class="k">if</span> <span class="n">dump</span> <span class="o">==</span> <span class="s1">&#39;time&#39;</span><span class="p">:</span>
+                        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;[{0: %H:%M:%S}] Starting batch &quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">datetime</span><span class="o">.</span><span class="n">datetime</span><span class="o">.</span><span class="n">now</span><span class="p">())</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">bcount</span><span class="p">))</span>
+                    <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">is_multivariate</span><span class="p">:</span>
+                        <span class="n">mdata</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="n">ct</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">:</span><span class="n">ct</span> <span class="o">+</span> <span class="n">batch_size</span><span class="p">]</span>
+                    <span class="k">else</span><span class="p">:</span>
+                        <span class="n">mdata</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="n">ct</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span> <span class="p">:</span> <span class="n">ct</span> <span class="o">+</span> <span class="n">batch_size</span><span class="p">]</span>
+
+                    <span class="bp">self</span><span class="o">.</span><span class="n">train</span><span class="p">(</span><span class="n">mdata</span><span class="p">,</span> <span class="o">**</span><span class="n">kw</span><span class="p">)</span>
+
+                    <span class="k">if</span> <span class="n">batch_save</span><span class="p">:</span>
+                        <span class="n">Util</span><span class="o">.</span><span class="n">persist_obj</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span><span class="n">file_path</span><span class="p">)</span>
+
+                    <span class="k">if</span> <span class="n">dump</span> <span class="o">==</span> <span class="s1">&#39;time&#39;</span><span class="p">:</span>
+                        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;[{0: %H:%M:%S}] Finish batch &quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">datetime</span><span class="o">.</span><span class="n">datetime</span><span class="o">.</span><span class="n">now</span><span class="p">())</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">bcount</span><span class="p">))</span>
+
+                    <span class="n">bcount</span> <span class="o">+=</span> <span class="mi">1</span>
+
+            <span class="k">else</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">train</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kw</span><span class="p">)</span>
+
+            <span class="k">if</span> <span class="n">dump</span> <span class="o">==</span> <span class="s1">&#39;time&#39;</span><span class="p">:</span>
+                <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;[{0: %H:%M:%S}] Finish training&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">datetime</span><span class="o">.</span><span class="n">datetime</span><span class="o">.</span><span class="n">now</span><span class="p">()))</span>
+
+        <span class="k">if</span> <span class="n">save</span><span class="p">:</span>
+            <span class="n">Util</span><span class="o">.</span><span class="n">persist_obj</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">file_path</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="FTS.clone_parameters"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.fts.FTS.clone_parameters">[docs]</a>    <span class="k">def</span> <span class="nf">clone_parameters</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">model</span><span class="p">):</span>
+        <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Import the parameters values from other model</span>
+
+<span class="sd">        :param model: a model to clone the parameters</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">order</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">order</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">partitioner</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">lags</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">lags</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">shortname</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">shortname</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">name</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">detail</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">detail</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">is_high_order</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">is_high_order</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">min_order</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">min_order</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">has_seasonality</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">has_seasonality</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">has_point_forecasting</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">has_point_forecasting</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">has_interval_forecasting</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">has_interval_forecasting</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">has_probability_forecasting</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">has_probability_forecasting</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">is_multivariate</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">is_multivariate</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">dump</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">dump</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">transformations</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">transformations</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">transformations_param</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">transformations_param</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">original_max</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">original_max</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">original_min</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">original_min</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">auto_update</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">auto_update</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">benchmark_only</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">benchmark_only</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">indexer</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">indexer</span></div>
+
+<div class="viewcode-block" id="FTS.append_rule"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.fts.FTS.append_rule">[docs]</a>    <span class="k">def</span> <span class="nf">append_rule</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">flrg</span><span class="p">):</span>
+        <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Append FLRG rule to the model</span>
+
+<span class="sd">        :param flrg: rule</span>
+<span class="sd">        :return:</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="k">if</span> <span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()]</span> <span class="o">=</span> <span class="n">flrg</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">flrg</span><span class="o">.</span><span class="n">RHS</span><span class="p">,</span> <span class="p">(</span><span class="nb">list</span><span class="p">,</span> <span class="nb">set</span><span class="p">)):</span>
+                <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">flrg</span><span class="o">.</span><span class="n">RHS</span><span class="p">:</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()]</span><span class="o">.</span><span class="n">append_rhs</span><span class="p">(</span><span class="n">k</span><span class="p">)</span>
+            <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">flrg</span><span class="o">.</span><span class="n">RHS</span><span class="p">,</span> <span class="nb">dict</span><span class="p">):</span>
+                <span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">value</span> <span class="ow">in</span> <span class="n">flrg</span><span class="o">.</span><span class="n">RHS</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()]</span><span class="o">.</span><span class="n">append_rhs</span><span class="p">(</span><span class="n">key</span><span class="p">,</span> <span class="n">count</span><span class="o">=</span><span class="n">value</span><span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()]</span><span class="o">.</span><span class="n">append_rhs</span><span class="p">(</span><span class="n">flrg</span><span class="o">.</span><span class="n">RHS</span><span class="p">)</span></div>
+
+<div class="viewcode-block" id="FTS.merge"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.fts.FTS.merge">[docs]</a>    <span class="k">def</span> <span class="nf">merge</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">model</span><span class="p">):</span>
+        <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Merge the FLRG rules from other model</span>
+
+<span class="sd">        :param model: source model</span>
+<span class="sd">        :return:</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">flrg</span> <span class="ow">in</span> <span class="n">model</span><span class="o">.</span><span class="n">flrgs</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">append_rule</span><span class="p">(</span><span class="n">flrg</span><span class="p">)</span></div>
+
+<div class="viewcode-block" id="FTS.append_transformation"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.fts.FTS.append_transformation">[docs]</a>    <span class="k">def</span> <span class="nf">append_transformation</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">transformation</span><span class="p">):</span>
+        <span class="k">if</span> <span class="n">transformation</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">transformations</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">transformation</span><span class="p">)</span></div>
+
+<div class="viewcode-block" id="FTS.apply_transformations"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.fts.FTS.apply_transformations">[docs]</a>    <span class="k">def</span> <span class="nf">apply_transformations</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">params</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">updateUoD</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Apply the data transformations for data preprocessing</span>
+
+<span class="sd">        :param data: input data</span>
+<span class="sd">        :param params: transformation parameters</span>
+<span class="sd">        :param updateUoD:</span>
+<span class="sd">        :param kwargs:</span>
+<span class="sd">        :return: preprocessed data</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="n">ndata</span> <span class="o">=</span> <span class="n">data</span>
+        <span class="k">if</span> <span class="n">updateUoD</span><span class="p">:</span>
+            <span class="k">if</span> <span class="nb">min</span><span class="p">(</span><span class="n">data</span><span class="p">)</span> <span class="o">&lt;</span> <span class="mi">0</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">original_min</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="n">data</span><span class="p">)</span> <span class="o">*</span> <span class="mf">1.1</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">original_min</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="n">data</span><span class="p">)</span> <span class="o">*</span> <span class="mf">0.9</span>
+
+            <span class="k">if</span> <span class="nb">max</span><span class="p">(</span><span class="n">data</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">original_max</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">data</span><span class="p">)</span> <span class="o">*</span> <span class="mf">1.1</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">original_max</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">data</span><span class="p">)</span> <span class="o">*</span> <span class="mf">0.9</span>
+
+        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">transformations</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">params</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="n">params</span> <span class="o">=</span> <span class="p">[</span> <span class="kc">None</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">transformations</span><span class="p">]</span>
+
+            <span class="k">for</span> <span class="n">c</span><span class="p">,</span> <span class="n">t</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">transformations</span><span class="p">,</span> <span class="n">start</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
+                <span class="n">ndata</span> <span class="o">=</span> <span class="n">t</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">ndata</span><span class="p">,</span> <span class="n">params</span><span class="p">[</span><span class="n">c</span><span class="p">],</span> <span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">ndata</span></div>
+
+<div class="viewcode-block" id="FTS.apply_inverse_transformations"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.fts.FTS.apply_inverse_transformations">[docs]</a>    <span class="k">def</span> <span class="nf">apply_inverse_transformations</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">params</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Apply the data transformations for data postprocessing</span>
+
+<span class="sd">        :param data: input data</span>
+<span class="sd">        :param params: transformation parameters</span>
+<span class="sd">        :param updateUoD:</span>
+<span class="sd">        :param kwargs:</span>
+<span class="sd">        :return: postprocessed data</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">transformations</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">params</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="n">params</span> <span class="o">=</span> <span class="p">[</span><span class="kc">None</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">transformations</span><span class="p">]</span>
+
+            <span class="k">for</span> <span class="n">c</span><span class="p">,</span> <span class="n">t</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="nb">reversed</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">transformations</span><span class="p">),</span> <span class="n">start</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
+                <span class="n">ndata</span> <span class="o">=</span> <span class="n">t</span><span class="o">.</span><span class="n">inverse</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">params</span><span class="p">[</span><span class="n">c</span><span class="p">],</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+
+            <span class="k">return</span> <span class="n">ndata</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">data</span></div>
+
+<div class="viewcode-block" id="FTS.get_UoD"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.fts.FTS.get_UoD">[docs]</a>    <span class="k">def</span> <span class="nf">get_UoD</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Returns the interval of the known bounds of the universe of discourse (UoD), i. e.,</span>
+<span class="sd">        the known minimum and maximum values of the time series.</span>
+
+<span class="sd">        :return: A set with the lower and the upper bounds of the UoD</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">return</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">min</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">max</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">return</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">original_min</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">original_max</span><span class="p">)</span></div>
+        
+<div class="viewcode-block" id="FTS.offset"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.fts.FTS.offset">[docs]</a>    <span class="k">def</span> <span class="nf">offset</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Returns the number of lags to skip in the input test data in order to synchronize it with</span>
+<span class="sd">        the forecasted values given by the predict function. This is necessary due to the order of the</span>
+<span class="sd">        model, among other parameters.</span>
+
+<span class="sd">        :return: An integer with the number of lags to skip</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">is_high_order</span><span class="p">:</span>
+            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">return</span> <span class="mi">1</span></div>
+
+    <span class="k">def</span> <span class="fm">__str__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        String representation of the model</span>
+
+<span class="sd">        :return: a string containing the name of the model and the learned rules</span>
+<span class="sd">        (if the model was already trained)</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="n">tmp</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">+</span> <span class="s2">&quot;:</span><span class="se">\n</span><span class="s2">&quot;</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">type</span> <span class="o">==</span> <span class="s1">&#39;common&#39;</span><span class="p">:</span>
+            <span class="k">for</span> <span class="n">r</span> <span class="ow">in</span> <span class="nb">sorted</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">,</span> <span class="n">key</span><span class="o">=</span><span class="k">lambda</span> <span class="n">key</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">key</span><span class="p">]</span><span class="o">.</span><span class="n">get_midpoint</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">)):</span>
+                <span class="n">tmp</span> <span class="o">=</span> <span class="s2">&quot;</span><span class="si">{0}{1}</span><span class="se">\n</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">tmp</span><span class="p">,</span> <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">r</span><span class="p">]))</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">for</span> <span class="n">r</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">:</span>
+                <span class="n">tmp</span> <span class="o">=</span> <span class="s2">&quot;</span><span class="si">{0}{1}</span><span class="se">\n</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">tmp</span><span class="p">,</span> <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">r</span><span class="p">]))</span>
+        <span class="k">return</span> <span class="n">tmp</span>
+
+    <span class="k">def</span> <span class="fm">__len__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        The length (number of rules) of the model</span>
+
+<span class="sd">        :return: number of rules</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">return</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">)</span>
+
+<div class="viewcode-block" id="FTS.len_total"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.fts.FTS.len_total">[docs]</a>    <span class="k">def</span> <span class="nf">len_total</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Total length of the model, adding the number of terms in all rules</span>
+
+<span class="sd">        :return:</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">return</span> <span class="nb">sum</span><span class="p">([</span><span class="nb">len</span><span class="p">(</span><span class="n">k</span><span class="p">)</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">])</span></div>
+
+<div class="viewcode-block" id="FTS.reset_calculated_values"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.fts.FTS.reset_calculated_values">[docs]</a>    <span class="k">def</span> <span class="nf">reset_calculated_values</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Reset all pre-calculated values on the FLRG&#39;s</span>
+
+<span class="sd">        :return:</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="k">for</span> <span class="n">flrg</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">flrg</span><span class="p">]</span><span class="o">.</span><span class="n">reset_calculated_values</span><span class="p">()</span></div>
+
+<div class="viewcode-block" id="FTS.append_log"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.fts.FTS.append_log">[docs]</a>    <span class="k">def</span> <span class="nf">append_log</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span><span class="n">operation</span><span class="p">,</span> <span class="n">value</span><span class="p">):</span>
+        <span class="k">pass</span></div></div>
+
+
+
+
+
+
+</pre></div>
+
+            <div class="clearer"></div>
+          </div>
+        </div>
+      </div>
+      <div class="sphinxsidebar" role="navigation" aria-label="main navigation">
+        <div class="sphinxsidebarwrapper">
+<div id="searchbox" style="display: none" role="search">
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+    </div>
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+<script>$('#searchbox').show(0);</script>
+        </div>
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+          <a href="../../../genindex.html" title="General Index"
+             >index</a></li>
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+          <a href="../../../py-modindex.html" title="Python Module Index"
+             >modules</a> |</li>
+        <li class="nav-item nav-item-0"><a href="../../../index.html">pyFTS 1.6 documentation</a> &#187;</li>
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+        <li class="nav-item nav-item-this"><a href="">pyFTS.common.fts</a></li> 
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+        &#169; Copyright 2018, Machine Intelligence and Data Science Laboratory - UFMG - Brazil.
+      Created using <a href="https://www.sphinx-doc.org/">Sphinx</a> 3.1.2.
+    </div>
+  </body>
+</html>
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diff --git a/docs/build/html/_modules/pyFTS/distributed/dispy.html b/docs/build/html/_modules/pyFTS/distributed/dispy.html
new file mode 100644
index 0000000..aac5284
--- /dev/null
+++ b/docs/build/html/_modules/pyFTS/distributed/dispy.html
@@ -0,0 +1,271 @@
+
+
+<!doctype html>
+
+<html>
+  <head>
+    <meta charset="utf-8" />
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+    <title>pyFTS.distributed.dispy &#8212; pyFTS 1.6 documentation</title>
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+          <li class="nav-item nav-item-1"><a href="../../index.html" accesskey="U">Module code</a> &#187;</li>
+        <li class="nav-item nav-item-this"><a href="">pyFTS.distributed.dispy</a></li> 
+      </ul>
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+
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+      <div class="documentwrapper">
+        <div class="bodywrapper">
+          <div class="body" role="main">
+            
+  <h1>Source code for pyFTS.distributed.dispy</h1><div class="highlight"><pre>
+<span></span><span class="kn">import</span> <span class="nn">dispy</span> <span class="k">as</span> <span class="nn">dispy</span><span class="o">,</span> <span class="nn">dispy.httpd</span><span class="o">,</span> <span class="nn">logging</span>
+<span class="kn">from</span> <span class="nn">pyFTS.common</span> <span class="kn">import</span> <span class="n">Util</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+
+
+<div class="viewcode-block" id="start_dispy_cluster"><a class="viewcode-back" href="../../../pyFTS.distributed.html#pyFTS.distributed.dispy.start_dispy_cluster">[docs]</a><span class="k">def</span> <span class="nf">start_dispy_cluster</span><span class="p">(</span><span class="n">method</span><span class="p">,</span> <span class="n">nodes</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Start a new Dispy cluster on &#39;nodes&#39; to execute the method &#39;method&#39;</span>
+
+<span class="sd">    :param method: function to be executed on each cluster node</span>
+<span class="sd">    :param nodes: list of node names or IP&#39;s.</span>
+<span class="sd">    :return: the dispy cluster instance and the http_server for monitoring</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">cluster</span> <span class="o">=</span> <span class="n">dispy</span><span class="o">.</span><span class="n">JobCluster</span><span class="p">(</span><span class="n">method</span><span class="p">,</span> <span class="n">nodes</span><span class="o">=</span><span class="n">nodes</span><span class="p">,</span> <span class="n">loglevel</span><span class="o">=</span><span class="n">logging</span><span class="o">.</span><span class="n">DEBUG</span><span class="p">,</span> <span class="n">ping_interval</span><span class="o">=</span><span class="mi">1000</span><span class="p">)</span>
+
+    <span class="n">http_server</span> <span class="o">=</span> <span class="n">dispy</span><span class="o">.</span><span class="n">httpd</span><span class="o">.</span><span class="n">DispyHTTPServer</span><span class="p">(</span><span class="n">cluster</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">cluster</span><span class="p">,</span> <span class="n">http_server</span></div>
+
+
+<div class="viewcode-block" id="stop_dispy_cluster"><a class="viewcode-back" href="../../../pyFTS.distributed.html#pyFTS.distributed.dispy.stop_dispy_cluster">[docs]</a><span class="k">def</span> <span class="nf">stop_dispy_cluster</span><span class="p">(</span><span class="n">cluster</span><span class="p">,</span> <span class="n">http_server</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Stop a dispy cluster and http_server</span>
+
+<span class="sd">    :param cluster:</span>
+<span class="sd">    :param http_server:</span>
+<span class="sd">    :return:</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="c1">#cluster.wait()  # wait for all jobs to finish</span>
+
+    <span class="n">cluster</span><span class="o">.</span><span class="n">print_status</span><span class="p">()</span>
+
+    <span class="n">http_server</span><span class="o">.</span><span class="n">shutdown</span><span class="p">()</span>  <span class="c1"># this waits until browser gets all updates</span>
+    <span class="n">cluster</span><span class="o">.</span><span class="n">close</span><span class="p">()</span></div>
+
+
+<div class="viewcode-block" id="get_number_of_cpus"><a class="viewcode-back" href="../../../pyFTS.distributed.html#pyFTS.distributed.dispy.get_number_of_cpus">[docs]</a><span class="k">def</span> <span class="nf">get_number_of_cpus</span><span class="p">(</span><span class="n">cluster</span><span class="p">):</span>
+    <span class="n">cpus</span> <span class="o">=</span> <span class="mi">0</span>
+    <span class="k">for</span> <span class="n">dispy_node</span> <span class="ow">in</span> <span class="n">cluster</span><span class="o">.</span><span class="n">status</span><span class="p">()</span><span class="o">.</span><span class="n">nodes</span><span class="p">:</span>
+        <span class="n">cpus</span> <span class="o">+=</span> <span class="n">dispy_node</span><span class="o">.</span><span class="n">cpus</span>
+
+    <span class="k">return</span> <span class="n">cpus</span></div>
+
+
+<div class="viewcode-block" id="simple_model_train"><a class="viewcode-back" href="../../../pyFTS.distributed.html#pyFTS.distributed.dispy.simple_model_train">[docs]</a><span class="k">def</span> <span class="nf">simple_model_train</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">parameters</span><span class="p">):</span>
+    <span class="kn">import</span> <span class="nn">time</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Cluster function that receives a FTS instance &#39;model&#39; and train using the &#39;data&#39; and &#39;parameters&#39;</span>
+
+<span class="sd">    :param model: a FTS instance</span>
+<span class="sd">    :param data: training dataset</span>
+<span class="sd">    :param parameters: parameters for the training process</span>
+<span class="sd">    :return: the trained model</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">_start</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
+    <span class="n">model</span><span class="o">.</span><span class="n">train</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">parameters</span><span class="p">)</span>
+    <span class="n">_end</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
+    <span class="n">model</span><span class="o">.</span><span class="vm">__dict__</span><span class="p">[</span><span class="s1">&#39;training_time&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">_end</span> <span class="o">-</span> <span class="n">_start</span>
+    <span class="k">return</span> <span class="n">model</span></div>
+
+
+<div class="viewcode-block" id="distributed_train"><a class="viewcode-back" href="../../../pyFTS.distributed.html#pyFTS.distributed.dispy.distributed_train">[docs]</a><span class="k">def</span> <span class="nf">distributed_train</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">train_method</span><span class="p">,</span> <span class="n">nodes</span><span class="p">,</span> <span class="n">fts_method</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">num_batches</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span>
+                      <span class="n">train_parameters</span><span class="o">=</span><span class="p">{},</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+    <span class="kn">import</span> <span class="nn">dispy</span><span class="o">,</span> <span class="nn">dispy.httpd</span><span class="o">,</span> <span class="nn">datetime</span>
+
+    <span class="n">batch_save</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;batch_save&#39;</span><span class="p">,</span> <span class="kc">False</span><span class="p">)</span>  <span class="c1"># save model between batches</span>
+
+    <span class="n">batch_save_interval</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;batch_save_interval&#39;</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+
+    <span class="n">file_path</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;file_path&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
+
+    <span class="n">cluster</span><span class="p">,</span> <span class="n">http_server</span> <span class="o">=</span> <span class="n">start_dispy_cluster</span><span class="p">(</span><span class="n">train_method</span><span class="p">,</span> <span class="n">nodes</span><span class="p">)</span>
+
+    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;[{0: %H:%M:%S}] Distrituted Train Started with </span><span class="si">{1}</span><span class="s2"> CPU&#39;s&quot;</span>
+          <span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">datetime</span><span class="o">.</span><span class="n">datetime</span><span class="o">.</span><span class="n">now</span><span class="p">(),</span> <span class="n">get_number_of_cpus</span><span class="p">(</span><span class="n">cluster</span><span class="p">)))</span>
+
+    <span class="n">jobs</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="n">n</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+    <span class="n">batch_size</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">n</span> <span class="o">/</span> <span class="n">num_batches</span><span class="p">)</span>
+    <span class="n">bcount</span> <span class="o">=</span> <span class="mi">1</span>
+    <span class="k">for</span> <span class="n">ct</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">order</span><span class="p">,</span> <span class="n">n</span><span class="p">,</span> <span class="n">batch_size</span><span class="p">):</span>
+        <span class="k">if</span> <span class="n">model</span><span class="o">.</span><span class="n">is_multivariate</span><span class="p">:</span>
+            <span class="n">ndata</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="n">ct</span> <span class="o">-</span> <span class="n">model</span><span class="o">.</span><span class="n">order</span><span class="p">:</span><span class="n">ct</span> <span class="o">+</span> <span class="n">batch_size</span><span class="p">]</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">ndata</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="n">ct</span> <span class="o">-</span> <span class="n">model</span><span class="o">.</span><span class="n">order</span><span class="p">:</span> <span class="n">ct</span> <span class="o">+</span> <span class="n">batch_size</span><span class="p">]</span>
+
+        <span class="n">tmp_model</span> <span class="o">=</span> <span class="n">fts_method</span><span class="p">()</span>
+
+        <span class="n">tmp_model</span><span class="o">.</span><span class="n">clone_parameters</span><span class="p">(</span><span class="n">model</span><span class="p">)</span>
+
+        <span class="n">job</span> <span class="o">=</span> <span class="n">cluster</span><span class="o">.</span><span class="n">submit</span><span class="p">(</span><span class="n">tmp_model</span><span class="p">,</span> <span class="n">ndata</span><span class="p">,</span> <span class="n">train_parameters</span><span class="p">)</span>
+        <span class="n">job</span><span class="o">.</span><span class="n">id</span> <span class="o">=</span> <span class="n">bcount</span>  <span class="c1"># associate an ID to identify jobs (if needed later)</span>
+        <span class="n">jobs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">job</span><span class="p">)</span>
+
+        <span class="n">bcount</span> <span class="o">+=</span> <span class="mi">1</span>
+
+    <span class="k">for</span> <span class="n">job</span> <span class="ow">in</span> <span class="n">jobs</span><span class="p">:</span>
+        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;[{0: %H:%M:%S}] Processing batch &quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">datetime</span><span class="o">.</span><span class="n">datetime</span><span class="o">.</span><span class="n">now</span><span class="p">())</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">job</span><span class="o">.</span><span class="n">id</span><span class="p">))</span>
+        <span class="n">tmp</span> <span class="o">=</span> <span class="n">job</span><span class="p">()</span>
+        <span class="k">if</span> <span class="n">job</span><span class="o">.</span><span class="n">status</span> <span class="o">==</span> <span class="n">dispy</span><span class="o">.</span><span class="n">DispyJob</span><span class="o">.</span><span class="n">Finished</span> <span class="ow">and</span> <span class="n">tmp</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">model</span><span class="o">.</span><span class="n">merge</span><span class="p">(</span><span class="n">tmp</span><span class="p">)</span>
+            <span class="k">if</span> <span class="s1">&#39;training_time&#39;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">model</span><span class="o">.</span><span class="vm">__dict__</span><span class="p">:</span>
+                <span class="n">model</span><span class="o">.</span><span class="vm">__dict__</span><span class="p">[</span><span class="s1">&#39;training_time&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
+            <span class="n">model</span><span class="o">.</span><span class="vm">__dict__</span><span class="p">[</span><span class="s1">&#39;training_time&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">tmp</span><span class="o">.</span><span class="vm">__dict__</span><span class="p">[</span><span class="s1">&#39;training_time&#39;</span><span class="p">])</span>
+
+            <span class="k">if</span> <span class="n">batch_save</span> <span class="ow">and</span> <span class="p">(</span><span class="n">job</span><span class="o">.</span><span class="n">id</span> <span class="o">%</span> <span class="n">batch_save_interval</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+                <span class="n">Util</span><span class="o">.</span><span class="n">persist_obj</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">file_path</span><span class="p">)</span>
+
+        <span class="k">else</span><span class="p">:</span>
+            <span class="nb">print</span><span class="p">(</span><span class="n">job</span><span class="o">.</span><span class="n">exception</span><span class="p">)</span>
+            <span class="nb">print</span><span class="p">(</span><span class="n">job</span><span class="o">.</span><span class="n">stdout</span><span class="p">)</span>
+
+        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;[{0: %H:%M:%S}] Finished batch &quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">datetime</span><span class="o">.</span><span class="n">datetime</span><span class="o">.</span><span class="n">now</span><span class="p">())</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">job</span><span class="o">.</span><span class="n">id</span><span class="p">))</span>
+
+    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;[{0: %H:%M:%S}] Distrituted Train Finished&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">datetime</span><span class="o">.</span><span class="n">datetime</span><span class="o">.</span><span class="n">now</span><span class="p">()))</span>
+
+    <span class="n">stop_dispy_cluster</span><span class="p">(</span><span class="n">cluster</span><span class="p">,</span> <span class="n">http_server</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">model</span></div>
+
+
+<div class="viewcode-block" id="simple_model_predict"><a class="viewcode-back" href="../../../pyFTS.distributed.html#pyFTS.distributed.dispy.simple_model_predict">[docs]</a><span class="k">def</span> <span class="nf">simple_model_predict</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">parameters</span><span class="p">):</span>
+    <span class="kn">import</span> <span class="nn">time</span>
+    <span class="n">_start</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
+    <span class="n">forecasts</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">parameters</span><span class="p">)</span>
+    <span class="n">_stop</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
+    <span class="k">return</span> <span class="n">forecasts</span><span class="p">,</span> <span class="n">_stop</span> <span class="o">-</span> <span class="n">_start</span></div>
+
+
+<div class="viewcode-block" id="distributed_predict"><a class="viewcode-back" href="../../../pyFTS.distributed.html#pyFTS.distributed.dispy.distributed_predict">[docs]</a><span class="k">def</span> <span class="nf">distributed_predict</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">parameters</span><span class="p">,</span> <span class="n">nodes</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">num_batches</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+    <span class="kn">import</span> <span class="nn">dispy</span><span class="o">,</span> <span class="nn">dispy.httpd</span>
+
+    <span class="n">cluster</span><span class="p">,</span> <span class="n">http_server</span> <span class="o">=</span> <span class="n">start_dispy_cluster</span><span class="p">(</span><span class="n">simple_model_predict</span><span class="p">,</span> <span class="n">nodes</span><span class="p">)</span>
+
+    <span class="n">jobs</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="n">n</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+    <span class="n">batch_size</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">n</span> <span class="o">/</span> <span class="n">num_batches</span><span class="p">)</span>
+    <span class="n">bcount</span> <span class="o">=</span> <span class="mi">1</span>
+    <span class="k">for</span> <span class="n">ct</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">order</span><span class="p">,</span> <span class="n">n</span><span class="p">,</span> <span class="n">batch_size</span><span class="p">):</span>
+        <span class="k">if</span> <span class="n">model</span><span class="o">.</span><span class="n">is_multivariate</span><span class="p">:</span>
+            <span class="n">ndata</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="n">ct</span> <span class="o">-</span> <span class="n">model</span><span class="o">.</span><span class="n">order</span><span class="p">:</span><span class="n">ct</span> <span class="o">+</span> <span class="n">batch_size</span><span class="p">]</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">ndata</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="n">ct</span> <span class="o">-</span> <span class="n">model</span><span class="o">.</span><span class="n">order</span><span class="p">:</span> <span class="n">ct</span> <span class="o">+</span> <span class="n">batch_size</span><span class="p">]</span>
+
+        <span class="n">job</span> <span class="o">=</span> <span class="n">cluster</span><span class="o">.</span><span class="n">submit</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">ndata</span><span class="p">,</span> <span class="n">parameters</span><span class="p">)</span>
+        <span class="n">job</span><span class="o">.</span><span class="n">id</span> <span class="o">=</span> <span class="n">bcount</span>  <span class="c1"># associate an ID to identify jobs (if needed later)</span>
+        <span class="n">jobs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">job</span><span class="p">)</span>
+
+        <span class="n">bcount</span> <span class="o">+=</span> <span class="mi">1</span>
+
+    <span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
+
+    <span class="k">for</span> <span class="n">job</span> <span class="ow">in</span> <span class="n">jobs</span><span class="p">:</span>
+        <span class="n">tmp</span> <span class="o">=</span> <span class="n">job</span><span class="p">()</span>
+        <span class="k">if</span> <span class="n">job</span><span class="o">.</span><span class="n">status</span> <span class="o">==</span> <span class="n">dispy</span><span class="o">.</span><span class="n">DispyJob</span><span class="o">.</span><span class="n">Finished</span> <span class="ow">and</span> <span class="n">tmp</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">job</span><span class="o">.</span><span class="n">id</span> <span class="o">&lt;</span> <span class="n">batch_size</span><span class="p">:</span>
+                <span class="n">ret</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">tmp</span><span class="p">[</span><span class="mi">0</span><span class="p">][:</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">ret</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">tmp</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
+
+            <span class="k">if</span> <span class="s1">&#39;forecasting_time&#39;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">model</span><span class="o">.</span><span class="vm">__dict__</span><span class="p">:</span>
+                <span class="n">model</span><span class="o">.</span><span class="vm">__dict__</span><span class="p">[</span><span class="s1">&#39;forecasting_time&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
+            <span class="n">model</span><span class="o">.</span><span class="vm">__dict__</span><span class="p">[</span><span class="s1">&#39;forecasting_time&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">tmp</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
+
+        <span class="k">else</span><span class="p">:</span>
+            <span class="nb">print</span><span class="p">(</span><span class="n">job</span><span class="o">.</span><span class="n">exception</span><span class="p">)</span>
+            <span class="nb">print</span><span class="p">(</span><span class="n">job</span><span class="o">.</span><span class="n">stdout</span><span class="p">)</span>
+
+    <span class="n">stop_dispy_cluster</span><span class="p">(</span><span class="n">cluster</span><span class="p">,</span> <span class="n">http_server</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">ret</span></div>
+</pre></div>
+
+            <div class="clearer"></div>
+          </div>
+        </div>
+      </div>
+      <div class="sphinxsidebar" role="navigation" aria-label="main navigation">
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+<div id="searchbox" style="display: none" role="search">
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+        <li class="nav-item nav-item-0"><a href="../../../index.html">pyFTS 1.6 documentation</a> &#187;</li>
+          <li class="nav-item nav-item-1"><a href="../../index.html" >Module code</a> &#187;</li>
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+  </body>
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\ No newline at end of file
diff --git a/docs/build/html/_modules/pyFTS/hyperparam/Evolutionary.html b/docs/build/html/_modules/pyFTS/hyperparam/Evolutionary.html
new file mode 100644
index 0000000..9b4bc45
--- /dev/null
+++ b/docs/build/html/_modules/pyFTS/hyperparam/Evolutionary.html
@@ -0,0 +1,725 @@
+
+
+<!doctype html>
+
+<html>
+  <head>
+    <meta charset="utf-8" />
+    <meta name="viewport" content="width=device-width, initial-scale=1.0"><script type="text/javascript">
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+    <title>pyFTS.hyperparam.Evolutionary &#8212; pyFTS 1.6 documentation</title>
+    <link rel="stylesheet" href="../../../_static/bizstyle.css" type="text/css" />
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+          <a href="../../../py-modindex.html" title="Python Module Index"
+             >modules</a> |</li>
+        <li class="nav-item nav-item-0"><a href="../../../index.html">pyFTS 1.6 documentation</a> &#187;</li>
+          <li class="nav-item nav-item-1"><a href="../../index.html" accesskey="U">Module code</a> &#187;</li>
+        <li class="nav-item nav-item-this"><a href="">pyFTS.hyperparam.Evolutionary</a></li> 
+      </ul>
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+      <div class="documentwrapper">
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+          <div class="body" role="main">
+            
+  <h1>Source code for pyFTS.hyperparam.Evolutionary</h1><div class="highlight"><pre>
+<span></span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">Distributed Evolutionary Hyperparameter Optimization (DEHO) for MVFTS</span>
+<span class="sd">&quot;&quot;&quot;</span>
+
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+<span class="kn">import</span> <span class="nn">math</span>
+<span class="kn">import</span> <span class="nn">time</span>
+<span class="kn">from</span> <span class="nn">functools</span> <span class="kn">import</span> <span class="n">reduce</span>
+<span class="kn">from</span> <span class="nn">operator</span> <span class="kn">import</span> <span class="n">itemgetter</span>
+
+<span class="kn">import</span> <span class="nn">random</span>
+<span class="kn">from</span> <span class="nn">pyFTS.common</span> <span class="kn">import</span> <span class="n">Util</span>
+<span class="kn">from</span> <span class="nn">pyFTS.benchmarks</span> <span class="kn">import</span> <span class="n">Measures</span>
+<span class="kn">from</span> <span class="nn">pyFTS.partitioners</span> <span class="kn">import</span> <span class="n">Grid</span><span class="p">,</span> <span class="n">Entropy</span>  <span class="c1"># , Huarng</span>
+<span class="kn">from</span> <span class="nn">pyFTS.common</span> <span class="kn">import</span> <span class="n">Membership</span>
+<span class="kn">from</span> <span class="nn">pyFTS.models</span> <span class="kn">import</span> <span class="n">hofts</span><span class="p">,</span> <span class="n">ifts</span><span class="p">,</span> <span class="n">pwfts</span>
+<span class="kn">from</span> <span class="nn">pyFTS.hyperparam</span> <span class="kn">import</span> <span class="n">Util</span> <span class="k">as</span> <span class="n">hUtil</span>
+
+
+<span class="n">__measures</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;f1&#39;</span><span class="p">,</span> <span class="s1">&#39;f2&#39;</span><span class="p">,</span> <span class="s1">&#39;rmse&#39;</span><span class="p">,</span> <span class="s1">&#39;size&#39;</span><span class="p">]</span>
+
+
+<div class="viewcode-block" id="genotype"><a class="viewcode-back" href="../../../pyFTS.hyperparam.html#pyFTS.hyperparam.Evolutionary.genotype">[docs]</a><span class="k">def</span> <span class="nf">genotype</span><span class="p">(</span><span class="n">mf</span><span class="p">,</span> <span class="n">npart</span><span class="p">,</span> <span class="n">partitioner</span><span class="p">,</span> <span class="n">order</span><span class="p">,</span> <span class="n">alpha</span><span class="p">,</span> <span class="n">lags</span><span class="p">,</span> <span class="n">f1</span><span class="p">,</span> <span class="n">f2</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Create the individual genotype</span>
+
+<span class="sd">    :param mf: membership function</span>
+<span class="sd">    :param npart: number of partitions</span>
+<span class="sd">    :param partitioner: partitioner method</span>
+<span class="sd">    :param order: model order</span>
+<span class="sd">    :param alpha: alpha-cut</span>
+<span class="sd">    :param lags: array with lag indexes</span>
+<span class="sd">    :param f1: accuracy fitness value</span>
+<span class="sd">    :param f2: parsimony fitness value</span>
+<span class="sd">    :return: the genotype, a dictionary with all hyperparameters</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">ind</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span><span class="n">mf</span><span class="o">=</span><span class="n">mf</span><span class="p">,</span> <span class="n">npart</span><span class="o">=</span><span class="n">npart</span><span class="p">,</span> <span class="n">partitioner</span><span class="o">=</span><span class="n">partitioner</span><span class="p">,</span> <span class="n">order</span><span class="o">=</span><span class="n">order</span><span class="p">,</span>
+               <span class="n">alpha</span><span class="o">=</span><span class="n">alpha</span><span class="p">,</span> <span class="n">lags</span><span class="o">=</span><span class="n">lags</span><span class="p">,</span> <span class="n">f1</span><span class="o">=</span><span class="n">f1</span><span class="p">,</span> <span class="n">f2</span><span class="o">=</span><span class="n">f2</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">ind</span></div>
+
+
+<div class="viewcode-block" id="random_genotype"><a class="viewcode-back" href="../../../pyFTS.hyperparam.html#pyFTS.hyperparam.Evolutionary.random_genotype">[docs]</a><span class="k">def</span> <span class="nf">random_genotype</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Create random genotype</span>
+
+<span class="sd">    :return: the genotype, a dictionary with all hyperparameters</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">order</span> <span class="o">=</span> <span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
+    <span class="n">lags</span> <span class="o">=</span> <span class="p">[</span><span class="n">k</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">order</span><span class="o">+</span><span class="mi">1</span><span class="p">)]</span>
+    <span class="k">return</span> <span class="n">genotype</span><span class="p">(</span>
+        <span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">4</span><span class="p">),</span>
+        <span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">100</span><span class="p">),</span>
+        <span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span>
+        <span class="n">order</span><span class="p">,</span>
+        <span class="n">random</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="o">.</span><span class="mi">5</span><span class="p">),</span>
+        <span class="n">lags</span><span class="p">,</span>
+        <span class="kc">None</span><span class="p">,</span>
+        <span class="kc">None</span>
+    <span class="p">)</span></div>
+
+
+<span class="c1">#</span>
+<div class="viewcode-block" id="initial_population"><a class="viewcode-back" href="../../../pyFTS.hyperparam.html#pyFTS.hyperparam.Evolutionary.initial_population">[docs]</a><span class="k">def</span> <span class="nf">initial_population</span><span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Create a random population of size n</span>
+
+<span class="sd">    :param n: the size of the population</span>
+<span class="sd">    :return: a list with n random individuals</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">create_random_individual</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;random_individual&#39;</span><span class="p">,</span> <span class="n">random_genotype</span><span class="p">)</span>
+
+    <span class="n">pop</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n</span><span class="p">):</span>
+        <span class="n">pop</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">create_random_individual</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">))</span>
+    <span class="k">return</span> <span class="n">pop</span></div>
+
+
+<div class="viewcode-block" id="phenotype"><a class="viewcode-back" href="../../../pyFTS.hyperparam.html#pyFTS.hyperparam.Evolutionary.phenotype">[docs]</a><span class="k">def</span> <span class="nf">phenotype</span><span class="p">(</span><span class="n">individual</span><span class="p">,</span> <span class="n">train</span><span class="p">,</span> <span class="n">fts_method</span><span class="p">,</span> <span class="n">parameters</span><span class="o">=</span><span class="p">{},</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Instantiate the genotype, creating a fitted model with the genotype hyperparameters</span>
+
+<span class="sd">    :param individual: a genotype</span>
+<span class="sd">    :param train: the training dataset</span>
+<span class="sd">    :param fts_method: the FTS method </span>
+<span class="sd">    :param parameters: dict with model specific arguments for fit method.</span>
+<span class="sd">    :return: a fitted FTS model</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="kn">from</span> <span class="nn">pyFTS.models</span> <span class="kn">import</span> <span class="n">hofts</span><span class="p">,</span> <span class="n">ifts</span><span class="p">,</span> <span class="n">pwfts</span>
+
+    <span class="k">if</span> <span class="n">individual</span><span class="p">[</span><span class="s1">&#39;mf&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
+        <span class="n">mf</span> <span class="o">=</span> <span class="n">Membership</span><span class="o">.</span><span class="n">trimf</span>
+    <span class="k">elif</span> <span class="n">individual</span><span class="p">[</span><span class="s1">&#39;mf&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
+        <span class="n">mf</span> <span class="o">=</span> <span class="n">Membership</span><span class="o">.</span><span class="n">trapmf</span>
+    <span class="k">elif</span> <span class="n">individual</span><span class="p">[</span><span class="s1">&#39;mf&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="mi">3</span> <span class="ow">and</span> <span class="n">individual</span><span class="p">[</span><span class="s1">&#39;partitioner&#39;</span><span class="p">]</span> <span class="o">!=</span> <span class="mi">2</span><span class="p">:</span>
+        <span class="n">mf</span> <span class="o">=</span> <span class="n">Membership</span><span class="o">.</span><span class="n">gaussmf</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">mf</span> <span class="o">=</span> <span class="n">Membership</span><span class="o">.</span><span class="n">trimf</span>
+
+    <span class="k">if</span> <span class="n">individual</span><span class="p">[</span><span class="s1">&#39;partitioner&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
+        <span class="n">partitioner</span> <span class="o">=</span> <span class="n">Grid</span><span class="o">.</span><span class="n">GridPartitioner</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">train</span><span class="p">,</span> <span class="n">npart</span><span class="o">=</span><span class="n">individual</span><span class="p">[</span><span class="s1">&#39;npart&#39;</span><span class="p">],</span> <span class="n">func</span><span class="o">=</span><span class="n">mf</span><span class="p">)</span>
+    <span class="k">elif</span> <span class="n">individual</span><span class="p">[</span><span class="s1">&#39;partitioner&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
+        <span class="n">partitioner</span> <span class="o">=</span> <span class="n">Entropy</span><span class="o">.</span><span class="n">EntropyPartitioner</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">train</span><span class="p">,</span> <span class="n">npart</span><span class="o">=</span><span class="n">individual</span><span class="p">[</span><span class="s1">&#39;npart&#39;</span><span class="p">],</span> <span class="n">func</span><span class="o">=</span><span class="n">mf</span><span class="p">)</span>
+
+    <span class="n">model</span> <span class="o">=</span> <span class="n">fts_method</span><span class="p">(</span><span class="n">partitioner</span><span class="o">=</span><span class="n">partitioner</span><span class="p">,</span>
+                                       <span class="n">lags</span><span class="o">=</span><span class="n">individual</span><span class="p">[</span><span class="s1">&#39;lags&#39;</span><span class="p">],</span>
+                                       <span class="n">alpha_cut</span><span class="o">=</span><span class="n">individual</span><span class="p">[</span><span class="s1">&#39;alpha&#39;</span><span class="p">],</span>
+                                       <span class="n">order</span><span class="o">=</span><span class="n">individual</span><span class="p">[</span><span class="s1">&#39;order&#39;</span><span class="p">])</span>
+
+    <span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">train</span><span class="p">,</span> <span class="o">**</span><span class="n">parameters</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">model</span></div>
+
+
+<div class="viewcode-block" id="evaluate"><a class="viewcode-back" href="../../../pyFTS.hyperparam.html#pyFTS.hyperparam.Evolutionary.evaluate">[docs]</a><span class="k">def</span> <span class="nf">evaluate</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">individual</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Evaluate an individual using a sliding window cross validation over the dataset.</span>
+
+<span class="sd">    :param dataset: Evaluation dataset</span>
+<span class="sd">    :param individual: genotype to be tested</span>
+<span class="sd">    :param window_size: The length of scrolling window for train/test on dataset</span>
+<span class="sd">    :param train_rate: The train/test split ([0,1])</span>
+<span class="sd">    :param increment_rate: The increment of the scrolling window, relative to the window_size ([0,1])</span>
+<span class="sd">    :param parameters: dict with model specific arguments for fit method.</span>
+<span class="sd">    :return: a tuple (len_lags, rmse) with the parsimony fitness value and the accuracy fitness value</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="kn">from</span> <span class="nn">pyFTS.models</span> <span class="kn">import</span> <span class="n">hofts</span><span class="p">,</span> <span class="n">ifts</span><span class="p">,</span> <span class="n">pwfts</span>
+    <span class="kn">from</span> <span class="nn">pyFTS.common</span> <span class="kn">import</span> <span class="n">Util</span>
+    <span class="kn">from</span> <span class="nn">pyFTS.benchmarks</span> <span class="kn">import</span> <span class="n">Measures</span>
+    <span class="kn">from</span> <span class="nn">pyFTS.hyperparam.Evolutionary</span> <span class="kn">import</span> <span class="n">phenotype</span><span class="p">,</span> <span class="n">__measures</span>
+    <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+
+    <span class="n">window_size</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;window_size&#39;</span><span class="p">,</span> <span class="mi">800</span><span class="p">)</span>
+    <span class="n">train_rate</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;train_rate&#39;</span><span class="p">,</span> <span class="o">.</span><span class="mi">8</span><span class="p">)</span>
+    <span class="n">increment_rate</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;increment_rate&#39;</span><span class="p">,</span> <span class="o">.</span><span class="mi">2</span><span class="p">)</span>
+    <span class="n">fts_method</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;fts_method&#39;</span><span class="p">,</span> <span class="n">hofts</span><span class="o">.</span><span class="n">WeightedHighOrderFTS</span><span class="p">)</span>
+    <span class="n">parameters</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;parameters&#39;</span><span class="p">,{})</span>
+
+    <span class="k">if</span> <span class="n">individual</span><span class="p">[</span><span class="s1">&#39;f1&#39;</span><span class="p">]</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">individual</span><span class="p">[</span><span class="s1">&#39;f2&#39;</span><span class="p">]</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="k">return</span> <span class="p">{</span> <span class="n">key</span><span class="p">:</span> <span class="n">individual</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">__measures</span> <span class="p">}</span>
+
+    <span class="n">errors</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="n">lengths</span> <span class="o">=</span> <span class="p">[]</span>
+
+    <span class="k">for</span> <span class="n">count</span><span class="p">,</span> <span class="n">train</span><span class="p">,</span> <span class="n">test</span> <span class="ow">in</span> <span class="n">Util</span><span class="o">.</span><span class="n">sliding_window</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">window_size</span><span class="p">,</span> <span class="n">train</span><span class="o">=</span><span class="n">train_rate</span><span class="p">,</span> <span class="n">inc</span><span class="o">=</span><span class="n">increment_rate</span><span class="p">):</span>
+
+        <span class="k">try</span><span class="p">:</span>
+
+            <span class="n">model</span> <span class="o">=</span> <span class="n">phenotype</span><span class="p">(</span><span class="n">individual</span><span class="p">,</span> <span class="n">train</span><span class="p">,</span> <span class="n">fts_method</span><span class="o">=</span><span class="n">fts_method</span><span class="p">,</span> <span class="n">parameters</span><span class="o">=</span><span class="n">parameters</span><span class="p">)</span>
+
+            <span class="n">forecasts</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">test</span><span class="p">)</span>
+
+            <span class="n">rmse</span> <span class="o">=</span> <span class="n">Measures</span><span class="o">.</span><span class="n">rmse</span><span class="p">(</span><span class="n">test</span><span class="p">[</span><span class="n">model</span><span class="o">.</span><span class="n">max_lag</span><span class="p">:],</span> <span class="n">forecasts</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
+            <span class="n">lengths</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">model</span><span class="p">))</span>
+
+            <span class="n">errors</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">rmse</span><span class="p">)</span>
+
+        <span class="k">except</span><span class="p">:</span>
+            <span class="n">lengths</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">)</span>
+            <span class="n">errors</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">)</span>
+
+    <span class="k">try</span><span class="p">:</span>
+        <span class="n">_lags</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">lags</span><span class="p">)</span> <span class="o">*</span> <span class="mi">100</span>
+
+        <span class="n">_rmse</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nanmean</span><span class="p">(</span><span class="n">errors</span><span class="p">)</span>
+        <span class="n">_len</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nanmean</span><span class="p">(</span><span class="n">lengths</span><span class="p">)</span>
+
+        <span class="n">f1</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">([</span><span class="o">.</span><span class="mi">6</span> <span class="o">*</span> <span class="n">_rmse</span><span class="p">,</span> <span class="o">.</span><span class="mi">4</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">nanstd</span><span class="p">(</span><span class="n">errors</span><span class="p">)])</span>
+        <span class="n">f2</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">([</span><span class="o">.</span><span class="mi">4</span> <span class="o">*</span> <span class="n">_len</span><span class="p">,</span> <span class="o">.</span><span class="mi">6</span> <span class="o">*</span> <span class="n">_lags</span><span class="p">])</span>
+
+        <span class="k">return</span> <span class="p">{</span><span class="s1">&#39;f1&#39;</span><span class="p">:</span> <span class="n">f1</span><span class="p">,</span> <span class="s1">&#39;f2&#39;</span><span class="p">:</span> <span class="n">f2</span><span class="p">,</span> <span class="s1">&#39;rmse&#39;</span><span class="p">:</span> <span class="n">_rmse</span><span class="p">,</span> <span class="s1">&#39;size&#39;</span><span class="p">:</span> <span class="n">_len</span> <span class="p">}</span>
+    <span class="k">except</span><span class="p">:</span>
+        <span class="k">return</span> <span class="p">{</span><span class="s1">&#39;f1&#39;</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">inf</span><span class="p">,</span> <span class="s1">&#39;f2&#39;</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">inf</span><span class="p">,</span> <span class="s1">&#39;rmse&#39;</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">inf</span><span class="p">,</span> <span class="s1">&#39;size&#39;</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">inf</span><span class="p">}</span></div>
+
+
+<div class="viewcode-block" id="tournament"><a class="viewcode-back" href="../../../pyFTS.hyperparam.html#pyFTS.hyperparam.Evolutionary.tournament">[docs]</a><span class="k">def</span> <span class="nf">tournament</span><span class="p">(</span><span class="n">population</span><span class="p">,</span> <span class="n">objective</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Simple tournament selection strategy.</span>
+
+<span class="sd">    :param population: the population</span>
+<span class="sd">    :param objective: the objective to be considered on tournament</span>
+<span class="sd">    :return:</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">n</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">population</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span>
+
+    <span class="n">r1</span> <span class="o">=</span> <span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">n</span><span class="p">)</span> <span class="k">if</span> <span class="n">n</span> <span class="o">&gt;</span> <span class="mi">2</span> <span class="k">else</span> <span class="mi">0</span>
+    <span class="n">r2</span> <span class="o">=</span> <span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">n</span><span class="p">)</span> <span class="k">if</span> <span class="n">n</span> <span class="o">&gt;</span> <span class="mi">2</span> <span class="k">else</span> <span class="mi">1</span>
+    <span class="n">ix</span> <span class="o">=</span> <span class="n">r1</span> <span class="k">if</span> <span class="n">population</span><span class="p">[</span><span class="n">r1</span><span class="p">][</span><span class="n">objective</span><span class="p">]</span> <span class="o">&lt;</span> <span class="n">population</span><span class="p">[</span><span class="n">r2</span><span class="p">][</span><span class="n">objective</span><span class="p">]</span> <span class="k">else</span> <span class="n">r2</span>
+    <span class="k">return</span> <span class="n">population</span><span class="p">[</span><span class="n">ix</span><span class="p">]</span></div>
+
+
+<div class="viewcode-block" id="double_tournament"><a class="viewcode-back" href="../../../pyFTS.hyperparam.html#pyFTS.hyperparam.Evolutionary.double_tournament">[docs]</a><span class="k">def</span> <span class="nf">double_tournament</span><span class="p">(</span><span class="n">population</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Double tournament selection strategy.</span>
+
+<span class="sd">    :param population:</span>
+<span class="sd">    :return:</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">ancestor1</span> <span class="o">=</span> <span class="n">tournament</span><span class="p">(</span><span class="n">population</span><span class="p">,</span> <span class="s1">&#39;f1&#39;</span><span class="p">)</span>
+    <span class="n">ancestor2</span> <span class="o">=</span> <span class="n">tournament</span><span class="p">(</span><span class="n">population</span><span class="p">,</span> <span class="s1">&#39;f1&#39;</span><span class="p">)</span>
+
+    <span class="n">selected</span> <span class="o">=</span> <span class="n">tournament</span><span class="p">([</span><span class="n">ancestor1</span><span class="p">,</span> <span class="n">ancestor2</span><span class="p">],</span> <span class="s1">&#39;f2&#39;</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">selected</span></div>
+
+
+<div class="viewcode-block" id="lag_crossover2"><a class="viewcode-back" href="../../../pyFTS.hyperparam.html#pyFTS.hyperparam.Evolutionary.lag_crossover2">[docs]</a><span class="k">def</span> <span class="nf">lag_crossover2</span><span class="p">(</span><span class="n">best</span><span class="p">,</span> <span class="n">worst</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Cross over two lag genes</span>
+
+<span class="sd">    :param best: best genotype</span>
+<span class="sd">    :param worst: worst genotype</span>
+<span class="sd">    :return: a tuple (order, lags)</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">order</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="o">.</span><span class="mi">7</span> <span class="o">*</span> <span class="n">best</span><span class="p">[</span><span class="s1">&#39;order&#39;</span><span class="p">]</span> <span class="o">+</span> <span class="o">.</span><span class="mi">3</span> <span class="o">*</span> <span class="n">worst</span><span class="p">[</span><span class="s1">&#39;order&#39;</span><span class="p">]))</span>
+    <span class="n">lags</span> <span class="o">=</span> <span class="p">[]</span>
+
+    <span class="n">min_order</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="n">best</span><span class="p">[</span><span class="s1">&#39;order&#39;</span><span class="p">],</span> <span class="n">worst</span><span class="p">[</span><span class="s1">&#39;order&#39;</span><span class="p">])</span>
+
+    <span class="n">max_order</span> <span class="o">=</span> <span class="n">best</span> <span class="k">if</span> <span class="n">best</span><span class="p">[</span><span class="s1">&#39;order&#39;</span><span class="p">]</span> <span class="o">&gt;</span> <span class="n">min_order</span> <span class="k">else</span> <span class="n">worst</span>
+
+    <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">order</span><span class="p">):</span>
+        <span class="k">if</span> <span class="n">k</span> <span class="o">&lt;</span> <span class="n">min_order</span><span class="p">:</span>
+            <span class="n">lags</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="o">.</span><span class="mi">7</span> <span class="o">*</span> <span class="n">best</span><span class="p">[</span><span class="s1">&#39;lags&#39;</span><span class="p">][</span><span class="n">k</span><span class="p">]</span> <span class="o">+</span> <span class="o">.</span><span class="mi">3</span> <span class="o">*</span> <span class="n">worst</span><span class="p">[</span><span class="s1">&#39;lags&#39;</span><span class="p">][</span><span class="n">k</span><span class="p">])))</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">lags</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">max_order</span><span class="p">[</span><span class="s1">&#39;lags&#39;</span><span class="p">][</span><span class="n">k</span><span class="p">])</span>
+
+    <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">order</span><span class="p">):</span>
+        <span class="k">while</span> <span class="n">lags</span><span class="p">[</span><span class="n">k</span> <span class="o">-</span> <span class="mi">1</span><span class="p">]</span> <span class="o">&gt;=</span> <span class="n">lags</span><span class="p">[</span><span class="n">k</span><span class="p">]:</span>
+            <span class="n">lags</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">+=</span> <span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">10</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">order</span><span class="p">,</span> <span class="n">lags</span></div>
+
+
+<div class="viewcode-block" id="crossover"><a class="viewcode-back" href="../../../pyFTS.hyperparam.html#pyFTS.hyperparam.Evolutionary.crossover">[docs]</a><span class="k">def</span> <span class="nf">crossover</span><span class="p">(</span><span class="n">population</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Crossover operation between two parents</span>
+
+<span class="sd">    :param population: the original population</span>
+<span class="sd">    :return: a genotype</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="kn">import</span> <span class="nn">random</span>
+
+    <span class="n">n</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">population</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span>
+
+    <span class="n">r1</span><span class="p">,</span> <span class="n">r2</span> <span class="o">=</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span>
+    <span class="k">while</span> <span class="n">r1</span> <span class="o">==</span> <span class="n">r2</span><span class="p">:</span>
+        <span class="n">r1</span> <span class="o">=</span> <span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">n</span><span class="p">)</span>
+        <span class="n">r2</span> <span class="o">=</span> <span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">n</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="n">population</span><span class="p">[</span><span class="n">r1</span><span class="p">][</span><span class="s1">&#39;f1&#39;</span><span class="p">]</span> <span class="o">&lt;</span> <span class="n">population</span><span class="p">[</span><span class="n">r2</span><span class="p">][</span><span class="s1">&#39;f1&#39;</span><span class="p">]:</span>
+        <span class="n">best</span> <span class="o">=</span> <span class="n">population</span><span class="p">[</span><span class="n">r1</span><span class="p">]</span>
+        <span class="n">worst</span> <span class="o">=</span> <span class="n">population</span><span class="p">[</span><span class="n">r2</span><span class="p">]</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">best</span> <span class="o">=</span> <span class="n">population</span><span class="p">[</span><span class="n">r2</span><span class="p">]</span>
+        <span class="n">worst</span> <span class="o">=</span> <span class="n">population</span><span class="p">[</span><span class="n">r1</span><span class="p">]</span>
+
+    <span class="n">npart</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="o">.</span><span class="mi">7</span> <span class="o">*</span> <span class="n">best</span><span class="p">[</span><span class="s1">&#39;npart&#39;</span><span class="p">]</span> <span class="o">+</span> <span class="o">.</span><span class="mi">3</span> <span class="o">*</span> <span class="n">worst</span><span class="p">[</span><span class="s1">&#39;npart&#39;</span><span class="p">]))</span>
+    <span class="n">alpha</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="o">.</span><span class="mi">7</span> <span class="o">*</span> <span class="n">best</span><span class="p">[</span><span class="s1">&#39;alpha&#39;</span><span class="p">]</span> <span class="o">+</span> <span class="o">.</span><span class="mi">3</span> <span class="o">*</span> <span class="n">worst</span><span class="p">[</span><span class="s1">&#39;alpha&#39;</span><span class="p">])</span>
+
+    <span class="n">rnd</span> <span class="o">=</span> <span class="n">random</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+    <span class="n">mf</span> <span class="o">=</span> <span class="n">best</span><span class="p">[</span><span class="s1">&#39;mf&#39;</span><span class="p">]</span> <span class="k">if</span> <span class="n">rnd</span> <span class="o">&lt;</span> <span class="o">.</span><span class="mi">7</span> <span class="k">else</span> <span class="n">worst</span><span class="p">[</span><span class="s1">&#39;mf&#39;</span><span class="p">]</span>
+
+    <span class="n">rnd</span> <span class="o">=</span> <span class="n">random</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+    <span class="n">partitioner</span> <span class="o">=</span> <span class="n">best</span><span class="p">[</span><span class="s1">&#39;partitioner&#39;</span><span class="p">]</span> <span class="k">if</span> <span class="n">rnd</span> <span class="o">&lt;</span> <span class="o">.</span><span class="mi">7</span> <span class="k">else</span> <span class="n">worst</span><span class="p">[</span><span class="s1">&#39;partitioner&#39;</span><span class="p">]</span>
+
+    <span class="n">order</span><span class="p">,</span> <span class="n">lags</span> <span class="o">=</span> <span class="n">lag_crossover2</span><span class="p">(</span><span class="n">best</span><span class="p">,</span> <span class="n">worst</span><span class="p">)</span>
+
+    <span class="n">descendent</span> <span class="o">=</span> <span class="n">genotype</span><span class="p">(</span><span class="n">mf</span><span class="p">,</span> <span class="n">npart</span><span class="p">,</span> <span class="n">partitioner</span><span class="p">,</span> <span class="n">order</span><span class="p">,</span> <span class="n">alpha</span><span class="p">,</span> <span class="n">lags</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">descendent</span></div>
+
+
+<div class="viewcode-block" id="mutation_lags"><a class="viewcode-back" href="../../../pyFTS.hyperparam.html#pyFTS.hyperparam.Evolutionary.mutation_lags">[docs]</a><span class="k">def</span> <span class="nf">mutation_lags</span><span class="p">(</span><span class="n">lags</span><span class="p">,</span> <span class="n">order</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Mutation operation for lags gene</span>
+
+<span class="sd">    :param lags:</span>
+<span class="sd">    :param order:</span>
+<span class="sd">    :return:</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">try</span><span class="p">:</span>
+        <span class="n">l</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">lags</span><span class="p">)</span>
+        <span class="n">new</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="k">for</span> <span class="n">lag</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">order</span><span class="p">):</span>
+            <span class="k">if</span> <span class="n">lag</span> <span class="o">&lt;</span> <span class="n">l</span><span class="p">:</span>
+                <span class="n">new</span><span class="o">.</span><span class="n">append</span><span class="p">(</span> <span class="nb">min</span><span class="p">(</span><span class="mi">50</span><span class="p">,</span> <span class="nb">max</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="nb">int</span><span class="p">(</span><span class="n">lags</span><span class="p">[</span><span class="n">lag</span><span class="p">]</span> <span class="o">+</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="o">-</span><span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">))))</span> <span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">new</span><span class="o">.</span><span class="n">append</span><span class="p">(</span> <span class="n">new</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span> <span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">order</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
+            <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">order</span><span class="p">):</span>
+                <span class="k">while</span> <span class="n">new</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">&lt;=</span> <span class="n">new</span><span class="p">[</span><span class="n">k</span> <span class="o">-</span> <span class="mi">1</span><span class="p">]:</span>
+                    <span class="n">new</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">new</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">+</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
+
+        <span class="k">return</span> <span class="n">new</span>
+    <span class="k">except</span> <span class="ne">Exception</span> <span class="k">as</span> <span class="n">ex</span><span class="p">:</span>
+        <span class="nb">print</span><span class="p">(</span><span class="n">lags</span><span class="p">,</span> <span class="n">order</span><span class="p">,</span> <span class="n">new</span><span class="p">,</span> <span class="n">lag</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="mutation"><a class="viewcode-back" href="../../../pyFTS.hyperparam.html#pyFTS.hyperparam.Evolutionary.mutation">[docs]</a><span class="k">def</span> <span class="nf">mutation</span><span class="p">(</span><span class="n">individual</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Mutation operator</span>
+
+<span class="sd">    :param individual: an individual genotype</span>
+<span class="sd">    :param pmut: individual probability o</span>
+<span class="sd">    :return:</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">individual</span><span class="p">[</span><span class="s1">&#39;npart&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="mi">50</span><span class="p">,</span> <span class="nb">max</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="nb">int</span><span class="p">(</span><span class="n">individual</span><span class="p">[</span><span class="s1">&#39;npart&#39;</span><span class="p">]</span> <span class="o">+</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">normal</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">4</span><span class="p">))))</span>
+    <span class="n">individual</span><span class="p">[</span><span class="s1">&#39;alpha&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="o">.</span><span class="mi">5</span><span class="p">,</span> <span class="nb">max</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">individual</span><span class="p">[</span><span class="s1">&#39;alpha&#39;</span><span class="p">]</span> <span class="o">+</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">normal</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="o">.</span><span class="mi">5</span><span class="p">)))</span>
+    <span class="n">individual</span><span class="p">[</span><span class="s1">&#39;mf&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
+    <span class="n">individual</span><span class="p">[</span><span class="s1">&#39;partitioner&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
+    <span class="n">individual</span><span class="p">[</span><span class="s1">&#39;order&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="nb">max</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="nb">int</span><span class="p">(</span><span class="n">individual</span><span class="p">[</span><span class="s1">&#39;order&#39;</span><span class="p">]</span> <span class="o">+</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">normal</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">))))</span>
+    <span class="c1"># Chama a função mutation_lags</span>
+    <span class="n">individual</span><span class="p">[</span><span class="s1">&#39;lags&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">mutation_lags</span><span class="p">(</span> <span class="n">individual</span><span class="p">[</span><span class="s1">&#39;lags&#39;</span><span class="p">],</span>  <span class="n">individual</span><span class="p">[</span><span class="s1">&#39;order&#39;</span><span class="p">])</span>
+
+    <span class="n">individual</span><span class="p">[</span><span class="s1">&#39;f1&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span>
+    <span class="n">individual</span><span class="p">[</span><span class="s1">&#39;f2&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span>
+
+    <span class="k">return</span> <span class="n">individual</span></div>
+
+
+<div class="viewcode-block" id="elitism"><a class="viewcode-back" href="../../../pyFTS.hyperparam.html#pyFTS.hyperparam.Evolutionary.elitism">[docs]</a><span class="k">def</span> <span class="nf">elitism</span><span class="p">(</span><span class="n">population</span><span class="p">,</span> <span class="n">new_population</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Elitism operation, always select the best individual of the population and discard the worst</span>
+
+<span class="sd">    :param population:</span>
+<span class="sd">    :param new_population:</span>
+<span class="sd">    :return:</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">population</span> <span class="o">=</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">population</span><span class="p">,</span> <span class="n">key</span><span class="o">=</span><span class="n">itemgetter</span><span class="p">(</span><span class="s1">&#39;f1&#39;</span><span class="p">))</span>
+    <span class="n">best</span> <span class="o">=</span> <span class="n">population</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+
+    <span class="n">new_population</span> <span class="o">=</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">new_population</span><span class="p">,</span> <span class="n">key</span><span class="o">=</span><span class="n">itemgetter</span><span class="p">(</span><span class="s1">&#39;f1&#39;</span><span class="p">))</span>
+    <span class="k">if</span> <span class="n">new_population</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="s2">&quot;f1&quot;</span><span class="p">]</span> <span class="o">&gt;</span> <span class="n">best</span><span class="p">[</span><span class="s2">&quot;f1&quot;</span><span class="p">]:</span>
+        <span class="n">new_population</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="n">best</span><span class="p">)</span>
+    <span class="k">elif</span> <span class="n">new_population</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="s2">&quot;f1&quot;</span><span class="p">]</span> <span class="o">==</span> <span class="n">best</span><span class="p">[</span><span class="s2">&quot;f1&quot;</span><span class="p">]</span> <span class="ow">and</span> <span class="n">new_population</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="s2">&quot;f2&quot;</span><span class="p">]</span> <span class="o">&gt;</span> <span class="n">best</span><span class="p">[</span><span class="s2">&quot;f2&quot;</span><span class="p">]:</span>
+        <span class="n">new_population</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">best</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">new_population</span></div>
+
+
+<div class="viewcode-block" id="GeneticAlgorithm"><a class="viewcode-back" href="../../../pyFTS.hyperparam.html#pyFTS.hyperparam.Evolutionary.GeneticAlgorithm">[docs]</a><span class="k">def</span> <span class="nf">GeneticAlgorithm</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Genetic algoritm for Distributed Evolutionary Hyperparameter Optimization (DEHO)</span>
+
+<span class="sd">    :param dataset: The time series to optimize the FTS</span>
+<span class="sd">    :keyword ngen: An integer value with the maximum number of generations, default value: 30</span>
+<span class="sd">    :keyword mgen: An integer value with the maximum number of generations without improvement to stop, default value 7</span>
+<span class="sd">    :keyword npop: An integer value with the population size, default value: 20</span>
+<span class="sd">    :keyword pcross: A float value between 0 and 1 with the probability of crossover, default: .5</span>
+<span class="sd">    :keyword psel: A float value between 0 and 1 with the probability of selection, default: .5</span>
+<span class="sd">    :keyword pmut: A float value between 0 and 1 with the probability of mutation, default: .3</span>
+<span class="sd">    :keyword fts_method: The FTS method to optimize</span>
+<span class="sd">    :keyword parameters: dict with model specific arguments for fts_method</span>
+<span class="sd">    :keyword elitism: A boolean value indicating if the best individual must always survive to next population</span>
+<span class="sd">    :keyword initial_operator: a function that receives npop and return a random population with size npop</span>
+<span class="sd">    :keyword evalutation_operator: a function that receives a dataset and an individual and return its fitness</span>
+<span class="sd">    :keyword selection_operator: a function that receives the whole population and return a selected individual</span>
+<span class="sd">    :keyword crossover_operator: a function that receives the whole population and return a descendent individual</span>
+<span class="sd">    :keyword mutation_operator: a function that receives one individual and return a changed individual</span>
+<span class="sd">    :keyword window_size: An integer value with the the length of scrolling window for train/test on dataset</span>
+<span class="sd">    :keyword train_rate: A float value between 0 and 1 with the train/test split ([0,1])</span>
+<span class="sd">    :keyword increment_rate: A float value between 0 and 1 with the the increment of the scrolling window,</span>
+<span class="sd">             relative to the window_size ([0,1])</span>
+<span class="sd">    :keyword collect_statistics: A boolean value indicating to collect statistics for each generation</span>
+<span class="sd">    :keyword distributed: A value indicating it the execution will be local and sequential (distributed=False),</span>
+<span class="sd">             or parallel and distributed (distributed=&#39;dispy&#39; or distributed=&#39;spark&#39;)</span>
+<span class="sd">    :keyword cluster: If distributed=&#39;dispy&#39; the list of cluster nodes, else if distributed=&#39;spark&#39; it is the master node</span>
+<span class="sd">    :return: the best genotype</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">statistics</span> <span class="o">=</span> <span class="p">[]</span>
+
+    <span class="n">ngen</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;ngen&#39;</span><span class="p">,</span><span class="mi">30</span><span class="p">)</span>
+    <span class="n">mgen</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;mgen&#39;</span><span class="p">,</span> <span class="mi">7</span><span class="p">)</span>
+    <span class="n">npop</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;npop&#39;</span><span class="p">,</span><span class="mi">20</span><span class="p">)</span>
+    <span class="n">psel</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;psel&#39;</span><span class="p">,</span> <span class="o">.</span><span class="mi">5</span><span class="p">)</span>
+    <span class="n">pcross</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;pcross&#39;</span><span class="p">,</span><span class="o">.</span><span class="mi">5</span><span class="p">)</span>
+    <span class="n">pmut</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;pmut&#39;</span><span class="p">,</span><span class="o">.</span><span class="mi">3</span><span class="p">)</span>
+    <span class="n">distributed</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;distributed&#39;</span><span class="p">,</span> <span class="kc">False</span><span class="p">)</span>
+
+    <span class="n">initial_operator</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;initial_operator&#39;</span><span class="p">,</span> <span class="n">initial_population</span><span class="p">)</span>
+    <span class="n">evaluation_operator</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;evaluation_operator&#39;</span><span class="p">,</span> <span class="n">evaluate</span><span class="p">)</span>
+    <span class="n">selection_operator</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;selection_operator&#39;</span><span class="p">,</span> <span class="n">double_tournament</span><span class="p">)</span>
+    <span class="n">crossover_operator</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;crossover_operator&#39;</span><span class="p">,</span> <span class="n">crossover</span><span class="p">)</span>
+    <span class="n">mutation_operator</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;mutation_operator&#39;</span><span class="p">,</span> <span class="n">mutation</span><span class="p">)</span>
+
+    <span class="n">_elitism</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;elitism&#39;</span><span class="p">,</span> <span class="kc">True</span><span class="p">)</span>
+
+    <span class="n">elitism_operator</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;elitism_operator&#39;</span><span class="p">,</span> <span class="n">elitism</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="n">distributed</span> <span class="o">==</span> <span class="s1">&#39;dispy&#39;</span><span class="p">:</span>
+        <span class="n">cluster</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s1">&#39;cluster&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
+
+    <span class="n">collect_statistics</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;collect_statistics&#39;</span><span class="p">,</span> <span class="kc">True</span><span class="p">)</span>
+
+    <span class="n">no_improvement_count</span> <span class="o">=</span> <span class="mi">0</span>
+
+    <span class="n">new_population</span> <span class="o">=</span> <span class="p">[]</span>
+
+    <span class="n">population</span> <span class="o">=</span> <span class="n">initial_operator</span><span class="p">(</span><span class="n">npop</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+
+    <span class="n">last_best</span> <span class="o">=</span> <span class="n">population</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+    <span class="n">best</span> <span class="o">=</span> <span class="n">population</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+
+    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Evaluating initial population </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()))</span>
+    <span class="k">if</span> <span class="ow">not</span> <span class="n">distributed</span><span class="p">:</span>
+        <span class="k">for</span> <span class="n">individual</span> <span class="ow">in</span> <span class="n">population</span><span class="p">:</span>
+            <span class="n">ret</span> <span class="o">=</span> <span class="n">evaluation_operator</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">individual</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+            <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">__measures</span><span class="p">:</span>
+                <span class="n">individual</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="o">=</span> <span class="n">ret</span><span class="p">[</span><span class="n">key</span><span class="p">]</span>
+    <span class="k">elif</span> <span class="n">distributed</span><span class="o">==</span><span class="s1">&#39;dispy&#39;</span><span class="p">:</span>
+        <span class="kn">from</span> <span class="nn">pyFTS.distributed</span> <span class="kn">import</span> <span class="n">dispy</span> <span class="k">as</span> <span class="n">dUtil</span>
+        <span class="kn">import</span> <span class="nn">dispy</span>
+        <span class="n">jobs</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="k">for</span> <span class="n">ct</span><span class="p">,</span> <span class="n">individual</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">population</span><span class="p">):</span>
+            <span class="n">job</span> <span class="o">=</span> <span class="n">cluster</span><span class="o">.</span><span class="n">submit</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">individual</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+            <span class="n">job</span><span class="o">.</span><span class="n">id</span> <span class="o">=</span> <span class="n">ct</span>
+            <span class="n">jobs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">job</span><span class="p">)</span>
+        <span class="k">for</span> <span class="n">job</span> <span class="ow">in</span> <span class="n">jobs</span><span class="p">:</span>
+            <span class="n">result</span> <span class="o">=</span> <span class="n">job</span><span class="p">()</span>
+            <span class="k">if</span> <span class="n">job</span><span class="o">.</span><span class="n">status</span> <span class="o">==</span> <span class="n">dispy</span><span class="o">.</span><span class="n">DispyJob</span><span class="o">.</span><span class="n">Finished</span> <span class="ow">and</span> <span class="n">result</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">__measures</span><span class="p">:</span>
+                    <span class="n">population</span><span class="p">[</span><span class="n">job</span><span class="o">.</span><span class="n">id</span><span class="p">][</span><span class="n">key</span><span class="p">]</span> <span class="o">=</span> <span class="n">result</span><span class="p">[</span><span class="n">key</span><span class="p">]</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="nb">print</span><span class="p">(</span><span class="n">job</span><span class="o">.</span><span class="n">exception</span><span class="p">)</span>
+                <span class="nb">print</span><span class="p">(</span><span class="n">job</span><span class="o">.</span><span class="n">stdout</span><span class="p">)</span>
+
+    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">ngen</span><span class="p">):</span>
+        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;GENERATION </span><span class="si">{}</span><span class="s2"> </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()))</span>
+
+        <span class="n">generation_statistics</span> <span class="o">=</span> <span class="p">{}</span>
+
+        <span class="c1"># Selection</span>
+        <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="n">npop</span> <span class="o">*</span> <span class="n">psel</span><span class="p">)):</span>
+            <span class="n">new_population</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">selection_operator</span><span class="p">(</span><span class="n">population</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">))</span>
+
+        <span class="c1"># Crossover</span>
+        <span class="n">new</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="n">npop</span> <span class="o">*</span> <span class="n">pcross</span><span class="p">)):</span>
+            <span class="n">new</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">crossover_operator</span><span class="p">(</span><span class="n">new_population</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">))</span>
+
+        <span class="n">new_population</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">new</span><span class="p">)</span>
+
+        <span class="c1"># Mutation</span>
+        <span class="k">for</span> <span class="n">ct</span><span class="p">,</span> <span class="n">individual</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">new_population</span><span class="p">):</span>
+            <span class="n">rnd</span> <span class="o">=</span> <span class="n">random</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+            <span class="k">if</span> <span class="n">rnd</span> <span class="o">&lt;</span> <span class="n">pmut</span><span class="p">:</span>
+                <span class="n">new_population</span><span class="p">[</span><span class="n">ct</span><span class="p">]</span> <span class="o">=</span> <span class="n">mutation_operator</span><span class="p">(</span><span class="n">individual</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+
+        <span class="c1"># Evaluation</span>
+        <span class="k">if</span> <span class="n">collect_statistics</span><span class="p">:</span>
+            <span class="n">stats</span> <span class="o">=</span> <span class="p">{}</span>
+            <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">__measures</span><span class="p">:</span>
+                <span class="n">stats</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
+
+        <span class="k">if</span> <span class="ow">not</span> <span class="n">distributed</span><span class="p">:</span>
+            <span class="k">for</span> <span class="n">individual</span> <span class="ow">in</span> <span class="n">new_population</span><span class="p">:</span>
+                <span class="n">ret</span> <span class="o">=</span> <span class="n">evaluation_operator</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">individual</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+                <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">__measures</span><span class="p">:</span>
+                    <span class="n">individual</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="o">=</span> <span class="n">ret</span><span class="p">[</span><span class="n">key</span><span class="p">]</span>
+                    <span class="k">if</span> <span class="n">collect_statistics</span><span class="p">:</span> <span class="n">stats</span><span class="p">[</span><span class="n">key</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">ret</span><span class="p">[</span><span class="n">key</span><span class="p">])</span>
+
+        <span class="k">elif</span> <span class="n">distributed</span> <span class="o">==</span> <span class="s1">&#39;dispy&#39;</span><span class="p">:</span>
+            <span class="n">jobs</span> <span class="o">=</span> <span class="p">[]</span>
+
+            <span class="k">for</span> <span class="n">ct</span><span class="p">,</span> <span class="n">individual</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">new_population</span><span class="p">):</span>
+                <span class="n">job</span> <span class="o">=</span> <span class="n">cluster</span><span class="o">.</span><span class="n">submit</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">individual</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+                <span class="n">job</span><span class="o">.</span><span class="n">id</span> <span class="o">=</span> <span class="n">ct</span>
+                <span class="n">jobs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">job</span><span class="p">)</span>
+            <span class="k">for</span> <span class="n">job</span> <span class="ow">in</span> <span class="n">jobs</span><span class="p">:</span>
+                <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;job id </span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">job</span><span class="o">.</span><span class="n">id</span><span class="p">))</span>
+                <span class="n">result</span> <span class="o">=</span> <span class="n">job</span><span class="p">()</span>
+                <span class="k">if</span> <span class="n">job</span><span class="o">.</span><span class="n">status</span> <span class="o">==</span> <span class="n">dispy</span><span class="o">.</span><span class="n">DispyJob</span><span class="o">.</span><span class="n">Finished</span> <span class="ow">and</span> <span class="n">result</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                    <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">__measures</span><span class="p">:</span>
+                        <span class="n">new_population</span><span class="p">[</span><span class="n">job</span><span class="o">.</span><span class="n">id</span><span class="p">][</span><span class="n">key</span><span class="p">]</span> <span class="o">=</span> <span class="n">result</span><span class="p">[</span><span class="n">key</span><span class="p">]</span>
+                        <span class="k">if</span> <span class="n">collect_statistics</span><span class="p">:</span> <span class="n">stats</span><span class="p">[</span><span class="n">key</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">result</span><span class="p">[</span><span class="n">key</span><span class="p">])</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="nb">print</span><span class="p">(</span><span class="n">job</span><span class="o">.</span><span class="n">exception</span><span class="p">)</span>
+                    <span class="nb">print</span><span class="p">(</span><span class="n">job</span><span class="o">.</span><span class="n">stdout</span><span class="p">)</span>
+
+
+        <span class="k">if</span> <span class="n">collect_statistics</span><span class="p">:</span>
+            <span class="n">mean_stats</span> <span class="o">=</span> <span class="p">{</span><span class="n">key</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">nanmedian</span><span class="p">(</span><span class="n">stats</span><span class="p">[</span><span class="n">key</span><span class="p">])</span> <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">__measures</span> <span class="p">}</span>
+
+            <span class="n">generation_statistics</span><span class="p">[</span><span class="s1">&#39;population&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">mean_stats</span>
+
+        <span class="c1"># Elitism</span>
+        <span class="k">if</span> <span class="n">_elitism</span><span class="p">:</span>
+            <span class="n">population</span> <span class="o">=</span> <span class="n">elitism_operator</span><span class="p">(</span><span class="n">population</span><span class="p">,</span> <span class="n">new_population</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+
+        <span class="n">population</span> <span class="o">=</span> <span class="n">population</span><span class="p">[:</span><span class="n">npop</span><span class="p">]</span>
+
+        <span class="n">new_population</span> <span class="o">=</span> <span class="p">[]</span>
+
+        <span class="n">last_best</span> <span class="o">=</span> <span class="n">best</span>
+
+        <span class="n">best</span> <span class="o">=</span> <span class="n">population</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+
+        <span class="k">if</span> <span class="n">collect_statistics</span><span class="p">:</span>
+            <span class="n">generation_statistics</span><span class="p">[</span><span class="s1">&#39;best&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="p">{</span><span class="n">key</span><span class="p">:</span> <span class="n">best</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">__measures</span> <span class="p">}</span>
+
+            <span class="n">statistics</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">generation_statistics</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">last_best</span><span class="p">[</span><span class="s1">&#39;f1&#39;</span><span class="p">]</span> <span class="o">&lt;=</span> <span class="n">best</span><span class="p">[</span><span class="s1">&#39;f1&#39;</span><span class="p">]</span> <span class="ow">and</span> <span class="n">last_best</span><span class="p">[</span><span class="s1">&#39;f2&#39;</span><span class="p">]</span> <span class="o">&lt;=</span> <span class="n">best</span><span class="p">[</span><span class="s1">&#39;f2&#39;</span><span class="p">]:</span>
+            <span class="n">no_improvement_count</span> <span class="o">+=</span> <span class="mi">1</span>
+            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;WITHOUT IMPROVEMENT </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">no_improvement_count</span><span class="p">))</span>
+            <span class="n">pmut</span> <span class="o">+=</span> <span class="o">.</span><span class="mi">05</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">no_improvement_count</span> <span class="o">=</span> <span class="mi">0</span>
+            <span class="n">pcross</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;pcross&#39;</span><span class="p">,</span> <span class="o">.</span><span class="mi">5</span><span class="p">)</span>
+            <span class="n">pmut</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;pmut&#39;</span><span class="p">,</span> <span class="o">.</span><span class="mi">3</span><span class="p">)</span>
+            <span class="nb">print</span><span class="p">(</span><span class="n">best</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">no_improvement_count</span> <span class="o">==</span> <span class="n">mgen</span><span class="p">:</span>
+            <span class="k">break</span>
+
+    <span class="k">return</span> <span class="n">best</span><span class="p">,</span> <span class="n">statistics</span></div>
+
+
+<div class="viewcode-block" id="process_experiment"><a class="viewcode-back" href="../../../pyFTS.hyperparam.html#pyFTS.hyperparam.Evolutionary.process_experiment">[docs]</a><span class="k">def</span> <span class="nf">process_experiment</span><span class="p">(</span><span class="n">fts_method</span><span class="p">,</span> <span class="n">result</span><span class="p">,</span> <span class="n">datasetname</span><span class="p">,</span> <span class="n">conn</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Persist the results of an DEHO execution in sqlite database (best hyperparameters) and json file (generation statistics)</span>
+
+<span class="sd">    :param fts_method:</span>
+<span class="sd">    :param result:</span>
+<span class="sd">    :param datasetname:</span>
+<span class="sd">    :param conn:</span>
+<span class="sd">    :return:</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">log_result</span><span class="p">(</span><span class="n">conn</span><span class="p">,</span> <span class="n">datasetname</span><span class="p">,</span> <span class="n">fts_method</span><span class="p">,</span> <span class="n">result</span><span class="p">[</span><span class="s1">&#39;individual&#39;</span><span class="p">])</span>
+    <span class="n">persist_statistics</span><span class="p">(</span><span class="n">datasetname</span><span class="p">,</span> <span class="n">result</span><span class="p">[</span><span class="s1">&#39;statistics&#39;</span><span class="p">])</span>
+    <span class="k">return</span> <span class="n">result</span><span class="p">[</span><span class="s1">&#39;individual&#39;</span><span class="p">]</span></div>
+
+
+<div class="viewcode-block" id="persist_statistics"><a class="viewcode-back" href="../../../pyFTS.hyperparam.html#pyFTS.hyperparam.Evolutionary.persist_statistics">[docs]</a><span class="k">def</span> <span class="nf">persist_statistics</span><span class="p">(</span><span class="n">datasetname</span><span class="p">,</span> <span class="n">statistics</span><span class="p">):</span>
+    <span class="kn">import</span> <span class="nn">json</span>
+    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="s1">&#39;statistics_</span><span class="si">{}</span><span class="s1">.json&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">datasetname</span><span class="p">),</span> <span class="s1">&#39;w&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">file</span><span class="p">:</span>
+        <span class="n">file</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="n">json</span><span class="o">.</span><span class="n">dumps</span><span class="p">(</span><span class="n">statistics</span><span class="p">))</span></div>
+
+
+<div class="viewcode-block" id="log_result"><a class="viewcode-back" href="../../../pyFTS.hyperparam.html#pyFTS.hyperparam.Evolutionary.log_result">[docs]</a><span class="k">def</span> <span class="nf">log_result</span><span class="p">(</span><span class="n">conn</span><span class="p">,</span> <span class="n">datasetname</span><span class="p">,</span> <span class="n">fts_method</span><span class="p">,</span> <span class="n">result</span><span class="p">):</span>
+    <span class="n">metrics</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;rmse&#39;</span><span class="p">,</span> <span class="s1">&#39;size&#39;</span><span class="p">,</span> <span class="s1">&#39;time&#39;</span><span class="p">]</span>
+    <span class="k">for</span> <span class="n">metric</span> <span class="ow">in</span> <span class="n">metrics</span><span class="p">:</span>
+        <span class="n">record</span> <span class="o">=</span> <span class="p">(</span><span class="n">datasetname</span><span class="p">,</span> <span class="s1">&#39;Evolutive&#39;</span><span class="p">,</span> <span class="n">fts_method</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="n">result</span><span class="p">[</span><span class="s1">&#39;mf&#39;</span><span class="p">],</span>
+                  <span class="n">result</span><span class="p">[</span><span class="s1">&#39;order&#39;</span><span class="p">],</span> <span class="n">result</span><span class="p">[</span><span class="s1">&#39;partitioner&#39;</span><span class="p">],</span> <span class="n">result</span><span class="p">[</span><span class="s1">&#39;npart&#39;</span><span class="p">],</span>
+                  <span class="n">result</span><span class="p">[</span><span class="s1">&#39;alpha&#39;</span><span class="p">],</span> <span class="nb">str</span><span class="p">(</span><span class="n">result</span><span class="p">[</span><span class="s1">&#39;lags&#39;</span><span class="p">]),</span> <span class="n">metric</span><span class="p">,</span> <span class="n">result</span><span class="p">[</span><span class="n">metric</span><span class="p">])</span>
+
+        <span class="nb">print</span><span class="p">(</span><span class="n">record</span><span class="p">)</span>
+
+        <span class="n">hUtil</span><span class="o">.</span><span class="n">insert_hyperparam</span><span class="p">(</span><span class="n">record</span><span class="p">,</span> <span class="n">conn</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="execute"><a class="viewcode-back" href="../../../pyFTS.hyperparam.html#pyFTS.hyperparam.Evolutionary.execute">[docs]</a><span class="k">def</span> <span class="nf">execute</span><span class="p">(</span><span class="n">datasetname</span><span class="p">,</span> <span class="n">dataset</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Batch execution of Distributed Evolutionary Hyperparameter Optimization (DEHO) for monovariate methods</span>
+
+<span class="sd">    :param datasetname:</span>
+<span class="sd">    :param dataset: The time series to optimize the FTS</span>
+<span class="sd">    :keyword file:</span>
+<span class="sd">    :keyword experiments:</span>
+<span class="sd">    :keyword distributed:</span>
+<span class="sd">    :keyword ngen: An integer value with the maximum number of generations, default value: 30</span>
+<span class="sd">    :keyword mgen: An integer value with the maximum number of generations without improvement to stop, default value 7</span>
+<span class="sd">    :keyword npop: An integer value with the population size, default value: 20</span>
+<span class="sd">    :keyword pcross: A float value between 0 and 1 with the probability of crossover, default: .5</span>
+<span class="sd">    :keyword psel: A float value between 0 and 1 with the probability of selection, default: .5</span>
+<span class="sd">    :keyword pmut: A float value between 0 and 1 with the probability of mutation, default: .3</span>
+<span class="sd">    :keyword fts_method: The FTS method to optimize</span>
+<span class="sd">    :keyword parameters: dict with model specific arguments for fts_method</span>
+<span class="sd">    :keyword elitism: A boolean value indicating if the best individual must always survive to next population</span>
+<span class="sd">    :keyword initial_operator: a function that receives npop and return a random population with size npop</span>
+<span class="sd">    :keyword random_individual: create an random genotype</span>
+<span class="sd">    :keyword evalutation_operator: a function that receives a dataset and an individual and return its fitness</span>
+<span class="sd">    :keyword selection_operator: a function that receives the whole population and return a selected individual</span>
+<span class="sd">    :keyword crossover_operator: a function that receives the whole population and return a descendent individual</span>
+<span class="sd">    :keyword mutation_operator: a function that receives one individual and return a changed individual</span>
+<span class="sd">    :keyword window_size: An integer value with the the length of scrolling window for train/test on dataset</span>
+<span class="sd">    :keyword train_rate: A float value between 0 and 1 with the train/test split ([0,1])</span>
+<span class="sd">    :keyword increment_rate: A float value between 0 and 1 with the the increment of the scrolling window,</span>
+<span class="sd">             relative to the window_size ([0,1])</span>
+<span class="sd">    :keyword collect_statistics: A boolean value indicating to collect statistics for each generation</span>
+<span class="sd">    :keyword distributed: A value indicating it the execution will be local and sequential (distributed=False),</span>
+<span class="sd">             or parallel and distributed (distributed=&#39;dispy&#39; or distributed=&#39;spark&#39;)</span>
+<span class="sd">    :keyword cluster: If distributed=&#39;dispy&#39; the list of cluster nodes, else if distributed=&#39;spark&#39; it is the master node</span>
+<span class="sd">    :return: the best genotype</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">file</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;file&#39;</span><span class="p">,</span> <span class="s1">&#39;hyperparam.db&#39;</span><span class="p">)</span>
+
+    <span class="n">conn</span> <span class="o">=</span> <span class="n">hUtil</span><span class="o">.</span><span class="n">open_hyperparam_db</span><span class="p">(</span><span class="n">file</span><span class="p">)</span>
+
+    <span class="n">experiments</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;experiments&#39;</span><span class="p">,</span> <span class="mi">30</span><span class="p">)</span>
+
+    <span class="n">distributed</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;distributed&#39;</span><span class="p">,</span> <span class="kc">False</span><span class="p">)</span>
+
+    <span class="n">fts_method</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;fts_method&#39;</span><span class="p">,</span> <span class="n">hofts</span><span class="o">.</span><span class="n">WeightedHighOrderFTS</span><span class="p">)</span>
+    <span class="n">shortname</span> <span class="o">=</span> <span class="nb">str</span><span class="p">(</span><span class="n">fts_method</span><span class="o">.</span><span class="vm">__module__</span><span class="p">)</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;.&#39;</span><span class="p">)[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
+
+    <span class="k">if</span> <span class="n">distributed</span> <span class="o">==</span> <span class="s1">&#39;dispy&#39;</span><span class="p">:</span>
+        <span class="kn">from</span> <span class="nn">pyFTS.distributed</span> <span class="kn">import</span> <span class="n">dispy</span> <span class="k">as</span> <span class="n">dUtil</span>
+        <span class="n">nodes</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;nodes&#39;</span><span class="p">,</span> <span class="p">[</span><span class="s1">&#39;127.0.0.1&#39;</span><span class="p">])</span>
+        <span class="n">cluster</span><span class="p">,</span> <span class="n">http_server</span> <span class="o">=</span> <span class="n">dUtil</span><span class="o">.</span><span class="n">start_dispy_cluster</span><span class="p">(</span><span class="n">evaluate</span><span class="p">,</span> <span class="n">nodes</span><span class="o">=</span><span class="n">nodes</span><span class="p">)</span>
+        <span class="n">kwargs</span><span class="p">[</span><span class="s1">&#39;cluster&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">cluster</span>
+
+    <span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">experiments</span><span class="p">):</span>
+        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Experiment </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">i</span><span class="p">))</span>
+
+        <span class="n">start</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
+        <span class="n">ret</span><span class="p">,</span> <span class="n">statistics</span> <span class="o">=</span> <span class="n">GeneticAlgorithm</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+        <span class="n">end</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
+        <span class="n">ret</span><span class="p">[</span><span class="s1">&#39;time&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">end</span> <span class="o">-</span> <span class="n">start</span>
+        <span class="n">experiment</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;individual&#39;</span><span class="p">:</span> <span class="n">ret</span><span class="p">,</span> <span class="s1">&#39;statistics&#39;</span><span class="p">:</span> <span class="n">statistics</span><span class="p">}</span>
+
+        <span class="n">ret</span> <span class="o">=</span> <span class="n">process_experiment</span><span class="p">(</span><span class="n">shortname</span><span class="p">,</span> <span class="n">experiment</span><span class="p">,</span> <span class="n">datasetname</span><span class="p">,</span> <span class="n">conn</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="n">distributed</span> <span class="o">==</span> <span class="s1">&#39;dispy&#39;</span><span class="p">:</span>
+        <span class="n">dUtil</span><span class="o">.</span><span class="n">stop_dispy_cluster</span><span class="p">(</span><span class="n">cluster</span><span class="p">,</span> <span class="n">http_server</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">ret</span></div>
+</pre></div>
+
+            <div class="clearer"></div>
+          </div>
+        </div>
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+      <div class="sphinxsidebar" role="navigation" aria-label="main navigation">
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diff --git a/docs/build/html/_modules/pyFTS/hyperparam/GridSearch.html b/docs/build/html/_modules/pyFTS/hyperparam/GridSearch.html
new file mode 100644
index 0000000..f052f42
--- /dev/null
+++ b/docs/build/html/_modules/pyFTS/hyperparam/GridSearch.html
@@ -0,0 +1,272 @@
+
+
+<!doctype html>
+
+<html>
+  <head>
+    <meta charset="utf-8" />
+    <meta name="viewport" content="width=device-width, initial-scale=1.0"><script type="text/javascript">
+
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+    <title>pyFTS.hyperparam.GridSearch &#8212; pyFTS 1.6 documentation</title>
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+        <li class="nav-item nav-item-this"><a href="">pyFTS.hyperparam.GridSearch</a></li> 
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+        <div class="bodywrapper">
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+            
+  <h1>Source code for pyFTS.hyperparam.GridSearch</h1><div class="highlight"><pre>
+<span></span>
+<span class="kn">from</span> <span class="nn">pyFTS.common</span> <span class="kn">import</span> <span class="n">Util</span><span class="p">,</span> <span class="n">Membership</span>
+<span class="kn">from</span> <span class="nn">pyFTS.models</span> <span class="kn">import</span> <span class="n">hofts</span>
+<span class="kn">from</span> <span class="nn">pyFTS.partitioners</span> <span class="kn">import</span> <span class="n">Grid</span><span class="p">,</span> <span class="n">Entropy</span>
+<span class="kn">from</span> <span class="nn">pyFTS.benchmarks</span> <span class="kn">import</span> <span class="n">Measures</span>
+<span class="kn">from</span> <span class="nn">pyFTS.hyperparam</span> <span class="kn">import</span> <span class="n">Util</span> <span class="k">as</span> <span class="n">hUtil</span>
+
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">from</span> <span class="nn">itertools</span> <span class="kn">import</span> <span class="n">product</span>
+
+
+<div class="viewcode-block" id="dict_individual"><a class="viewcode-back" href="../../../pyFTS.hyperparam.html#pyFTS.hyperparam.GridSearch.dict_individual">[docs]</a><span class="k">def</span> <span class="nf">dict_individual</span><span class="p">(</span><span class="n">mf</span><span class="p">,</span> <span class="n">partitioner</span><span class="p">,</span> <span class="n">partitions</span><span class="p">,</span> <span class="n">order</span><span class="p">,</span> <span class="n">lags</span><span class="p">,</span> <span class="n">alpha_cut</span><span class="p">):</span>
+    <span class="k">return</span> <span class="p">{</span>
+        <span class="s1">&#39;mf&#39;</span><span class="p">:</span> <span class="n">mf</span><span class="p">,</span>
+        <span class="s1">&#39;partitioner&#39;</span><span class="p">:</span> <span class="n">partitioner</span><span class="p">,</span>
+        <span class="s1">&#39;npart&#39;</span><span class="p">:</span> <span class="n">partitions</span><span class="p">,</span>
+        <span class="s1">&#39;alpha&#39;</span><span class="p">:</span> <span class="n">alpha_cut</span><span class="p">,</span>
+        <span class="s1">&#39;order&#39;</span><span class="p">:</span> <span class="n">order</span><span class="p">,</span>
+        <span class="s1">&#39;lags&#39;</span><span class="p">:</span> <span class="n">lags</span>
+    <span class="p">}</span></div>
+
+
+<div class="viewcode-block" id="cluster_method"><a class="viewcode-back" href="../../../pyFTS.hyperparam.html#pyFTS.hyperparam.GridSearch.cluster_method">[docs]</a><span class="k">def</span> <span class="nf">cluster_method</span><span class="p">(</span><span class="n">individual</span><span class="p">,</span> <span class="n">dataset</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+    <span class="kn">from</span> <span class="nn">pyFTS.common</span> <span class="kn">import</span> <span class="n">Util</span><span class="p">,</span> <span class="n">Membership</span>
+    <span class="kn">from</span> <span class="nn">pyFTS.models</span> <span class="kn">import</span> <span class="n">hofts</span>
+    <span class="kn">from</span> <span class="nn">pyFTS.partitioners</span> <span class="kn">import</span> <span class="n">Grid</span><span class="p">,</span> <span class="n">Entropy</span>
+    <span class="kn">from</span> <span class="nn">pyFTS.benchmarks</span> <span class="kn">import</span> <span class="n">Measures</span>
+    <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+
+    <span class="k">if</span> <span class="n">individual</span><span class="p">[</span><span class="s1">&#39;mf&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
+        <span class="n">mf</span> <span class="o">=</span> <span class="n">Membership</span><span class="o">.</span><span class="n">trimf</span>
+    <span class="k">elif</span> <span class="n">individual</span><span class="p">[</span><span class="s1">&#39;mf&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
+        <span class="n">mf</span> <span class="o">=</span> <span class="n">Membership</span><span class="o">.</span><span class="n">trapmf</span>
+    <span class="k">elif</span> <span class="n">individual</span><span class="p">[</span><span class="s1">&#39;mf&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="mi">3</span> <span class="ow">and</span> <span class="n">individual</span><span class="p">[</span><span class="s1">&#39;partitioner&#39;</span><span class="p">]</span> <span class="o">!=</span> <span class="mi">2</span><span class="p">:</span>
+        <span class="n">mf</span> <span class="o">=</span> <span class="n">Membership</span><span class="o">.</span><span class="n">gaussmf</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">mf</span> <span class="o">=</span> <span class="n">Membership</span><span class="o">.</span><span class="n">trimf</span>
+
+    <span class="n">window_size</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;window_size&#39;</span><span class="p">,</span> <span class="mi">800</span><span class="p">)</span>
+    <span class="n">train_rate</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;train_rate&#39;</span><span class="p">,</span> <span class="o">.</span><span class="mi">8</span><span class="p">)</span>
+    <span class="n">increment_rate</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;increment_rate&#39;</span><span class="p">,</span> <span class="o">.</span><span class="mi">2</span><span class="p">)</span>
+    <span class="n">parameters</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;parameters&#39;</span><span class="p">,</span> <span class="p">{})</span>
+
+    <span class="n">errors</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="n">sizes</span> <span class="o">=</span> <span class="p">[]</span>
+
+    <span class="k">for</span> <span class="n">count</span><span class="p">,</span> <span class="n">train</span><span class="p">,</span> <span class="n">test</span> <span class="ow">in</span> <span class="n">Util</span><span class="o">.</span><span class="n">sliding_window</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">window_size</span><span class="p">,</span> <span class="n">train</span><span class="o">=</span><span class="n">train_rate</span><span class="p">,</span> <span class="n">inc</span><span class="o">=</span><span class="n">increment_rate</span><span class="p">):</span>
+
+        <span class="k">if</span> <span class="n">individual</span><span class="p">[</span><span class="s1">&#39;partitioner&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
+            <span class="n">partitioner</span> <span class="o">=</span> <span class="n">Grid</span><span class="o">.</span><span class="n">GridPartitioner</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">train</span><span class="p">,</span> <span class="n">npart</span><span class="o">=</span><span class="n">individual</span><span class="p">[</span><span class="s1">&#39;npart&#39;</span><span class="p">],</span> <span class="n">func</span><span class="o">=</span><span class="n">mf</span><span class="p">)</span>
+        <span class="k">elif</span> <span class="n">individual</span><span class="p">[</span><span class="s1">&#39;partitioner&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
+            <span class="n">npart</span> <span class="o">=</span> <span class="n">individual</span><span class="p">[</span><span class="s1">&#39;npart&#39;</span><span class="p">]</span> <span class="k">if</span> <span class="n">individual</span><span class="p">[</span><span class="s1">&#39;npart&#39;</span><span class="p">]</span> <span class="o">&gt;</span> <span class="mi">10</span> <span class="k">else</span> <span class="mi">10</span>
+            <span class="n">partitioner</span> <span class="o">=</span> <span class="n">Entropy</span><span class="o">.</span><span class="n">EntropyPartitioner</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">train</span><span class="p">,</span> <span class="n">npart</span><span class="o">=</span><span class="n">npart</span><span class="p">,</span> <span class="n">func</span><span class="o">=</span><span class="n">mf</span><span class="p">)</span>
+
+        <span class="n">model</span> <span class="o">=</span> <span class="n">hofts</span><span class="o">.</span><span class="n">WeightedHighOrderFTS</span><span class="p">(</span><span class="n">partitioner</span><span class="o">=</span><span class="n">partitioner</span><span class="p">,</span>
+                                   <span class="n">lags</span><span class="o">=</span><span class="n">individual</span><span class="p">[</span><span class="s1">&#39;lags&#39;</span><span class="p">],</span>
+                                   <span class="n">alpha_cut</span><span class="o">=</span><span class="n">individual</span><span class="p">[</span><span class="s1">&#39;alpha&#39;</span><span class="p">],</span>
+                                   <span class="n">order</span><span class="o">=</span><span class="n">individual</span><span class="p">[</span><span class="s1">&#39;order&#39;</span><span class="p">])</span>
+        <span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">train</span><span class="p">)</span>
+
+        <span class="n">forecasts</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">test</span><span class="p">)</span>
+
+        <span class="c1">#rmse, mape, u = Measures.get_point_statistics(test, model)</span>
+        <span class="n">rmse</span> <span class="o">=</span> <span class="n">Measures</span><span class="o">.</span><span class="n">rmse</span><span class="p">(</span><span class="n">test</span><span class="p">[</span><span class="n">model</span><span class="o">.</span><span class="n">max_lag</span><span class="p">:],</span> <span class="n">forecasts</span><span class="p">)</span>
+
+        <span class="n">size</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">model</span><span class="p">)</span>
+
+        <span class="n">errors</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">rmse</span><span class="p">)</span>
+        <span class="n">sizes</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">size</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="p">{</span><span class="s1">&#39;parameters&#39;</span><span class="p">:</span> <span class="n">individual</span><span class="p">,</span> <span class="s1">&#39;rmse&#39;</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">nanmean</span><span class="p">(</span><span class="n">errors</span><span class="p">),</span> <span class="s1">&#39;size&#39;</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">nanmean</span><span class="p">(</span><span class="n">size</span><span class="p">)}</span></div>
+
+
+<div class="viewcode-block" id="process_jobs"><a class="viewcode-back" href="../../../pyFTS.hyperparam.html#pyFTS.hyperparam.GridSearch.process_jobs">[docs]</a><span class="k">def</span> <span class="nf">process_jobs</span><span class="p">(</span><span class="n">jobs</span><span class="p">,</span> <span class="n">datasetname</span><span class="p">,</span> <span class="n">conn</span><span class="p">):</span>
+    <span class="kn">from</span> <span class="nn">pyFTS.distributed</span> <span class="kn">import</span> <span class="n">dispy</span> <span class="k">as</span> <span class="n">dUtil</span>
+    <span class="kn">import</span> <span class="nn">dispy</span>
+    <span class="k">for</span> <span class="n">ct</span><span class="p">,</span> <span class="n">job</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">jobs</span><span class="p">):</span>
+        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Processing job </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">ct</span><span class="p">))</span>
+        <span class="n">result</span> <span class="o">=</span> <span class="n">job</span><span class="p">()</span>
+        <span class="k">if</span> <span class="n">job</span><span class="o">.</span><span class="n">status</span> <span class="o">==</span> <span class="n">dispy</span><span class="o">.</span><span class="n">DispyJob</span><span class="o">.</span><span class="n">Finished</span> <span class="ow">and</span> <span class="n">result</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Processing result of </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">result</span><span class="p">))</span>
+            
+            <span class="n">metrics</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;rmse&#39;</span><span class="p">:</span> <span class="n">result</span><span class="p">[</span><span class="s1">&#39;rmse&#39;</span><span class="p">],</span> <span class="s1">&#39;size&#39;</span><span class="p">:</span> <span class="n">result</span><span class="p">[</span><span class="s1">&#39;size&#39;</span><span class="p">]}</span>
+            
+            <span class="k">for</span> <span class="n">metric</span> <span class="ow">in</span> <span class="n">metrics</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
+
+                <span class="n">param</span> <span class="o">=</span> <span class="n">result</span><span class="p">[</span><span class="s1">&#39;parameters&#39;</span><span class="p">]</span>
+
+                <span class="n">record</span> <span class="o">=</span> <span class="p">(</span><span class="n">datasetname</span><span class="p">,</span> <span class="s1">&#39;GridSearch&#39;</span><span class="p">,</span> <span class="s1">&#39;WHOFTS&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="n">param</span><span class="p">[</span><span class="s1">&#39;mf&#39;</span><span class="p">],</span>
+                          <span class="n">param</span><span class="p">[</span><span class="s1">&#39;order&#39;</span><span class="p">],</span> <span class="n">param</span><span class="p">[</span><span class="s1">&#39;partitioner&#39;</span><span class="p">],</span> <span class="n">param</span><span class="p">[</span><span class="s1">&#39;npart&#39;</span><span class="p">],</span>
+                          <span class="n">param</span><span class="p">[</span><span class="s1">&#39;alpha&#39;</span><span class="p">],</span> <span class="nb">str</span><span class="p">(</span><span class="n">param</span><span class="p">[</span><span class="s1">&#39;lags&#39;</span><span class="p">]),</span> <span class="n">metric</span><span class="p">,</span> <span class="n">metrics</span><span class="p">[</span><span class="n">metric</span><span class="p">])</span>
+
+
+                <span class="n">hUtil</span><span class="o">.</span><span class="n">insert_hyperparam</span><span class="p">(</span><span class="n">record</span><span class="p">,</span> <span class="n">conn</span><span class="p">)</span>
+
+        <span class="k">else</span><span class="p">:</span>
+            <span class="nb">print</span><span class="p">(</span><span class="n">job</span><span class="o">.</span><span class="n">exception</span><span class="p">)</span>
+            <span class="nb">print</span><span class="p">(</span><span class="n">job</span><span class="o">.</span><span class="n">stdout</span><span class="p">)</span></div>
+    
+
+<div class="viewcode-block" id="execute"><a class="viewcode-back" href="../../../pyFTS.hyperparam.html#pyFTS.hyperparam.GridSearch.execute">[docs]</a><span class="k">def</span> <span class="nf">execute</span><span class="p">(</span><span class="n">hyperparams</span><span class="p">,</span> <span class="n">datasetname</span><span class="p">,</span> <span class="n">dataset</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+    <span class="kn">from</span> <span class="nn">pyFTS.distributed</span> <span class="kn">import</span> <span class="n">dispy</span> <span class="k">as</span> <span class="n">dUtil</span>
+    <span class="kn">import</span> <span class="nn">dispy</span>
+
+    <span class="n">nodes</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;nodes&#39;</span><span class="p">,[</span><span class="s1">&#39;127.0.0.1&#39;</span><span class="p">])</span>
+
+    <span class="n">individuals</span> <span class="o">=</span> <span class="p">[]</span>
+
+    <span class="k">if</span> <span class="s1">&#39;lags&#39;</span> <span class="ow">in</span> <span class="n">hyperparams</span><span class="p">:</span>
+        <span class="n">lags</span> <span class="o">=</span> <span class="n">hyperparams</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s1">&#39;lags&#39;</span><span class="p">)</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">lags</span> <span class="o">=</span> <span class="p">[</span><span class="n">k</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">50</span><span class="p">)]</span>
+
+    <span class="n">keys_sorted</span> <span class="o">=</span> <span class="p">[</span><span class="n">k</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">hyperparams</span><span class="o">.</span><span class="n">keys</span><span class="p">())]</span>
+
+    <span class="n">index</span> <span class="o">=</span> <span class="p">{}</span>
+    <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">keys_sorted</span><span class="p">)):</span>
+        <span class="n">index</span><span class="p">[</span><span class="n">keys_sorted</span><span class="p">[</span><span class="n">k</span><span class="p">]]</span> <span class="o">=</span> <span class="n">k</span>
+        
+    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Evaluation order: </span><span class="se">\n</span><span class="s2"> </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">index</span><span class="p">))</span>
+
+    <span class="n">hp_values</span> <span class="o">=</span> <span class="p">[</span>
+        <span class="p">[</span><span class="n">v</span> <span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">hyperparams</span><span class="p">[</span><span class="n">hp</span><span class="p">]]</span>
+        <span class="k">for</span> <span class="n">hp</span> <span class="ow">in</span> <span class="n">keys_sorted</span>
+    <span class="p">]</span>
+    
+    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Evaluation values: </span><span class="se">\n</span><span class="s2"> </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">hp_values</span><span class="p">))</span>
+    
+    <span class="n">cluster</span><span class="p">,</span> <span class="n">http_server</span> <span class="o">=</span> <span class="n">dUtil</span><span class="o">.</span><span class="n">start_dispy_cluster</span><span class="p">(</span><span class="n">cluster_method</span><span class="p">,</span> <span class="n">nodes</span><span class="o">=</span><span class="n">nodes</span><span class="p">)</span>
+    <span class="n">file</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;file&#39;</span><span class="p">,</span> <span class="s1">&#39;hyperparam.db&#39;</span><span class="p">)</span>
+
+    <span class="n">conn</span> <span class="o">=</span> <span class="n">hUtil</span><span class="o">.</span><span class="n">open_hyperparam_db</span><span class="p">(</span><span class="n">file</span><span class="p">)</span>
+
+    <span class="k">for</span> <span class="n">instance</span> <span class="ow">in</span> <span class="n">product</span><span class="p">(</span><span class="o">*</span><span class="n">hp_values</span><span class="p">):</span>
+        <span class="n">partitions</span> <span class="o">=</span> <span class="n">instance</span><span class="p">[</span><span class="n">index</span><span class="p">[</span><span class="s1">&#39;partitions&#39;</span><span class="p">]]</span>
+        <span class="n">partitioner</span> <span class="o">=</span> <span class="n">instance</span><span class="p">[</span><span class="n">index</span><span class="p">[</span><span class="s1">&#39;partitioner&#39;</span><span class="p">]]</span>
+        <span class="n">mf</span> <span class="o">=</span> <span class="n">instance</span><span class="p">[</span><span class="n">index</span><span class="p">[</span><span class="s1">&#39;mf&#39;</span><span class="p">]]</span>
+        <span class="n">alpha_cut</span> <span class="o">=</span> <span class="n">instance</span><span class="p">[</span><span class="n">index</span><span class="p">[</span><span class="s1">&#39;alpha&#39;</span><span class="p">]]</span>
+        <span class="n">order</span> <span class="o">=</span> <span class="n">instance</span><span class="p">[</span><span class="n">index</span><span class="p">[</span><span class="s1">&#39;order&#39;</span><span class="p">]]</span>
+        <span class="n">count</span> <span class="o">=</span> <span class="mi">0</span>
+        <span class="k">for</span> <span class="n">lag1</span> <span class="ow">in</span> <span class="n">lags</span><span class="p">:</span> <span class="c1"># o é o lag1</span>
+            <span class="n">_lags</span> <span class="o">=</span> <span class="p">[</span><span class="n">lag1</span><span class="p">]</span>
+            <span class="n">count</span> <span class="o">+=</span> <span class="mi">1</span>
+            <span class="k">if</span> <span class="n">order</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
+                <span class="k">for</span> <span class="n">lag2</span> <span class="ow">in</span> <span class="n">lags</span><span class="p">:</span>  <span class="c1"># o é o lag1</span>
+                    <span class="n">_lags2</span> <span class="o">=</span> <span class="p">[</span><span class="n">lag1</span><span class="p">,</span> <span class="n">lag1</span><span class="o">+</span><span class="n">lag2</span><span class="p">]</span>
+                    <span class="n">count</span> <span class="o">+=</span> <span class="mi">1</span>
+                    <span class="k">if</span> <span class="n">order</span> <span class="o">&gt;</span> <span class="mi">2</span><span class="p">:</span>
+                        <span class="k">for</span> <span class="n">lag3</span> <span class="ow">in</span> <span class="n">lags</span><span class="p">:</span>  <span class="c1"># o é o lag1</span>
+                            <span class="n">count</span> <span class="o">+=</span> <span class="mi">1</span>
+                            <span class="n">_lags3</span> <span class="o">=</span> <span class="p">[</span><span class="n">lag1</span><span class="p">,</span> <span class="n">lag1</span> <span class="o">+</span> <span class="n">lag2</span><span class="p">,</span> <span class="n">lag1</span> <span class="o">+</span> <span class="n">lag2</span><span class="o">+</span><span class="n">lag3</span> <span class="p">]</span>
+                            <span class="n">individuals</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">dict_individual</span><span class="p">(</span><span class="n">mf</span><span class="p">,</span> <span class="n">partitioner</span><span class="p">,</span> <span class="n">partitions</span><span class="p">,</span> <span class="n">order</span><span class="p">,</span> <span class="n">_lags3</span><span class="p">,</span> <span class="n">alpha_cut</span><span class="p">))</span>
+                    <span class="k">else</span><span class="p">:</span>
+                        <span class="n">individuals</span><span class="o">.</span><span class="n">append</span><span class="p">(</span>
+                            <span class="n">dict_individual</span><span class="p">(</span><span class="n">mf</span><span class="p">,</span> <span class="n">partitioner</span><span class="p">,</span> <span class="n">partitions</span><span class="p">,</span> <span class="n">order</span><span class="p">,</span> <span class="n">_lags2</span><span class="p">,</span> <span class="n">alpha_cut</span><span class="p">))</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">individuals</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">dict_individual</span><span class="p">(</span><span class="n">mf</span><span class="p">,</span> <span class="n">partitioner</span><span class="p">,</span> <span class="n">partitions</span><span class="p">,</span> <span class="n">order</span><span class="p">,</span> <span class="n">_lags</span><span class="p">,</span> <span class="n">alpha_cut</span><span class="p">))</span>
+                
+            <span class="k">if</span> <span class="n">count</span> <span class="o">&gt;</span> <span class="mi">10</span><span class="p">:</span>
+                <span class="n">jobs</span> <span class="o">=</span> <span class="p">[]</span>
+
+                <span class="k">for</span> <span class="n">ind</span> <span class="ow">in</span> <span class="n">individuals</span><span class="p">:</span>
+                    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Testing individual </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">ind</span><span class="p">))</span>
+                    <span class="n">job</span> <span class="o">=</span> <span class="n">cluster</span><span class="o">.</span><span class="n">submit</span><span class="p">(</span><span class="n">ind</span><span class="p">,</span> <span class="n">dataset</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+                    <span class="n">jobs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">job</span><span class="p">)</span>
+                    
+                <span class="n">process_jobs</span><span class="p">(</span><span class="n">jobs</span><span class="p">,</span> <span class="n">datasetname</span><span class="p">,</span> <span class="n">conn</span><span class="p">)</span>
+                
+                <span class="n">count</span> <span class="o">=</span> <span class="mi">0</span>
+                
+                <span class="n">individuals</span> <span class="o">=</span> <span class="p">[]</span>
+
+    <span class="n">dUtil</span><span class="o">.</span><span class="n">stop_dispy_cluster</span><span class="p">(</span><span class="n">cluster</span><span class="p">,</span> <span class="n">http_server</span><span class="p">)</span></div>
+</pre></div>
+
+            <div class="clearer"></div>
+          </div>
+        </div>
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+      <div class="sphinxsidebar" role="navigation" aria-label="main navigation">
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diff --git a/docs/build/html/_modules/pyFTS/models/chen.html b/docs/build/html/_modules/pyFTS/models/chen.html
new file mode 100644
index 0000000..5bf3e9f
--- /dev/null
+++ b/docs/build/html/_modules/pyFTS/models/chen.html
@@ -0,0 +1,189 @@
+
+
+<!doctype html>
+
+<html>
+  <head>
+    <meta charset="utf-8" />
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+        <li class="nav-item nav-item-this"><a href="">pyFTS.models.chen</a></li> 
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+            
+  <h1>Source code for pyFTS.models.chen</h1><div class="highlight"><pre>
+<span></span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">First Order Conventional Fuzzy Time Series by Chen (1996)</span>
+
+<span class="sd">S.-M. Chen, “Forecasting enrollments based on fuzzy time series,” Fuzzy Sets Syst., vol. 81, no. 3, pp. 311–319, 1996.</span>
+<span class="sd">&quot;&quot;&quot;</span>
+
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">from</span> <span class="nn">pyFTS.common</span> <span class="kn">import</span> <span class="n">FuzzySet</span><span class="p">,</span> <span class="n">FLR</span><span class="p">,</span> <span class="n">fts</span><span class="p">,</span> <span class="n">flrg</span>
+
+
+<div class="viewcode-block" id="ConventionalFLRG"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.chen.ConventionalFLRG">[docs]</a><span class="k">class</span> <span class="nc">ConventionalFLRG</span><span class="p">(</span><span class="n">flrg</span><span class="o">.</span><span class="n">FLRG</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;First Order Conventional Fuzzy Logical Relationship Group&quot;&quot;&quot;</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">LHS</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">(</span><span class="n">ConventionalFLRG</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">LHS</span> <span class="o">=</span> <span class="n">LHS</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span> <span class="o">=</span> <span class="nb">set</span><span class="p">()</span>
+
+<div class="viewcode-block" id="ConventionalFLRG.get_key"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.chen.ConventionalFLRG.get_key">[docs]</a>    <span class="k">def</span> <span class="nf">get_key</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sets</span><span class="p">):</span>
+        <span class="k">return</span> <span class="n">sets</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">LHS</span><span class="p">]</span><span class="o">.</span><span class="n">name</span></div>
+
+<div class="viewcode-block" id="ConventionalFLRG.append_rhs"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.chen.ConventionalFLRG.append_rhs">[docs]</a>    <span class="k">def</span> <span class="nf">append_rhs</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">c</span><span class="p">)</span></div>
+
+    <span class="k">def</span> <span class="fm">__str__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="n">tmp</span> <span class="o">=</span> <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">LHS</span><span class="p">)</span> <span class="o">+</span> <span class="s2">&quot; -&gt; &quot;</span>
+        <span class="n">tmp2</span> <span class="o">=</span> <span class="s2">&quot;&quot;</span>
+        <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="nb">sorted</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="p">,</span> <span class="n">key</span><span class="o">=</span><span class="k">lambda</span> <span class="n">s</span><span class="p">:</span> <span class="n">s</span><span class="p">):</span>
+            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">tmp2</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
+                <span class="n">tmp2</span> <span class="o">=</span> <span class="n">tmp2</span> <span class="o">+</span> <span class="s2">&quot;,&quot;</span>
+            <span class="n">tmp2</span> <span class="o">=</span> <span class="n">tmp2</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">c</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">tmp</span> <span class="o">+</span> <span class="n">tmp2</span></div>
+
+
+<div class="viewcode-block" id="ConventionalFTS"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.chen.ConventionalFTS">[docs]</a><span class="k">class</span> <span class="nc">ConventionalFTS</span><span class="p">(</span><span class="n">fts</span><span class="o">.</span><span class="n">FTS</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;Conventional Fuzzy Time Series&quot;&quot;&quot;</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">(</span><span class="n">ConventionalFTS</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">order</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="s2">&quot;Conventional FTS&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">detail</span> <span class="o">=</span> <span class="s2">&quot;Chen&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">shortname</span> <span class="o">=</span> <span class="s2">&quot;CFTS&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span> <span class="o">=</span> <span class="p">{}</span>
+
+<div class="viewcode-block" id="ConventionalFTS.generate_flrg"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.chen.ConventionalFTS.generate_flrg">[docs]</a>    <span class="k">def</span> <span class="nf">generate_flrg</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">flrs</span><span class="p">):</span>
+        <span class="k">for</span> <span class="n">flr</span> <span class="ow">in</span> <span class="n">flrs</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">flr</span><span class="o">.</span><span class="n">LHS</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">flr</span><span class="o">.</span><span class="n">LHS</span><span class="p">]</span><span class="o">.</span><span class="n">append_rhs</span><span class="p">(</span><span class="n">flr</span><span class="o">.</span><span class="n">RHS</span><span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">flr</span><span class="o">.</span><span class="n">LHS</span><span class="p">]</span> <span class="o">=</span> <span class="n">ConventionalFLRG</span><span class="p">(</span><span class="n">flr</span><span class="o">.</span><span class="n">LHS</span><span class="p">)</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">flr</span><span class="o">.</span><span class="n">LHS</span><span class="p">]</span><span class="o">.</span><span class="n">append_rhs</span><span class="p">(</span><span class="n">flr</span><span class="o">.</span><span class="n">RHS</span><span class="p">)</span></div>
+
+<div class="viewcode-block" id="ConventionalFTS.train"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.chen.ConventionalFTS.train">[docs]</a>    <span class="k">def</span> <span class="nf">train</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+
+        <span class="n">tmpdata</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">fuzzyfy</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s1">&#39;maximum&#39;</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s1">&#39;sets&#39;</span><span class="p">)</span>
+        <span class="n">flrs</span> <span class="o">=</span> <span class="n">FLR</span><span class="o">.</span><span class="n">generate_non_recurrent_flrs</span><span class="p">(</span><span class="n">tmpdata</span><span class="p">,</span> <span class="n">steps</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">standard_horizon</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">generate_flrg</span><span class="p">(</span><span class="n">flrs</span><span class="p">)</span></div>
+
+<div class="viewcode-block" id="ConventionalFTS.forecast"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.chen.ConventionalFTS.forecast">[docs]</a>    <span class="k">def</span> <span class="nf">forecast</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ndata</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+
+        <span class="n">explain</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;explain&#39;</span><span class="p">,</span><span class="kc">False</span><span class="p">)</span>
+
+        <span class="n">l</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">ndata</span><span class="p">)</span> <span class="k">if</span> <span class="ow">not</span> <span class="n">explain</span> <span class="k">else</span> <span class="mi">1</span>
+
+        <span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
+
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">l</span><span class="p">):</span>
+
+            <span class="n">actual</span> <span class="o">=</span> <span class="n">FuzzySet</span><span class="o">.</span><span class="n">get_maximum_membership_fuzzyset</span><span class="p">(</span><span class="n">ndata</span><span class="p">[</span><span class="n">k</span><span class="p">],</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">)</span>
+
+            <span class="k">if</span> <span class="n">explain</span><span class="p">:</span>
+                <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Fuzzyfication:</span><span class="se">\n\n</span><span class="s2"> </span><span class="si">{}</span><span class="s2"> -&gt; </span><span class="si">{}</span><span class="s2"> </span><span class="se">\n</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">ndata</span><span class="p">[</span><span class="n">k</span><span class="p">],</span> <span class="n">actual</span><span class="o">.</span><span class="n">name</span><span class="p">))</span>
+
+            <span class="k">if</span> <span class="n">actual</span><span class="o">.</span><span class="n">name</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">:</span>
+                <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">actual</span><span class="o">.</span><span class="n">centroid</span><span class="p">)</span>
+
+                <span class="k">if</span> <span class="n">explain</span><span class="p">:</span>
+                    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Rules:</span><span class="se">\n\n</span><span class="s2"> </span><span class="si">{}</span><span class="s2"> -&gt; </span><span class="si">{}</span><span class="s2"> (Naïve)</span><span class="se">\t</span><span class="s2"> Midpoint: </span><span class="si">{}</span><span class="s2">  </span><span class="se">\n\n</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">actual</span><span class="o">.</span><span class="n">name</span><span class="p">,</span> <span class="n">actual</span><span class="o">.</span><span class="n">name</span><span class="p">,</span><span class="n">actual</span><span class="o">.</span><span class="n">centroid</span><span class="p">))</span>
+
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">_flrg</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">actual</span><span class="o">.</span><span class="n">name</span><span class="p">]</span>
+
+                <span class="n">mp</span> <span class="o">=</span> <span class="n">_flrg</span><span class="o">.</span><span class="n">get_midpoint</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">)</span>
+
+                <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">mp</span><span class="p">)</span>
+
+                <span class="k">if</span> <span class="n">explain</span><span class="p">:</span>
+                    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Rules:</span><span class="se">\n\n</span><span class="s2"> </span><span class="si">{}</span><span class="s2"> </span><span class="se">\t</span><span class="s2"> Midpoint: </span><span class="si">{}</span><span class="s2"> </span><span class="se">\n</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">_flrg</span><span class="p">),</span> <span class="n">mp</span><span class="p">))</span>
+
+                    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Deffuzyfied value: </span><span class="si">{}</span><span class="s2"> </span><span class="se">\n</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">mp</span><span class="p">))</span>
+
+        <span class="k">return</span> <span class="n">ret</span></div></div>
+</pre></div>
+
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diff --git a/docs/build/html/_modules/pyFTS/models/cheng.html b/docs/build/html/_modules/pyFTS/models/cheng.html
new file mode 100644
index 0000000..3462e63
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+
+
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+            
+  <h1>Source code for pyFTS.models.cheng</h1><div class="highlight"><pre>
+<span></span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">Trend Weighted Fuzzy Time Series by Cheng, Chen and Wu (2009)</span>
+
+<span class="sd">C.-H. Cheng, Y.-S. Chen, and Y.-L. Wu, “Forecasting innovation diffusion of products using trend-weighted fuzzy time-series model,” </span>
+<span class="sd">Expert Syst. Appl., vol. 36, no. 2, pp. 1826–1832, 2009.</span>
+<span class="sd">&quot;&quot;&quot;</span>
+
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">from</span> <span class="nn">pyFTS.common</span> <span class="kn">import</span> <span class="n">FuzzySet</span><span class="p">,</span> <span class="n">FLR</span><span class="p">,</span> <span class="n">fts</span>
+<span class="kn">from</span> <span class="nn">pyFTS.models</span> <span class="kn">import</span> <span class="n">yu</span>
+
+
+<div class="viewcode-block" id="TrendWeightedFLRG"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.cheng.TrendWeightedFLRG">[docs]</a><span class="k">class</span> <span class="nc">TrendWeightedFLRG</span><span class="p">(</span><span class="n">yu</span><span class="o">.</span><span class="n">WeightedFLRG</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    First Order Trend Weighted Fuzzy Logical Relationship Group</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">LHS</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">(</span><span class="n">TrendWeightedFLRG</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">LHS</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">w</span> <span class="o">=</span> <span class="kc">None</span>
+
+<div class="viewcode-block" id="TrendWeightedFLRG.weights"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.cheng.TrendWeightedFLRG.weights">[docs]</a>    <span class="k">def</span> <span class="nf">weights</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sets</span><span class="p">):</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">w</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">count_nochange</span> <span class="o">=</span> <span class="mf">0.0</span>
+            <span class="n">count_up</span> <span class="o">=</span> <span class="mf">0.0</span>
+            <span class="n">count_down</span> <span class="o">=</span> <span class="mf">0.0</span>
+            <span class="n">weights</span> <span class="o">=</span> <span class="p">[]</span>
+
+            <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="p">:</span>
+                <span class="n">tmp</span> <span class="o">=</span> <span class="mi">0</span>
+                <span class="k">if</span> <span class="n">sets</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">LHS</span><span class="p">]</span><span class="o">.</span><span class="n">centroid</span> <span class="o">==</span> <span class="n">sets</span><span class="p">[</span><span class="n">c</span><span class="p">]</span><span class="o">.</span><span class="n">centroid</span><span class="p">:</span>
+                    <span class="n">count_nochange</span> <span class="o">+=</span> <span class="mf">1.0</span>
+                    <span class="n">tmp</span> <span class="o">=</span> <span class="n">count_nochange</span>
+                <span class="k">elif</span> <span class="n">sets</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">LHS</span><span class="p">]</span><span class="o">.</span><span class="n">centroid</span> <span class="o">&gt;</span> <span class="n">sets</span><span class="p">[</span><span class="n">c</span><span class="p">]</span><span class="o">.</span><span class="n">centroid</span><span class="p">:</span>
+                    <span class="n">count_down</span> <span class="o">+=</span> <span class="mf">1.0</span>
+                    <span class="n">tmp</span> <span class="o">=</span> <span class="n">count_down</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">count_up</span> <span class="o">+=</span> <span class="mf">1.0</span>
+                    <span class="n">tmp</span> <span class="o">=</span> <span class="n">count_up</span>
+                <span class="n">weights</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">tmp</span><span class="p">)</span>
+
+            <span class="n">tot</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">(</span><span class="n">weights</span><span class="p">)</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">w</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">k</span> <span class="o">/</span> <span class="n">tot</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">weights</span><span class="p">])</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">w</span></div></div>
+
+
+<div class="viewcode-block" id="TrendWeightedFTS"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.cheng.TrendWeightedFTS">[docs]</a><span class="k">class</span> <span class="nc">TrendWeightedFTS</span><span class="p">(</span><span class="n">yu</span><span class="o">.</span><span class="n">WeightedFTS</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;First Order Trend Weighted Fuzzy Time Series&quot;&quot;&quot;</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">(</span><span class="n">TrendWeightedFTS</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">shortname</span> <span class="o">=</span> <span class="s2">&quot;TWFTS&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="s2">&quot;Trend Weighted FTS&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">detail</span> <span class="o">=</span> <span class="s2">&quot;Cheng&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">is_high_order</span> <span class="o">=</span> <span class="kc">False</span>
+
+<div class="viewcode-block" id="TrendWeightedFTS.generate_FLRG"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.cheng.TrendWeightedFTS.generate_FLRG">[docs]</a>    <span class="k">def</span> <span class="nf">generate_FLRG</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">flrs</span><span class="p">):</span>
+        <span class="k">for</span> <span class="n">flr</span> <span class="ow">in</span> <span class="n">flrs</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">flr</span><span class="o">.</span><span class="n">LHS</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">flr</span><span class="o">.</span><span class="n">LHS</span><span class="p">]</span><span class="o">.</span><span class="n">append_rhs</span><span class="p">(</span><span class="n">flr</span><span class="o">.</span><span class="n">RHS</span><span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">flr</span><span class="o">.</span><span class="n">LHS</span><span class="p">]</span> <span class="o">=</span> <span class="n">TrendWeightedFLRG</span><span class="p">(</span><span class="n">flr</span><span class="o">.</span><span class="n">LHS</span><span class="p">)</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">flr</span><span class="o">.</span><span class="n">LHS</span><span class="p">]</span><span class="o">.</span><span class="n">append_rhs</span><span class="p">(</span><span class="n">flr</span><span class="o">.</span><span class="n">RHS</span><span class="p">)</span></div></div>
+</pre></div>
+
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+
+
+<!doctype html>
+
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+        <li class="nav-item nav-item-this"><a href="">pyFTS.models.ensemble.ensemble</a></li> 
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+            
+  <h1>Source code for pyFTS.models.ensemble.ensemble</h1><div class="highlight"><pre>
+<span></span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">EnsembleFTS wraps several FTS methods to ensemble their forecasts, providing point,</span>
+<span class="sd">interval and probabilistic forecasting.</span>
+
+<span class="sd">Silva, P. C. L et al. Probabilistic Forecasting with Seasonal Ensemble Fuzzy Time-Series</span>
+<span class="sd">XIII Brazilian Congress on Computational Intelligence, 2017. Rio de Janeiro, Brazil.</span>
+<span class="sd">&quot;&quot;&quot;</span>
+
+
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+<span class="kn">from</span> <span class="nn">pyFTS.common</span> <span class="kn">import</span> <span class="n">SortedCollection</span><span class="p">,</span> <span class="n">fts</span><span class="p">,</span> <span class="n">tree</span>
+<span class="kn">from</span> <span class="nn">pyFTS.models</span> <span class="kn">import</span> <span class="n">chen</span><span class="p">,</span> <span class="n">cheng</span><span class="p">,</span> <span class="n">hofts</span><span class="p">,</span> <span class="n">hwang</span><span class="p">,</span> <span class="n">ismailefendi</span><span class="p">,</span> <span class="n">sadaei</span><span class="p">,</span> <span class="n">song</span><span class="p">,</span> <span class="n">yu</span>
+<span class="kn">from</span> <span class="nn">pyFTS.probabilistic</span> <span class="kn">import</span> <span class="n">ProbabilityDistribution</span>
+<span class="kn">from</span> <span class="nn">pyFTS.partitioners</span> <span class="kn">import</span> <span class="n">Grid</span>
+<span class="kn">import</span> <span class="nn">scipy.stats</span> <span class="k">as</span> <span class="nn">st</span>
+<span class="kn">from</span> <span class="nn">itertools</span> <span class="kn">import</span> <span class="n">product</span>
+
+
+<div class="viewcode-block" id="sampler"><a class="viewcode-back" href="../../../../pyFTS.models.ensemble.html#pyFTS.models.ensemble.ensemble.sampler">[docs]</a><span class="k">def</span> <span class="nf">sampler</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">quantiles</span><span class="p">,</span> <span class="n">bounds</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
+    <span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="k">for</span> <span class="n">qt</span> <span class="ow">in</span> <span class="n">quantiles</span><span class="p">:</span>
+        <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">nanpercentile</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">q</span><span class="o">=</span><span class="n">qt</span> <span class="o">*</span> <span class="mi">100</span><span class="p">))</span>
+    <span class="k">if</span> <span class="n">bounds</span><span class="p">:</span>
+        <span class="n">ret</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="nb">min</span><span class="p">(</span><span class="n">data</span><span class="p">))</span>
+        <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">max</span><span class="p">(</span><span class="n">data</span><span class="p">))</span>
+    <span class="k">return</span> <span class="n">ret</span></div>
+
+
+<div class="viewcode-block" id="EnsembleFTS"><a class="viewcode-back" href="../../../../pyFTS.models.ensemble.html#pyFTS.models.ensemble.ensemble.EnsembleFTS">[docs]</a><span class="k">class</span> <span class="nc">EnsembleFTS</span><span class="p">(</span><span class="n">fts</span><span class="o">.</span><span class="n">FTS</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Ensemble FTS</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">(</span><span class="n">EnsembleFTS</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">shortname</span> <span class="o">=</span> <span class="s2">&quot;EnsembleFTS&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="s2">&quot;Ensemble FTS&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span> <span class="o">=</span> <span class="p">{}</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">is_wrapper</span> <span class="o">=</span> <span class="kc">True</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">has_point_forecasting</span> <span class="o">=</span> <span class="kc">True</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">has_interval_forecasting</span> <span class="o">=</span> <span class="kc">True</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">has_probability_forecasting</span> <span class="o">=</span> <span class="kc">True</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">is_high_order</span> <span class="o">=</span> <span class="kc">True</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">models</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="sd">&quot;&quot;&quot;A list of FTS models, the ensemble components&quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">parameters</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="sd">&quot;&quot;&quot;A list with the parameters for each component model&quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">alpha</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;alpha&quot;</span><span class="p">,</span> <span class="mf">0.05</span><span class="p">)</span>
+        <span class="sd">&quot;&quot;&quot;The quantiles &quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">point_method</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;point_method&#39;</span><span class="p">,</span> <span class="s1">&#39;mean&#39;</span><span class="p">)</span>
+        <span class="sd">&quot;&quot;&quot;The method used to mix the several model&#39;s forecasts into a unique point forecast. Options: mean, median, quantile, exponential&quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">interval_method</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;interval_method&#39;</span><span class="p">,</span> <span class="s1">&#39;quantile&#39;</span><span class="p">)</span>
+        <span class="sd">&quot;&quot;&quot;The method used to mix the several model&#39;s forecasts into a interval forecast. Options: quantile, extremum, normal&quot;&quot;&quot;</span>
+
+<div class="viewcode-block" id="EnsembleFTS.append_model"><a class="viewcode-back" href="../../../../pyFTS.models.ensemble.html#pyFTS.models.ensemble.ensemble.EnsembleFTS.append_model">[docs]</a>    <span class="k">def</span> <span class="nf">append_model</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">model</span><span class="p">):</span>
+        <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Append a new trained model to the ensemble</span>
+
+<span class="sd">        :param model: FTS model</span>
+
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">models</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">model</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">model</span><span class="o">.</span><span class="n">order</span> <span class="o">&gt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">order</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">order</span>
+
+        <span class="k">if</span> <span class="n">model</span><span class="o">.</span><span class="n">is_multivariate</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">is_multivariate</span> <span class="o">=</span> <span class="kc">True</span>
+
+        <span class="k">if</span> <span class="n">model</span><span class="o">.</span><span class="n">has_seasonality</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">has_seasonality</span> <span class="o">=</span> <span class="kc">True</span>
+
+        <span class="k">if</span> <span class="n">model</span><span class="o">.</span><span class="n">original_min</span> <span class="o">&lt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">original_min</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">original_min</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">original_min</span>
+        <span class="k">elif</span> <span class="n">model</span><span class="o">.</span><span class="n">original_max</span> <span class="o">&gt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">original_max</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">original_max</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">original_max</span></div>
+
+
+<div class="viewcode-block" id="EnsembleFTS.get_UoD"><a class="viewcode-back" href="../../../../pyFTS.models.ensemble.html#pyFTS.models.ensemble.ensemble.EnsembleFTS.get_UoD">[docs]</a>    <span class="k">def</span> <span class="nf">get_UoD</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">original_min</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">original_max</span><span class="p">]</span></div>
+
+<div class="viewcode-block" id="EnsembleFTS.train"><a class="viewcode-back" href="../../../../pyFTS.models.ensemble.html#pyFTS.models.ensemble.ensemble.EnsembleFTS.train">[docs]</a>    <span class="k">def</span> <span class="nf">train</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="k">pass</span></div>
+
+<div class="viewcode-block" id="EnsembleFTS.get_models_forecasts"><a class="viewcode-back" href="../../../../pyFTS.models.ensemble.html#pyFTS.models.ensemble.ensemble.EnsembleFTS.get_models_forecasts">[docs]</a>    <span class="k">def</span> <span class="nf">get_models_forecasts</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span><span class="n">data</span><span class="p">):</span>
+        <span class="n">tmp</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="k">for</span> <span class="n">model</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">models</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">model</span><span class="o">.</span><span class="n">is_multivariate</span> <span class="ow">or</span> <span class="n">model</span><span class="o">.</span><span class="n">has_seasonality</span><span class="p">:</span>
+                <span class="n">forecast</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">forecast</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+
+                <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">)</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">indexer</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                    <span class="n">data</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">indexer</span><span class="o">.</span><span class="n">get_data</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+
+                <span class="n">sample</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="o">-</span><span class="n">model</span><span class="o">.</span><span class="n">order</span><span class="p">:]</span>
+                <span class="n">forecast</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">sample</span><span class="p">)</span>
+                <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">forecast</span><span class="p">,</span> <span class="p">(</span><span class="nb">list</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">))</span> <span class="ow">and</span> <span class="nb">len</span><span class="p">(</span><span class="n">forecast</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
+                    <span class="n">forecast</span> <span class="o">=</span> <span class="n">forecast</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
+                <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">forecast</span><span class="p">,</span> <span class="p">(</span><span class="nb">list</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">))</span> <span class="ow">and</span> <span class="nb">len</span><span class="p">(</span><span class="n">forecast</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+                    <span class="n">forecast</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
+            <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">forecast</span><span class="p">,</span> <span class="nb">list</span><span class="p">):</span>
+                <span class="n">tmp</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">forecast</span><span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">tmp</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">forecast</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">tmp</span></div>
+
+<div class="viewcode-block" id="EnsembleFTS.get_point"><a class="viewcode-back" href="../../../../pyFTS.models.ensemble.html#pyFTS.models.ensemble.ensemble.EnsembleFTS.get_point">[docs]</a>    <span class="k">def</span> <span class="nf">get_point</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span><span class="n">forecasts</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">point_method</span> <span class="o">==</span> <span class="s1">&#39;mean&#39;</span><span class="p">:</span>
+            <span class="n">ret</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nanmean</span><span class="p">(</span><span class="n">forecasts</span><span class="p">)</span>
+        <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">point_method</span> <span class="o">==</span> <span class="s1">&#39;median&#39;</span><span class="p">:</span>
+            <span class="n">ret</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nanpercentile</span><span class="p">(</span><span class="n">forecasts</span><span class="p">,</span> <span class="mi">50</span><span class="p">)</span>
+        <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">point_method</span> <span class="o">==</span> <span class="s1">&#39;quantile&#39;</span><span class="p">:</span>
+            <span class="n">alpha</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;alpha&quot;</span><span class="p">,</span><span class="mf">0.05</span><span class="p">)</span>
+            <span class="n">ret</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nanpercentile</span><span class="p">(</span><span class="n">forecasts</span><span class="p">,</span> <span class="n">alpha</span><span class="o">*</span><span class="mi">100</span><span class="p">)</span>
+        <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">point_method</span> <span class="o">==</span> <span class="s1">&#39;exponential&#39;</span><span class="p">:</span>
+            <span class="n">l</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">models</span><span class="p">)</span>
+            <span class="k">if</span> <span class="n">l</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
+                <span class="k">return</span> <span class="n">forecasts</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+            <span class="n">w</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="p">(</span><span class="n">l</span> <span class="o">-</span> <span class="n">k</span><span class="p">))</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">l</span><span class="p">)])</span>
+            <span class="n">w</span> <span class="o">=</span> <span class="n">w</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">(</span><span class="n">w</span><span class="p">)</span>
+            <span class="n">ret</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">([</span><span class="n">w</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">*</span> <span class="n">forecasts</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">l</span><span class="p">)])</span>
+
+        <span class="k">return</span> <span class="n">ret</span></div>
+
+<div class="viewcode-block" id="EnsembleFTS.get_interval"><a class="viewcode-back" href="../../../../pyFTS.models.ensemble.html#pyFTS.models.ensemble.ensemble.EnsembleFTS.get_interval">[docs]</a>    <span class="k">def</span> <span class="nf">get_interval</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">forecasts</span><span class="p">):</span>
+        <span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">interval_method</span> <span class="o">==</span> <span class="s1">&#39;extremum&#39;</span><span class="p">:</span>
+            <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">([</span><span class="nb">min</span><span class="p">(</span><span class="n">forecasts</span><span class="p">),</span> <span class="nb">max</span><span class="p">(</span><span class="n">forecasts</span><span class="p">)])</span>
+        <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">interval_method</span> <span class="o">==</span> <span class="s1">&#39;quantile&#39;</span><span class="p">:</span>
+            <span class="n">qt_lo</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nanpercentile</span><span class="p">(</span><span class="n">forecasts</span><span class="p">,</span> <span class="n">q</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">alpha</span> <span class="o">*</span> <span class="mi">100</span><span class="p">)</span>
+            <span class="n">qt_up</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nanpercentile</span><span class="p">(</span><span class="n">forecasts</span><span class="p">,</span> <span class="n">q</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">alpha</span><span class="p">)</span> <span class="o">*</span> <span class="mi">100</span><span class="p">)</span>
+            <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">([</span><span class="n">qt_lo</span><span class="p">,</span> <span class="n">qt_up</span><span class="p">])</span>
+        <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">interval_method</span> <span class="o">==</span> <span class="s1">&#39;normal&#39;</span><span class="p">:</span>
+            <span class="n">mu</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nanmean</span><span class="p">(</span><span class="n">forecasts</span><span class="p">)</span>
+            <span class="n">sigma</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">nanvar</span><span class="p">(</span><span class="n">forecasts</span><span class="p">))</span>
+            <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">mu</span> <span class="o">+</span> <span class="n">st</span><span class="o">.</span><span class="n">norm</span><span class="o">.</span><span class="n">ppf</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">alpha</span><span class="p">)</span> <span class="o">*</span> <span class="n">sigma</span><span class="p">)</span>
+            <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">mu</span> <span class="o">+</span> <span class="n">st</span><span class="o">.</span><span class="n">norm</span><span class="o">.</span><span class="n">ppf</span><span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">alpha</span><span class="p">)</span> <span class="o">*</span> <span class="n">sigma</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">ret</span></div>
+
+<div class="viewcode-block" id="EnsembleFTS.get_distribution_interquantile"><a class="viewcode-back" href="../../../../pyFTS.models.ensemble.html#pyFTS.models.ensemble.ensemble.EnsembleFTS.get_distribution_interquantile">[docs]</a>    <span class="k">def</span> <span class="nf">get_distribution_interquantile</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span><span class="n">forecasts</span><span class="p">,</span> <span class="n">alpha</span><span class="p">):</span>
+        <span class="n">size</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">forecasts</span><span class="p">)</span>
+        <span class="n">qt_lower</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">ceil</span><span class="p">(</span><span class="n">size</span> <span class="o">*</span> <span class="n">alpha</span><span class="p">))</span> <span class="o">-</span> <span class="mi">1</span>
+        <span class="n">qt_upper</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">ceil</span><span class="p">(</span><span class="n">size</span> <span class="o">*</span> <span class="p">(</span><span class="mi">1</span><span class="o">-</span> <span class="n">alpha</span><span class="p">)))</span> <span class="o">-</span> <span class="mi">1</span>
+
+        <span class="n">ret</span> <span class="o">=</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">forecasts</span><span class="p">)[</span><span class="n">qt_lower</span> <span class="p">:</span> <span class="n">qt_upper</span><span class="p">]</span>
+
+        <span class="k">return</span> <span class="n">ret</span></div>
+
+<div class="viewcode-block" id="EnsembleFTS.forecast"><a class="viewcode-back" href="../../../../pyFTS.models.ensemble.html#pyFTS.models.ensemble.ensemble.EnsembleFTS.forecast">[docs]</a>    <span class="k">def</span> <span class="nf">forecast</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+
+        <span class="k">if</span> <span class="s2">&quot;method&quot;</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">point_method</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;method&#39;</span><span class="p">,</span><span class="s1">&#39;mean&#39;</span><span class="p">)</span>
+
+        <span class="n">l</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+        <span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
+
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">,</span> <span class="n">l</span><span class="o">+</span><span class="mi">1</span><span class="p">):</span>
+            <span class="n">sample</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="n">k</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span> <span class="p">:</span> <span class="n">k</span><span class="p">]</span>
+            <span class="n">tmp</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_models_forecasts</span><span class="p">(</span><span class="n">sample</span><span class="p">)</span>
+            <span class="n">point</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_point</span><span class="p">(</span><span class="n">tmp</span><span class="p">)</span>
+            <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">point</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">ret</span></div>
+
+<div class="viewcode-block" id="EnsembleFTS.forecast_interval"><a class="viewcode-back" href="../../../../pyFTS.models.ensemble.html#pyFTS.models.ensemble.ensemble.EnsembleFTS.forecast_interval">[docs]</a>    <span class="k">def</span> <span class="nf">forecast_interval</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+
+        <span class="k">if</span> <span class="s2">&quot;method&quot;</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">interval_method</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;method&#39;</span><span class="p">,</span><span class="s1">&#39;quantile&#39;</span><span class="p">)</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">alpha</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;alpha&#39;</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">alpha</span><span class="p">)</span>
+
+        <span class="n">l</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+
+        <span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
+
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">,</span> <span class="n">l</span><span class="o">+</span><span class="mi">1</span><span class="p">):</span>
+            <span class="n">sample</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="n">k</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span> <span class="p">:</span> <span class="n">k</span><span class="p">]</span>
+            <span class="n">tmp</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_models_forecasts</span><span class="p">(</span><span class="n">sample</span><span class="p">)</span>
+            <span class="n">interval</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_interval</span><span class="p">(</span><span class="n">tmp</span><span class="p">)</span>
+            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">interval</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
+                <span class="n">interval</span> <span class="o">=</span> <span class="n">interval</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
+            <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">interval</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">ret</span></div>
+
+<div class="viewcode-block" id="EnsembleFTS.forecast_ahead_interval"><a class="viewcode-back" href="../../../../pyFTS.models.ensemble.html#pyFTS.models.ensemble.ensemble.EnsembleFTS.forecast_ahead_interval">[docs]</a>    <span class="k">def</span> <span class="nf">forecast_ahead_interval</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">steps</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+
+        <span class="k">if</span> <span class="s1">&#39;method&#39;</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">interval_method</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;method&#39;</span><span class="p">,</span><span class="s1">&#39;quantile&#39;</span><span class="p">)</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">alpha</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;alpha&#39;</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">alpha</span><span class="p">)</span>
+
+        <span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
+
+        <span class="n">start</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;start_at&#39;</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">)</span>
+
+        <span class="n">sample</span> <span class="o">=</span> <span class="p">[[</span><span class="n">k</span><span class="p">]</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">data</span><span class="p">[</span><span class="n">start</span><span class="p">:</span> <span class="n">start</span><span class="o">+</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">]]</span>
+
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">,</span> <span class="n">steps</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">):</span>
+            <span class="n">forecasts</span> <span class="o">=</span> <span class="p">[]</span>
+
+            <span class="n">lags</span> <span class="o">=</span> <span class="p">[]</span>
+            <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">):</span>
+                <span class="n">lags</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">sample</span><span class="p">[</span><span class="n">i</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">])</span>
+
+            <span class="c1"># Trace the possible paths</span>
+            <span class="k">for</span> <span class="n">path</span> <span class="ow">in</span> <span class="n">product</span><span class="p">(</span><span class="o">*</span><span class="n">lags</span><span class="p">):</span>
+                <span class="n">forecasts</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">get_models_forecasts</span><span class="p">(</span><span class="n">path</span><span class="p">))</span>
+
+            <span class="n">sample</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">sampler</span><span class="p">(</span><span class="n">forecasts</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="o">.</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">),</span> <span class="n">bounds</span><span class="o">=</span><span class="kc">True</span><span class="p">))</span>
+
+            <span class="n">interval</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_interval</span><span class="p">(</span><span class="n">forecasts</span><span class="p">)</span>
+
+            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">interval</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
+                <span class="n">interval</span> <span class="o">=</span> <span class="n">interval</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+
+            <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">interval</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">ret</span><span class="p">[</span><span class="o">-</span><span class="n">steps</span><span class="p">:]</span></div>
+
+<div class="viewcode-block" id="EnsembleFTS.forecast_distribution"><a class="viewcode-back" href="../../../../pyFTS.models.ensemble.html#pyFTS.models.ensemble.ensemble.EnsembleFTS.forecast_distribution">[docs]</a>    <span class="k">def</span> <span class="nf">forecast_distribution</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
+
+        <span class="n">smooth</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;smooth&quot;</span><span class="p">,</span> <span class="s2">&quot;KDE&quot;</span><span class="p">)</span>
+        <span class="n">alpha</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;alpha&quot;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
+
+        <span class="n">uod</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_UoD</span><span class="p">()</span>
+
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">)):</span>
+
+            <span class="n">sample</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="n">k</span><span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span> <span class="p">:</span> <span class="n">k</span><span class="p">]</span>
+
+            <span class="n">forecasts</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_models_forecasts</span><span class="p">(</span><span class="n">sample</span><span class="p">)</span>
+
+            <span class="k">if</span> <span class="n">alpha</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="n">forecasts</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ravel</span><span class="p">(</span><span class="n">forecasts</span><span class="p">)</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">forecasts</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_distribution_interquantile</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">ravel</span><span class="p">(</span><span class="n">forecasts</span><span class="p">)</span><span class="o">.</span><span class="n">tolist</span><span class="p">(),</span> <span class="n">alpha</span><span class="p">)</span>
+
+            <span class="n">dist</span> <span class="o">=</span> <span class="n">ProbabilityDistribution</span><span class="o">.</span><span class="n">ProbabilityDistribution</span><span class="p">(</span><span class="n">smooth</span><span class="p">,</span> <span class="n">uod</span><span class="o">=</span><span class="n">uod</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="n">forecasts</span><span class="p">,</span>
+                                                                   <span class="n">name</span><span class="o">=</span><span class="s2">&quot;&quot;</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+
+            <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">dist</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">ret</span></div>
+
+<div class="viewcode-block" id="EnsembleFTS.forecast_ahead_distribution"><a class="viewcode-back" href="../../../../pyFTS.models.ensemble.html#pyFTS.models.ensemble.ensemble.EnsembleFTS.forecast_ahead_distribution">[docs]</a>    <span class="k">def</span> <span class="nf">forecast_ahead_distribution</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">steps</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="k">if</span> <span class="s1">&#39;method&#39;</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">point_method</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;method&#39;</span><span class="p">,</span><span class="s1">&#39;mean&#39;</span><span class="p">)</span>
+
+        <span class="n">smooth</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;smooth&quot;</span><span class="p">,</span> <span class="s2">&quot;histogram&quot;</span><span class="p">)</span>
+        <span class="n">alpha</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;alpha&quot;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
+
+        <span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
+
+        <span class="n">start</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;start_at&#39;</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">)</span>
+
+        <span class="n">uod</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_UoD</span><span class="p">()</span>
+
+        <span class="n">sample</span> <span class="o">=</span> <span class="p">[[</span><span class="n">k</span><span class="p">]</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">data</span><span class="p">[</span><span class="n">start</span><span class="p">:</span> <span class="n">start</span><span class="o">+</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">]]</span>
+
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">,</span> <span class="n">steps</span><span class="o">+</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">):</span>
+            <span class="n">forecasts</span> <span class="o">=</span> <span class="p">[]</span>
+
+            <span class="n">lags</span> <span class="o">=</span> <span class="p">[]</span>
+            <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">):</span>
+                <span class="n">lags</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">sample</span><span class="p">[</span><span class="n">i</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">])</span>
+
+            <span class="c1"># Trace the possible paths</span>
+            <span class="k">for</span> <span class="n">path</span> <span class="ow">in</span> <span class="n">product</span><span class="p">(</span><span class="o">*</span><span class="n">lags</span><span class="p">):</span>
+                <span class="n">forecasts</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">get_models_forecasts</span><span class="p">(</span><span class="n">path</span><span class="p">))</span>
+
+            <span class="n">sample</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">sampler</span><span class="p">(</span><span class="n">forecasts</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="o">.</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">),</span> <span class="n">bounds</span><span class="o">=</span><span class="kc">True</span><span class="p">))</span>
+
+            <span class="k">if</span> <span class="n">alpha</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="n">forecasts</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ravel</span><span class="p">(</span><span class="n">forecasts</span><span class="p">)</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">forecasts</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_distribution_interquantile</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">ravel</span><span class="p">(</span><span class="n">forecasts</span><span class="p">)</span><span class="o">.</span><span class="n">tolist</span><span class="p">(),</span> <span class="n">alpha</span><span class="p">)</span>
+
+            <span class="n">dist</span> <span class="o">=</span> <span class="n">ProbabilityDistribution</span><span class="o">.</span><span class="n">ProbabilityDistribution</span><span class="p">(</span><span class="n">smooth</span><span class="p">,</span> <span class="n">uod</span><span class="o">=</span><span class="n">uod</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="n">forecasts</span><span class="p">,</span>
+                                                                   <span class="n">name</span><span class="o">=</span><span class="s2">&quot;&quot;</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+
+            <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">dist</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">ret</span><span class="p">[</span><span class="o">-</span><span class="n">steps</span><span class="p">:]</span></div></div>
+
+
+<div class="viewcode-block" id="SimpleEnsembleFTS"><a class="viewcode-back" href="../../../../pyFTS.models.ensemble.html#pyFTS.models.ensemble.ensemble.SimpleEnsembleFTS">[docs]</a><span class="k">class</span> <span class="nc">SimpleEnsembleFTS</span><span class="p">(</span><span class="n">EnsembleFTS</span><span class="p">):</span>
+    <span class="sd">&#39;&#39;&#39;</span>
+<span class="sd">    An homogeneous FTS method ensemble with variations on partitionings and orders.</span>
+<span class="sd">    &#39;&#39;&#39;</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">(</span><span class="n">SimpleEnsembleFTS</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">method</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;fts_method&#39;</span><span class="p">,</span> <span class="n">hofts</span><span class="o">.</span><span class="n">WeightedHighOrderFTS</span><span class="p">)</span>
+        <span class="sd">&quot;&quot;&quot;FTS method class that will be used on internal models&quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">partitioner_method</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;partitioner_method&#39;</span><span class="p">,</span> <span class="n">Grid</span><span class="o">.</span><span class="n">GridPartitioner</span><span class="p">)</span>
+        <span class="sd">&quot;&quot;&quot;UoD partitioner class that will be used on internal methods&quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">partitions</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;partitions&#39;</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">15</span><span class="p">,</span><span class="mi">35</span><span class="p">,</span><span class="mi">10</span><span class="p">))</span>
+        <span class="sd">&quot;&quot;&quot;Possible variations of number of partitions on internal models&quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">orders</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;orders&#39;</span><span class="p">,</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">])</span>
+        <span class="sd">&quot;&quot;&quot;Possible variations of order on internal models&quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">uod_clip</span> <span class="o">=</span> <span class="kc">False</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">shortname</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;name&#39;</span><span class="p">,</span> <span class="s1">&#39;EnsembleFTS-&#39;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">method</span><span class="o">.</span><span class="vm">__module__</span><span class="p">)</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;.&#39;</span><span class="p">)[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
+
+<div class="viewcode-block" id="SimpleEnsembleFTS.train"><a class="viewcode-back" href="../../../../pyFTS.models.ensemble.html#pyFTS.models.ensemble.ensemble.SimpleEnsembleFTS.train">[docs]</a>    <span class="k">def</span> <span class="nf">train</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitions</span><span class="p">:</span>
+            <span class="n">fs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner_method</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">data</span><span class="p">,</span> <span class="n">npart</span><span class="o">=</span><span class="n">k</span><span class="p">)</span>
+
+            <span class="k">for</span> <span class="n">order</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">orders</span><span class="p">:</span>
+                <span class="n">tmp</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">method</span><span class="p">(</span><span class="n">partitioner</span><span class="o">=</span><span class="n">fs</span><span class="p">,</span> <span class="n">order</span><span class="o">=</span><span class="n">order</span><span class="p">)</span>
+
+                <span class="n">tmp</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+
+                <span class="bp">self</span><span class="o">.</span><span class="n">append_model</span><span class="p">(</span><span class="n">tmp</span><span class="p">)</span></div></div>
+
+
+<div class="viewcode-block" id="AllMethodEnsembleFTS"><a class="viewcode-back" href="../../../../pyFTS.models.ensemble.html#pyFTS.models.ensemble.ensemble.AllMethodEnsembleFTS">[docs]</a><span class="k">class</span> <span class="nc">AllMethodEnsembleFTS</span><span class="p">(</span><span class="n">EnsembleFTS</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Creates an EnsembleFTS with all point forecast methods, sharing the same partitioner</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">(</span><span class="n">AllMethodEnsembleFTS</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">min_order</span> <span class="o">=</span> <span class="mi">3</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">shortname</span> <span class="o">=</span><span class="s2">&quot;Ensemble FTS&quot;</span>
+
+<div class="viewcode-block" id="AllMethodEnsembleFTS.set_transformations"><a class="viewcode-back" href="../../../../pyFTS.models.ensemble.html#pyFTS.models.ensemble.ensemble.AllMethodEnsembleFTS.set_transformations">[docs]</a>    <span class="k">def</span> <span class="nf">set_transformations</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">model</span><span class="p">):</span>
+        <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">transformations</span><span class="p">:</span>
+            <span class="n">model</span><span class="o">.</span><span class="n">append_transformation</span><span class="p">(</span><span class="n">t</span><span class="p">)</span></div>
+
+<div class="viewcode-block" id="AllMethodEnsembleFTS.train"><a class="viewcode-back" href="../../../../pyFTS.models.ensemble.html#pyFTS.models.ensemble.ensemble.AllMethodEnsembleFTS.train">[docs]</a>    <span class="k">def</span> <span class="nf">train</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="n">fo_methods</span> <span class="o">=</span> <span class="p">[</span><span class="n">song</span><span class="o">.</span><span class="n">ConventionalFTS</span><span class="p">,</span> <span class="n">chen</span><span class="o">.</span><span class="n">ConventionalFTS</span><span class="p">,</span> <span class="n">yu</span><span class="o">.</span><span class="n">WeightedFTS</span><span class="p">,</span> <span class="n">cheng</span><span class="o">.</span><span class="n">TrendWeightedFTS</span><span class="p">,</span>
+                      <span class="n">sadaei</span><span class="o">.</span><span class="n">ExponentialyWeightedFTS</span><span class="p">,</span> <span class="n">ismailefendi</span><span class="o">.</span><span class="n">ImprovedWeightedFTS</span><span class="p">]</span>
+
+        <span class="n">ho_methods</span> <span class="o">=</span> <span class="p">[</span><span class="n">hofts</span><span class="o">.</span><span class="n">HighOrderFTS</span><span class="p">,</span> <span class="n">hwang</span><span class="o">.</span><span class="n">HighOrderFTS</span><span class="p">]</span>
+
+        <span class="k">for</span> <span class="n">method</span> <span class="ow">in</span> <span class="n">fo_methods</span><span class="p">:</span>
+            <span class="n">model</span> <span class="o">=</span> <span class="n">method</span><span class="p">(</span><span class="n">partitioner</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="p">)</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">set_transformations</span><span class="p">(</span><span class="n">model</span><span class="p">)</span>
+            <span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">append_model</span><span class="p">(</span><span class="n">model</span><span class="p">)</span>
+
+        <span class="k">for</span> <span class="n">method</span> <span class="ow">in</span> <span class="n">ho_methods</span><span class="p">:</span>
+            <span class="k">for</span> <span class="n">o</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="o">+</span><span class="mi">1</span><span class="p">):</span>
+                <span class="n">model</span> <span class="o">=</span> <span class="n">method</span><span class="p">(</span><span class="n">partitioner</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="p">)</span>
+                <span class="k">if</span> <span class="n">model</span><span class="o">.</span><span class="n">min_order</span> <span class="o">&gt;=</span> <span class="n">o</span><span class="p">:</span>
+                    <span class="n">model</span><span class="o">.</span><span class="n">order</span> <span class="o">=</span> <span class="n">o</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">set_transformations</span><span class="p">(</span><span class="n">model</span><span class="p">)</span>
+                    <span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">append_model</span><span class="p">(</span><span class="n">model</span><span class="p">)</span></div></div>
+
+
+
+
+</pre></div>
+
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diff --git a/docs/build/html/_modules/pyFTS/models/ensemble/multiseasonal.html b/docs/build/html/_modules/pyFTS/models/ensemble/multiseasonal.html
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@@ -0,0 +1,192 @@
+
+
+<!doctype html>
+
+<html>
+  <head>
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+    <title>pyFTS.models.ensemble.multiseasonal &#8212; pyFTS 1.6 documentation</title>
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+        <li class="nav-item nav-item-this"><a href="">pyFTS.models.ensemble.multiseasonal</a></li> 
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+            
+  <h1>Source code for pyFTS.models.ensemble.multiseasonal</h1><div class="highlight"><pre>
+<span></span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">Silva, P. C. L et al. Probabilistic Forecasting with Seasonal Ensemble Fuzzy Time-Series</span>
+<span class="sd">XIII Brazilian Congress on Computational Intelligence, 2017. Rio de Janeiro, Brazil.</span>
+<span class="sd">&quot;&quot;&quot;</span>
+
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">from</span> <span class="nn">pyFTS.common</span> <span class="kn">import</span> <span class="n">Util</span> <span class="k">as</span> <span class="n">cUtil</span>
+<span class="kn">from</span> <span class="nn">pyFTS.models.ensemble</span> <span class="kn">import</span> <span class="n">ensemble</span>
+<span class="kn">from</span> <span class="nn">pyFTS.models.seasonal</span> <span class="kn">import</span> <span class="n">cmsfts</span>
+<span class="kn">from</span> <span class="nn">pyFTS.probabilistic</span> <span class="kn">import</span> <span class="n">ProbabilityDistribution</span>
+<span class="kn">from</span> <span class="nn">copy</span> <span class="kn">import</span> <span class="n">deepcopy</span>
+<span class="kn">from</span> <span class="nn">joblib</span> <span class="kn">import</span> <span class="n">Parallel</span><span class="p">,</span> <span class="n">delayed</span>
+<span class="kn">import</span> <span class="nn">multiprocessing</span>
+
+
+<div class="viewcode-block" id="train_individual_model"><a class="viewcode-back" href="../../../../pyFTS.models.ensemble.html#pyFTS.models.ensemble.multiseasonal.train_individual_model">[docs]</a><span class="k">def</span> <span class="nf">train_individual_model</span><span class="p">(</span><span class="n">partitioner</span><span class="p">,</span> <span class="n">train_data</span><span class="p">,</span> <span class="n">indexer</span><span class="p">):</span>
+    <span class="n">pttr</span> <span class="o">=</span> <span class="nb">str</span><span class="p">(</span><span class="n">partitioner</span><span class="o">.</span><span class="vm">__module__</span><span class="p">)</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;.&#39;</span><span class="p">)[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
+    <span class="n">diff</span> <span class="o">=</span> <span class="s2">&quot;_diff&quot;</span> <span class="k">if</span> <span class="n">partitioner</span><span class="o">.</span><span class="n">transformation</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">else</span> <span class="s2">&quot;&quot;</span>
+    <span class="n">_key</span> <span class="o">=</span> <span class="s2">&quot;msfts_&quot;</span> <span class="o">+</span> <span class="n">pttr</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">partitioner</span><span class="o">.</span><span class="n">partitions</span><span class="p">)</span> <span class="o">+</span> <span class="n">diff</span> <span class="o">+</span> <span class="s2">&quot;_&quot;</span> <span class="o">+</span> <span class="n">indexer</span><span class="o">.</span><span class="n">name</span>
+
+    <span class="nb">print</span><span class="p">(</span><span class="n">_key</span><span class="p">)</span>
+
+    <span class="n">model</span> <span class="o">=</span> <span class="n">cmsfts</span><span class="o">.</span><span class="n">ContextualMultiSeasonalFTS</span><span class="p">(</span><span class="n">_key</span><span class="p">,</span> <span class="n">indexer</span><span class="o">=</span><span class="n">indexer</span><span class="p">)</span>
+    <span class="n">model</span><span class="o">.</span><span class="n">append_transformation</span><span class="p">(</span><span class="n">partitioner</span><span class="o">.</span><span class="n">transformation</span><span class="p">)</span>
+    <span class="n">model</span><span class="o">.</span><span class="n">train</span><span class="p">(</span><span class="n">train_data</span><span class="p">,</span> <span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">,</span> <span class="n">order</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+
+    <span class="n">cUtil</span><span class="o">.</span><span class="n">persist_obj</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="s2">&quot;models/&quot;</span><span class="o">+</span><span class="n">_key</span><span class="o">+</span><span class="s2">&quot;.pkl&quot;</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">model</span></div>
+
+
+<div class="viewcode-block" id="SeasonalEnsembleFTS"><a class="viewcode-back" href="../../../../pyFTS.models.ensemble.html#pyFTS.models.ensemble.multiseasonal.SeasonalEnsembleFTS">[docs]</a><span class="k">class</span> <span class="nc">SeasonalEnsembleFTS</span><span class="p">(</span><span class="n">ensemble</span><span class="o">.</span><span class="n">EnsembleFTS</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">name</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">(</span><span class="n">SeasonalEnsembleFTS</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s2">&quot;Seasonal Ensemble FTS&quot;</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">min_order</span> <span class="o">=</span> <span class="mi">1</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">indexers</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">partitioners</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">is_multivariate</span> <span class="o">=</span> <span class="kc">True</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">has_seasonality</span> <span class="o">=</span> <span class="kc">True</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">has_probability_forecasting</span> <span class="o">=</span> <span class="kc">True</span>
+
+<div class="viewcode-block" id="SeasonalEnsembleFTS.update_uod"><a class="viewcode-back" href="../../../../pyFTS.models.ensemble.html#pyFTS.models.ensemble.multiseasonal.SeasonalEnsembleFTS.update_uod">[docs]</a>    <span class="k">def</span> <span class="nf">update_uod</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">original_max</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">indexer</span><span class="o">.</span><span class="n">get_data</span><span class="p">(</span><span class="n">data</span><span class="p">))</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">original_min</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">indexer</span><span class="o">.</span><span class="n">get_data</span><span class="p">(</span><span class="n">data</span><span class="p">))</span></div>
+
+<div class="viewcode-block" id="SeasonalEnsembleFTS.train"><a class="viewcode-back" href="../../../../pyFTS.models.ensemble.html#pyFTS.models.ensemble.multiseasonal.SeasonalEnsembleFTS.train">[docs]</a>    <span class="k">def</span> <span class="nf">train</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">original_max</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">indexer</span><span class="o">.</span><span class="n">get_data</span><span class="p">(</span><span class="n">data</span><span class="p">))</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">original_min</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">indexer</span><span class="o">.</span><span class="n">get_data</span><span class="p">(</span><span class="n">data</span><span class="p">))</span>
+
+        <span class="n">num_cores</span> <span class="o">=</span> <span class="n">multiprocessing</span><span class="o">.</span><span class="n">cpu_count</span><span class="p">()</span>
+
+        <span class="n">pool</span> <span class="o">=</span> <span class="p">{}</span>
+        <span class="n">count</span> <span class="o">=</span> <span class="mi">0</span>
+        <span class="k">for</span> <span class="n">ix</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">indexers</span><span class="p">:</span>
+            <span class="k">for</span> <span class="n">pt</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioners</span><span class="p">:</span>
+                <span class="n">pool</span><span class="p">[</span><span class="n">count</span><span class="p">]</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;ix&#39;</span><span class="p">:</span> <span class="n">ix</span><span class="p">,</span> <span class="s1">&#39;pt&#39;</span><span class="p">:</span> <span class="n">pt</span><span class="p">}</span>
+                <span class="n">count</span> <span class="o">+=</span> <span class="mi">1</span>
+
+        <span class="n">results</span> <span class="o">=</span> <span class="n">Parallel</span><span class="p">(</span><span class="n">n_jobs</span><span class="o">=</span><span class="n">num_cores</span><span class="p">)(</span>
+            <span class="n">delayed</span><span class="p">(</span><span class="n">train_individual_model</span><span class="p">)(</span><span class="n">deepcopy</span><span class="p">(</span><span class="n">pool</span><span class="p">[</span><span class="n">m</span><span class="p">][</span><span class="s1">&#39;pt&#39;</span><span class="p">]),</span> <span class="n">data</span><span class="p">,</span> <span class="n">deepcopy</span><span class="p">(</span><span class="n">pool</span><span class="p">[</span><span class="n">m</span><span class="p">][</span><span class="s1">&#39;ix&#39;</span><span class="p">]))</span>
+            <span class="k">for</span> <span class="n">m</span> <span class="ow">in</span> <span class="n">pool</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span>
+
+        <span class="k">for</span> <span class="n">tmp</span> <span class="ow">in</span> <span class="n">results</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">append_model</span><span class="p">(</span><span class="n">tmp</span><span class="p">)</span>
+
+        <span class="n">cUtil</span><span class="o">.</span><span class="n">persist_obj</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s2">&quot;models/&quot;</span><span class="o">+</span><span class="bp">self</span><span class="o">.</span><span class="n">name</span><span class="o">+</span><span class="s2">&quot;.pkl&quot;</span><span class="p">)</span></div>
+
+<div class="viewcode-block" id="SeasonalEnsembleFTS.forecast_distribution"><a class="viewcode-back" href="../../../../pyFTS.models.ensemble.html#pyFTS.models.ensemble.multiseasonal.SeasonalEnsembleFTS.forecast_distribution">[docs]</a>    <span class="k">def</span> <span class="nf">forecast_distribution</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+
+        <span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
+
+        <span class="n">smooth</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;smooth&quot;</span><span class="p">,</span> <span class="s2">&quot;KDE&quot;</span><span class="p">)</span>
+        <span class="n">alpha</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;alpha&quot;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
+
+        <span class="n">uod</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_UoD</span><span class="p">()</span>
+
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">data</span><span class="o">.</span><span class="n">index</span><span class="p">:</span>
+
+            <span class="n">tmp</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_models_forecasts</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">ix</span><span class="p">[</span><span class="n">k</span><span class="p">])</span>
+
+            <span class="k">if</span> <span class="n">alpha</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="n">tmp</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ravel</span><span class="p">(</span><span class="n">tmp</span><span class="p">)</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">tmp</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_distribution_interquantile</span><span class="p">(</span> <span class="n">np</span><span class="o">.</span><span class="n">ravel</span><span class="p">(</span><span class="n">tmp</span><span class="p">)</span><span class="o">.</span><span class="n">tolist</span><span class="p">(),</span> <span class="n">alpha</span><span class="p">)</span>
+
+            <span class="n">name</span> <span class="o">=</span> <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">indexer</span><span class="o">.</span><span class="n">get_index</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">ix</span><span class="p">[</span><span class="n">k</span><span class="p">]))</span>
+
+            <span class="n">dist</span> <span class="o">=</span> <span class="n">ProbabilityDistribution</span><span class="o">.</span><span class="n">ProbabilityDistribution</span><span class="p">(</span><span class="n">smooth</span><span class="p">,</span> <span class="n">uod</span><span class="o">=</span><span class="n">uod</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="n">tmp</span><span class="p">,</span>
+                                                                   <span class="n">name</span><span class="o">=</span><span class="n">name</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+
+            <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">dist</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">ret</span></div></div>
+</pre></div>
+
+            <div class="clearer"></div>
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new file mode 100644
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@@ -0,0 +1,395 @@
+
+
+<!doctype html>
+
+<html>
+  <head>
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+        <li class="nav-item nav-item-this"><a href="">pyFTS.models.hofts</a></li> 
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+            
+  <h1>Source code for pyFTS.models.hofts</h1><div class="highlight"><pre>
+<span></span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">High Order FTS</span>
+
+<span class="sd">Severiano, S. A. Jr; Silva, P. C. L.; Sadaei, H. J.; Guimarães, F. G. Very Short-term Solar Forecasting</span>
+<span class="sd">using Fuzzy Time Series. 2017 IEEE International Conference on Fuzzy Systems. DOI10.1109/FUZZ-IEEE.2017.8015732</span>
+<span class="sd">&quot;&quot;&quot;</span>
+
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">from</span> <span class="nn">pyFTS.common</span> <span class="kn">import</span> <span class="n">FuzzySet</span><span class="p">,</span> <span class="n">FLR</span><span class="p">,</span> <span class="n">fts</span><span class="p">,</span> <span class="n">flrg</span>
+<span class="kn">from</span> <span class="nn">itertools</span> <span class="kn">import</span> <span class="n">product</span>
+
+
+<div class="viewcode-block" id="HighOrderFLRG"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.hofts.HighOrderFLRG">[docs]</a><span class="k">class</span> <span class="nc">HighOrderFLRG</span><span class="p">(</span><span class="n">flrg</span><span class="o">.</span><span class="n">FLRG</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;Conventional High Order Fuzzy Logical Relationship Group&quot;&quot;&quot;</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">order</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">(</span><span class="n">HighOrderFLRG</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">order</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">LHS</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span> <span class="o">=</span> <span class="p">{}</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">strlhs</span> <span class="o">=</span> <span class="s2">&quot;&quot;</span>
+
+<div class="viewcode-block" id="HighOrderFLRG.append_rhs"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.hofts.HighOrderFLRG.append_rhs">[docs]</a>    <span class="k">def</span> <span class="nf">append_rhs</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="k">if</span> <span class="n">c</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="p">[</span><span class="n">c</span><span class="p">]</span> <span class="o">=</span> <span class="n">c</span></div>
+
+<div class="viewcode-block" id="HighOrderFLRG.append_lhs"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.hofts.HighOrderFLRG.append_lhs">[docs]</a>    <span class="k">def</span> <span class="nf">append_lhs</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">c</span><span class="p">):</span>
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">c</span><span class="p">,(</span><span class="nb">tuple</span><span class="p">,</span><span class="nb">list</span><span class="p">)):</span>
+            <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">c</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">LHS</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">k</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">LHS</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">c</span><span class="p">)</span></div>
+
+    <span class="k">def</span> <span class="fm">__str__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="n">tmp</span> <span class="o">=</span> <span class="s2">&quot;&quot;</span>
+        <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="nb">sorted</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="p">):</span>
+            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">tmp</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
+                <span class="n">tmp</span> <span class="o">=</span> <span class="n">tmp</span> <span class="o">+</span> <span class="s2">&quot;,&quot;</span>
+            <span class="n">tmp</span> <span class="o">=</span> <span class="n">tmp</span> <span class="o">+</span> <span class="n">c</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_key</span><span class="p">()</span> <span class="o">+</span> <span class="s2">&quot; -&gt; &quot;</span> <span class="o">+</span> <span class="n">tmp</span>
+
+
+    <span class="k">def</span> <span class="fm">__len__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="WeightedHighOrderFLRG"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.hofts.WeightedHighOrderFLRG">[docs]</a><span class="k">class</span> <span class="nc">WeightedHighOrderFLRG</span><span class="p">(</span><span class="n">flrg</span><span class="o">.</span><span class="n">FLRG</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;Weighted High Order Fuzzy Logical Relationship Group&quot;&quot;&quot;</span>
+
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">order</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">(</span><span class="n">WeightedHighOrderFLRG</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">order</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">LHS</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span> <span class="o">=</span> <span class="p">{}</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">count</span> <span class="o">=</span> <span class="mf">0.0</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">strlhs</span> <span class="o">=</span> <span class="s2">&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">w</span> <span class="o">=</span> <span class="kc">None</span>
+
+<div class="viewcode-block" id="WeightedHighOrderFLRG.append_rhs"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.hofts.WeightedHighOrderFLRG.append_rhs">[docs]</a>    <span class="k">def</span> <span class="nf">append_rhs</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">fset</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="n">count</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;count&#39;</span><span class="p">,</span><span class="mf">1.0</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">fset</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="p">[</span><span class="n">fset</span><span class="p">]</span> <span class="o">=</span> <span class="n">count</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="p">[</span><span class="n">fset</span><span class="p">]</span> <span class="o">+=</span> <span class="n">count</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">count</span> <span class="o">+=</span> <span class="n">count</span></div>
+
+<div class="viewcode-block" id="WeightedHighOrderFLRG.append_lhs"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.hofts.WeightedHighOrderFLRG.append_lhs">[docs]</a>    <span class="k">def</span> <span class="nf">append_lhs</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">c</span><span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">LHS</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">c</span><span class="p">)</span></div>
+
+<div class="viewcode-block" id="WeightedHighOrderFLRG.weights"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.hofts.WeightedHighOrderFLRG.weights">[docs]</a>    <span class="k">def</span> <span class="nf">weights</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">w</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">w</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="p">[</span><span class="n">c</span><span class="p">]</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">count</span> <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="o">.</span><span class="n">keys</span><span class="p">()])</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">w</span></div>
+
+<div class="viewcode-block" id="WeightedHighOrderFLRG.get_midpoint"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.hofts.WeightedHighOrderFLRG.get_midpoint">[docs]</a>    <span class="k">def</span> <span class="nf">get_midpoint</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sets</span><span class="p">):</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">midpoint</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">mp</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">sets</span><span class="p">[</span><span class="n">c</span><span class="p">]</span><span class="o">.</span><span class="n">centroid</span> <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="o">.</span><span class="n">keys</span><span class="p">()])</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">midpoint</span> <span class="o">=</span> <span class="n">mp</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">weights</span><span class="p">())</span>
+
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">midpoint</span></div>
+
+<div class="viewcode-block" id="WeightedHighOrderFLRG.get_lower"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.hofts.WeightedHighOrderFLRG.get_lower">[docs]</a>    <span class="k">def</span> <span class="nf">get_lower</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sets</span><span class="p">):</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">lower</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">lw</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">sets</span><span class="p">[</span><span class="n">s</span><span class="p">]</span><span class="o">.</span><span class="n">lower</span> <span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="o">.</span><span class="n">keys</span><span class="p">()])</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">lower</span> <span class="o">=</span> <span class="n">lw</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">weights</span><span class="p">())</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">lower</span></div>
+
+<div class="viewcode-block" id="WeightedHighOrderFLRG.get_upper"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.hofts.WeightedHighOrderFLRG.get_upper">[docs]</a>    <span class="k">def</span> <span class="nf">get_upper</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sets</span><span class="p">):</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">upper</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">up</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">sets</span><span class="p">[</span><span class="n">s</span><span class="p">]</span><span class="o">.</span><span class="n">upper</span> <span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="o">.</span><span class="n">keys</span><span class="p">()])</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">upper</span> <span class="o">=</span> <span class="n">up</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">weights</span><span class="p">())</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">upper</span></div>
+
+    <span class="k">def</span> <span class="fm">__str__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="n">_str</span> <span class="o">=</span> <span class="s2">&quot;&quot;</span>
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
+            <span class="n">_str</span> <span class="o">+=</span> <span class="s2">&quot;, &quot;</span> <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">_str</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span> <span class="k">else</span> <span class="s2">&quot;&quot;</span>
+            <span class="n">_str</span> <span class="o">+=</span> <span class="n">k</span> <span class="o">+</span> <span class="s2">&quot; (&quot;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">count</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span> <span class="o">+</span> <span class="s2">&quot;)&quot;</span>
+
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_key</span><span class="p">()</span> <span class="o">+</span> <span class="s2">&quot; -&gt; &quot;</span> <span class="o">+</span> <span class="n">_str</span>
+
+    <span class="k">def</span> <span class="fm">__len__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="HighOrderFTS"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.hofts.HighOrderFTS">[docs]</a><span class="k">class</span> <span class="nc">HighOrderFTS</span><span class="p">(</span><span class="n">fts</span><span class="o">.</span><span class="n">FTS</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;Conventional High Order Fuzzy Time Series&quot;&quot;&quot;</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">(</span><span class="n">HighOrderFTS</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="s2">&quot;High Order FTS&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">shortname</span> <span class="o">=</span> <span class="s2">&quot;HOFTS&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">detail</span> <span class="o">=</span> <span class="s2">&quot;Severiano, Silva, Sadaei and Guimarães&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">is_high_order</span> <span class="o">=</span> <span class="kc">True</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">min_order</span> <span class="o">=</span> <span class="mi">1</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;order&quot;</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">min_order</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">configure_lags</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+
+<div class="viewcode-block" id="HighOrderFTS.configure_lags"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.hofts.HighOrderFTS.configure_lags">[docs]</a>    <span class="k">def</span> <span class="nf">configure_lags</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="k">if</span> <span class="s2">&quot;order&quot;</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">order</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;order&quot;</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">min_order</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="s2">&quot;lags&quot;</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">lags</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;lags&quot;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">lags</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">lags</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">lags</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span></div>
+
+<div class="viewcode-block" id="HighOrderFTS.generate_lhs_flrg"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.hofts.HighOrderFTS.generate_lhs_flrg">[docs]</a>    <span class="k">def</span> <span class="nf">generate_lhs_flrg</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sample</span><span class="p">,</span> <span class="n">explain</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
+
+        <span class="n">nsample</span> <span class="o">=</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">fuzzyfy</span><span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s2">&quot;sets&quot;</span><span class="p">,</span> <span class="n">alpha_cut</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">alpha_cut</span><span class="p">)</span>
+                   <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">sample</span><span class="p">]</span>
+
+        <span class="k">if</span> <span class="n">explain</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">append_log</span><span class="p">(</span><span class="s2">&quot;Fuzzyfication&quot;</span><span class="p">,</span><span class="s2">&quot;</span><span class="si">{}</span><span class="s2"> -&gt; </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">sample</span><span class="p">,</span> <span class="n">nsample</span><span class="p">))</span>
+
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">generate_lhs_flrg_fuzzyfied</span><span class="p">(</span><span class="n">nsample</span><span class="p">,</span> <span class="n">explain</span><span class="p">)</span></div>
+
+<div class="viewcode-block" id="HighOrderFTS.generate_lhs_flrg_fuzzyfied"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.hofts.HighOrderFTS.generate_lhs_flrg_fuzzyfied">[docs]</a>    <span class="k">def</span> <span class="nf">generate_lhs_flrg_fuzzyfied</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sample</span><span class="p">,</span> <span class="n">explain</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
+        <span class="n">lags</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="n">flrgs</span> <span class="o">=</span> <span class="p">[]</span>
+
+        <span class="k">for</span> <span class="n">ct</span><span class="p">,</span> <span class="n">o</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">lags</span><span class="p">):</span>
+            <span class="n">lhs</span> <span class="o">=</span> <span class="n">sample</span><span class="p">[</span><span class="n">o</span> <span class="o">-</span> <span class="mi">1</span><span class="p">]</span>
+            <span class="n">lags</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">lhs</span><span class="p">)</span>
+
+            <span class="k">if</span> <span class="n">explain</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">append_log</span><span class="p">(</span><span class="s2">&quot;Ordering Lags&quot;</span><span class="p">,</span> <span class="s2">&quot;Lag </span><span class="si">{}</span><span class="s2"> Value </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">o</span><span class="p">,</span> <span class="n">lhs</span><span class="p">))</span>
+
+        <span class="c1"># Trace the possible paths</span>
+        <span class="k">for</span> <span class="n">path</span> <span class="ow">in</span> <span class="n">product</span><span class="p">(</span><span class="o">*</span><span class="n">lags</span><span class="p">):</span>
+            <span class="n">flrg</span> <span class="o">=</span> <span class="n">HighOrderFLRG</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">)</span>
+
+            <span class="k">for</span> <span class="n">lhs</span> <span class="ow">in</span> <span class="n">path</span><span class="p">:</span>
+                <span class="n">flrg</span><span class="o">.</span><span class="n">append_lhs</span><span class="p">(</span><span class="n">lhs</span><span class="p">)</span>
+
+            <span class="n">flrgs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">flrg</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">flrgs</span></div>
+
+<div class="viewcode-block" id="HighOrderFTS.generate_flrg"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.hofts.HighOrderFTS.generate_flrg">[docs]</a>    <span class="k">def</span> <span class="nf">generate_flrg</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">):</span>
+        <span class="n">_tmp_steps</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">standard_horizon</span> <span class="o">-</span> <span class="mi">1</span>
+        <span class="n">l</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">,</span> <span class="n">l</span> <span class="o">-</span> <span class="n">_tmp_steps</span><span class="p">):</span>
+
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">dump</span><span class="p">:</span> <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;FLR: &quot;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">k</span><span class="p">))</span>
+
+            <span class="n">sample</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="n">k</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">:</span> <span class="n">k</span><span class="p">]</span>
+
+            <span class="n">rhs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">fuzzyfy</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="n">k</span><span class="o">+</span><span class="n">_tmp_steps</span><span class="p">],</span> <span class="n">mode</span><span class="o">=</span><span class="s2">&quot;sets&quot;</span><span class="p">,</span> <span class="n">alpha_cut</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">alpha_cut</span><span class="p">)</span>
+
+            <span class="n">flrgs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">generate_lhs_flrg</span><span class="p">(</span><span class="n">sample</span><span class="p">)</span>
+
+            <span class="k">for</span> <span class="n">flrg</span> <span class="ow">in</span> <span class="n">flrgs</span><span class="p">:</span>
+                <span class="k">if</span> <span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">:</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()]</span> <span class="o">=</span> <span class="n">flrg</span><span class="p">;</span>
+
+                <span class="k">for</span> <span class="n">st</span> <span class="ow">in</span> <span class="n">rhs</span><span class="p">:</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()]</span><span class="o">.</span><span class="n">append_rhs</span><span class="p">(</span><span class="n">st</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="HighOrderFTS.generate_flrg_fuzzyfied"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.hofts.HighOrderFTS.generate_flrg_fuzzyfied">[docs]</a>    <span class="k">def</span> <span class="nf">generate_flrg_fuzzyfied</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">):</span>
+        <span class="n">_tmp_steps</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">standard_horizon</span> <span class="o">-</span> <span class="mi">1</span>
+        <span class="n">l</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">,</span> <span class="n">l</span><span class="p">):</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">dump</span><span class="p">:</span> <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;FLR: &quot;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">k</span><span class="p">))</span>
+
+            <span class="n">sample</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="n">k</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">:</span> <span class="n">k</span><span class="p">]</span>
+
+            <span class="n">rhs</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="n">k</span><span class="o">+</span><span class="n">_tmp_steps</span><span class="p">]</span>
+
+            <span class="n">flrgs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">generate_lhs_flrg_fuzzyfied</span><span class="p">(</span><span class="n">sample</span><span class="p">)</span>
+
+            <span class="k">for</span> <span class="n">flrg</span> <span class="ow">in</span> <span class="n">flrgs</span><span class="p">:</span>
+
+                <span class="k">if</span> <span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">:</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()]</span> <span class="o">=</span> <span class="n">flrg</span>
+
+                <span class="k">for</span> <span class="n">st</span> <span class="ow">in</span> <span class="n">rhs</span><span class="p">:</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()]</span><span class="o">.</span><span class="n">append_rhs</span><span class="p">(</span><span class="n">st</span><span class="p">)</span></div>
+
+<div class="viewcode-block" id="HighOrderFTS.train"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.hofts.HighOrderFTS.train">[docs]</a>    <span class="k">def</span> <span class="nf">train</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">configure_lags</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;fuzzyfied&#39;</span><span class="p">,</span><span class="kc">False</span><span class="p">):</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">generate_flrg</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">generate_flrg_fuzzyfied</span><span class="p">(</span><span class="n">data</span><span class="p">)</span></div>
+
+<div class="viewcode-block" id="HighOrderFTS.forecast"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.hofts.HighOrderFTS.forecast">[docs]</a>    <span class="k">def</span> <span class="nf">forecast</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ndata</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+
+        <span class="n">explain</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;explain&#39;</span><span class="p">,</span> <span class="kc">False</span><span class="p">)</span>
+
+        <span class="n">fuzzyfied</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;fuzzyfied&#39;</span><span class="p">,</span> <span class="kc">False</span><span class="p">)</span>
+
+        <span class="n">mode</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;mode&#39;</span><span class="p">,</span> <span class="s1">&#39;mean&#39;</span><span class="p">)</span>
+
+        <span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
+
+        <span class="n">l</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">ndata</span><span class="p">)</span> <span class="k">if</span> <span class="ow">not</span> <span class="n">explain</span> <span class="k">else</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span> <span class="o">+</span> <span class="mi">1</span>
+
+        <span class="k">if</span> <span class="n">l</span> <span class="o">&lt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">ndata</span>
+        <span class="k">elif</span> <span class="n">l</span> <span class="o">==</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">:</span>
+            <span class="n">l</span> <span class="o">+=</span> <span class="mi">1</span>
+
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">,</span> <span class="n">l</span><span class="p">):</span>
+
+            <span class="n">sample</span> <span class="o">=</span> <span class="n">ndata</span><span class="p">[</span><span class="n">k</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">:</span> <span class="n">k</span><span class="p">]</span>
+
+            <span class="k">if</span> <span class="ow">not</span> <span class="n">fuzzyfied</span><span class="p">:</span>
+                <span class="n">flrgs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">generate_lhs_flrg</span><span class="p">(</span><span class="n">sample</span><span class="p">,</span> <span class="n">explain</span><span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">flrgs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">generate_lhs_flrg_fuzzyfied</span><span class="p">(</span><span class="n">sample</span><span class="p">,</span> <span class="n">explain</span><span class="p">)</span>
+
+            <span class="n">midpoints</span> <span class="o">=</span> <span class="p">[]</span>
+            <span class="n">memberships</span> <span class="o">=</span> <span class="p">[]</span>
+            <span class="k">for</span> <span class="n">flrg</span> <span class="ow">in</span> <span class="n">flrgs</span><span class="p">:</span>
+
+                <span class="k">if</span> <span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">:</span>
+                    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">flrg</span><span class="o">.</span><span class="n">LHS</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
+                        <span class="n">mp</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="n">flrg</span><span class="o">.</span><span class="n">LHS</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]]</span><span class="o">.</span><span class="n">centroid</span>
+                        <span class="n">mv</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="n">flrg</span><span class="o">.</span><span class="n">LHS</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]]</span><span class="o">.</span><span class="n">membership</span><span class="p">(</span><span class="n">sample</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span> <span class="k">if</span> <span class="ow">not</span> <span class="n">fuzzyfied</span> <span class="k">else</span> <span class="kc">None</span>
+                        <span class="n">midpoints</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">mp</span><span class="p">)</span>
+                        <span class="n">memberships</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">mv</span><span class="p">)</span>
+
+                        <span class="k">if</span> <span class="n">explain</span><span class="p">:</span>
+                            <span class="bp">self</span><span class="o">.</span><span class="n">append_log</span><span class="p">(</span><span class="s2">&quot;Rule Matching&quot;</span><span class="p">,</span> <span class="s2">&quot;</span><span class="si">{}</span><span class="s2"> -&gt; </span><span class="si">{}</span><span class="s2"> (Naïve) Midpoint: </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">flrg</span><span class="o">.</span><span class="n">LHS</span><span class="p">),</span> <span class="n">flrg</span><span class="o">.</span><span class="n">LHS</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span>
+                                                                                            <span class="n">mp</span><span class="p">))</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">flrg</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()]</span>
+                    <span class="n">mp</span> <span class="o">=</span> <span class="n">flrg</span><span class="o">.</span><span class="n">get_midpoint</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">)</span>
+                    <span class="n">mv</span> <span class="o">=</span> <span class="n">flrg</span><span class="o">.</span><span class="n">get_membership</span><span class="p">(</span><span class="n">sample</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">)</span> <span class="k">if</span> <span class="ow">not</span> <span class="n">fuzzyfied</span> <span class="k">else</span> <span class="kc">None</span>
+                    <span class="n">midpoints</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">mp</span><span class="p">)</span>
+                    <span class="n">memberships</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">mv</span><span class="p">)</span>
+
+                    <span class="k">if</span> <span class="n">explain</span><span class="p">:</span>
+                        <span class="bp">self</span><span class="o">.</span><span class="n">append_log</span><span class="p">(</span><span class="s2">&quot;Rule Matching&quot;</span><span class="p">,</span> <span class="s2">&quot;</span><span class="si">{}</span><span class="s2">, Midpoint: </span><span class="si">{}</span><span class="s2"> Membership: </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">(),</span> <span class="n">mp</span><span class="p">,</span> <span class="n">mv</span><span class="p">))</span>
+
+            <span class="k">if</span> <span class="n">mode</span> <span class="o">==</span> <span class="s2">&quot;mean&quot;</span> <span class="ow">or</span> <span class="n">fuzzyfied</span><span class="p">:</span>
+                <span class="n">final</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nanmean</span><span class="p">(</span><span class="n">midpoints</span><span class="p">)</span>
+                <span class="k">if</span> <span class="n">explain</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">append_log</span><span class="p">(</span><span class="s2">&quot;Deffuzyfication&quot;</span><span class="p">,</span> <span class="s2">&quot;By Mean: </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">final</span><span class="p">))</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">final</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">midpoints</span><span class="p">,</span> <span class="n">memberships</span><span class="p">)</span><span class="o">/</span><span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">(</span><span class="n">memberships</span><span class="p">)</span>
+                <span class="k">if</span> <span class="n">explain</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">append_log</span><span class="p">(</span><span class="s2">&quot;Deffuzyfication&quot;</span><span class="p">,</span> <span class="s2">&quot;By Memberships: </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">final</span><span class="p">))</span>
+
+            <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">final</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">ret</span></div></div>
+
+
+<div class="viewcode-block" id="WeightedHighOrderFTS"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.hofts.WeightedHighOrderFTS">[docs]</a><span class="k">class</span> <span class="nc">WeightedHighOrderFTS</span><span class="p">(</span><span class="n">HighOrderFTS</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;Weighted High Order Fuzzy Time Series&quot;&quot;&quot;</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">(</span><span class="n">WeightedHighOrderFTS</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="s2">&quot;Weighted High Order FTS&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">shortname</span> <span class="o">=</span> <span class="s2">&quot;WHOFTS&quot;</span>
+
+<div class="viewcode-block" id="WeightedHighOrderFTS.generate_lhs_flrg_fuzzyfied"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.hofts.WeightedHighOrderFTS.generate_lhs_flrg_fuzzyfied">[docs]</a>    <span class="k">def</span> <span class="nf">generate_lhs_flrg_fuzzyfied</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sample</span><span class="p">,</span> <span class="n">explain</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
+        <span class="n">lags</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="n">flrgs</span> <span class="o">=</span> <span class="p">[]</span>
+
+        <span class="k">for</span> <span class="n">ct</span><span class="p">,</span> <span class="n">o</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">lags</span><span class="p">):</span>
+            <span class="n">lags</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">sample</span><span class="p">[</span><span class="n">o</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
+
+            <span class="k">if</span> <span class="n">explain</span><span class="p">:</span>
+                <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\t</span><span class="s2"> (Lag </span><span class="si">{}</span><span class="s2">) </span><span class="si">{}</span><span class="s2"> </span><span class="se">\n</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">o</span><span class="p">,</span> <span class="n">sample</span><span class="p">[</span><span class="n">o</span><span class="o">-</span><span class="mi">1</span><span class="p">]))</span>
+
+        <span class="c1"># Trace the possible paths</span>
+        <span class="k">for</span> <span class="n">path</span> <span class="ow">in</span> <span class="n">product</span><span class="p">(</span><span class="o">*</span><span class="n">lags</span><span class="p">):</span>
+            <span class="n">flrg</span> <span class="o">=</span> <span class="n">WeightedHighOrderFLRG</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">)</span>
+
+            <span class="k">for</span> <span class="n">lhs</span> <span class="ow">in</span> <span class="n">path</span><span class="p">:</span>
+                <span class="n">flrg</span><span class="o">.</span><span class="n">append_lhs</span><span class="p">(</span><span class="n">lhs</span><span class="p">)</span>
+
+            <span class="n">flrgs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">flrg</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">flrgs</span></div></div>
+</pre></div>
+
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+  <h1>Source code for pyFTS.models.hwang</h1><div class="highlight"><pre>
+<span></span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">High Order Fuzzy Time Series by Hwang, Chen and Lee (1998)</span>
+
+<span class="sd">Jeng-Ren Hwang, Shyi-Ming Chen, and Chia-Hoang Lee, “Handling forecasting problems using fuzzy time series,” </span>
+<span class="sd">Fuzzy Sets Syst., no. 100, pp. 217–228, 1998.</span>
+<span class="sd">&quot;&quot;&quot;</span>
+
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">from</span> <span class="nn">pyFTS.common</span> <span class="kn">import</span> <span class="n">FuzzySet</span><span class="p">,</span> <span class="n">FLR</span><span class="p">,</span> <span class="n">Transformations</span><span class="p">,</span> <span class="n">fts</span>
+
+
+<div class="viewcode-block" id="HighOrderFTS"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.hwang.HighOrderFTS">[docs]</a><span class="k">class</span> <span class="nc">HighOrderFTS</span><span class="p">(</span><span class="n">fts</span><span class="o">.</span><span class="n">FTS</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">(</span><span class="n">HighOrderFTS</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">is_high_order</span> <span class="o">=</span> <span class="kc">True</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">min_order</span> <span class="o">=</span> <span class="mi">2</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="s2">&quot;Hwang High Order FTS&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">shortname</span> <span class="o">=</span> <span class="s2">&quot;Hwang&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">detail</span> <span class="o">=</span> <span class="s2">&quot;Hwang&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">configure_lags</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+
+<div class="viewcode-block" id="HighOrderFTS.configure_lags"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.hwang.HighOrderFTS.configure_lags">[docs]</a>    <span class="k">def</span> <span class="nf">configure_lags</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="k">if</span> <span class="s2">&quot;order&quot;</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">order</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;order&quot;</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span></div>
+
+<div class="viewcode-block" id="HighOrderFTS.forecast"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.hwang.HighOrderFTS.forecast">[docs]</a>    <span class="k">def</span> <span class="nf">forecast</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ndata</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+
+        <span class="n">l</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">sets</span><span class="p">)</span>
+
+        <span class="n">cn</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mf">0.0</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">l</span><span class="p">)])</span>
+        <span class="n">ow</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mf">0.0</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">l</span><span class="p">)]</span> <span class="k">for</span> <span class="n">z</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)])</span>
+        <span class="n">rn</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mf">0.0</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">l</span><span class="p">)]</span> <span class="k">for</span> <span class="n">z</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)])</span>
+        <span class="n">ft</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mf">0.0</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">l</span><span class="p">)])</span>
+
+        <span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
+
+        <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">ndata</span><span class="p">)):</span>
+
+            <span class="k">for</span> <span class="n">ix</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">l</span><span class="p">):</span>
+                <span class="n">s</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">ordered_sets</span><span class="p">[</span><span class="n">ix</span><span class="p">]</span>
+                <span class="n">cn</span><span class="p">[</span><span class="n">ix</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="n">s</span><span class="p">]</span><span class="o">.</span><span class="n">membership</span><span class="p">(</span> <span class="n">FuzzySet</span><span class="o">.</span><span class="n">grant_bounds</span><span class="p">(</span><span class="n">ndata</span><span class="p">[</span><span class="n">t</span><span class="p">],</span> <span class="bp">self</span><span class="o">.</span><span class="n">sets</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">ordered_sets</span><span class="p">))</span>
+                <span class="k">for</span> <span class="n">w</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="o">-</span><span class="mi">1</span><span class="p">):</span>
+                    <span class="n">ow</span><span class="p">[</span><span class="n">w</span><span class="p">,</span> <span class="n">ix</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="n">s</span><span class="p">]</span><span class="o">.</span><span class="n">membership</span><span class="p">(</span><span class="n">FuzzySet</span><span class="o">.</span><span class="n">grant_bounds</span><span class="p">(</span><span class="n">ndata</span><span class="p">[</span><span class="n">t</span> <span class="o">-</span> <span class="n">w</span><span class="p">],</span> <span class="bp">self</span><span class="o">.</span><span class="n">sets</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">ordered_sets</span><span class="p">))</span>
+                    <span class="n">rn</span><span class="p">[</span><span class="n">w</span><span class="p">,</span> <span class="n">ix</span><span class="p">]</span> <span class="o">=</span> <span class="n">ow</span><span class="p">[</span><span class="n">w</span><span class="p">,</span> <span class="n">ix</span><span class="p">]</span> <span class="o">*</span> <span class="n">cn</span><span class="p">[</span><span class="n">ix</span><span class="p">]</span>
+                    <span class="n">ft</span><span class="p">[</span><span class="n">ix</span><span class="p">]</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">ft</span><span class="p">[</span><span class="n">ix</span><span class="p">],</span> <span class="n">rn</span><span class="p">[</span><span class="n">w</span><span class="p">,</span> <span class="n">ix</span><span class="p">])</span>
+            <span class="n">mft</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">ft</span><span class="p">)</span>
+            <span class="n">out</span> <span class="o">=</span> <span class="mf">0.0</span>
+            <span class="n">count</span> <span class="o">=</span> <span class="mf">0.0</span>
+            <span class="k">for</span> <span class="n">ix</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">l</span><span class="p">):</span>
+                <span class="n">s</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">ordered_sets</span><span class="p">[</span><span class="n">ix</span><span class="p">]</span>
+                <span class="k">if</span> <span class="n">ft</span><span class="p">[</span><span class="n">ix</span><span class="p">]</span> <span class="o">==</span> <span class="n">mft</span><span class="p">:</span>
+                    <span class="n">out</span> <span class="o">=</span> <span class="n">out</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="n">s</span><span class="p">]</span><span class="o">.</span><span class="n">centroid</span>
+                    <span class="n">count</span> <span class="o">+=</span> <span class="mf">1.0</span>
+            <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">out</span> <span class="o">/</span> <span class="n">count</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">ret</span></div>
+
+<div class="viewcode-block" id="HighOrderFTS.train"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.hwang.HighOrderFTS.train">[docs]</a>    <span class="k">def</span> <span class="nf">train</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">sets</span> <span class="o">==</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">sets</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">configure_lags</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div></div>
+</pre></div>
+
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+
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+        <li class="nav-item nav-item-this"><a href="">pyFTS.models.ifts</a></li> 
+      </ul>
+    </div>  
+
+    <div class="document">
+      <div class="documentwrapper">
+        <div class="bodywrapper">
+          <div class="body" role="main">
+            
+  <h1>Source code for pyFTS.models.ifts</h1><div class="highlight"><pre>
+<span></span><span class="ch">#!/usr/bin/python</span>
+<span class="c1"># -*- coding: utf8 -*-</span>
+
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">High Order Interval Fuzzy Time Series</span>
+
+<span class="sd">SILVA, Petrônio CL; SADAEI, Hossein Javedani; GUIMARÃES, Frederico Gadelha. Interval Forecasting with Fuzzy Time Series.</span>
+<span class="sd">In: Computational Intelligence (SSCI), 2016 IEEE Symposium Series on. IEEE, 2016. p. 1-8.</span>
+<span class="sd">&quot;&quot;&quot;</span>
+
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">from</span> <span class="nn">pyFTS.common</span> <span class="kn">import</span> <span class="n">FuzzySet</span><span class="p">,</span> <span class="n">FLR</span><span class="p">,</span> <span class="n">fts</span><span class="p">,</span> <span class="n">tree</span>
+<span class="kn">from</span> <span class="nn">pyFTS.models</span> <span class="kn">import</span> <span class="n">hofts</span>
+
+
+<div class="viewcode-block" id="IntervalFTS"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.ifts.IntervalFTS">[docs]</a><span class="k">class</span> <span class="nc">IntervalFTS</span><span class="p">(</span><span class="n">hofts</span><span class="o">.</span><span class="n">HighOrderFTS</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    High Order Interval Fuzzy Time Series</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">(</span><span class="n">IntervalFTS</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">shortname</span> <span class="o">=</span> <span class="s2">&quot;IFTS&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="s2">&quot;Interval FTS&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">detail</span> <span class="o">=</span> <span class="s2">&quot;Silva, P.; Guimarães, F.; Sadaei, H. (2016)&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span> <span class="o">=</span> <span class="p">{}</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">has_point_forecasting</span> <span class="o">=</span> <span class="kc">False</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">has_interval_forecasting</span> <span class="o">=</span> <span class="kc">True</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">is_high_order</span> <span class="o">=</span> <span class="kc">True</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">min_order</span> <span class="o">=</span> <span class="mi">1</span>
+
+<div class="viewcode-block" id="IntervalFTS.get_upper"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.ifts.IntervalFTS.get_upper">[docs]</a>    <span class="k">def</span> <span class="nf">get_upper</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">flrg</span><span class="p">):</span>
+        <span class="n">ret</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
+        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">flrg</span><span class="o">.</span><span class="n">LHS</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">:</span>
+                <span class="n">tmp</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()]</span>
+                <span class="n">ret</span> <span class="o">=</span> <span class="n">tmp</span><span class="o">.</span><span class="n">get_upper</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">ret</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="n">flrg</span><span class="o">.</span><span class="n">LHS</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]]</span><span class="o">.</span><span class="n">upper</span>
+        <span class="k">return</span> <span class="n">ret</span></div>
+
+<div class="viewcode-block" id="IntervalFTS.get_lower"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.ifts.IntervalFTS.get_lower">[docs]</a>    <span class="k">def</span> <span class="nf">get_lower</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">flrg</span><span class="p">):</span>
+        <span class="n">ret</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
+        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">flrg</span><span class="o">.</span><span class="n">LHS</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">:</span>
+                <span class="n">tmp</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()]</span>
+                <span class="n">ret</span> <span class="o">=</span> <span class="n">tmp</span><span class="o">.</span><span class="n">get_lower</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">ret</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="n">flrg</span><span class="o">.</span><span class="n">LHS</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]]</span><span class="o">.</span><span class="n">lower</span>
+        <span class="k">return</span> <span class="n">ret</span></div>
+
+<div class="viewcode-block" id="IntervalFTS.get_sequence_membership"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.ifts.IntervalFTS.get_sequence_membership">[docs]</a>    <span class="k">def</span> <span class="nf">get_sequence_membership</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">fuzzySets</span><span class="p">):</span>
+        <span class="n">mb</span> <span class="o">=</span> <span class="p">[</span><span class="n">fuzzySets</span><span class="p">[</span><span class="n">k</span><span class="p">]</span><span class="o">.</span><span class="n">membership</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="n">k</span><span class="p">])</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">))]</span>
+        <span class="k">return</span> <span class="n">mb</span></div>
+
+<div class="viewcode-block" id="IntervalFTS.forecast_interval"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.ifts.IntervalFTS.forecast_interval">[docs]</a>    <span class="k">def</span> <span class="nf">forecast_interval</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ndata</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+
+        <span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
+
+        <span class="n">l</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">ndata</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">l</span> <span class="o">&lt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">ndata</span>
+
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">,</span> <span class="n">l</span><span class="o">+</span><span class="mi">1</span><span class="p">):</span>
+
+            <span class="n">sample</span> <span class="o">=</span> <span class="n">ndata</span><span class="p">[</span><span class="n">k</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">:</span> <span class="n">k</span><span class="p">]</span>
+
+            <span class="n">flrgs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">generate_lhs_flrg</span><span class="p">(</span><span class="n">sample</span><span class="p">)</span>
+
+            <span class="n">up</span> <span class="o">=</span> <span class="p">[]</span>
+            <span class="n">lo</span> <span class="o">=</span> <span class="p">[]</span>
+            <span class="n">affected_flrgs_memberships</span> <span class="o">=</span> <span class="p">[]</span>
+
+            <span class="k">for</span> <span class="n">flrg</span> <span class="ow">in</span> <span class="n">flrgs</span><span class="p">:</span>
+                <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">flrg</span><span class="o">.</span><span class="n">LHS</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
+
+                    <span class="n">mv</span> <span class="o">=</span> <span class="n">flrg</span><span class="o">.</span><span class="n">get_membership</span><span class="p">(</span><span class="n">sample</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">)</span>
+                    <span class="n">up</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">mv</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_upper</span><span class="p">(</span><span class="n">flrg</span><span class="p">))</span>
+                    <span class="n">lo</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">mv</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_lower</span><span class="p">(</span><span class="n">flrg</span><span class="p">))</span>
+                    <span class="n">affected_flrgs_memberships</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">mv</span><span class="p">)</span>
+
+            <span class="c1"># gerar o intervalo</span>
+            <span class="n">norm</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">(</span><span class="n">affected_flrgs_memberships</span><span class="p">)</span>
+            <span class="n">lo_</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">(</span><span class="n">lo</span><span class="p">)</span> <span class="o">/</span> <span class="n">norm</span>
+            <span class="n">up_</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">(</span><span class="n">up</span><span class="p">)</span> <span class="o">/</span> <span class="n">norm</span>
+            <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">([</span><span class="n">lo_</span><span class="p">,</span> <span class="n">up_</span><span class="p">])</span>
+
+        <span class="k">return</span> <span class="n">ret</span></div>
+
+<div class="viewcode-block" id="IntervalFTS.forecast_ahead_interval"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.ifts.IntervalFTS.forecast_ahead_interval">[docs]</a>    <span class="k">def</span> <span class="nf">forecast_ahead_interval</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">steps</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="n">start</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;start_at&#39;</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
+        <span class="n">ret</span> <span class="o">=</span> <span class="p">[[</span><span class="n">x</span><span class="p">,</span> <span class="n">x</span><span class="p">]</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">data</span><span class="p">[</span><span class="n">start</span><span class="p">:</span><span class="n">start</span><span class="o">+</span><span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">]]</span>
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span> <span class="o">+</span> <span class="n">steps</span><span class="p">):</span>
+            <span class="n">interval_lower</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">clip_uod</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">forecast_interval</span><span class="p">([</span><span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">ret</span><span class="p">[</span><span class="n">k</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">:</span> <span class="n">k</span><span class="p">]])[</span><span class="mi">0</span><span class="p">])</span>
+            <span class="n">interval_upper</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">clip_uod</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">forecast_interval</span><span class="p">([</span><span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">ret</span><span class="p">[</span><span class="n">k</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">:</span> <span class="n">k</span><span class="p">]])[</span><span class="mi">0</span><span class="p">])</span>
+            <span class="n">interval</span> <span class="o">=</span> <span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">nanmin</span><span class="p">(</span><span class="n">interval_lower</span><span class="p">),</span> <span class="n">np</span><span class="o">.</span><span class="n">nanmax</span><span class="p">(</span><span class="n">interval_upper</span><span class="p">)]</span>
+            <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">interval</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">ret</span><span class="p">[</span><span class="o">-</span><span class="n">steps</span><span class="p">:]</span></div></div>
+
+
+<div class="viewcode-block" id="WeightedIntervalFTS"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.ifts.WeightedIntervalFTS">[docs]</a><span class="k">class</span> <span class="nc">WeightedIntervalFTS</span><span class="p">(</span><span class="n">hofts</span><span class="o">.</span><span class="n">WeightedHighOrderFTS</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Weighted High Order Interval Fuzzy Time Series</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">(</span><span class="n">WeightedIntervalFTS</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">shortname</span> <span class="o">=</span> <span class="s2">&quot;WIFTS&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="s2">&quot;Weighted Interval FTS&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">detail</span> <span class="o">=</span> <span class="s2">&quot;Silva, P.; Guimarães, F.; Sadaei, H. (2016)&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span> <span class="o">=</span> <span class="p">{}</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">has_point_forecasting</span> <span class="o">=</span> <span class="kc">False</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">has_interval_forecasting</span> <span class="o">=</span> <span class="kc">True</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">is_high_order</span> <span class="o">=</span> <span class="kc">True</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">min_order</span> <span class="o">=</span> <span class="mi">1</span>
+
+<div class="viewcode-block" id="WeightedIntervalFTS.get_upper"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.ifts.WeightedIntervalFTS.get_upper">[docs]</a>    <span class="k">def</span> <span class="nf">get_upper</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">flrg</span><span class="p">):</span>
+        <span class="n">ret</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
+        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">flrg</span><span class="o">.</span><span class="n">LHS</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">:</span>
+                <span class="n">tmp</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()]</span>
+                <span class="n">ret</span> <span class="o">=</span> <span class="n">tmp</span><span class="o">.</span><span class="n">get_upper</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">ret</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="n">flrg</span><span class="o">.</span><span class="n">LHS</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]]</span><span class="o">.</span><span class="n">upper</span>
+        <span class="k">return</span> <span class="n">ret</span></div>
+
+<div class="viewcode-block" id="WeightedIntervalFTS.get_lower"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.ifts.WeightedIntervalFTS.get_lower">[docs]</a>    <span class="k">def</span> <span class="nf">get_lower</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">flrg</span><span class="p">):</span>
+        <span class="n">ret</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
+        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">flrg</span><span class="o">.</span><span class="n">LHS</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">:</span>
+                <span class="n">tmp</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()]</span>
+                <span class="n">ret</span> <span class="o">=</span> <span class="n">tmp</span><span class="o">.</span><span class="n">get_lower</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">ret</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="n">flrg</span><span class="o">.</span><span class="n">LHS</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]]</span><span class="o">.</span><span class="n">lower</span>
+        <span class="k">return</span> <span class="n">ret</span></div>
+
+<div class="viewcode-block" id="WeightedIntervalFTS.get_sequence_membership"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.ifts.WeightedIntervalFTS.get_sequence_membership">[docs]</a>    <span class="k">def</span> <span class="nf">get_sequence_membership</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">fuzzySets</span><span class="p">):</span>
+        <span class="n">mb</span> <span class="o">=</span> <span class="p">[</span><span class="n">fuzzySets</span><span class="p">[</span><span class="n">k</span><span class="p">]</span><span class="o">.</span><span class="n">membership</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="n">k</span><span class="p">])</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">))]</span>
+        <span class="k">return</span> <span class="n">mb</span></div>
+
+<div class="viewcode-block" id="WeightedIntervalFTS.forecast_interval"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.ifts.WeightedIntervalFTS.forecast_interval">[docs]</a>    <span class="k">def</span> <span class="nf">forecast_interval</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ndata</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
+
+        <span class="n">l</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">ndata</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">l</span> <span class="o">&lt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">ndata</span>
+
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">,</span> <span class="n">l</span><span class="o">+</span><span class="mi">1</span><span class="p">):</span>
+
+            <span class="n">sample</span> <span class="o">=</span> <span class="n">ndata</span><span class="p">[</span><span class="n">k</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">:</span> <span class="n">k</span><span class="p">]</span>
+
+            <span class="n">flrgs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">generate_lhs_flrg</span><span class="p">(</span><span class="n">sample</span><span class="p">)</span>
+
+            <span class="n">up</span> <span class="o">=</span> <span class="p">[]</span>
+            <span class="n">lo</span> <span class="o">=</span> <span class="p">[]</span>
+            <span class="n">affected_flrgs_memberships</span> <span class="o">=</span> <span class="p">[]</span>
+
+            <span class="k">for</span> <span class="n">flrg</span> <span class="ow">in</span> <span class="n">flrgs</span><span class="p">:</span>
+                <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">flrg</span><span class="o">.</span><span class="n">LHS</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
+
+                    <span class="n">mv</span> <span class="o">=</span> <span class="n">flrg</span><span class="o">.</span><span class="n">get_membership</span><span class="p">(</span><span class="n">sample</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">)</span>
+                    <span class="n">up</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">mv</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_upper</span><span class="p">(</span><span class="n">flrg</span><span class="p">))</span>
+                    <span class="n">lo</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">mv</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_lower</span><span class="p">(</span><span class="n">flrg</span><span class="p">))</span>
+                    <span class="n">affected_flrgs_memberships</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">mv</span><span class="p">)</span>
+
+            <span class="c1"># gerar o intervalo</span>
+            <span class="n">norm</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">(</span><span class="n">affected_flrgs_memberships</span><span class="p">)</span>
+            <span class="n">lo_</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">(</span><span class="n">lo</span><span class="p">)</span> <span class="o">/</span> <span class="n">norm</span>
+            <span class="n">up_</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">(</span><span class="n">up</span><span class="p">)</span> <span class="o">/</span> <span class="n">norm</span>
+            <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">([</span><span class="n">lo_</span><span class="p">,</span> <span class="n">up_</span><span class="p">])</span>
+
+        <span class="k">return</span> <span class="n">ret</span></div>
+
+<div class="viewcode-block" id="WeightedIntervalFTS.forecast_ahead_interval"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.ifts.WeightedIntervalFTS.forecast_ahead_interval">[docs]</a>    <span class="k">def</span> <span class="nf">forecast_ahead_interval</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">steps</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="n">start</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;start_at&#39;</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
+        <span class="n">ret</span> <span class="o">=</span> <span class="p">[[</span><span class="n">x</span><span class="p">,</span> <span class="n">x</span><span class="p">]</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">data</span><span class="p">[</span><span class="n">start</span><span class="p">:</span><span class="n">start</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">]]</span>
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span> <span class="o">+</span> <span class="n">steps</span><span class="p">):</span>
+            <span class="n">interval_lower</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">clip_uod</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">forecast_interval</span><span class="p">([</span><span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">ret</span><span class="p">[</span><span class="n">k</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">:</span> <span class="n">k</span><span class="p">]])[</span><span class="mi">0</span><span class="p">])</span>
+            <span class="n">interval_upper</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">clip_uod</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">forecast_interval</span><span class="p">([</span><span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">ret</span><span class="p">[</span><span class="n">k</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">:</span> <span class="n">k</span><span class="p">]])[</span><span class="mi">0</span><span class="p">])</span>
+            <span class="n">interval</span> <span class="o">=</span> <span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">nanmin</span><span class="p">(</span><span class="n">interval_lower</span><span class="p">),</span> <span class="n">np</span><span class="o">.</span><span class="n">nanmax</span><span class="p">(</span><span class="n">interval_upper</span><span class="p">)]</span>
+            <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">interval</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">ret</span><span class="p">[</span><span class="o">-</span><span class="n">steps</span><span class="p">:]</span></div></div>
+
+
+</pre></div>
+
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@@ -0,0 +1,224 @@
+
+
+<!doctype html>
+
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+        <li class="nav-item nav-item-this"><a href="">pyFTS.models.incremental.IncrementalEnsemble</a></li> 
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+  <h1>Source code for pyFTS.models.incremental.IncrementalEnsemble</h1><div class="highlight"><pre>
+<span></span><span class="sd">&#39;&#39;&#39;</span>
+<span class="sd">Time Variant/Incremental Ensemble of FTS methods</span>
+<span class="sd">&#39;&#39;&#39;</span>
+
+
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+<span class="kn">from</span> <span class="nn">pyFTS.common</span> <span class="kn">import</span> <span class="n">FuzzySet</span><span class="p">,</span> <span class="n">FLR</span><span class="p">,</span> <span class="n">fts</span><span class="p">,</span> <span class="n">flrg</span>
+<span class="kn">from</span> <span class="nn">pyFTS.partitioners</span> <span class="kn">import</span> <span class="n">Grid</span>
+<span class="kn">from</span> <span class="nn">pyFTS.models</span> <span class="kn">import</span> <span class="n">hofts</span>
+<span class="kn">from</span> <span class="nn">pyFTS.models.ensemble</span> <span class="kn">import</span> <span class="n">ensemble</span>
+
+
+<div class="viewcode-block" id="IncrementalEnsembleFTS"><a class="viewcode-back" href="../../../../pyFTS.models.incremental.html#pyFTS.models.incremental.IncrementalEnsemble.IncrementalEnsembleFTS">[docs]</a><span class="k">class</span> <span class="nc">IncrementalEnsembleFTS</span><span class="p">(</span><span class="n">ensemble</span><span class="o">.</span><span class="n">EnsembleFTS</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Time Variant/Incremental Ensemble of FTS methods</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">(</span><span class="n">IncrementalEnsembleFTS</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">shortname</span> <span class="o">=</span> <span class="s2">&quot;IncrementalEnsembleFTS&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="s2">&quot;Incremental Ensemble FTS&quot;</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">order</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;order&#39;</span><span class="p">,</span><span class="mi">1</span><span class="p">)</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">partitioner_method</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;partitioner_method&#39;</span><span class="p">,</span> <span class="n">Grid</span><span class="o">.</span><span class="n">GridPartitioner</span><span class="p">)</span>
+        <span class="sd">&quot;&quot;&quot;The partitioner method to be called when a new model is build&quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">partitioner_params</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;partitioner_params&#39;</span><span class="p">,</span> <span class="p">{</span><span class="s1">&#39;npart&#39;</span><span class="p">:</span> <span class="mi">10</span><span class="p">})</span>
+        <span class="sd">&quot;&quot;&quot;The partitioner method parameters&quot;&quot;&quot;</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">fts_method</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;fts_method&#39;</span><span class="p">,</span> <span class="n">hofts</span><span class="o">.</span><span class="n">WeightedHighOrderFTS</span><span class="p">)</span>
+        <span class="sd">&quot;&quot;&quot;The FTS method to be called when a new model is build&quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">fts_params</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;fts_params&#39;</span><span class="p">,</span> <span class="p">{})</span>
+        <span class="sd">&quot;&quot;&quot;The FTS method specific parameters&quot;&quot;&quot;</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">window_length</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;window_length&#39;</span><span class="p">,</span> <span class="mi">100</span><span class="p">)</span>
+        <span class="sd">&quot;&quot;&quot;The memory window length&quot;&quot;&quot;</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">batch_size</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;batch_size&#39;</span><span class="p">,</span> <span class="mi">10</span><span class="p">)</span>
+        <span class="sd">&quot;&quot;&quot;The batch interval between each retraining&quot;&quot;&quot;</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">num_models</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;num_models&#39;</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span>
+        <span class="sd">&quot;&quot;&quot;The number of models to hold in the ensemble&quot;&quot;&quot;</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">point_method</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;point_method&#39;</span><span class="p">,</span> <span class="s1">&#39;exponential&#39;</span><span class="p">)</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">is_high_order</span> <span class="o">=</span> <span class="kc">True</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">uod_clip</span> <span class="o">=</span> <span class="kc">False</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">window_length</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span>
+
+<div class="viewcode-block" id="IncrementalEnsembleFTS.offset"><a class="viewcode-back" href="../../../../pyFTS.models.incremental.html#pyFTS.models.incremental.IncrementalEnsemble.IncrementalEnsembleFTS.offset">[docs]</a>    <span class="k">def</span> <span class="nf">offset</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span></div>
+
+<div class="viewcode-block" id="IncrementalEnsembleFTS.train"><a class="viewcode-back" href="../../../../pyFTS.models.incremental.html#pyFTS.models.incremental.IncrementalEnsemble.IncrementalEnsembleFTS.train">[docs]</a>    <span class="k">def</span> <span class="nf">train</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+
+        <span class="n">partitioner</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner_method</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="bp">self</span><span class="o">.</span><span class="n">partitioner_params</span><span class="p">)</span>
+        <span class="n">model</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">fts_method</span><span class="p">(</span><span class="n">partitioner</span><span class="o">=</span><span class="n">partitioner</span><span class="p">,</span> <span class="o">**</span><span class="bp">self</span><span class="o">.</span><span class="n">fts_params</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">model</span><span class="o">.</span><span class="n">is_high_order</span><span class="p">:</span>
+            <span class="n">model</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">fts_method</span><span class="p">(</span><span class="n">partitioner</span><span class="o">=</span><span class="n">partitioner</span><span class="p">,</span> <span class="n">order</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">,</span> <span class="o">**</span><span class="bp">self</span><span class="o">.</span><span class="n">fts_params</span><span class="p">)</span>
+        <span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">append_model</span><span class="p">(</span><span class="n">model</span><span class="p">)</span>
+        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">models</span><span class="p">)</span> <span class="o">&gt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_models</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">models</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span></div>
+
+<div class="viewcode-block" id="IncrementalEnsembleFTS.forecast"><a class="viewcode-back" href="../../../../pyFTS.models.incremental.html#pyFTS.models.incremental.IncrementalEnsemble.IncrementalEnsembleFTS.forecast">[docs]</a>    <span class="k">def</span> <span class="nf">forecast</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="n">l</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+        <span class="n">no_update</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;no_update&#39;</span><span class="p">,</span> <span class="kc">False</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">no_update</span><span class="p">:</span>
+            <span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
+            <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">,</span> <span class="n">l</span><span class="o">+</span><span class="mi">1</span><span class="p">):</span>
+                <span class="n">sample</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="n">k</span><span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">:</span> <span class="n">k</span><span class="p">]</span>
+                <span class="n">tmp</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_models_forecasts</span><span class="p">(</span><span class="n">sample</span><span class="p">)</span>
+                <span class="n">point</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_point</span><span class="p">(</span><span class="n">tmp</span><span class="p">)</span>
+                <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">point</span><span class="p">)</span>
+            <span class="k">return</span> <span class="n">ret</span>
+
+        <span class="n">data_window</span> <span class="o">=</span> <span class="p">[]</span>
+
+        <span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
+
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">,</span> <span class="n">l</span><span class="p">):</span>
+
+            <span class="n">k2</span> <span class="o">=</span> <span class="n">k</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span>
+
+            <span class="n">data_window</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="n">k2</span><span class="p">])</span>
+
+            <span class="k">if</span> <span class="n">k2</span> <span class="o">&gt;=</span> <span class="bp">self</span><span class="o">.</span><span class="n">window_length</span><span class="p">:</span>
+                <span class="n">data_window</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
+
+            <span class="k">if</span> <span class="n">k</span> <span class="o">%</span> <span class="bp">self</span><span class="o">.</span><span class="n">batch_size</span> <span class="o">==</span> <span class="mi">0</span> <span class="ow">and</span> <span class="n">k2</span> <span class="o">&gt;=</span> <span class="bp">self</span><span class="o">.</span><span class="n">window_length</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">train</span><span class="p">(</span><span class="n">data_window</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+
+            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">models</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
+                <span class="n">sample</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="n">k2</span><span class="p">:</span> <span class="n">k</span><span class="p">]</span>
+                <span class="n">tmp</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_models_forecasts</span><span class="p">(</span><span class="n">sample</span><span class="p">)</span>
+                <span class="n">point</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_point</span><span class="p">(</span><span class="n">tmp</span><span class="p">)</span>
+                <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">point</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">ret</span></div>
+
+<div class="viewcode-block" id="IncrementalEnsembleFTS.forecast_ahead"><a class="viewcode-back" href="../../../../pyFTS.models.incremental.html#pyFTS.models.incremental.IncrementalEnsemble.IncrementalEnsembleFTS.forecast_ahead">[docs]</a>    <span class="k">def</span> <span class="nf">forecast_ahead</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">steps</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">)</span> <span class="o">&lt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">data</span>
+
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span>
+            <span class="n">data</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
+
+        <span class="n">start</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;start_at&#39;</span><span class="p">,</span><span class="mi">0</span><span class="p">)</span>
+
+        <span class="n">ret</span> <span class="o">=</span> <span class="n">data</span><span class="p">[:</span><span class="n">start</span><span class="o">+</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">]</span>
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">start</span><span class="o">+</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">,</span> <span class="n">steps</span><span class="o">+</span><span class="n">start</span><span class="o">+</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">):</span>
+            <span class="n">tmp</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">forecast</span><span class="p">(</span><span class="n">ret</span><span class="p">[</span><span class="n">k</span><span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">:</span><span class="n">k</span><span class="p">],</span> <span class="n">no_update</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+
+            <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">tmp</span><span class="p">,(</span><span class="nb">list</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">)):</span>
+                <span class="n">tmp</span> <span class="o">=</span> <span class="n">tmp</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
+
+            <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">tmp</span><span class="p">)</span>
+            <span class="n">data</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">tmp</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">ret</span><span class="p">[</span><span class="o">-</span><span class="n">steps</span><span class="p">:]</span></div></div>
+
+
+
+
+
+
+</pre></div>
+
+            <div class="clearer"></div>
+          </div>
+        </div>
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+      <div class="sphinxsidebar" role="navigation" aria-label="main navigation">
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diff --git a/docs/build/html/_modules/pyFTS/models/incremental/TimeVariant.html b/docs/build/html/_modules/pyFTS/models/incremental/TimeVariant.html
new file mode 100644
index 0000000..88a3cac
--- /dev/null
+++ b/docs/build/html/_modules/pyFTS/models/incremental/TimeVariant.html
@@ -0,0 +1,215 @@
+
+
+<!doctype html>
+
+<html>
+  <head>
+    <meta charset="utf-8" />
+    <meta name="viewport" content="width=device-width, initial-scale=1.0"><script type="text/javascript">
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+        <li class="nav-item nav-item-this"><a href="">pyFTS.models.incremental.TimeVariant</a></li> 
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+            
+  <h1>Source code for pyFTS.models.incremental.TimeVariant</h1><div class="highlight"><pre>
+<span></span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">Meta model that wraps another FTS method and continously retrain it using a data window with</span>
+<span class="sd">the most recent data</span>
+<span class="sd">&quot;&quot;&quot;</span>
+
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">from</span> <span class="nn">pyFTS.common</span> <span class="kn">import</span> <span class="n">FuzzySet</span><span class="p">,</span> <span class="n">FLR</span><span class="p">,</span> <span class="n">fts</span><span class="p">,</span> <span class="n">flrg</span>
+<span class="kn">from</span> <span class="nn">pyFTS.partitioners</span> <span class="kn">import</span> <span class="n">Grid</span>
+
+
+<div class="viewcode-block" id="Retrainer"><a class="viewcode-back" href="../../../../pyFTS.models.incremental.html#pyFTS.models.incremental.TimeVariant.Retrainer">[docs]</a><span class="k">class</span> <span class="nc">Retrainer</span><span class="p">(</span><span class="n">fts</span><span class="o">.</span><span class="n">FTS</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Meta model for incremental/online learning that retrain its internal model after</span>
+<span class="sd">    data windows controlled by the parameter &#39;batch_size&#39;, using as the training data a</span>
+<span class="sd">    window of recent lags, whose size is controlled by the parameter &#39;window_length&#39;.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">(</span><span class="n">Retrainer</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">partitioner_method</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;partitioner_method&#39;</span><span class="p">,</span> <span class="n">Grid</span><span class="o">.</span><span class="n">GridPartitioner</span><span class="p">)</span>
+        <span class="sd">&quot;&quot;&quot;The partitioner method to be called when a new model is build&quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">partitioner_params</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;partitioner_params&#39;</span><span class="p">,</span> <span class="p">{</span><span class="s1">&#39;npart&#39;</span><span class="p">:</span> <span class="mi">10</span><span class="p">})</span>
+        <span class="sd">&quot;&quot;&quot;The partitioner method parameters&quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="sd">&quot;&quot;&quot;The most recent trained partitioner&quot;&quot;&quot;</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">fts_method</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;fts_method&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
+        <span class="sd">&quot;&quot;&quot;The FTS method to be called when a new model is build&quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">fts_params</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;fts_params&#39;</span><span class="p">,</span> <span class="p">{})</span>
+        <span class="sd">&quot;&quot;&quot;The FTS method specific parameters&quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">model</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="sd">&quot;&quot;&quot;The most recent trained model&quot;&quot;&quot;</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">window_length</span>  <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;window_length&#39;</span><span class="p">,</span><span class="mi">100</span><span class="p">)</span>
+        <span class="sd">&quot;&quot;&quot;The memory window length&quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">auto_update</span> <span class="o">=</span> <span class="kc">False</span>
+        <span class="sd">&quot;&quot;&quot;If true the model is updated at each time and not recreated&quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">batch_size</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;batch_size&#39;</span><span class="p">,</span> <span class="mi">10</span><span class="p">)</span>
+        <span class="sd">&quot;&quot;&quot;The batch interval between each retraining&quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">is_high_order</span> <span class="o">=</span> <span class="kc">True</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">is_time_variant</span> <span class="o">=</span> <span class="kc">True</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">uod_clip</span> <span class="o">=</span> <span class="kc">False</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">window_length</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">is_wrapper</span> <span class="o">=</span> <span class="kc">True</span>
+
+<div class="viewcode-block" id="Retrainer.train"><a class="viewcode-back" href="../../../../pyFTS.models.incremental.html#pyFTS.models.incremental.TimeVariant.Retrainer.train">[docs]</a>    <span class="k">def</span> <span class="nf">train</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner_method</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="bp">self</span><span class="o">.</span><span class="n">partitioner_params</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">model</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">fts_method</span><span class="p">(</span><span class="n">partitioner</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="p">,</span> <span class="o">**</span><span class="bp">self</span><span class="o">.</span><span class="n">fts_params</span><span class="p">)</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">is_high_order</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">order</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">fts_method</span><span class="p">(</span><span class="n">partitioner</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="p">,</span>
+                                                            <span class="n">order</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">,</span> <span class="o">**</span><span class="bp">self</span><span class="o">.</span><span class="n">fts_params</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">shortname</span> <span class="o">=</span> <span class="s2">&quot;TimeVariant - &quot;</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">shortname</span></div>
+
+<div class="viewcode-block" id="Retrainer.forecast"><a class="viewcode-back" href="../../../../pyFTS.models.incremental.html#pyFTS.models.incremental.TimeVariant.Retrainer.forecast">[docs]</a>    <span class="k">def</span> <span class="nf">forecast</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="n">l</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+
+        <span class="n">no_update</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;no_update&#39;</span><span class="p">,</span><span class="kc">False</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">no_update</span><span class="p">:</span>
+            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+
+        <span class="n">horizon</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">window_length</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span>
+
+        <span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
+
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">horizon</span><span class="p">,</span> <span class="n">l</span><span class="o">+</span><span class="mi">1</span><span class="p">):</span>
+            <span class="n">_train</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="n">k</span> <span class="o">-</span> <span class="n">horizon</span><span class="p">:</span> <span class="n">k</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">]</span>
+            <span class="n">_test</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="n">k</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">:</span> <span class="n">k</span><span class="p">]</span>
+
+            <span class="k">if</span> <span class="n">k</span> <span class="o">%</span> <span class="bp">self</span><span class="o">.</span><span class="n">batch_size</span> <span class="o">==</span> <span class="mi">0</span> <span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">auto_update</span><span class="p">:</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">train</span><span class="p">(</span><span class="n">_train</span><span class="p">)</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">train</span><span class="p">(</span><span class="n">_train</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+
+            <span class="n">ret</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">_test</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">))</span>
+
+        <span class="k">return</span> <span class="n">ret</span></div>
+
+<div class="viewcode-block" id="Retrainer.forecast_ahead"><a class="viewcode-back" href="../../../../pyFTS.models.incremental.html#pyFTS.models.incremental.TimeVariant.Retrainer.forecast_ahead">[docs]</a>    <span class="k">def</span> <span class="nf">forecast_ahead</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">steps</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">)</span> <span class="o">&lt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">data</span>
+
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span>
+            <span class="n">data</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
+
+        <span class="n">start</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;start_at&#39;</span><span class="p">,</span><span class="mi">0</span><span class="p">)</span>
+
+        <span class="n">ret</span> <span class="o">=</span> <span class="n">data</span><span class="p">[:</span><span class="n">start</span><span class="o">+</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">]</span>
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">start</span><span class="o">+</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">,</span> <span class="n">steps</span><span class="o">+</span><span class="n">start</span><span class="o">+</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">):</span>
+            <span class="n">tmp</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">forecast</span><span class="p">(</span><span class="n">ret</span><span class="p">[</span><span class="n">k</span><span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">:</span><span class="n">k</span><span class="p">],</span> <span class="n">no_update</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+
+            <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">tmp</span><span class="p">,(</span><span class="nb">list</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">)):</span>
+                <span class="n">tmp</span> <span class="o">=</span> <span class="n">tmp</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
+
+            <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">tmp</span><span class="p">)</span>
+            <span class="n">data</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">tmp</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">ret</span><span class="p">[</span><span class="o">-</span><span class="n">steps</span><span class="p">:]</span></div>
+
+<div class="viewcode-block" id="Retrainer.offset"><a class="viewcode-back" href="../../../../pyFTS.models.incremental.html#pyFTS.models.incremental.TimeVariant.Retrainer.offset">[docs]</a>    <span class="k">def</span> <span class="nf">offset</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span></div>
+
+    <span class="k">def</span> <span class="fm">__str__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="sd">&quot;&quot;&quot;String representation of the model&quot;&quot;&quot;</span>
+
+        <span class="k">return</span> <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="fm">__len__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        The length (number of rules) of the model</span>
+
+<span class="sd">        :return: number of rules</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">return</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="p">)</span></div>
+</pre></div>
+
+            <div class="clearer"></div>
+          </div>
+        </div>
+      </div>
+      <div class="sphinxsidebar" role="navigation" aria-label="main navigation">
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+      <h3>Navigation</h3>
+      <ul>
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+          <a href="../../../../genindex.html" title="General Index"
+             >index</a></li>
+        <li class="right" >
+          <a href="../../../../py-modindex.html" title="Python Module Index"
+             >modules</a> |</li>
+        <li class="nav-item nav-item-0"><a href="../../../../index.html">pyFTS 1.6 documentation</a> &#187;</li>
+          <li class="nav-item nav-item-1"><a href="../../../index.html" >Module code</a> &#187;</li>
+        <li class="nav-item nav-item-this"><a href="">pyFTS.models.incremental.TimeVariant</a></li> 
+      </ul>
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+    <div class="footer" role="contentinfo">
+        &#169; Copyright 2018, Machine Intelligence and Data Science Laboratory - UFMG - Brazil.
+      Created using <a href="https://www.sphinx-doc.org/">Sphinx</a> 3.1.2.
+    </div>
+  </body>
+</html>
\ No newline at end of file
diff --git a/docs/build/html/_modules/pyFTS/models/ismailefendi.html b/docs/build/html/_modules/pyFTS/models/ismailefendi.html
new file mode 100644
index 0000000..b59e220
--- /dev/null
+++ b/docs/build/html/_modules/pyFTS/models/ismailefendi.html
@@ -0,0 +1,211 @@
+
+
+<!doctype html>
+
+<html>
+  <head>
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+  <h1>Source code for pyFTS.models.ismailefendi</h1><div class="highlight"><pre>
+<span></span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">First Order Improved Weighted Fuzzy Time Series by Efendi, Ismail and Deris (2013)</span>
+
+<span class="sd">R. Efendi, Z. Ismail, and M. M. Deris, “Improved weight Fuzzy Time Series as used in the exchange rates forecasting of </span>
+<span class="sd">US Dollar to Ringgit Malaysia,” Int. J. Comput. Intell. Appl., vol. 12, no. 1, p. 1350005, 2013.</span>
+<span class="sd">&quot;&quot;&quot;</span>
+
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">from</span> <span class="nn">pyFTS.common</span> <span class="kn">import</span> <span class="n">FuzzySet</span><span class="p">,</span> <span class="n">FLR</span><span class="p">,</span> <span class="n">fts</span><span class="p">,</span> <span class="n">flrg</span>
+
+
+<div class="viewcode-block" id="ImprovedWeightedFLRG"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.ismailefendi.ImprovedWeightedFLRG">[docs]</a><span class="k">class</span> <span class="nc">ImprovedWeightedFLRG</span><span class="p">(</span><span class="n">flrg</span><span class="o">.</span><span class="n">FLRG</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;First Order Improved Weighted Fuzzy Logical Relationship Group&quot;&quot;&quot;</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">LHS</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">(</span><span class="n">ImprovedWeightedFLRG</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">LHS</span> <span class="o">=</span> <span class="n">LHS</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span> <span class="o">=</span> <span class="p">{}</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">rhs_counts</span> <span class="o">=</span> <span class="p">{}</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">count</span> <span class="o">=</span> <span class="mf">0.0</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">w</span> <span class="o">=</span> <span class="kc">None</span>
+
+<div class="viewcode-block" id="ImprovedWeightedFLRG.append_rhs"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.ismailefendi.ImprovedWeightedFLRG.append_rhs">[docs]</a>    <span class="k">def</span> <span class="nf">append_rhs</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="n">count</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;count&#39;</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">c</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="p">[</span><span class="n">c</span><span class="p">]</span> <span class="o">=</span> <span class="n">c</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">rhs_counts</span><span class="p">[</span><span class="n">c</span><span class="p">]</span> <span class="o">=</span> <span class="n">count</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">rhs_counts</span><span class="p">[</span><span class="n">c</span><span class="p">]</span> <span class="o">+=</span> <span class="n">count</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">count</span> <span class="o">+=</span> <span class="n">count</span></div>
+
+<div class="viewcode-block" id="ImprovedWeightedFLRG.weights"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.ismailefendi.ImprovedWeightedFLRG.weights">[docs]</a>    <span class="k">def</span> <span class="nf">weights</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">w</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">w</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="bp">self</span><span class="o">.</span><span class="n">rhs_counts</span><span class="p">[</span><span class="n">c</span><span class="p">]</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">count</span> <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="o">.</span><span class="n">keys</span><span class="p">()])</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">w</span></div>
+
+    <span class="k">def</span> <span class="fm">__str__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="n">tmp</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">LHS</span> <span class="o">+</span> <span class="s2">&quot; -&gt; &quot;</span>
+        <span class="n">tmp2</span> <span class="o">=</span> <span class="s2">&quot;&quot;</span>
+        <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="nb">sorted</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="o">.</span><span class="n">keys</span><span class="p">()):</span>
+            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">tmp2</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
+                <span class="n">tmp2</span> <span class="o">=</span> <span class="n">tmp2</span> <span class="o">+</span> <span class="s2">&quot;,&quot;</span>
+            <span class="n">tmp2</span> <span class="o">=</span> <span class="n">tmp2</span> <span class="o">+</span> <span class="n">c</span> <span class="o">+</span> <span class="s2">&quot;(&quot;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">rhs_counts</span><span class="p">[</span><span class="n">c</span><span class="p">]</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">count</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span> <span class="o">+</span> <span class="s2">&quot;)&quot;</span>
+        <span class="k">return</span> <span class="n">tmp</span> <span class="o">+</span> <span class="n">tmp2</span>
+
+    <span class="k">def</span> <span class="fm">__len__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="ImprovedWeightedFTS"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.ismailefendi.ImprovedWeightedFTS">[docs]</a><span class="k">class</span> <span class="nc">ImprovedWeightedFTS</span><span class="p">(</span><span class="n">fts</span><span class="o">.</span><span class="n">FTS</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;First Order Improved Weighted Fuzzy Time Series&quot;&quot;&quot;</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">(</span><span class="n">ImprovedWeightedFTS</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">order</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s2">&quot;IWFTS&quot;</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="s2">&quot;Improved Weighted FTS&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">detail</span> <span class="o">=</span> <span class="s2">&quot;Ismail &amp; Efendi&quot;</span>
+
+<div class="viewcode-block" id="ImprovedWeightedFTS.generate_flrg"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.ismailefendi.ImprovedWeightedFTS.generate_flrg">[docs]</a>    <span class="k">def</span> <span class="nf">generate_flrg</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">flrs</span><span class="p">):</span>
+        <span class="k">for</span> <span class="n">flr</span> <span class="ow">in</span> <span class="n">flrs</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">flr</span><span class="o">.</span><span class="n">LHS</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">flr</span><span class="o">.</span><span class="n">LHS</span><span class="p">]</span><span class="o">.</span><span class="n">append_rhs</span><span class="p">(</span><span class="n">flr</span><span class="o">.</span><span class="n">RHS</span><span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">flr</span><span class="o">.</span><span class="n">LHS</span><span class="p">]</span> <span class="o">=</span> <span class="n">ImprovedWeightedFLRG</span><span class="p">(</span><span class="n">flr</span><span class="o">.</span><span class="n">LHS</span><span class="p">)</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">flr</span><span class="o">.</span><span class="n">LHS</span><span class="p">]</span><span class="o">.</span><span class="n">append_rhs</span><span class="p">(</span><span class="n">flr</span><span class="o">.</span><span class="n">RHS</span><span class="p">)</span></div>
+
+<div class="viewcode-block" id="ImprovedWeightedFTS.train"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.ismailefendi.ImprovedWeightedFTS.train">[docs]</a>    <span class="k">def</span> <span class="nf">train</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ndata</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+
+        <span class="n">tmpdata</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">fuzzyfy</span><span class="p">(</span><span class="n">ndata</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s1">&#39;maximum&#39;</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s1">&#39;sets&#39;</span><span class="p">)</span>
+        <span class="n">flrs</span> <span class="o">=</span> <span class="n">FLR</span><span class="o">.</span><span class="n">generate_recurrent_flrs</span><span class="p">(</span><span class="n">tmpdata</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">generate_flrg</span><span class="p">(</span><span class="n">flrs</span><span class="p">)</span></div>
+
+<div class="viewcode-block" id="ImprovedWeightedFTS.forecast"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.ismailefendi.ImprovedWeightedFTS.forecast">[docs]</a>    <span class="k">def</span> <span class="nf">forecast</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ndata</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+
+        <span class="n">explain</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;explain&#39;</span><span class="p">,</span> <span class="kc">False</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">ordered_sets</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">ordered_sets</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">ordered_sets</span> <span class="o">=</span> <span class="n">FuzzySet</span><span class="o">.</span><span class="n">set_ordered</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">)</span>
+
+        <span class="n">ndata</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">ndata</span><span class="p">)</span>
+
+        <span class="n">l</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">ndata</span><span class="p">)</span> <span class="k">if</span> <span class="ow">not</span> <span class="n">explain</span> <span class="k">else</span> <span class="mi">1</span>
+
+        <span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
+
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">l</span><span class="p">):</span>
+
+            <span class="n">actual</span> <span class="o">=</span> <span class="n">FuzzySet</span><span class="o">.</span><span class="n">get_maximum_membership_fuzzyset</span><span class="p">(</span><span class="n">ndata</span><span class="p">[</span><span class="n">k</span><span class="p">],</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">,</span> <span class="n">ordered_sets</span><span class="p">)</span>
+
+            <span class="k">if</span> <span class="n">explain</span><span class="p">:</span>
+                <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Fuzzyfication:</span><span class="se">\n\n</span><span class="s2"> </span><span class="si">{}</span><span class="s2"> -&gt; </span><span class="si">{}</span><span class="s2"> </span><span class="se">\n</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">ndata</span><span class="p">[</span><span class="n">k</span><span class="p">],</span> <span class="n">actual</span><span class="o">.</span><span class="n">name</span><span class="p">))</span>
+
+            <span class="k">if</span> <span class="n">actual</span><span class="o">.</span><span class="n">name</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">:</span>
+                <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">actual</span><span class="o">.</span><span class="n">centroid</span><span class="p">)</span>
+
+                <span class="k">if</span> <span class="n">explain</span><span class="p">:</span>
+                    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Rules:</span><span class="se">\n\n</span><span class="s2"> </span><span class="si">{}</span><span class="s2"> -&gt; </span><span class="si">{}</span><span class="s2"> (Naïve)</span><span class="se">\t</span><span class="s2"> Midpoint: </span><span class="si">{}</span><span class="s2">  </span><span class="se">\n\n</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">actual</span><span class="o">.</span><span class="n">name</span><span class="p">,</span> <span class="n">actual</span><span class="o">.</span><span class="n">name</span><span class="p">,</span><span class="n">actual</span><span class="o">.</span><span class="n">centroid</span><span class="p">))</span>
+
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">flrg</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">actual</span><span class="o">.</span><span class="n">name</span><span class="p">]</span>
+                <span class="n">mp</span> <span class="o">=</span> <span class="n">flrg</span><span class="o">.</span><span class="n">get_midpoints</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">)</span>
+
+                <span class="n">final</span> <span class="o">=</span> <span class="n">mp</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">flrg</span><span class="o">.</span><span class="n">weights</span><span class="p">())</span>
+
+                <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">final</span><span class="p">)</span>
+
+                <span class="k">if</span> <span class="n">explain</span><span class="p">:</span>
+                    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Rules:</span><span class="se">\n\n</span><span class="s2"> </span><span class="si">{}</span><span class="s2"> </span><span class="se">\n\n</span><span class="s2"> &quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">flrg</span><span class="p">)))</span>
+                    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Midpoints: </span><span class="se">\n\n</span><span class="s2"> </span><span class="si">{}</span><span class="se">\n\n</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">mp</span><span class="p">))</span>
+
+                    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Deffuzyfied value: </span><span class="si">{}</span><span class="s2"> </span><span class="se">\n</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">final</span><span class="p">))</span>
+
+        <span class="k">return</span> <span class="n">ret</span></div></div>
+</pre></div>
+
+            <div class="clearer"></div>
+          </div>
+        </div>
+      </div>
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+            
+  <h1>Source code for pyFTS.models.multivariate.cmvfts</h1><div class="highlight"><pre>
+<span></span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+<span class="kn">from</span> <span class="nn">pyFTS.common</span> <span class="kn">import</span> <span class="n">FuzzySet</span><span class="p">,</span> <span class="n">FLR</span><span class="p">,</span> <span class="n">fts</span><span class="p">,</span> <span class="n">flrg</span>
+<span class="kn">from</span> <span class="nn">pyFTS.models</span> <span class="kn">import</span> <span class="n">hofts</span>
+<span class="kn">from</span> <span class="nn">pyFTS.models.multivariate</span> <span class="kn">import</span> <span class="n">mvfts</span><span class="p">,</span> <span class="n">grid</span><span class="p">,</span> <span class="n">common</span>
+<span class="kn">from</span> <span class="nn">types</span> <span class="kn">import</span> <span class="n">LambdaType</span>
+
+
+<div class="viewcode-block" id="ClusteredMVFTS"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.cmvfts.ClusteredMVFTS">[docs]</a><span class="k">class</span> <span class="nc">ClusteredMVFTS</span><span class="p">(</span><span class="n">mvfts</span><span class="o">.</span><span class="n">MVFTS</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Meta model for high order, clustered multivariate FTS</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">(</span><span class="n">ClusteredMVFTS</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">fts_method</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;fts_method&#39;</span><span class="p">,</span> <span class="n">hofts</span><span class="o">.</span><span class="n">WeightedHighOrderFTS</span><span class="p">)</span>
+        <span class="sd">&quot;&quot;&quot;The FTS method to be called when a new model is build&quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">fts_params</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;fts_params&#39;</span><span class="p">,</span> <span class="p">{})</span>
+        <span class="sd">&quot;&quot;&quot;The FTS method specific parameters&quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">model</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="sd">&quot;&quot;&quot;The most recent trained model&quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">knn</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;knn&#39;</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">is_high_order</span> <span class="o">=</span> <span class="kc">True</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">is_clustered</span> <span class="o">=</span> <span class="kc">True</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">order</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;order&quot;</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">lags</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;lags&quot;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">alpha_cut</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;alpha_cut&#39;</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">)</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">shortname</span> <span class="o">=</span> <span class="s2">&quot;ClusteredMVFTS&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="s2">&quot;Clustered Multivariate FTS&quot;</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">pre_fuzzyfy</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;pre_fuzzyfy&#39;</span><span class="p">,</span> <span class="kc">True</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">fuzzyfy_mode</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;fuzzyfy_mode&#39;</span><span class="p">,</span> <span class="s1">&#39;sets&#39;</span><span class="p">)</span>
+
+<div class="viewcode-block" id="ClusteredMVFTS.fuzzyfy"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.cmvfts.ClusteredMVFTS.fuzzyfy">[docs]</a>    <span class="k">def</span> <span class="nf">fuzzyfy</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span><span class="n">data</span><span class="p">):</span>
+        <span class="n">ndata</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="k">for</span> <span class="n">index</span><span class="p">,</span> <span class="n">row</span> <span class="ow">in</span> <span class="n">data</span><span class="o">.</span><span class="n">iterrows</span><span class="p">()</span> <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">)</span> <span class="k">else</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">data</span><span class="p">):</span>
+            <span class="n">data_point</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">format_data</span><span class="p">(</span><span class="n">row</span><span class="p">)</span>
+            <span class="n">ndata</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">fuzzyfy</span><span class="p">(</span><span class="n">data_point</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">fuzzyfy_mode</span><span class="p">))</span>
+
+        <span class="k">return</span> <span class="n">ndata</span></div>
+
+<div class="viewcode-block" id="ClusteredMVFTS.train"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.cmvfts.ClusteredMVFTS.train">[docs]</a>    <span class="k">def</span> <span class="nf">train</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">fts_params</span><span class="p">[</span><span class="s1">&#39;order&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">model</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">fts_method</span><span class="p">(</span><span class="n">partitioner</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="p">,</span> <span class="o">**</span><span class="bp">self</span><span class="o">.</span><span class="n">fts_params</span><span class="p">)</span>
+
+        <span class="n">ndata</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">apply_transformations</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+
+        <span class="n">ndata</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">check_data</span><span class="p">(</span><span class="n">ndata</span><span class="p">)</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">train</span><span class="p">(</span><span class="n">ndata</span><span class="p">,</span> <span class="n">fuzzyfied</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">pre_fuzzyfy</span><span class="p">)</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">prune</span><span class="p">()</span></div>
+
+<div class="viewcode-block" id="ClusteredMVFTS.check_data"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.cmvfts.ClusteredMVFTS.check_data">[docs]</a>    <span class="k">def</span> <span class="nf">check_data</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">):</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">pre_fuzzyfy</span><span class="p">:</span>
+            <span class="n">ndata</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">fuzzyfy</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">ndata</span> <span class="o">=</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">format_data</span><span class="p">(</span><span class="n">k</span><span class="p">)</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">data</span><span class="o">.</span><span class="n">to_dict</span><span class="p">(</span><span class="s1">&#39;records&#39;</span><span class="p">)]</span>
+
+        <span class="k">return</span> <span class="n">ndata</span></div>
+
+<div class="viewcode-block" id="ClusteredMVFTS.forecast"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.cmvfts.ClusteredMVFTS.forecast">[docs]</a>    <span class="k">def</span> <span class="nf">forecast</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+
+        <span class="n">ndata1</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">apply_transformations</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+
+        <span class="n">ndata</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">check_data</span><span class="p">(</span><span class="n">ndata1</span><span class="p">)</span>
+
+        <span class="n">pre_fuzz</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;pre_fuzzyfy&#39;</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">pre_fuzzyfy</span><span class="p">)</span>
+
+        <span class="n">ret</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">forecast</span><span class="p">(</span><span class="n">ndata</span><span class="p">,</span> <span class="n">fuzzyfied</span><span class="o">=</span><span class="n">pre_fuzz</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+
+        <span class="n">ret</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">target_variable</span><span class="o">.</span><span class="n">apply_inverse_transformations</span><span class="p">(</span><span class="n">ret</span><span class="p">,</span>
+                                                                 <span class="n">params</span><span class="o">=</span><span class="n">data</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">target_variable</span><span class="o">.</span><span class="n">data_label</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">ret</span></div>
+
+<div class="viewcode-block" id="ClusteredMVFTS.forecast_interval"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.cmvfts.ClusteredMVFTS.forecast_interval">[docs]</a>    <span class="k">def</span> <span class="nf">forecast_interval</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+
+        <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">has_interval_forecasting</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span><span class="s2">&quot;The internal method does not support interval forecasting!&quot;</span><span class="p">)</span>
+
+        <span class="n">data</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">check_data</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+
+        <span class="n">pre_fuzz</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;pre_fuzzyfy&#39;</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">pre_fuzzyfy</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">forecast_interval</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">fuzzyfied</span><span class="o">=</span><span class="n">pre_fuzz</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>
+
+
+
+<div class="viewcode-block" id="ClusteredMVFTS.forecast_distribution"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.cmvfts.ClusteredMVFTS.forecast_distribution">[docs]</a>    <span class="k">def</span> <span class="nf">forecast_distribution</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+
+        <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">has_probability_forecasting</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span><span class="s2">&quot;The internal method does not support probabilistic forecasting!&quot;</span><span class="p">)</span>
+
+        <span class="n">data</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">check_data</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+
+        <span class="n">pre_fuzz</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;pre_fuzzyfy&#39;</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">pre_fuzzyfy</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">forecast_distribution</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">fuzzyfied</span><span class="o">=</span><span class="n">pre_fuzz</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>
+
+<div class="viewcode-block" id="ClusteredMVFTS.forecast_ahead_distribution"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.cmvfts.ClusteredMVFTS.forecast_ahead_distribution">[docs]</a>    <span class="k">def</span> <span class="nf">forecast_ahead_distribution</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">steps</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+
+        <span class="n">generators</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;generators&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">generators</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span><span class="s1">&#39;You must provide parameter </span><span class="se">\&#39;</span><span class="s1">generators</span><span class="se">\&#39;</span><span class="s1">! generators is a dict where the keys&#39;</span> <span class="o">+</span>
+                            <span class="s1">&#39; are the dataframe column names (except the target_variable) and the values are &#39;</span> <span class="o">+</span>
+                            <span class="s1">&#39;lambda functions that accept one value (the actual value of the variable) &#39;</span>
+                            <span class="s1">&#39; and return the next value or trained FTS models that accept the actual values and &#39;</span>
+                            <span class="s1">&#39;forecast new ones.&#39;</span><span class="p">)</span>
+
+        <span class="n">ndata</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">apply_transformations</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+
+        <span class="n">start</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;start_at&#39;</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
+
+        <span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="n">sample</span> <span class="o">=</span> <span class="n">ndata</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="n">start</span><span class="p">:</span> <span class="n">start</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">]</span>
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">steps</span><span class="p">):</span>
+            <span class="n">tmp</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">forecast_distribution</span><span class="p">(</span><span class="n">sample</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">:],</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
+
+            <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">tmp</span><span class="p">)</span>
+
+            <span class="n">new_data_point</span> <span class="o">=</span> <span class="p">{}</span>
+
+            <span class="k">for</span> <span class="n">data_label</span> <span class="ow">in</span> <span class="n">generators</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
+                <span class="k">if</span> <span class="n">data_label</span> <span class="o">!=</span> <span class="bp">self</span><span class="o">.</span><span class="n">target_variable</span><span class="o">.</span><span class="n">data_label</span><span class="p">:</span>
+                    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">generators</span><span class="p">[</span><span class="n">data_label</span><span class="p">],</span> <span class="n">LambdaType</span><span class="p">):</span>
+                        <span class="n">last_data_point</span> <span class="o">=</span> <span class="n">sample</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
+                        <span class="n">new_data_point</span><span class="p">[</span><span class="n">data_label</span><span class="p">]</span> <span class="o">=</span> <span class="n">generators</span><span class="p">[</span><span class="n">data_label</span><span class="p">](</span><span class="n">last_data_point</span><span class="p">[</span><span class="n">data_label</span><span class="p">])</span>
+
+                    <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">generators</span><span class="p">[</span><span class="n">data_label</span><span class="p">],</span> <span class="n">fts</span><span class="o">.</span><span class="n">FTS</span><span class="p">):</span>
+                        <span class="n">gen_model</span> <span class="o">=</span> <span class="n">generators</span><span class="p">[</span><span class="n">data_label</span><span class="p">]</span>
+                        <span class="n">last_data_point</span> <span class="o">=</span> <span class="n">sample</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="o">-</span><span class="n">gen_model</span><span class="o">.</span><span class="n">order</span><span class="p">:]</span>
+
+                        <span class="k">if</span> <span class="ow">not</span> <span class="n">gen_model</span><span class="o">.</span><span class="n">is_multivariate</span><span class="p">:</span>
+                            <span class="n">last_data_point</span> <span class="o">=</span> <span class="n">last_data_point</span><span class="p">[</span><span class="n">data_label</span><span class="p">]</span><span class="o">.</span><span class="n">values</span>
+
+                        <span class="n">new_data_point</span><span class="p">[</span><span class="n">data_label</span><span class="p">]</span> <span class="o">=</span> <span class="n">gen_model</span><span class="o">.</span><span class="n">forecast</span><span class="p">(</span><span class="n">last_data_point</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
+
+            <span class="n">new_data_point</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">target_variable</span><span class="o">.</span><span class="n">data_label</span><span class="p">]</span> <span class="o">=</span> <span class="n">tmp</span><span class="o">.</span><span class="n">expected_value</span><span class="p">()</span>
+
+            <span class="n">sample</span> <span class="o">=</span> <span class="n">sample</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">new_data_point</span><span class="p">,</span> <span class="n">ignore_index</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">ret</span><span class="p">[</span><span class="o">-</span><span class="n">steps</span><span class="p">:]</span></div>
+
+<div class="viewcode-block" id="ClusteredMVFTS.forecast_multivariate"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.cmvfts.ClusteredMVFTS.forecast_multivariate">[docs]</a>    <span class="k">def</span> <span class="nf">forecast_multivariate</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+
+        <span class="n">ndata</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">check_data</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+
+        <span class="n">generators</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;generators&#39;</span><span class="p">,</span> <span class="p">{})</span>
+
+        <span class="n">already_processed_cols</span> <span class="o">=</span> <span class="p">[]</span>
+
+        <span class="n">ret</span> <span class="o">=</span> <span class="p">{}</span>
+
+        <span class="n">ret</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">target_variable</span><span class="o">.</span><span class="n">data_label</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">forecast</span><span class="p">(</span><span class="n">ndata</span><span class="p">,</span> <span class="n">fuzzyfied</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">pre_fuzzyfy</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+
+        <span class="k">for</span> <span class="n">var</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">explanatory_variables</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">var</span><span class="o">.</span><span class="n">data_label</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">already_processed_cols</span><span class="p">:</span>
+                <span class="k">if</span> <span class="n">var</span><span class="o">.</span><span class="n">data_label</span> <span class="ow">in</span> <span class="n">generators</span><span class="p">:</span>
+                    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">generators</span><span class="p">[</span><span class="n">var</span><span class="o">.</span><span class="n">data_label</span><span class="p">],</span> <span class="n">LambdaType</span><span class="p">):</span>
+                        <span class="n">fx</span> <span class="o">=</span> <span class="n">generators</span><span class="p">[</span><span class="n">var</span><span class="o">.</span><span class="n">data_label</span><span class="p">]</span>
+                        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="n">var</span><span class="o">.</span><span class="n">data_label</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">)</span> <span class="o">&gt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">:</span>
+                            <span class="n">ret</span><span class="p">[</span><span class="n">var</span><span class="o">.</span><span class="n">data_label</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="n">fx</span><span class="p">(</span><span class="n">k</span><span class="p">)</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">data</span><span class="p">[</span><span class="n">var</span><span class="o">.</span><span class="n">data_label</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">:]]</span>
+                        <span class="k">else</span><span class="p">:</span>
+                            <span class="n">ret</span><span class="p">[</span><span class="n">var</span><span class="o">.</span><span class="n">data_label</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="n">fx</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="n">var</span><span class="o">.</span><span class="n">data_label</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])]</span>
+                    <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">generators</span><span class="p">[</span><span class="n">var</span><span class="o">.</span><span class="n">data_label</span><span class="p">],</span> <span class="n">fts</span><span class="o">.</span><span class="n">FTS</span><span class="p">):</span>
+                        <span class="n">model</span> <span class="o">=</span> <span class="n">generators</span><span class="p">[</span><span class="n">var</span><span class="o">.</span><span class="n">data_label</span><span class="p">]</span>
+                        <span class="k">if</span> <span class="ow">not</span> <span class="n">model</span><span class="o">.</span><span class="n">is_multivariate</span><span class="p">:</span>
+                            <span class="n">ret</span><span class="p">[</span><span class="n">var</span><span class="o">.</span><span class="n">data_label</span><span class="p">]</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">forecast</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="n">var</span><span class="o">.</span><span class="n">data_label</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">)</span>
+                        <span class="k">else</span><span class="p">:</span>
+                            <span class="n">ret</span><span class="p">[</span><span class="n">var</span><span class="o">.</span><span class="n">data_label</span><span class="p">]</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">forecast</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+                <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">target_variable</span><span class="o">.</span><span class="n">name</span> <span class="o">!=</span> <span class="n">var</span><span class="o">.</span><span class="n">name</span><span class="p">:</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">target_variable</span> <span class="o">=</span> <span class="n">var</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">change_target_variable</span><span class="p">(</span><span class="n">var</span><span class="p">)</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">partitioner</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">reset_calculated_values</span><span class="p">()</span>
+                    <span class="n">ret</span><span class="p">[</span><span class="n">var</span><span class="o">.</span><span class="n">data_label</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">forecast</span><span class="p">(</span><span class="n">ndata</span><span class="p">,</span> <span class="n">fuzzyfied</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">pre_fuzzyfy</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+
+                <span class="n">already_processed_cols</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">var</span><span class="o">.</span><span class="n">data_label</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">ret</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="n">ret</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span></div>
+
+<div class="viewcode-block" id="ClusteredMVFTS.forecast_ahead_multivariate"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.cmvfts.ClusteredMVFTS.forecast_ahead_multivariate">[docs]</a>    <span class="k">def</span> <span class="nf">forecast_ahead_multivariate</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">steps</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+
+        <span class="n">ndata</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">apply_transformations</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+
+        <span class="n">start</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;start_at&#39;</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
+
+        <span class="n">ret</span> <span class="o">=</span> <span class="n">ndata</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="n">start</span><span class="p">:</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="o">+</span><span class="n">start</span><span class="p">]</span>
+
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">steps</span><span class="p">):</span>
+            <span class="n">sample</span> <span class="o">=</span> <span class="n">ret</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="n">k</span><span class="p">:</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="o">+</span><span class="n">k</span><span class="p">]</span>
+            <span class="n">tmp</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">forecast_multivariate</span><span class="p">(</span><span class="n">sample</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+            <span class="n">ret</span> <span class="o">=</span> <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">tmp</span><span class="p">,</span> <span class="n">ignore_index</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">ret</span></div>
+
+    <span class="k">def</span> <span class="fm">__str__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="sd">&quot;&quot;&quot;String representation of the model&quot;&quot;&quot;</span>
+        <span class="k">return</span> <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="fm">__len__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        The length (number of rules) of the model</span>
+
+<span class="sd">        :return: number of rules</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">return</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="p">)</span></div>
+
+</pre></div>
+
+            <div class="clearer"></div>
+          </div>
+        </div>
+      </div>
+      <div class="sphinxsidebar" role="navigation" aria-label="main navigation">
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+        <li class="nav-item nav-item-this"><a href="">pyFTS.models.multivariate.cmvfts</a></li> 
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+        &#169; Copyright 2018, Machine Intelligence and Data Science Laboratory - UFMG - Brazil.
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diff --git a/docs/build/html/_modules/pyFTS/models/multivariate/granular.html b/docs/build/html/_modules/pyFTS/models/multivariate/granular.html
new file mode 100644
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+  <h1>Source code for pyFTS.models.multivariate.granular</h1><div class="highlight"><pre>
+<span></span><span class="kn">from</span> <span class="nn">pyFTS.models.multivariate</span> <span class="kn">import</span> <span class="n">cmvfts</span><span class="p">,</span> <span class="n">grid</span>
+<span class="kn">from</span> <span class="nn">pyFTS.models</span> <span class="kn">import</span> <span class="n">hofts</span>
+
+
+<div class="viewcode-block" id="GranularWMVFTS"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.granular.GranularWMVFTS">[docs]</a><span class="k">class</span> <span class="nc">GranularWMVFTS</span><span class="p">(</span><span class="n">cmvfts</span><span class="o">.</span><span class="n">ClusteredMVFTS</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Granular multivariate weighted high order FTS</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">(</span><span class="n">GranularWMVFTS</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">fts_method</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;fts_method&#39;</span><span class="p">,</span> <span class="n">hofts</span><span class="o">.</span><span class="n">WeightedHighOrderFTS</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">model</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="sd">&quot;&quot;&quot;The most recent trained model&quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">knn</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;knn&#39;</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">order</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;order&quot;</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">shortname</span> <span class="o">=</span> <span class="s2">&quot;FIG-FTS&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="s2">&quot;Fuzzy Information Granular FTS&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">mode</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;mode&#39;</span><span class="p">,</span><span class="s1">&#39;sets&#39;</span><span class="p">)</span>
+
+<div class="viewcode-block" id="GranularWMVFTS.train"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.granular.GranularWMVFTS.train">[docs]</a>    <span class="k">def</span> <span class="nf">train</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span> <span class="o">=</span> <span class="n">grid</span><span class="o">.</span><span class="n">IncrementalGridCluster</span><span class="p">(</span>
+            <span class="n">explanatory_variables</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">explanatory_variables</span><span class="p">,</span>
+            <span class="n">target_variable</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">target_variable</span><span class="p">,</span>
+            <span class="n">neighbors</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">knn</span><span class="p">)</span>
+        <span class="nb">super</span><span class="p">(</span><span class="n">GranularWMVFTS</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="n">train</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">mode</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div></div>
+
+</pre></div>
+
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diff --git a/docs/build/html/_modules/pyFTS/models/multivariate/mvfts.html b/docs/build/html/_modules/pyFTS/models/multivariate/mvfts.html
new file mode 100644
index 0000000..2eaec27
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@@ -0,0 +1,427 @@
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+  <h1>Source code for pyFTS.models.multivariate.mvfts</h1><div class="highlight"><pre>
+<span></span><span class="kn">from</span> <span class="nn">pyFTS.common</span> <span class="kn">import</span> <span class="n">fts</span><span class="p">,</span> <span class="n">FuzzySet</span><span class="p">,</span> <span class="n">FLR</span><span class="p">,</span> <span class="n">Membership</span>
+<span class="kn">from</span> <span class="nn">pyFTS.partitioners</span> <span class="kn">import</span> <span class="n">Grid</span>
+<span class="kn">from</span> <span class="nn">pyFTS.models.multivariate</span> <span class="kn">import</span> <span class="n">FLR</span> <span class="k">as</span> <span class="n">MVFLR</span><span class="p">,</span> <span class="n">common</span><span class="p">,</span> <span class="n">flrg</span> <span class="k">as</span> <span class="n">mvflrg</span>
+<span class="kn">from</span> <span class="nn">itertools</span> <span class="kn">import</span> <span class="n">product</span>
+<span class="kn">from</span> <span class="nn">types</span> <span class="kn">import</span> <span class="n">LambdaType</span>
+<span class="kn">from</span> <span class="nn">copy</span> <span class="kn">import</span> <span class="n">deepcopy</span>
+
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+
+
+<div class="viewcode-block" id="product_dict"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.mvfts.product_dict">[docs]</a><span class="k">def</span> <span class="nf">product_dict</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Code by Seth Johnson</span>
+<span class="sd">    :param kwargs:</span>
+<span class="sd">    :return:</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">keys</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">keys</span><span class="p">()</span>
+    <span class="n">vals</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">values</span><span class="p">()</span>
+    <span class="k">for</span> <span class="n">instance</span> <span class="ow">in</span> <span class="n">product</span><span class="p">(</span><span class="o">*</span><span class="n">vals</span><span class="p">):</span>
+        <span class="k">yield</span> <span class="nb">dict</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="n">keys</span><span class="p">,</span> <span class="n">instance</span><span class="p">))</span></div>
+
+
+<div class="viewcode-block" id="MVFTS"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.mvfts.MVFTS">[docs]</a><span class="k">class</span> <span class="nc">MVFTS</span><span class="p">(</span><span class="n">fts</span><span class="o">.</span><span class="n">FTS</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Multivariate extension of Chen&#39;s ConventionalFTS method</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">(</span><span class="n">MVFTS</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">explanatory_variables</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;explanatory_variables&#39;</span><span class="p">,[])</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">target_variable</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;target_variable&#39;</span><span class="p">,</span><span class="kc">None</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span> <span class="o">=</span> <span class="p">{}</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">is_multivariate</span> <span class="o">=</span> <span class="kc">True</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">shortname</span> <span class="o">=</span> <span class="s2">&quot;MVFTS&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="s2">&quot;Multivariate FTS&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">uod_clip</span> <span class="o">=</span> <span class="kc">False</span>
+
+<div class="viewcode-block" id="MVFTS.append_transformation"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.mvfts.MVFTS.append_transformation">[docs]</a>    <span class="k">def</span> <span class="nf">append_transformation</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">transformation</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="n">transformation</span><span class="o">.</span><span class="n">is_multivariate</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span><span class="s1">&#39;The transformation is not multivariate&#39;</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">transformations</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">transformation</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">transformations_param</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">kwargs</span><span class="p">)</span></div>
+
+<div class="viewcode-block" id="MVFTS.append_variable"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.mvfts.MVFTS.append_variable">[docs]</a>    <span class="k">def</span> <span class="nf">append_variable</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">var</span><span class="p">):</span>
+        <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Append a new endogenous variable to the model</span>
+
+<span class="sd">        :param var: variable object</span>
+<span class="sd">        :return:</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">explanatory_variables</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">var</span><span class="p">)</span></div>
+
+<div class="viewcode-block" id="MVFTS.format_data"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.mvfts.MVFTS.format_data">[docs]</a>    <span class="k">def</span> <span class="nf">format_data</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">):</span>
+        <span class="n">ndata</span> <span class="o">=</span> <span class="p">{}</span>
+        <span class="k">for</span> <span class="n">var</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">explanatory_variables</span><span class="p">:</span>
+            <span class="n">ndata</span><span class="p">[</span><span class="n">var</span><span class="o">.</span><span class="n">name</span><span class="p">]</span> <span class="o">=</span> <span class="n">var</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">extractor</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="n">var</span><span class="o">.</span><span class="n">data_label</span><span class="p">])</span>
+
+        <span class="k">return</span> <span class="n">ndata</span></div>
+
+<div class="viewcode-block" id="MVFTS.apply_transformations"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.mvfts.MVFTS.apply_transformations">[docs]</a>    <span class="k">def</span> <span class="nf">apply_transformations</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">params</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">updateUoD</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="n">ndata</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">copy</span><span class="p">(</span><span class="n">deep</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+        <span class="k">for</span> <span class="n">ct</span><span class="p">,</span> <span class="n">transformation</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">transformations</span><span class="p">):</span>
+            <span class="n">ndata</span> <span class="o">=</span> <span class="n">transformation</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">ndata</span><span class="p">,</span> <span class="o">**</span><span class="bp">self</span><span class="o">.</span><span class="n">transformations_param</span><span class="p">[</span><span class="n">ct</span><span class="p">])</span>
+
+        <span class="k">for</span> <span class="n">var</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">explanatory_variables</span><span class="p">:</span>
+            <span class="k">try</span><span class="p">:</span>
+                <span class="n">values</span> <span class="o">=</span> <span class="n">ndata</span><span class="p">[</span><span class="n">var</span><span class="o">.</span><span class="n">data_label</span><span class="p">]</span><span class="o">.</span><span class="n">values</span> <span class="c1">#if isinstance(ndata, pd.DataFrame) else ndata[var.data_label]</span>
+                <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">uod_clip</span> <span class="ow">and</span> <span class="n">var</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">type</span> <span class="o">==</span> <span class="s1">&#39;common&#39;</span><span class="p">:</span>
+                    <span class="n">ndata</span><span class="p">[</span><span class="n">var</span><span class="o">.</span><span class="n">data_label</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">clip</span><span class="p">(</span><span class="n">values</span><span class="p">,</span>
+                                                    <span class="n">var</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">min</span><span class="p">,</span> <span class="n">var</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">max</span><span class="p">)</span>
+
+                <span class="n">ndata</span><span class="p">[</span><span class="n">var</span><span class="o">.</span><span class="n">data_label</span><span class="p">]</span> <span class="o">=</span> <span class="n">var</span><span class="o">.</span><span class="n">apply_transformations</span><span class="p">(</span><span class="n">values</span><span class="p">)</span>
+            <span class="k">except</span><span class="p">:</span>
+                <span class="k">pass</span>
+
+        <span class="k">return</span> <span class="n">ndata</span></div>
+
+<div class="viewcode-block" id="MVFTS.generate_lhs_flrs"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.mvfts.MVFTS.generate_lhs_flrs">[docs]</a>    <span class="k">def</span> <span class="nf">generate_lhs_flrs</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">):</span>
+        <span class="n">flrs</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="n">lags</span> <span class="o">=</span> <span class="p">{}</span>
+        <span class="k">for</span> <span class="n">vc</span><span class="p">,</span> <span class="n">var</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">explanatory_variables</span><span class="p">):</span>
+            <span class="n">data_point</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="n">var</span><span class="o">.</span><span class="n">name</span><span class="p">]</span>
+            <span class="n">lags</span><span class="p">[</span><span class="n">var</span><span class="o">.</span><span class="n">name</span><span class="p">]</span> <span class="o">=</span> <span class="n">common</span><span class="o">.</span><span class="n">fuzzyfy_instance</span><span class="p">(</span><span class="n">data_point</span><span class="p">,</span> <span class="n">var</span><span class="p">,</span> <span class="n">tuples</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+
+        <span class="k">for</span> <span class="n">path</span> <span class="ow">in</span> <span class="n">product_dict</span><span class="p">(</span><span class="o">**</span><span class="n">lags</span><span class="p">):</span>
+            <span class="n">flr</span> <span class="o">=</span> <span class="n">MVFLR</span><span class="o">.</span><span class="n">FLR</span><span class="p">()</span>
+
+            <span class="n">flr</span><span class="o">.</span><span class="n">LHS</span> <span class="o">=</span> <span class="n">path</span>
+
+            <span class="c1">#for var, fset in path.items():</span>
+            <span class="c1">#    flr.set_lhs(var, fset)</span>
+
+            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">flr</span><span class="o">.</span><span class="n">LHS</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">explanatory_variables</span><span class="p">):</span>
+                <span class="n">flrs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">flr</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">flrs</span></div>
+
+<div class="viewcode-block" id="MVFTS.generate_flrs"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.mvfts.MVFTS.generate_flrs">[docs]</a>    <span class="k">def</span> <span class="nf">generate_flrs</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">):</span>
+        <span class="n">flrs</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="k">for</span> <span class="n">ct</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">index</span><span class="p">)):</span>
+            <span class="n">ix</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">index</span><span class="p">[</span><span class="n">ct</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
+            <span class="n">data_point</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">format_data</span><span class="p">(</span> <span class="n">data</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">ix</span><span class="p">]</span> <span class="p">)</span>
+
+            <span class="n">tmp_flrs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">generate_lhs_flrs</span><span class="p">(</span><span class="n">data_point</span><span class="p">)</span>
+
+            <span class="n">target_ix</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">index</span><span class="p">[</span><span class="n">ct</span><span class="p">]</span>
+            <span class="n">target_point</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">target_variable</span><span class="o">.</span><span class="n">data_label</span><span class="p">][</span><span class="n">target_ix</span><span class="p">]</span>
+            <span class="n">target</span> <span class="o">=</span> <span class="n">common</span><span class="o">.</span><span class="n">fuzzyfy_instance</span><span class="p">(</span><span class="n">target_point</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">target_variable</span><span class="p">)</span>
+
+            <span class="k">for</span> <span class="n">flr</span> <span class="ow">in</span> <span class="n">tmp_flrs</span><span class="p">:</span>
+                <span class="k">for</span> <span class="n">v</span><span class="p">,</span> <span class="n">s</span> <span class="ow">in</span> <span class="n">target</span><span class="p">:</span>
+                    <span class="n">new_flr</span> <span class="o">=</span> <span class="n">deepcopy</span><span class="p">(</span><span class="n">flr</span><span class="p">)</span>
+                    <span class="n">new_flr</span><span class="o">.</span><span class="n">set_rhs</span><span class="p">(</span><span class="n">s</span><span class="p">)</span>
+                    <span class="n">flrs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">new_flr</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">flrs</span></div>
+
+<div class="viewcode-block" id="MVFTS.generate_flrg"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.mvfts.MVFTS.generate_flrg">[docs]</a>    <span class="k">def</span> <span class="nf">generate_flrg</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">flrs</span><span class="p">):</span>
+        <span class="k">for</span> <span class="n">flr</span> <span class="ow">in</span> <span class="n">flrs</span><span class="p">:</span>
+            <span class="n">flrg</span> <span class="o">=</span> <span class="n">mvflrg</span><span class="o">.</span><span class="n">FLRG</span><span class="p">(</span><span class="n">lhs</span><span class="o">=</span><span class="n">flr</span><span class="o">.</span><span class="n">LHS</span><span class="p">)</span>
+
+            <span class="k">if</span> <span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()]</span> <span class="o">=</span> <span class="n">flrg</span>
+
+            <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()]</span><span class="o">.</span><span class="n">append_rhs</span><span class="p">(</span><span class="n">flr</span><span class="o">.</span><span class="n">RHS</span><span class="p">)</span></div>
+
+<div class="viewcode-block" id="MVFTS.train"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.mvfts.MVFTS.train">[docs]</a>    <span class="k">def</span> <span class="nf">train</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+
+        <span class="n">ndata</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">apply_transformations</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+
+        <span class="n">flrs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">generate_flrs</span><span class="p">(</span><span class="n">ndata</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">generate_flrg</span><span class="p">(</span><span class="n">flrs</span><span class="p">)</span></div>
+
+<div class="viewcode-block" id="MVFTS.forecast"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.mvfts.MVFTS.forecast">[docs]</a>    <span class="k">def</span> <span class="nf">forecast</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="n">ndata</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">apply_transformations</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+        <span class="n">c</span> <span class="o">=</span> <span class="mi">0</span>
+        <span class="k">for</span> <span class="n">index</span><span class="p">,</span> <span class="n">row</span> <span class="ow">in</span> <span class="n">ndata</span><span class="o">.</span><span class="n">iterrows</span><span class="p">()</span> <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">ndata</span><span class="p">,</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">)</span> <span class="k">else</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">ndata</span><span class="p">):</span>
+            <span class="n">data_point</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">format_data</span><span class="p">(</span><span class="n">row</span><span class="p">)</span>
+            <span class="n">flrs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">generate_lhs_flrs</span><span class="p">(</span><span class="n">data_point</span><span class="p">)</span>
+            <span class="n">mvs</span> <span class="o">=</span> <span class="p">[]</span>
+            <span class="n">mps</span> <span class="o">=</span> <span class="p">[]</span>
+            <span class="k">for</span> <span class="n">flr</span> <span class="ow">in</span> <span class="n">flrs</span><span class="p">:</span>
+                <span class="n">flrg</span> <span class="o">=</span> <span class="n">mvflrg</span><span class="o">.</span><span class="n">FLRG</span><span class="p">(</span><span class="n">lhs</span><span class="o">=</span><span class="n">flr</span><span class="o">.</span><span class="n">LHS</span><span class="p">)</span>
+                <span class="k">if</span> <span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">:</span>
+                    <span class="c1">#Naïve approach is applied when no rules were found</span>
+                    <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">target_variable</span><span class="o">.</span><span class="n">name</span> <span class="ow">in</span> <span class="n">flrg</span><span class="o">.</span><span class="n">LHS</span><span class="p">:</span>
+                        <span class="n">fs</span> <span class="o">=</span> <span class="n">flrg</span><span class="o">.</span><span class="n">LHS</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">target_variable</span><span class="o">.</span><span class="n">name</span><span class="p">]</span>
+                        <span class="n">fset</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">target_variable</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="n">fs</span><span class="p">]</span>
+                        <span class="n">mp</span> <span class="o">=</span> <span class="n">fset</span><span class="o">.</span><span class="n">centroid</span>
+                        <span class="n">mv</span> <span class="o">=</span> <span class="n">fset</span><span class="o">.</span><span class="n">membership</span><span class="p">(</span><span class="n">data_point</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">target_variable</span><span class="o">.</span><span class="n">name</span><span class="p">])</span>
+                        <span class="n">mvs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">mv</span><span class="p">)</span>
+                        <span class="n">mps</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">mp</span><span class="p">)</span>
+                    <span class="k">else</span><span class="p">:</span>
+                        <span class="n">mvs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="mf">0.</span><span class="p">)</span>
+                        <span class="n">mps</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="mf">0.</span><span class="p">)</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">_flrg</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()]</span>
+                    <span class="n">mvs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">_flrg</span><span class="o">.</span><span class="n">get_membership</span><span class="p">(</span><span class="n">data_point</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">explanatory_variables</span><span class="p">))</span>
+                    <span class="n">mps</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">_flrg</span><span class="o">.</span><span class="n">get_midpoint</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">target_variable</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">))</span>
+
+            <span class="n">mv</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">mvs</span><span class="p">)</span>
+            <span class="n">mp</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">mps</span><span class="p">)</span>
+
+            <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">mv</span><span class="p">,</span><span class="n">mp</span><span class="o">.</span><span class="n">T</span><span class="p">)</span><span class="o">/</span><span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">(</span><span class="n">mv</span><span class="p">))</span>
+
+        <span class="n">ret</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">target_variable</span><span class="o">.</span><span class="n">apply_inverse_transformations</span><span class="p">(</span><span class="n">ret</span><span class="p">,</span>
+                                                           <span class="n">params</span><span class="o">=</span><span class="n">data</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">target_variable</span><span class="o">.</span><span class="n">data_label</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">ret</span></div>
+
+<div class="viewcode-block" id="MVFTS.forecast_ahead"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.mvfts.MVFTS.forecast_ahead">[docs]</a>    <span class="k">def</span> <span class="nf">forecast_ahead</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">steps</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="n">generators</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;generators&#39;</span><span class="p">,</span><span class="kc">None</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">generators</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span><span class="s1">&#39;You must provide parameter </span><span class="se">\&#39;</span><span class="s1">generators</span><span class="se">\&#39;</span><span class="s1">! generators is a dict where the keys&#39;</span> <span class="o">+</span>
+                            <span class="s1">&#39; are the dataframe column names (except the target_variable) and the values are &#39;</span> <span class="o">+</span>
+                            <span class="s1">&#39;lambda functions that accept one value (the actual value of the variable) &#39;</span>
+                            <span class="s1">&#39; and return the next value or trained FTS models that accept the actual values and &#39;</span>
+                            <span class="s1">&#39;forecast new ones.&#39;</span><span class="p">)</span>
+
+        <span class="n">ndata</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">apply_transformations</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+
+        <span class="n">start</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;start_at&#39;</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
+
+        <span class="n">ndata</span> <span class="o">=</span> <span class="n">ndata</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="n">start</span><span class="p">:</span> <span class="n">start</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">]</span>
+        <span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">steps</span><span class="p">):</span>
+            <span class="n">sample</span> <span class="o">=</span> <span class="n">ndata</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">:]</span>
+            <span class="n">tmp</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">forecast</span><span class="p">(</span><span class="n">sample</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+
+            <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">tmp</span><span class="p">,</span> <span class="p">(</span><span class="nb">list</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">)):</span>
+                <span class="n">tmp</span> <span class="o">=</span> <span class="n">tmp</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
+
+            <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">tmp</span><span class="p">)</span>
+
+            <span class="n">new_data_point</span> <span class="o">=</span> <span class="p">{}</span>
+
+            <span class="k">for</span> <span class="n">data_label</span> <span class="ow">in</span> <span class="n">generators</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
+                <span class="k">if</span> <span class="n">data_label</span> <span class="o">!=</span> <span class="bp">self</span><span class="o">.</span><span class="n">target_variable</span><span class="o">.</span><span class="n">data_label</span><span class="p">:</span>
+                    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">generators</span><span class="p">[</span><span class="n">data_label</span><span class="p">],</span> <span class="n">LambdaType</span><span class="p">):</span>
+                        <span class="n">last_data_point</span> <span class="o">=</span> <span class="n">ndata</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">ndata</span><span class="o">.</span><span class="n">index</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]]</span>
+                        <span class="n">new_data_point</span><span class="p">[</span><span class="n">data_label</span><span class="p">]</span> <span class="o">=</span> <span class="n">generators</span><span class="p">[</span><span class="n">data_label</span><span class="p">](</span><span class="n">last_data_point</span><span class="p">[</span><span class="n">data_label</span><span class="p">])</span>
+                    <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">generators</span><span class="p">[</span><span class="n">data_label</span><span class="p">],</span> <span class="n">fts</span><span class="o">.</span><span class="n">FTS</span><span class="p">):</span>
+                        <span class="n">gen_model</span> <span class="o">=</span> <span class="n">generators</span><span class="p">[</span><span class="n">data_label</span><span class="p">]</span>
+                        <span class="n">last_data_point</span> <span class="o">=</span> <span class="n">sample</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="o">-</span><span class="n">gen_model</span><span class="o">.</span><span class="n">order</span><span class="p">:]</span>
+
+                        <span class="k">if</span> <span class="ow">not</span> <span class="n">gen_model</span><span class="o">.</span><span class="n">is_multivariate</span><span class="p">:</span>
+                            <span class="n">last_data_point</span> <span class="o">=</span> <span class="n">last_data_point</span><span class="p">[</span><span class="n">data_label</span><span class="p">]</span><span class="o">.</span><span class="n">values</span>
+
+                        <span class="n">new_data_point</span><span class="p">[</span><span class="n">data_label</span><span class="p">]</span> <span class="o">=</span> <span class="n">gen_model</span><span class="o">.</span><span class="n">forecast</span><span class="p">(</span><span class="n">last_data_point</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
+
+            <span class="n">new_data_point</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">target_variable</span><span class="o">.</span><span class="n">data_label</span><span class="p">]</span> <span class="o">=</span> <span class="n">tmp</span>
+
+            <span class="n">ndata</span> <span class="o">=</span> <span class="n">ndata</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">new_data_point</span><span class="p">,</span> <span class="n">ignore_index</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">ret</span><span class="p">[</span><span class="o">-</span><span class="n">steps</span><span class="p">:]</span></div>
+
+<div class="viewcode-block" id="MVFTS.forecast_interval"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.mvfts.MVFTS.forecast_interval">[docs]</a>    <span class="k">def</span> <span class="nf">forecast_interval</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="n">ndata</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">apply_transformations</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+        <span class="n">c</span> <span class="o">=</span> <span class="mi">0</span>
+        <span class="k">for</span> <span class="n">index</span><span class="p">,</span> <span class="n">row</span> <span class="ow">in</span> <span class="n">ndata</span><span class="o">.</span><span class="n">iterrows</span><span class="p">()</span> <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">ndata</span><span class="p">,</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">)</span> <span class="k">else</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">ndata</span><span class="p">):</span>
+            <span class="n">data_point</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">format_data</span><span class="p">(</span><span class="n">row</span><span class="p">)</span>
+            <span class="n">flrs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">generate_lhs_flrs</span><span class="p">(</span><span class="n">data_point</span><span class="p">)</span>
+            <span class="n">mvs</span> <span class="o">=</span> <span class="p">[]</span>
+            <span class="n">ups</span> <span class="o">=</span> <span class="p">[]</span>
+            <span class="n">los</span> <span class="o">=</span> <span class="p">[]</span>
+            <span class="k">for</span> <span class="n">flr</span> <span class="ow">in</span> <span class="n">flrs</span><span class="p">:</span>
+                <span class="n">flrg</span> <span class="o">=</span> <span class="n">mvflrg</span><span class="o">.</span><span class="n">FLRG</span><span class="p">(</span><span class="n">lhs</span><span class="o">=</span><span class="n">flr</span><span class="o">.</span><span class="n">LHS</span><span class="p">)</span>
+                <span class="k">if</span> <span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">:</span>
+                    <span class="c1">#Naïve approach is applied when no rules were found</span>
+                    <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">target_variable</span><span class="o">.</span><span class="n">name</span> <span class="ow">in</span> <span class="n">flrg</span><span class="o">.</span><span class="n">LHS</span><span class="p">:</span>
+                        <span class="n">fs</span> <span class="o">=</span> <span class="n">flrg</span><span class="o">.</span><span class="n">LHS</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">target_variable</span><span class="o">.</span><span class="n">name</span><span class="p">]</span>
+                        <span class="n">fset</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">target_variable</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="n">fs</span><span class="p">]</span>
+                        <span class="n">up</span> <span class="o">=</span> <span class="n">fset</span><span class="o">.</span><span class="n">upper</span>
+                        <span class="n">lo</span> <span class="o">=</span> <span class="n">fset</span><span class="o">.</span><span class="n">lower</span>
+                        <span class="n">mv</span> <span class="o">=</span> <span class="n">fset</span><span class="o">.</span><span class="n">membership</span><span class="p">(</span><span class="n">data_point</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">target_variable</span><span class="o">.</span><span class="n">name</span><span class="p">])</span>
+                        <span class="n">mvs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">mv</span><span class="p">)</span>
+                        <span class="n">ups</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">up</span><span class="p">)</span>
+                        <span class="n">los</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">lo</span><span class="p">)</span>
+                    <span class="k">else</span><span class="p">:</span>
+                        <span class="n">mvs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="mf">0.</span><span class="p">)</span>
+                        <span class="n">ups</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="mf">0.</span><span class="p">)</span>
+                        <span class="n">los</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="mf">0.</span><span class="p">)</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">_flrg</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()]</span>
+                    <span class="n">mvs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">_flrg</span><span class="o">.</span><span class="n">get_membership</span><span class="p">(</span><span class="n">data_point</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">explanatory_variables</span><span class="p">))</span>
+                    <span class="n">ups</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">_flrg</span><span class="o">.</span><span class="n">get_upper</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">target_variable</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">))</span>
+                    <span class="n">los</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">_flrg</span><span class="o">.</span><span class="n">get_lower</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">target_variable</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">))</span>
+
+            <span class="n">mv</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">mvs</span><span class="p">)</span>
+            <span class="n">up</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">mv</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">ups</span><span class="p">)</span><span class="o">.</span><span class="n">T</span><span class="p">)</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">(</span><span class="n">mv</span><span class="p">)</span>
+            <span class="n">lo</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">mv</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">los</span><span class="p">)</span><span class="o">.</span><span class="n">T</span><span class="p">)</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">(</span><span class="n">mv</span><span class="p">)</span>
+
+            <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">([</span><span class="n">lo</span><span class="p">,</span> <span class="n">up</span><span class="p">])</span>
+
+        <span class="n">ret</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">target_variable</span><span class="o">.</span><span class="n">apply_inverse_transformations</span><span class="p">(</span><span class="n">ret</span><span class="p">,</span>
+                                                           <span class="n">params</span><span class="o">=</span><span class="n">data</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">target_variable</span><span class="o">.</span><span class="n">data_label</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">ret</span></div>
+
+<div class="viewcode-block" id="MVFTS.forecast_ahead_interval"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.mvfts.MVFTS.forecast_ahead_interval">[docs]</a>    <span class="k">def</span> <span class="nf">forecast_ahead_interval</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">steps</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="n">generators</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;generators&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">generators</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span><span class="s1">&#39;You must provide parameter </span><span class="se">\&#39;</span><span class="s1">generators</span><span class="se">\&#39;</span><span class="s1">! generators is a dict where the keys&#39;</span> <span class="o">+</span>
+                            <span class="s1">&#39; are the dataframe column names (except the target_variable) and the values are &#39;</span> <span class="o">+</span>
+                            <span class="s1">&#39;lambda functions that accept one value (the actual value of the variable) &#39;</span>
+                            <span class="s1">&#39; and return the next value or trained FTS models that accept the actual values and &#39;</span>
+                            <span class="s1">&#39;forecast new ones.&#39;</span><span class="p">)</span>
+
+        <span class="n">ndata</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">apply_transformations</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+
+        <span class="n">start</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;start_at&#39;</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
+
+        <span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="n">ix</span> <span class="o">=</span> <span class="n">ndata</span><span class="o">.</span><span class="n">index</span><span class="p">[</span><span class="n">start</span><span class="p">:</span> <span class="n">start</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">]</span>
+        <span class="n">lo</span> <span class="o">=</span> <span class="n">ndata</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">ix</span><span class="p">]</span> <span class="c1">#[ndata.loc[k] for k in ix]</span>
+        <span class="n">up</span> <span class="o">=</span> <span class="n">ndata</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">ix</span><span class="p">]</span> <span class="c1">#[ndata.loc[k] for k in ix]</span>
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">steps</span><span class="p">):</span>
+            <span class="n">tmp_lo</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">forecast_interval</span><span class="p">(</span><span class="n">lo</span><span class="p">[</span><span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">:],</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
+            <span class="n">tmp_up</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">forecast_interval</span><span class="p">(</span><span class="n">up</span><span class="p">[</span><span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">:],</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
+
+            <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">([</span><span class="nb">min</span><span class="p">(</span><span class="n">tmp_lo</span><span class="p">),</span> <span class="nb">max</span><span class="p">(</span><span class="n">tmp_up</span><span class="p">)])</span>
+
+            <span class="n">new_data_point_lo</span> <span class="o">=</span> <span class="p">{}</span>
+            <span class="n">new_data_point_up</span> <span class="o">=</span> <span class="p">{}</span>
+
+            <span class="k">for</span> <span class="n">data_label</span> <span class="ow">in</span> <span class="n">generators</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
+                <span class="k">if</span> <span class="n">data_label</span> <span class="o">!=</span> <span class="bp">self</span><span class="o">.</span><span class="n">target_variable</span><span class="o">.</span><span class="n">data_label</span><span class="p">:</span>
+                    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">generators</span><span class="p">[</span><span class="n">data_label</span><span class="p">],</span> <span class="n">LambdaType</span><span class="p">):</span>
+                        <span class="n">last_data_point_lo</span> <span class="o">=</span> <span class="n">lo</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">lo</span><span class="o">.</span><span class="n">index</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]]</span>
+                        <span class="n">new_data_point_lo</span><span class="p">[</span><span class="n">data_label</span><span class="p">]</span> <span class="o">=</span> <span class="n">generators</span><span class="p">[</span><span class="n">data_label</span><span class="p">](</span><span class="n">last_data_point_lo</span><span class="p">[</span><span class="n">data_label</span><span class="p">])</span>
+                        <span class="n">last_data_point_up</span> <span class="o">=</span> <span class="n">up</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">up</span><span class="o">.</span><span class="n">index</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]]</span>
+                        <span class="n">new_data_point_up</span><span class="p">[</span><span class="n">data_label</span><span class="p">]</span> <span class="o">=</span> <span class="n">generators</span><span class="p">[</span><span class="n">data_label</span><span class="p">](</span><span class="n">last_data_point_up</span><span class="p">[</span><span class="n">data_label</span><span class="p">])</span>
+                    <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">generators</span><span class="p">[</span><span class="n">data_label</span><span class="p">],</span> <span class="n">fts</span><span class="o">.</span><span class="n">FTS</span><span class="p">):</span>
+                        <span class="n">model</span> <span class="o">=</span> <span class="n">generators</span><span class="p">[</span><span class="n">data_label</span><span class="p">]</span>
+                        <span class="n">last_data_point_lo</span> <span class="o">=</span> <span class="n">lo</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">lo</span><span class="o">.</span><span class="n">index</span><span class="p">[</span><span class="o">-</span><span class="n">model</span><span class="o">.</span><span class="n">order</span><span class="p">:]]</span>
+                        <span class="n">last_data_point_up</span> <span class="o">=</span> <span class="n">up</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">up</span><span class="o">.</span><span class="n">index</span><span class="p">[</span><span class="o">-</span><span class="n">model</span><span class="o">.</span><span class="n">order</span><span class="p">:]]</span>
+
+                        <span class="k">if</span> <span class="ow">not</span> <span class="n">model</span><span class="o">.</span><span class="n">is_multivariate</span><span class="p">:</span>
+                            <span class="n">last_data_point_lo</span> <span class="o">=</span> <span class="n">last_data_point_lo</span><span class="p">[</span><span class="n">data_label</span><span class="p">]</span><span class="o">.</span><span class="n">values</span>
+                            <span class="n">last_data_point_up</span> <span class="o">=</span> <span class="n">last_data_point_up</span><span class="p">[</span><span class="n">data_label</span><span class="p">]</span><span class="o">.</span><span class="n">values</span>
+
+                        <span class="n">new_data_point_lo</span><span class="p">[</span><span class="n">data_label</span><span class="p">]</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">forecast</span><span class="p">(</span><span class="n">last_data_point_lo</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
+                        <span class="n">new_data_point_up</span><span class="p">[</span><span class="n">data_label</span><span class="p">]</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">forecast</span><span class="p">(</span><span class="n">last_data_point_up</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
+
+            <span class="n">new_data_point_lo</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">target_variable</span><span class="o">.</span><span class="n">data_label</span><span class="p">]</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="n">tmp_lo</span><span class="p">)</span>
+            <span class="n">new_data_point_up</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">target_variable</span><span class="o">.</span><span class="n">data_label</span><span class="p">]</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">tmp_up</span><span class="p">)</span>
+
+            <span class="n">lo</span> <span class="o">=</span> <span class="n">lo</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">new_data_point_lo</span><span class="p">,</span> <span class="n">ignore_index</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+            <span class="n">up</span> <span class="o">=</span> <span class="n">up</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">new_data_point_up</span><span class="p">,</span> <span class="n">ignore_index</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">ret</span><span class="p">[</span><span class="o">-</span><span class="n">steps</span><span class="p">:]</span></div>
+
+<div class="viewcode-block" id="MVFTS.clone_parameters"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.mvfts.MVFTS.clone_parameters">[docs]</a>    <span class="k">def</span> <span class="nf">clone_parameters</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">model</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">(</span><span class="n">MVFTS</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="n">clone_parameters</span><span class="p">(</span><span class="n">model</span><span class="p">)</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">explanatory_variables</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">explanatory_variables</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">target_variable</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">target_variable</span></div>
+
+    <span class="k">def</span> <span class="fm">__str__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="n">_str</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">+</span> <span class="s2">&quot;:</span><span class="se">\n</span><span class="s2">&quot;</span>
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
+            <span class="n">_str</span> <span class="o">+=</span> <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">k</span><span class="p">])</span> <span class="o">+</span> <span class="s2">&quot;</span><span class="se">\n</span><span class="s2">&quot;</span>
+
+        <span class="k">return</span> <span class="n">_str</span></div>
+
+
+</pre></div>
+
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+          </div>
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new file mode 100644
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@@ -0,0 +1,181 @@
+
+
+<!doctype html>
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+            
+  <h1>Source code for pyFTS.models.multivariate.variable</h1><div class="highlight"><pre>
+<span></span><span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+<span class="kn">from</span> <span class="nn">pyFTS.common</span> <span class="kn">import</span> <span class="n">fts</span><span class="p">,</span> <span class="n">FuzzySet</span><span class="p">,</span> <span class="n">FLR</span><span class="p">,</span> <span class="n">Membership</span><span class="p">,</span> <span class="n">tree</span>
+<span class="kn">from</span> <span class="nn">pyFTS.partitioners</span> <span class="kn">import</span> <span class="n">Grid</span>
+<span class="kn">from</span> <span class="nn">pyFTS.models.multivariate</span> <span class="kn">import</span> <span class="n">FLR</span> <span class="k">as</span> <span class="n">MVFLR</span>
+
+
+<div class="viewcode-block" id="Variable"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.variable.Variable">[docs]</a><span class="k">class</span> <span class="nc">Variable</span><span class="p">:</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    A variable of a fuzzy time series multivariate model. Each variable contains its own</span>
+<span class="sd">    transformations and partitioners.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">name</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="sd">&quot;&quot;&quot;</span>
+
+<span class="sd">        :param name:</span>
+<span class="sd">        :param \**kwargs: See below</span>
+
+<span class="sd">        :Keyword Arguments:</span>
+<span class="sd">            * *alias* -- Alternative name for the variable</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="n">name</span>
+        <span class="sd">&quot;&quot;&quot;A string with the name of the variable&quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">alias</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;alias&#39;</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">name</span><span class="p">)</span>
+        <span class="sd">&quot;&quot;&quot;A string with the alias of the variable&quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">data_label</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;data_label&#39;</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">name</span><span class="p">)</span>
+        <span class="sd">&quot;&quot;&quot;A string with the column name on DataFrame&quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">type</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;type&#39;</span><span class="p">,</span> <span class="s1">&#39;common&#39;</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">data_type</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;data_type&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
+        <span class="sd">&quot;&quot;&quot;The type of the data column on Pandas Dataframe&quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">mask</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;mask&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
+        <span class="sd">&quot;&quot;&quot;The mask for format the data column on Pandas Dataframe&quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">transformation</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;transformation&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
+        <span class="sd">&quot;&quot;&quot;Pre processing transformation for the variable&quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">transformation_params</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;transformation_params&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="sd">&quot;&quot;&quot;UoD partitioner for the variable data&quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">alpha_cut</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;alpha_cut&#39;</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">)</span>
+        <span class="sd">&quot;&quot;&quot;Minimal membership value to be considered on fuzzyfication process&quot;&quot;&quot;</span>
+
+
+        <span class="k">if</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;data&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">build</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+
+<div class="viewcode-block" id="Variable.build"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.variable.Variable.build">[docs]</a>    <span class="k">def</span> <span class="nf">build</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="sd">&quot;&quot;&quot;</span>
+
+<span class="sd">        :param kwargs:</span>
+<span class="sd">        :return:</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">fs</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;partitioner&#39;</span><span class="p">,</span> <span class="n">Grid</span><span class="o">.</span><span class="n">GridPartitioner</span><span class="p">)</span>
+        <span class="n">mf</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;func&#39;</span><span class="p">,</span> <span class="n">Membership</span><span class="o">.</span><span class="n">trimf</span><span class="p">)</span>
+        <span class="n">np</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;npart&#39;</span><span class="p">,</span> <span class="mi">10</span><span class="p">)</span>
+        <span class="n">data</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;data&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
+        <span class="n">kw</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;partitioner_specific&#39;</span><span class="p">,</span> <span class="p">{})</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span> <span class="o">=</span> <span class="n">fs</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">data</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">data_label</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span> <span class="n">npart</span><span class="o">=</span><span class="n">np</span><span class="p">,</span> <span class="n">func</span><span class="o">=</span><span class="n">mf</span><span class="p">,</span>
+                              <span class="n">transformation</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">transformation</span><span class="p">,</span> <span class="n">prefix</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">alias</span><span class="p">,</span>
+                              <span class="n">variable</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">name</span><span class="p">,</span> <span class="o">**</span><span class="n">kw</span><span class="p">)</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">+</span> <span class="s2">&quot; &quot;</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">name</span></div>
+
+<div class="viewcode-block" id="Variable.apply_transformations"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.variable.Variable.apply_transformations">[docs]</a>    <span class="k">def</span> <span class="nf">apply_transformations</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+
+        <span class="k">if</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;params&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">transformation_params</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;params&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">transformation</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">transformation</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">transformation_params</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">data</span></div>
+
+<div class="viewcode-block" id="Variable.apply_inverse_transformations"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.variable.Variable.apply_inverse_transformations">[docs]</a>    <span class="k">def</span> <span class="nf">apply_inverse_transformations</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+
+        <span class="k">if</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;params&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">transformation_params</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;params&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">transformation</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">transformation</span><span class="o">.</span><span class="n">inverse</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">transformation_params</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">data</span></div>
+
+    <span class="k">def</span> <span class="fm">__str__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">name</span></div>
+</pre></div>
+
+            <div class="clearer"></div>
+          </div>
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+        <li class="nav-item nav-item-0"><a href="../../../../index.html">pyFTS 1.6 documentation</a> &#187;</li>
+          <li class="nav-item nav-item-1"><a href="../../../index.html" >Module code</a> &#187;</li>
+        <li class="nav-item nav-item-this"><a href="">pyFTS.models.multivariate.variable</a></li> 
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+  </body>
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diff --git a/docs/build/html/_modules/pyFTS/models/multivariate/wmvfts.html b/docs/build/html/_modules/pyFTS/models/multivariate/wmvfts.html
new file mode 100644
index 0000000..6d528a1
--- /dev/null
+++ b/docs/build/html/_modules/pyFTS/models/multivariate/wmvfts.html
@@ -0,0 +1,178 @@
+
+
+<!doctype html>
+
+<html>
+  <head>
+    <meta charset="utf-8" />
+    <meta name="viewport" content="width=device-width, initial-scale=1.0"><script type="text/javascript">
+
+      var _gaq = _gaq || [];
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+    <title>pyFTS.models.multivariate.wmvfts &#8212; pyFTS 1.6 documentation</title>
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+        <li class="nav-item nav-item-this"><a href="">pyFTS.models.multivariate.wmvfts</a></li> 
+      </ul>
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+    <div class="document">
+      <div class="documentwrapper">
+        <div class="bodywrapper">
+          <div class="body" role="main">
+            
+  <h1>Source code for pyFTS.models.multivariate.wmvfts</h1><div class="highlight"><pre>
+<span></span><span class="kn">from</span> <span class="nn">pyFTS.common</span> <span class="kn">import</span> <span class="n">fts</span><span class="p">,</span> <span class="n">FuzzySet</span><span class="p">,</span> <span class="n">FLR</span><span class="p">,</span> <span class="n">Membership</span><span class="p">,</span> <span class="n">tree</span>
+<span class="kn">from</span> <span class="nn">pyFTS.partitioners</span> <span class="kn">import</span> <span class="n">Grid</span>
+<span class="kn">from</span> <span class="nn">pyFTS.models.multivariate</span> <span class="kn">import</span> <span class="n">mvfts</span><span class="p">,</span> <span class="n">FLR</span> <span class="k">as</span> <span class="n">MVFLR</span><span class="p">,</span> <span class="n">common</span><span class="p">,</span> <span class="n">flrg</span> <span class="k">as</span> <span class="n">mvflrg</span>
+
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+
+
+<div class="viewcode-block" id="WeightedFLRG"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.wmvfts.WeightedFLRG">[docs]</a><span class="k">class</span> <span class="nc">WeightedFLRG</span><span class="p">(</span><span class="n">mvflrg</span><span class="o">.</span><span class="n">FLRG</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Weighted Multivariate Fuzzy Logical Rule Group</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">(</span><span class="n">WeightedFLRG</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">order</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;order&#39;</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">LHS</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;lhs&#39;</span><span class="p">,</span> <span class="p">{})</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span> <span class="o">=</span> <span class="p">{}</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">count</span> <span class="o">=</span> <span class="mf">0.0</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">w</span> <span class="o">=</span> <span class="kc">None</span>
+
+<div class="viewcode-block" id="WeightedFLRG.append_rhs"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.wmvfts.WeightedFLRG.append_rhs">[docs]</a>    <span class="k">def</span> <span class="nf">append_rhs</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">fset</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="n">count</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;count&#39;</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">fset</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="p">[</span><span class="n">fset</span><span class="p">]</span> <span class="o">=</span> <span class="n">count</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="p">[</span><span class="n">fset</span><span class="p">]</span> <span class="o">+=</span> <span class="n">count</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">count</span> <span class="o">+=</span> <span class="n">count</span></div>
+
+<div class="viewcode-block" id="WeightedFLRG.weights"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.wmvfts.WeightedFLRG.weights">[docs]</a>    <span class="k">def</span> <span class="nf">weights</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">w</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">w</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="p">[</span><span class="n">c</span><span class="p">]</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">count</span> <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="o">.</span><span class="n">keys</span><span class="p">()])</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">w</span></div>
+
+<div class="viewcode-block" id="WeightedFLRG.get_midpoint"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.wmvfts.WeightedFLRG.get_midpoint">[docs]</a>    <span class="k">def</span> <span class="nf">get_midpoint</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sets</span><span class="p">):</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">midpoint</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">mp</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">sets</span><span class="p">[</span><span class="n">c</span><span class="p">]</span><span class="o">.</span><span class="n">centroid</span> <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="o">.</span><span class="n">keys</span><span class="p">()])</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">midpoint</span> <span class="o">=</span> <span class="n">mp</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">weights</span><span class="p">())</span>
+
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">midpoint</span></div>
+
+<div class="viewcode-block" id="WeightedFLRG.get_lower"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.wmvfts.WeightedFLRG.get_lower">[docs]</a>    <span class="k">def</span> <span class="nf">get_lower</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sets</span><span class="p">):</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">lower</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">lw</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">sets</span><span class="p">[</span><span class="n">s</span><span class="p">]</span><span class="o">.</span><span class="n">lower</span> <span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="o">.</span><span class="n">keys</span><span class="p">()])</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">lower</span> <span class="o">=</span> <span class="n">lw</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">weights</span><span class="p">())</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">lower</span></div>
+
+<div class="viewcode-block" id="WeightedFLRG.get_upper"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.wmvfts.WeightedFLRG.get_upper">[docs]</a>    <span class="k">def</span> <span class="nf">get_upper</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sets</span><span class="p">):</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">upper</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">up</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">sets</span><span class="p">[</span><span class="n">s</span><span class="p">]</span><span class="o">.</span><span class="n">upper</span> <span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="o">.</span><span class="n">keys</span><span class="p">()])</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">upper</span> <span class="o">=</span> <span class="n">up</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">weights</span><span class="p">())</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">upper</span></div>
+
+    <span class="k">def</span> <span class="fm">__str__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="n">_str</span> <span class="o">=</span> <span class="s2">&quot;&quot;</span>
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
+            <span class="n">_str</span> <span class="o">+=</span> <span class="s2">&quot;, &quot;</span> <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">_str</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span> <span class="k">else</span> <span class="s2">&quot;&quot;</span>
+            <span class="n">_str</span> <span class="o">+=</span> <span class="n">k</span> <span class="o">+</span> <span class="s2">&quot; (&quot;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span> <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">count</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span> <span class="o">+</span> <span class="s2">&quot;)&quot;</span>
+
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_key</span><span class="p">()</span> <span class="o">+</span> <span class="s2">&quot; -&gt; &quot;</span> <span class="o">+</span> <span class="n">_str</span></div>
+
+
+<div class="viewcode-block" id="WeightedMVFTS"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.wmvfts.WeightedMVFTS">[docs]</a><span class="k">class</span> <span class="nc">WeightedMVFTS</span><span class="p">(</span><span class="n">mvfts</span><span class="o">.</span><span class="n">MVFTS</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Weighted Multivariate FTS</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">(</span><span class="n">WeightedMVFTS</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">order</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">shortname</span> <span class="o">=</span> <span class="s2">&quot;WeightedMVFTS&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="s2">&quot;Weighted Multivariate FTS&quot;</span>
+
+<div class="viewcode-block" id="WeightedMVFTS.generate_flrg"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.wmvfts.WeightedMVFTS.generate_flrg">[docs]</a>    <span class="k">def</span> <span class="nf">generate_flrg</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">flrs</span><span class="p">):</span>
+        <span class="k">for</span> <span class="n">flr</span> <span class="ow">in</span> <span class="n">flrs</span><span class="p">:</span>
+            <span class="n">flrg</span> <span class="o">=</span> <span class="n">WeightedFLRG</span><span class="p">(</span><span class="n">lhs</span><span class="o">=</span><span class="n">flr</span><span class="o">.</span><span class="n">LHS</span><span class="p">)</span>
+
+            <span class="k">if</span> <span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()]</span> <span class="o">=</span> <span class="n">flrg</span>
+
+            <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()]</span><span class="o">.</span><span class="n">append_rhs</span><span class="p">(</span><span class="n">flr</span><span class="o">.</span><span class="n">RHS</span><span class="p">)</span></div></div>
+</pre></div>
+
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+        <li class="nav-item nav-item-this"><a href="">pyFTS.models.multivariate.wmvfts</a></li> 
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diff --git a/docs/build/html/_modules/pyFTS/models/nonstationary/cvfts.html b/docs/build/html/_modules/pyFTS/models/nonstationary/cvfts.html
new file mode 100644
index 0000000..29c3f72
--- /dev/null
+++ b/docs/build/html/_modules/pyFTS/models/nonstationary/cvfts.html
@@ -0,0 +1,371 @@
+
+
+<!doctype html>
+
+<html>
+  <head>
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+    <meta name="viewport" content="width=device-width, initial-scale=1.0"><script type="text/javascript">
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+    <title>pyFTS.models.nonstationary.cvfts &#8212; pyFTS 1.6 documentation</title>
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+        <li class="nav-item nav-item-this"><a href="">pyFTS.models.nonstationary.cvfts</a></li> 
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+            
+  <h1>Source code for pyFTS.models.nonstationary.cvfts</h1><div class="highlight"><pre>
+<span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">from</span> <span class="nn">pyFTS.models</span> <span class="kn">import</span> <span class="n">hofts</span>
+<span class="kn">from</span> <span class="nn">pyFTS.models.nonstationary</span> <span class="kn">import</span> <span class="n">common</span><span class="p">,</span><span class="n">nsfts</span>
+<span class="kn">from</span> <span class="nn">pyFTS.common</span> <span class="kn">import</span> <span class="n">FLR</span><span class="p">,</span> <span class="n">flrg</span><span class="p">,</span> <span class="n">tree</span>
+
+
+<div class="viewcode-block" id="HighOrderNonstationaryFLRG"><a class="viewcode-back" href="../../../../pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.cvfts.HighOrderNonstationaryFLRG">[docs]</a><span class="k">class</span> <span class="nc">HighOrderNonstationaryFLRG</span><span class="p">(</span><span class="n">hofts</span><span class="o">.</span><span class="n">HighOrderFTS</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;Conventional High Order Fuzzy Logical Relationship Group&quot;&quot;&quot;</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">order</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">(</span><span class="n">HighOrderNonstationaryFLRG</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">order</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">LHS</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span> <span class="o">=</span> <span class="p">{}</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">strlhs</span> <span class="o">=</span> <span class="s2">&quot;&quot;</span>
+
+<div class="viewcode-block" id="HighOrderNonstationaryFLRG.append_rhs"><a class="viewcode-back" href="../../../../pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.cvfts.HighOrderNonstationaryFLRG.append_rhs">[docs]</a>    <span class="k">def</span> <span class="nf">append_rhs</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="k">if</span> <span class="n">c</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="p">[</span><span class="n">c</span><span class="p">]</span> <span class="o">=</span> <span class="n">c</span></div>
+
+<div class="viewcode-block" id="HighOrderNonstationaryFLRG.append_lhs"><a class="viewcode-back" href="../../../../pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.cvfts.HighOrderNonstationaryFLRG.append_lhs">[docs]</a>    <span class="k">def</span> <span class="nf">append_lhs</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">c</span><span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">LHS</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">c</span><span class="p">)</span></div>
+
+    <span class="k">def</span> <span class="fm">__str__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="n">tmp</span> <span class="o">=</span> <span class="s2">&quot;&quot;</span>
+        <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="nb">sorted</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="p">):</span>
+            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">tmp</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
+                <span class="n">tmp</span> <span class="o">=</span> <span class="n">tmp</span> <span class="o">+</span> <span class="s2">&quot;,&quot;</span>
+            <span class="n">tmp</span> <span class="o">=</span> <span class="n">tmp</span> <span class="o">+</span> <span class="n">c</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_key</span><span class="p">()</span> <span class="o">+</span> <span class="s2">&quot; -&gt; &quot;</span> <span class="o">+</span> <span class="n">tmp</span>
+
+
+    <span class="k">def</span> <span class="fm">__len__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="ConditionalVarianceFTS"><a class="viewcode-back" href="../../../../pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.cvfts.ConditionalVarianceFTS">[docs]</a><span class="k">class</span> <span class="nc">ConditionalVarianceFTS</span><span class="p">(</span><span class="n">hofts</span><span class="o">.</span><span class="n">HighOrderFTS</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">(</span><span class="n">ConditionalVarianceFTS</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="s2">&quot;Conditional Variance FTS&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">shortname</span> <span class="o">=</span> <span class="s2">&quot;CVFTS &quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">detail</span> <span class="o">=</span> <span class="s2">&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span> <span class="o">=</span> <span class="p">{}</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">is_high_order</span> <span class="o">=</span> <span class="kc">False</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">append_transformation</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">transformation</span><span class="p">)</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">min_stack</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">]</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">max_stack</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">]</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">uod_clip</span> <span class="o">=</span> <span class="kc">False</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">order</span> <span class="o">=</span> <span class="mi">1</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">min_order</span> <span class="o">=</span> <span class="mi">1</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span> <span class="o">=</span> <span class="mi">1</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">inputs</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">forecasts</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">residuals</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">variance_residual</span> <span class="o">=</span> <span class="mf">0.</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">mean_residual</span> <span class="o">=</span> <span class="mf">0.</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">memory_window</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;memory_window&quot;</span><span class="p">,</span><span class="mi">5</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">sets</span> <span class="o">=</span> <span class="p">{}</span>
+
+<div class="viewcode-block" id="ConditionalVarianceFTS.train"><a class="viewcode-back" href="../../../../pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.cvfts.ConditionalVarianceFTS.train">[docs]</a>    <span class="k">def</span> <span class="nf">train</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ndata</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+
+        <span class="n">tmpdata</span> <span class="o">=</span> <span class="n">common</span><span class="o">.</span><span class="n">fuzzySeries</span><span class="p">(</span><span class="n">ndata</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">sets</span><span class="p">,</span>
+                                     <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">ordered_sets</span><span class="p">,</span>
+                                     <span class="n">method</span><span class="o">=</span><span class="s1">&#39;fuzzy&#39;</span><span class="p">,</span> <span class="n">const_t</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+        <span class="n">flrs</span> <span class="o">=</span> <span class="n">FLR</span><span class="o">.</span><span class="n">generate_non_recurrent_flrs</span><span class="p">(</span><span class="n">tmpdata</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">generate_flrg</span><span class="p">(</span><span class="n">flrs</span><span class="p">)</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">forecasts</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">forecast</span><span class="p">(</span><span class="n">ndata</span><span class="p">,</span> <span class="n">no_update</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">residuals</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">ndata</span><span class="p">[</span><span class="mi">1</span><span class="p">:])</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">forecasts</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">variance_residual</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">var</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">residuals</span><span class="p">)</span> <span class="c1"># np.max(self.residuals</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">mean_residual</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">residuals</span><span class="p">)</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">residuals</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">residuals</span><span class="p">[</span><span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">memory_window</span><span class="p">:]</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">forecasts</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">forecasts</span><span class="p">[</span><span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">memory_window</span><span class="p">:]</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">inputs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">ndata</span><span class="p">[</span><span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">memory_window</span><span class="p">:])</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span></div>
+
+
+<div class="viewcode-block" id="ConditionalVarianceFTS.generate_flrg"><a class="viewcode-back" href="../../../../pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.cvfts.ConditionalVarianceFTS.generate_flrg">[docs]</a>    <span class="k">def</span> <span class="nf">generate_flrg</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">flrs</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="k">for</span> <span class="n">flr</span> <span class="ow">in</span> <span class="n">flrs</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">flr</span><span class="o">.</span><span class="n">LHS</span><span class="o">.</span><span class="n">name</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">flr</span><span class="o">.</span><span class="n">LHS</span><span class="o">.</span><span class="n">name</span><span class="p">]</span><span class="o">.</span><span class="n">append_rhs</span><span class="p">(</span><span class="n">flr</span><span class="o">.</span><span class="n">RHS</span><span class="o">.</span><span class="n">name</span><span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">flr</span><span class="o">.</span><span class="n">LHS</span><span class="o">.</span><span class="n">name</span><span class="p">]</span> <span class="o">=</span> <span class="n">nsfts</span><span class="o">.</span><span class="n">ConventionalNonStationaryFLRG</span><span class="p">(</span><span class="n">flr</span><span class="o">.</span><span class="n">LHS</span><span class="o">.</span><span class="n">name</span><span class="p">)</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">flr</span><span class="o">.</span><span class="n">LHS</span><span class="o">.</span><span class="n">name</span><span class="p">]</span><span class="o">.</span><span class="n">append_rhs</span><span class="p">(</span><span class="n">flr</span><span class="o">.</span><span class="n">RHS</span><span class="o">.</span><span class="n">name</span><span class="p">)</span></div>
+
+
+    <span class="k">def</span> <span class="nf">_smooth</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">a</span><span class="p">):</span>
+        <span class="k">return</span> <span class="o">.</span><span class="mi">1</span> <span class="o">*</span> <span class="n">a</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="o">.</span><span class="mi">3</span> <span class="o">*</span> <span class="n">a</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="o">.</span><span class="mi">6</span> <span class="o">*</span> <span class="n">a</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span>
+
+<div class="viewcode-block" id="ConditionalVarianceFTS.perturbation_factors"><a class="viewcode-back" href="../../../../pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.cvfts.ConditionalVarianceFTS.perturbation_factors">[docs]</a>    <span class="k">def</span> <span class="nf">perturbation_factors</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="n">npart</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">)</span>
+        <span class="n">_max</span> <span class="o">=</span> <span class="mi">0</span>
+        <span class="n">_min</span> <span class="o">=</span> <span class="mi">0</span>
+        <span class="k">if</span> <span class="n">data</span> <span class="o">&lt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">original_min</span><span class="p">:</span>
+            <span class="n">_min</span> <span class="o">=</span> <span class="n">data</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">original_min</span> <span class="k">if</span> <span class="n">data</span> <span class="o">&lt;</span> <span class="mi">0</span> <span class="k">else</span> <span class="bp">self</span><span class="o">.</span><span class="n">original_min</span> <span class="o">-</span> <span class="n">data</span>
+        <span class="k">elif</span> <span class="n">data</span> <span class="o">&gt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">original_max</span><span class="p">:</span>
+            <span class="n">_max</span> <span class="o">=</span> <span class="n">data</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">original_max</span> <span class="k">if</span> <span class="n">data</span> <span class="o">&gt;</span> <span class="mi">0</span> <span class="k">else</span> <span class="bp">self</span><span class="o">.</span><span class="n">original_max</span> <span class="o">-</span> <span class="n">data</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">min_stack</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">min_stack</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">_min</span><span class="p">)</span>
+        <span class="n">_min</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">min_stack</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">max_stack</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">max_stack</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">_max</span><span class="p">)</span>
+        <span class="n">_max</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">max_stack</span><span class="p">)</span>
+
+        <span class="n">_range</span> <span class="o">=</span> <span class="p">(</span><span class="n">_max</span> <span class="o">-</span> <span class="n">_min</span><span class="p">)</span><span class="o">/</span><span class="mi">2</span>
+
+        <span class="n">translate</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="n">_min</span><span class="p">,</span> <span class="n">_max</span><span class="p">,</span> <span class="n">npart</span><span class="p">)</span>
+
+        <span class="n">var</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">std</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">residuals</span><span class="p">)</span>
+
+        <span class="n">var</span> <span class="o">=</span> <span class="mi">0</span> <span class="k">if</span> <span class="n">var</span> <span class="o">&lt;</span> <span class="mi">1</span> <span class="k">else</span> <span class="n">var</span>
+
+        <span class="n">loc</span> <span class="o">=</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">mean_residual</span> <span class="o">+</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">residuals</span><span class="p">))</span>
+
+        <span class="n">location</span> <span class="o">=</span> <span class="p">[</span><span class="n">_range</span> <span class="o">+</span> <span class="n">w</span> <span class="o">+</span> <span class="n">loc</span> <span class="o">+</span> <span class="n">k</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="o">-</span><span class="n">var</span><span class="p">,</span><span class="n">var</span><span class="p">,</span> <span class="n">npart</span><span class="p">)</span> <span class="k">for</span> <span class="n">w</span> <span class="ow">in</span> <span class="n">translate</span><span class="p">]</span>
+
+        <span class="n">scale</span> <span class="o">=</span> <span class="p">[</span><span class="nb">abs</span><span class="p">(</span><span class="n">location</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="n">location</span><span class="p">[</span><span class="mi">2</span><span class="p">])]</span>
+        <span class="n">scale</span><span class="o">.</span><span class="n">extend</span><span class="p">([</span><span class="nb">abs</span><span class="p">(</span><span class="n">location</span><span class="p">[</span><span class="n">k</span> <span class="o">-</span> <span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">location</span><span class="p">[</span><span class="n">k</span> <span class="o">+</span> <span class="mi">1</span><span class="p">])</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">npart</span><span class="p">)])</span>
+        <span class="n">scale</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">abs</span><span class="p">(</span><span class="n">location</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">location</span><span class="p">[</span><span class="o">-</span><span class="mi">3</span><span class="p">]))</span>
+
+        <span class="n">perturb</span> <span class="o">=</span> <span class="p">[[</span><span class="n">location</span><span class="p">[</span><span class="n">k</span><span class="p">],</span> <span class="n">scale</span><span class="p">[</span><span class="n">k</span><span class="p">]]</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">npart</span><span class="p">)]</span>
+
+        <span class="k">return</span> <span class="n">perturb</span></div>
+
+<div class="viewcode-block" id="ConditionalVarianceFTS.perturbation_factors__old"><a class="viewcode-back" href="../../../../pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.cvfts.ConditionalVarianceFTS.perturbation_factors__old">[docs]</a>    <span class="k">def</span> <span class="nf">perturbation_factors__old</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">):</span>
+        <span class="n">npart</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">)</span>
+        <span class="n">_max</span> <span class="o">=</span> <span class="mi">0</span>
+        <span class="n">_min</span> <span class="o">=</span> <span class="mi">0</span>
+        <span class="k">if</span> <span class="n">data</span> <span class="o">&lt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">original_min</span><span class="p">:</span>
+            <span class="n">_min</span> <span class="o">=</span> <span class="n">data</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">original_min</span> <span class="k">if</span> <span class="n">data</span> <span class="o">&lt;</span> <span class="mi">0</span> <span class="k">else</span> <span class="bp">self</span><span class="o">.</span><span class="n">original_min</span> <span class="o">-</span> <span class="n">data</span>
+        <span class="k">elif</span> <span class="n">data</span> <span class="o">&gt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">original_max</span><span class="p">:</span>
+            <span class="n">_max</span> <span class="o">=</span> <span class="n">data</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">original_max</span> <span class="k">if</span> <span class="n">data</span> <span class="o">&gt;</span> <span class="mi">0</span> <span class="k">else</span> <span class="bp">self</span><span class="o">.</span><span class="n">original_max</span> <span class="o">-</span> <span class="n">data</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">min_stack</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">min_stack</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="n">_min</span><span class="p">)</span>
+        <span class="n">_min</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">min_stack</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">max_stack</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">max_stack</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">_max</span><span class="p">)</span>
+        <span class="n">_max</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">max_stack</span><span class="p">)</span>
+
+        <span class="n">location</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="n">_min</span><span class="p">,</span> <span class="n">_max</span><span class="p">,</span> <span class="n">npart</span><span class="p">)</span>
+        <span class="n">scale</span> <span class="o">=</span> <span class="p">[</span><span class="nb">abs</span><span class="p">(</span><span class="n">location</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="n">location</span><span class="p">[</span><span class="mi">2</span><span class="p">])]</span>
+        <span class="n">scale</span><span class="o">.</span><span class="n">extend</span><span class="p">([</span><span class="nb">abs</span><span class="p">(</span><span class="n">location</span><span class="p">[</span><span class="n">k</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">location</span><span class="p">[</span><span class="n">k</span><span class="o">+</span><span class="mi">1</span><span class="p">])</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">npart</span><span class="p">)])</span>
+        <span class="n">scale</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">abs</span><span class="p">(</span><span class="n">location</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">location</span><span class="p">[</span><span class="o">-</span><span class="mi">3</span><span class="p">]))</span>
+
+        <span class="n">perturb</span> <span class="o">=</span> <span class="p">[[</span><span class="n">location</span><span class="p">[</span><span class="n">k</span><span class="p">],</span> <span class="n">scale</span><span class="p">[</span><span class="n">k</span><span class="p">]]</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">npart</span><span class="p">)]</span>
+
+        <span class="k">return</span> <span class="n">perturb</span></div>
+
+    <span class="k">def</span> <span class="nf">_fsset_key</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ix</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">ordered_sets</span><span class="p">[</span><span class="n">ix</span><span class="p">]</span>
+
+    <span class="k">def</span> <span class="nf">_affected_sets</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sample</span><span class="p">,</span> <span class="n">perturb</span><span class="p">):</span>
+
+        <span class="n">affected_sets</span> <span class="o">=</span> <span class="p">[[</span><span class="n">ct</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">_fsset_key</span><span class="p">(</span><span class="n">ct</span><span class="p">)]</span><span class="o">.</span><span class="n">membership</span><span class="p">(</span><span class="n">sample</span><span class="p">,</span> <span class="n">perturb</span><span class="p">[</span><span class="n">ct</span><span class="p">])]</span>
+                         <span class="k">for</span> <span class="n">ct</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">))</span>
+                         <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">_fsset_key</span><span class="p">(</span><span class="n">ct</span><span class="p">)]</span><span class="o">.</span><span class="n">membership</span><span class="p">(</span><span class="n">sample</span><span class="p">,</span> <span class="n">perturb</span><span class="p">[</span><span class="n">ct</span><span class="p">])</span> <span class="o">&gt;</span> <span class="mf">0.0</span><span class="p">]</span>
+
+        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">affected_sets</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+
+            <span class="k">if</span> <span class="n">sample</span> <span class="o">&lt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">lower_set</span><span class="p">()</span><span class="o">.</span><span class="n">get_lower</span><span class="p">(</span><span class="n">perturb</span><span class="p">[</span><span class="mi">0</span><span class="p">]):</span>
+                <span class="n">affected_sets</span><span class="o">.</span><span class="n">append</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">])</span>
+            <span class="k">elif</span> <span class="n">sample</span> <span class="o">&gt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">upper_set</span><span class="p">()</span><span class="o">.</span><span class="n">get_upper</span><span class="p">(</span><span class="n">perturb</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]):</span>
+                <span class="n">affected_sets</span><span class="o">.</span><span class="n">append</span><span class="p">([</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">sets</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">])</span>
+
+        <span class="k">return</span> <span class="n">affected_sets</span>
+
+<div class="viewcode-block" id="ConditionalVarianceFTS.forecast"><a class="viewcode-back" href="../../../../pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.cvfts.ConditionalVarianceFTS.forecast">[docs]</a>    <span class="k">def</span> <span class="nf">forecast</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ndata</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="n">l</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">ndata</span><span class="p">)</span>
+
+        <span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
+
+        <span class="n">no_update</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;no_update&quot;</span><span class="p">,</span><span class="kc">False</span><span class="p">)</span>
+
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">l</span><span class="p">):</span>
+
+            <span class="n">sample</span> <span class="o">=</span> <span class="n">ndata</span><span class="p">[</span><span class="n">k</span><span class="p">]</span>
+
+            <span class="k">if</span> <span class="ow">not</span> <span class="n">no_update</span><span class="p">:</span>
+                <span class="n">perturb</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">perturbation_factors</span><span class="p">(</span><span class="n">sample</span><span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">perturb</span> <span class="o">=</span> <span class="p">[[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">))]</span>
+
+            <span class="n">affected_sets</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_affected_sets</span><span class="p">(</span><span class="n">sample</span><span class="p">,</span> <span class="n">perturb</span><span class="p">)</span>
+
+            <span class="n">numerator</span> <span class="o">=</span> <span class="p">[]</span>
+            <span class="n">denominator</span> <span class="o">=</span> <span class="p">[]</span>
+
+            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">affected_sets</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
+                <span class="n">ix</span> <span class="o">=</span> <span class="n">affected_sets</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span>
+                <span class="n">aset</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">ordered_sets</span><span class="p">[</span><span class="n">ix</span><span class="p">]</span>
+                <span class="k">if</span> <span class="n">aset</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">:</span>
+                    <span class="n">numerator</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">aset</span><span class="p">]</span><span class="o">.</span><span class="n">get_midpoint</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">sets</span><span class="p">,</span> <span class="n">perturb</span><span class="p">[</span><span class="n">ix</span><span class="p">]))</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">fuzzy_set</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="n">aset</span><span class="p">]</span>
+                    <span class="n">numerator</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">fuzzy_set</span><span class="o">.</span><span class="n">get_midpoint</span><span class="p">(</span><span class="n">perturb</span><span class="p">[</span><span class="n">ix</span><span class="p">]))</span>
+                <span class="n">denominator</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="k">for</span> <span class="n">aset</span> <span class="ow">in</span> <span class="n">affected_sets</span><span class="p">:</span>
+                    <span class="n">ix</span> <span class="o">=</span> <span class="n">aset</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+                    <span class="n">fs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">ordered_sets</span><span class="p">[</span><span class="n">ix</span><span class="p">]</span>
+                    <span class="n">tdisp</span> <span class="o">=</span> <span class="n">perturb</span><span class="p">[</span><span class="n">ix</span><span class="p">]</span>
+                    <span class="k">if</span> <span class="n">fs</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">:</span>
+                        <span class="n">numerator</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">fs</span><span class="p">]</span><span class="o">.</span><span class="n">get_midpoint</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">sets</span><span class="p">,</span> <span class="n">tdisp</span><span class="p">)</span> <span class="o">*</span> <span class="n">aset</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
+                    <span class="k">else</span><span class="p">:</span>
+                        <span class="n">fuzzy_set</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="n">fs</span><span class="p">]</span>
+                        <span class="n">numerator</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">fuzzy_set</span><span class="o">.</span><span class="n">get_midpoint</span><span class="p">(</span><span class="n">tdisp</span><span class="p">)</span> <span class="o">*</span> <span class="n">aset</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
+                    <span class="n">denominator</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">aset</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
+
+            <span class="k">if</span> <span class="nb">sum</span><span class="p">(</span><span class="n">denominator</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
+                <span class="n">pto</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">(</span><span class="n">numerator</span><span class="p">)</span> <span class="o">/</span><span class="nb">sum</span><span class="p">(</span><span class="n">denominator</span><span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">pto</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">(</span><span class="n">numerator</span><span class="p">)</span>
+
+            <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">pto</span><span class="p">)</span>
+
+            <span class="k">if</span> <span class="ow">not</span> <span class="n">no_update</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">forecasts</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">pto</span><span class="p">)</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">residuals</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">inputs</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">forecasts</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">inputs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">sample</span><span class="p">)</span>
+
+                <span class="bp">self</span><span class="o">.</span><span class="n">inputs</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">forecasts</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">residuals</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">ret</span></div>
+
+
+<div class="viewcode-block" id="ConditionalVarianceFTS.forecast_interval"><a class="viewcode-back" href="../../../../pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.cvfts.ConditionalVarianceFTS.forecast_interval">[docs]</a>    <span class="k">def</span> <span class="nf">forecast_interval</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ndata</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="n">l</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">ndata</span><span class="p">)</span>
+
+        <span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
+
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">l</span><span class="p">):</span>
+
+            <span class="n">sample</span> <span class="o">=</span> <span class="n">ndata</span><span class="p">[</span><span class="n">k</span><span class="p">]</span>
+
+            <span class="n">perturb</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">perturbation_factors</span><span class="p">(</span><span class="n">sample</span><span class="p">)</span>
+
+            <span class="n">affected_sets</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_affected_sets</span><span class="p">(</span><span class="n">sample</span><span class="p">,</span> <span class="n">perturb</span><span class="p">)</span>
+
+            <span class="n">upper</span> <span class="o">=</span> <span class="p">[]</span>
+            <span class="n">lower</span> <span class="o">=</span> <span class="p">[]</span>
+
+            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">affected_sets</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
+                <span class="n">ix</span> <span class="o">=</span> <span class="n">affected_sets</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span>
+                <span class="n">aset</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">ordered_sets</span><span class="p">[</span><span class="n">ix</span><span class="p">]</span>
+                <span class="k">if</span> <span class="n">aset</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">:</span>
+                    <span class="n">lower</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">aset</span><span class="p">]</span><span class="o">.</span><span class="n">get_lower</span><span class="p">(</span><span class="n">perturb</span><span class="p">[</span><span class="n">ix</span><span class="p">]))</span>
+                    <span class="n">upper</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">aset</span><span class="p">]</span><span class="o">.</span><span class="n">get_upper</span><span class="p">(</span><span class="n">perturb</span><span class="p">[</span><span class="n">ix</span><span class="p">]))</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">fuzzy_set</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="n">aset</span><span class="p">]</span>
+                    <span class="n">lower</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">fuzzy_set</span><span class="o">.</span><span class="n">get_lower</span><span class="p">(</span><span class="n">perturb</span><span class="p">[</span><span class="n">ix</span><span class="p">]))</span>
+                    <span class="n">upper</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">fuzzy_set</span><span class="o">.</span><span class="n">get_upper</span><span class="p">(</span><span class="n">perturb</span><span class="p">[</span><span class="n">ix</span><span class="p">]))</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="k">for</span> <span class="n">aset</span> <span class="ow">in</span> <span class="n">affected_sets</span><span class="p">:</span>
+                    <span class="n">ix</span> <span class="o">=</span> <span class="n">aset</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+                    <span class="n">fs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">ordered_sets</span><span class="p">[</span><span class="n">ix</span><span class="p">]</span>
+                    <span class="n">tdisp</span> <span class="o">=</span> <span class="n">perturb</span><span class="p">[</span><span class="n">ix</span><span class="p">]</span>
+                    <span class="k">if</span> <span class="n">fs</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">:</span>
+                        <span class="n">lower</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">fs</span><span class="p">]</span><span class="o">.</span><span class="n">get_lower</span><span class="p">(</span><span class="n">tdisp</span><span class="p">)</span> <span class="o">*</span> <span class="n">aset</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
+                        <span class="n">upper</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">fs</span><span class="p">]</span><span class="o">.</span><span class="n">get_upper</span><span class="p">(</span><span class="n">tdisp</span><span class="p">)</span> <span class="o">*</span> <span class="n">aset</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
+                    <span class="k">else</span><span class="p">:</span>
+                        <span class="n">fuzzy_set</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="n">fs</span><span class="p">]</span>
+                        <span class="n">lower</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">fuzzy_set</span><span class="o">.</span><span class="n">get_lower</span><span class="p">(</span><span class="n">tdisp</span><span class="p">)</span> <span class="o">*</span> <span class="n">aset</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
+                        <span class="n">upper</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">fuzzy_set</span><span class="o">.</span><span class="n">get_upper</span><span class="p">(</span><span class="n">tdisp</span><span class="p">)</span> <span class="o">*</span> <span class="n">aset</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
+
+            <span class="n">itvl</span> <span class="o">=</span> <span class="p">[</span><span class="nb">sum</span><span class="p">(</span><span class="n">lower</span><span class="p">),</span> <span class="nb">sum</span><span class="p">(</span><span class="n">upper</span><span class="p">)]</span>
+
+            <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">itvl</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">ret</span></div></div>
+</pre></div>
+
+            <div class="clearer"></div>
+          </div>
+        </div>
+      </div>
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+  <h1>Source code for pyFTS.models.nonstationary.honsfts</h1><div class="highlight"><pre>
+<span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">from</span> <span class="nn">pyFTS.common</span> <span class="kn">import</span> <span class="n">FuzzySet</span><span class="p">,</span> <span class="n">FLR</span><span class="p">,</span> <span class="n">fts</span>
+<span class="kn">from</span> <span class="nn">pyFTS.models</span> <span class="kn">import</span> <span class="n">hofts</span>
+<span class="kn">from</span> <span class="nn">pyFTS.models.nonstationary</span> <span class="kn">import</span> <span class="n">common</span><span class="p">,</span> <span class="n">flrg</span><span class="p">,</span> <span class="n">nsfts</span>
+<span class="kn">from</span> <span class="nn">itertools</span> <span class="kn">import</span> <span class="n">product</span>
+
+
+<div class="viewcode-block" id="HighOrderNonStationaryFLRG"><a class="viewcode-back" href="../../../../pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.honsfts.HighOrderNonStationaryFLRG">[docs]</a><span class="k">class</span> <span class="nc">HighOrderNonStationaryFLRG</span><span class="p">(</span><span class="n">flrg</span><span class="o">.</span><span class="n">NonStationaryFLRG</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;First Order NonStationary Fuzzy Logical Relationship Group&quot;&quot;&quot;</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">order</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">(</span><span class="n">HighOrderNonStationaryFLRG</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">order</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">LHS</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span> <span class="o">=</span> <span class="p">{}</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">count</span> <span class="o">=</span> <span class="mf">0.0</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">strlhs</span> <span class="o">=</span> <span class="s2">&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">w</span> <span class="o">=</span> <span class="kc">None</span>
+
+<div class="viewcode-block" id="HighOrderNonStationaryFLRG.append_rhs"><a class="viewcode-back" href="../../../../pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.honsfts.HighOrderNonStationaryFLRG.append_rhs">[docs]</a>    <span class="k">def</span> <span class="nf">append_rhs</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">fset</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="n">count</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;count&#39;</span><span class="p">,</span><span class="mf">1.0</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">fset</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="p">[</span><span class="n">fset</span><span class="p">]</span> <span class="o">=</span> <span class="n">count</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="p">[</span><span class="n">fset</span><span class="p">]</span> <span class="o">+=</span> <span class="n">count</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">count</span> <span class="o">+=</span> <span class="n">count</span></div>
+
+<div class="viewcode-block" id="HighOrderNonStationaryFLRG.append_lhs"><a class="viewcode-back" href="../../../../pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.honsfts.HighOrderNonStationaryFLRG.append_lhs">[docs]</a>    <span class="k">def</span> <span class="nf">append_lhs</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">c</span><span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">LHS</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">c</span><span class="p">)</span></div>
+
+<div class="viewcode-block" id="HighOrderNonStationaryFLRG.weights"><a class="viewcode-back" href="../../../../pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.honsfts.HighOrderNonStationaryFLRG.weights">[docs]</a>    <span class="k">def</span> <span class="nf">weights</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">w</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">w</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="p">[</span><span class="n">c</span><span class="p">]</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">count</span> <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="o">.</span><span class="n">keys</span><span class="p">()])</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">w</span></div>
+
+<div class="viewcode-block" id="HighOrderNonStationaryFLRG.get_midpoint"><a class="viewcode-back" href="../../../../pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.honsfts.HighOrderNonStationaryFLRG.get_midpoint">[docs]</a>    <span class="k">def</span> <span class="nf">get_midpoint</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sets</span><span class="p">,</span> <span class="n">perturb</span><span class="p">):</span>
+        <span class="n">mp</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">sets</span><span class="p">[</span><span class="n">c</span><span class="p">]</span><span class="o">.</span><span class="n">get_midpoint</span><span class="p">(</span><span class="n">perturb</span><span class="p">)</span> <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="o">.</span><span class="n">keys</span><span class="p">()])</span>
+        <span class="n">midpoint</span> <span class="o">=</span> <span class="n">mp</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">weights</span><span class="p">())</span>
+        <span class="k">return</span> <span class="n">midpoint</span></div>
+
+<div class="viewcode-block" id="HighOrderNonStationaryFLRG.get_lower"><a class="viewcode-back" href="../../../../pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.honsfts.HighOrderNonStationaryFLRG.get_lower">[docs]</a>    <span class="k">def</span> <span class="nf">get_lower</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sets</span><span class="p">,</span> <span class="n">perturb</span><span class="p">):</span>
+        <span class="n">lw</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">sets</span><span class="p">[</span><span class="n">s</span><span class="p">]</span><span class="o">.</span><span class="n">get_lower</span><span class="p">(</span><span class="n">perturb</span><span class="p">)</span> <span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="o">.</span><span class="n">keys</span><span class="p">()])</span>
+        <span class="n">lower</span> <span class="o">=</span> <span class="n">lw</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">weights</span><span class="p">())</span>
+        <span class="k">return</span> <span class="n">lower</span></div>
+
+<div class="viewcode-block" id="HighOrderNonStationaryFLRG.get_upper"><a class="viewcode-back" href="../../../../pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.honsfts.HighOrderNonStationaryFLRG.get_upper">[docs]</a>    <span class="k">def</span> <span class="nf">get_upper</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sets</span><span class="p">,</span> <span class="n">perturb</span><span class="p">):</span>
+        <span class="n">up</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">sets</span><span class="p">[</span><span class="n">s</span><span class="p">]</span><span class="o">.</span><span class="n">get_upper</span><span class="p">(</span><span class="n">perturb</span><span class="p">)</span> <span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="o">.</span><span class="n">keys</span><span class="p">()])</span>
+        <span class="n">upper</span> <span class="o">=</span> <span class="n">up</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">weights</span><span class="p">())</span>
+        <span class="k">return</span> <span class="n">upper</span></div>
+
+    <span class="k">def</span> <span class="fm">__str__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="n">_str</span> <span class="o">=</span> <span class="s2">&quot;&quot;</span>
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
+            <span class="n">_str</span> <span class="o">+=</span> <span class="s2">&quot;, &quot;</span> <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">_str</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span> <span class="k">else</span> <span class="s2">&quot;&quot;</span>
+            <span class="n">_str</span> <span class="o">+=</span> <span class="n">k</span> <span class="o">+</span> <span class="s2">&quot; (&quot;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">count</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span> <span class="o">+</span> <span class="s2">&quot;)&quot;</span>
+
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_key</span><span class="p">()</span> <span class="o">+</span> <span class="s2">&quot; -&gt; &quot;</span> <span class="o">+</span> <span class="n">_str</span>
+
+    <span class="k">def</span> <span class="fm">__len__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="HighOrderNonStationaryFTS"><a class="viewcode-back" href="../../../../pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.honsfts.HighOrderNonStationaryFTS">[docs]</a><span class="k">class</span> <span class="nc">HighOrderNonStationaryFTS</span><span class="p">(</span><span class="n">nsfts</span><span class="o">.</span><span class="n">NonStationaryFTS</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;NonStationaryFTS Fuzzy Time Series&quot;&quot;&quot;</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">(</span><span class="n">HighOrderNonStationaryFTS</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="s2">&quot;High Order Non Stationary FTS&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">shortname</span> <span class="o">=</span> <span class="s2">&quot;HONSFTS&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">detail</span> <span class="o">=</span> <span class="s2">&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span> <span class="o">=</span> <span class="p">{}</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">is_high_order</span> <span class="o">=</span> <span class="kc">True</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">order</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;order&quot;</span><span class="p">,</span><span class="mi">2</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">configure_lags</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+
+<div class="viewcode-block" id="HighOrderNonStationaryFTS.configure_lags"><a class="viewcode-back" href="../../../../pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.honsfts.HighOrderNonStationaryFTS.configure_lags">[docs]</a>    <span class="k">def</span> <span class="nf">configure_lags</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="k">if</span> <span class="s2">&quot;order&quot;</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">order</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;order&quot;</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">min_order</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="s2">&quot;lags&quot;</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">lags</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;lags&quot;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">lags</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">lags</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">lags</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span></div>
+
+<div class="viewcode-block" id="HighOrderNonStationaryFTS.train"><a class="viewcode-back" href="../../../../pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.honsfts.HighOrderNonStationaryFTS.train">[docs]</a>    <span class="k">def</span> <span class="nf">train</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">generate_flrg</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">method</span> <span class="o">==</span> <span class="s1">&#39;conditional&#39;</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">forecasts</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">forecast</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">no_update</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">residuals</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">:])</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">forecasts</span><span class="p">)</span>
+
+            <span class="bp">self</span><span class="o">.</span><span class="n">variance_residual</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">var</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">residuals</span><span class="p">)</span>  <span class="c1"># np.max(self.residuals</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">mean_residual</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">residuals</span><span class="p">)</span>
+
+            <span class="bp">self</span><span class="o">.</span><span class="n">residuals</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">residuals</span><span class="p">[</span><span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">memory_window</span><span class="p">:]</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">forecasts</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">forecasts</span><span class="p">[</span><span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">memory_window</span><span class="p">:]</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">inputs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">memory_window</span><span class="p">:])</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span></div>
+
+<div class="viewcode-block" id="HighOrderNonStationaryFTS.generate_flrg"><a class="viewcode-back" href="../../../../pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.honsfts.HighOrderNonStationaryFTS.generate_flrg">[docs]</a>    <span class="k">def</span> <span class="nf">generate_flrg</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="n">l</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">,</span> <span class="n">l</span><span class="p">):</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">dump</span><span class="p">:</span> <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;FLR: &quot;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">k</span><span class="p">))</span>
+
+            <span class="n">sample</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="n">k</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">:</span> <span class="n">k</span><span class="p">]</span>
+
+            <span class="n">rhs</span> <span class="o">=</span> <span class="p">[</span><span class="n">key</span> <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">ordered_sets</span>
+                   <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="n">key</span><span class="p">]</span><span class="o">.</span><span class="n">membership</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="n">k</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">])</span> <span class="o">&gt;</span> <span class="mf">0.0</span><span class="p">]</span>
+
+            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">rhs</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+                <span class="n">rhs</span> <span class="o">=</span> <span class="p">[</span><span class="n">common</span><span class="o">.</span><span class="n">check_bounds</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="n">k</span><span class="p">],</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="p">,</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">])</span><span class="o">.</span><span class="n">name</span><span class="p">]</span>
+
+            <span class="n">lags</span> <span class="o">=</span> <span class="p">[]</span>
+
+            <span class="k">for</span> <span class="n">o</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">):</span>
+                <span class="n">tdisp</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">]</span>
+                <span class="n">lhs</span> <span class="o">=</span> <span class="p">[</span><span class="n">key</span> <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">ordered_sets</span>
+                   <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="n">key</span><span class="p">]</span><span class="o">.</span><span class="n">membership</span><span class="p">(</span><span class="n">sample</span><span class="p">[</span><span class="n">o</span><span class="p">],</span> <span class="n">tdisp</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mf">0.0</span><span class="p">]</span>
+
+                <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">lhs</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+                    <span class="n">lhs</span> <span class="o">=</span> <span class="p">[</span><span class="n">common</span><span class="o">.</span><span class="n">check_bounds</span><span class="p">(</span><span class="n">sample</span><span class="p">[</span><span class="n">o</span><span class="p">],</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="p">,</span> <span class="n">tdisp</span><span class="p">)</span><span class="o">.</span><span class="n">name</span><span class="p">]</span>
+
+                <span class="n">lags</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">lhs</span><span class="p">)</span>
+
+            <span class="c1"># Trace the possible paths</span>
+            <span class="k">for</span> <span class="n">path</span> <span class="ow">in</span> <span class="n">product</span><span class="p">(</span><span class="o">*</span><span class="n">lags</span><span class="p">):</span>
+                <span class="n">flrg</span> <span class="o">=</span> <span class="n">HighOrderNonStationaryFLRG</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">)</span>
+
+                <span class="k">for</span> <span class="n">c</span><span class="p">,</span> <span class="n">e</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="n">start</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
+                    <span class="n">flrg</span><span class="o">.</span><span class="n">append_lhs</span><span class="p">(</span><span class="n">e</span><span class="p">)</span>
+
+                <span class="k">if</span> <span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">:</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()]</span> <span class="o">=</span> <span class="n">flrg</span><span class="p">;</span>
+
+                <span class="k">for</span> <span class="n">st</span> <span class="ow">in</span> <span class="n">rhs</span><span class="p">:</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()]</span><span class="o">.</span><span class="n">append_rhs</span><span class="p">(</span><span class="n">st</span><span class="p">)</span></div>
+
+    <span class="k">def</span> <span class="nf">_affected_flrgs</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sample</span><span class="p">,</span> <span class="n">perturb</span><span class="p">):</span>
+
+        <span class="n">affected_flrgs</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="n">affected_flrgs_memberships</span> <span class="o">=</span> <span class="p">[]</span>
+
+        <span class="n">lags</span> <span class="o">=</span> <span class="p">[]</span>
+
+        <span class="k">for</span> <span class="n">ct</span><span class="p">,</span> <span class="n">dat</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">sample</span><span class="p">):</span>
+            <span class="n">affected_sets</span> <span class="o">=</span> <span class="p">[</span><span class="n">key</span> <span class="k">for</span> <span class="n">ct</span><span class="p">,</span> <span class="n">key</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">ordered_sets</span><span class="p">)</span>
+                             <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="n">key</span><span class="p">]</span><span class="o">.</span><span class="n">membership</span><span class="p">(</span><span class="n">dat</span><span class="p">,</span> <span class="n">perturb</span><span class="p">[</span><span class="n">ct</span><span class="p">])</span> <span class="o">&gt;</span> <span class="mf">0.0</span><span class="p">]</span>
+
+            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">affected_sets</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+
+                <span class="k">if</span> <span class="n">dat</span> <span class="o">&lt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">lower_set</span><span class="p">()</span><span class="o">.</span><span class="n">get_lower</span><span class="p">(</span><span class="n">perturb</span><span class="p">[</span><span class="mi">0</span><span class="p">]):</span>
+                    <span class="n">affected_sets</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">lower_set</span><span class="p">()</span><span class="o">.</span><span class="n">name</span><span class="p">)</span>
+                <span class="k">elif</span> <span class="n">dat</span> <span class="o">&gt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">upper_set</span><span class="p">()</span><span class="o">.</span><span class="n">get_upper</span><span class="p">(</span><span class="n">perturb</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]):</span>
+                    <span class="n">affected_sets</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">upper_set</span><span class="p">()</span><span class="o">.</span><span class="n">name</span><span class="p">)</span>
+
+            <span class="n">lags</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">affected_sets</span><span class="p">)</span>
+
+        <span class="c1"># Build the tree with all possible paths</span>
+
+        <span class="c1"># Trace the possible paths</span>
+        <span class="k">for</span> <span class="n">path</span> <span class="ow">in</span> <span class="n">product</span><span class="p">(</span><span class="o">*</span><span class="n">lags</span><span class="p">):</span>
+
+            <span class="n">flrg</span> <span class="o">=</span> <span class="n">HighOrderNonStationaryFLRG</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">)</span>
+
+            <span class="k">for</span> <span class="n">kk</span> <span class="ow">in</span> <span class="n">path</span><span class="p">:</span>
+                <span class="n">flrg</span><span class="o">.</span><span class="n">append_lhs</span><span class="p">(</span><span class="n">kk</span><span class="p">)</span>
+
+            <span class="n">affected_flrgs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">flrg</span><span class="p">)</span>
+            <span class="n">mv</span> <span class="o">=</span> <span class="p">[]</span>
+            <span class="k">for</span> <span class="n">ct</span><span class="p">,</span> <span class="n">dat</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">sample</span><span class="p">):</span>
+                <span class="n">fset</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="n">flrg</span><span class="o">.</span><span class="n">LHS</span><span class="p">[</span><span class="n">ct</span><span class="p">]]</span>
+                <span class="n">ix</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">ordered_sets</span><span class="o">.</span><span class="n">index</span><span class="p">(</span><span class="n">flrg</span><span class="o">.</span><span class="n">LHS</span><span class="p">[</span><span class="n">ct</span><span class="p">])</span>
+                <span class="n">tmp</span> <span class="o">=</span> <span class="n">fset</span><span class="o">.</span><span class="n">membership</span><span class="p">(</span><span class="n">dat</span><span class="p">,</span> <span class="n">perturb</span><span class="p">[</span><span class="n">ix</span><span class="p">])</span>
+
+                <span class="n">mv</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">tmp</span><span class="p">)</span>
+
+            <span class="n">affected_flrgs_memberships</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">prod</span><span class="p">(</span><span class="n">mv</span><span class="p">))</span>
+
+        <span class="k">return</span> <span class="p">[</span><span class="n">affected_flrgs</span><span class="p">,</span> <span class="n">affected_flrgs_memberships</span><span class="p">]</span>
+
+<div class="viewcode-block" id="HighOrderNonStationaryFTS.forecast"><a class="viewcode-back" href="../../../../pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.honsfts.HighOrderNonStationaryFTS.forecast">[docs]</a>    <span class="k">def</span> <span class="nf">forecast</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ndata</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+
+        <span class="n">explain</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;explain&#39;</span><span class="p">,</span> <span class="kc">False</span><span class="p">)</span>
+
+        <span class="n">time_displacement</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;time_displacement&quot;</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
+
+        <span class="n">window_size</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;window_size&quot;</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+
+        <span class="n">no_update</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;no_update&quot;</span><span class="p">,</span> <span class="kc">False</span><span class="p">)</span>
+
+        <span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
+
+        <span class="n">l</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">ndata</span><span class="p">)</span> <span class="k">if</span> <span class="ow">not</span> <span class="n">explain</span> <span class="k">else</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span> <span class="o">+</span> <span class="mi">1</span>
+
+        <span class="k">if</span> <span class="n">l</span> <span class="o">&lt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">ndata</span>
+        <span class="k">elif</span> <span class="n">l</span> <span class="o">==</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">:</span>
+            <span class="n">l</span> <span class="o">+=</span> <span class="mi">1</span>
+
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">,</span> <span class="n">l</span><span class="p">):</span>
+
+            <span class="n">sample</span> <span class="o">=</span> <span class="n">ndata</span><span class="p">[</span><span class="n">k</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">:</span> <span class="n">k</span><span class="p">]</span>
+
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">method</span> <span class="o">==</span> <span class="s1">&#39;unconditional&#39;</span><span class="p">:</span>
+                <span class="n">perturb</span> <span class="o">=</span> <span class="n">common</span><span class="o">.</span><span class="n">window_index</span><span class="p">(</span><span class="n">k</span> <span class="o">+</span> <span class="n">time_displacement</span><span class="p">,</span> <span class="n">window_size</span><span class="p">)</span>
+            <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">method</span> <span class="o">==</span> <span class="s1">&#39;conditional&#39;</span><span class="p">:</span>
+                <span class="k">if</span> <span class="n">no_update</span><span class="p">:</span>
+                    <span class="n">perturb</span> <span class="o">=</span> <span class="p">[[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">partitions</span><span class="p">)]</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">perturb</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">conditional_perturbation_factors</span><span class="p">(</span><span class="n">sample</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
+
+            <span class="n">affected_flrgs</span><span class="p">,</span> <span class="n">affected_flrgs_memberships</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_affected_flrgs</span><span class="p">(</span><span class="n">sample</span><span class="p">,</span> <span class="n">perturb</span><span class="p">)</span>
+
+            <span class="n">tmp</span> <span class="o">=</span> <span class="p">[]</span>
+
+            <span class="n">perturb2</span> <span class="o">=</span> <span class="n">perturb</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">affected_flrgs</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+                <span class="n">tmp</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">common</span><span class="o">.</span><span class="n">check_bounds</span><span class="p">(</span><span class="n">sample</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">,</span> <span class="n">perturb2</span><span class="p">))</span>
+            <span class="k">elif</span> <span class="nb">len</span><span class="p">(</span><span class="n">affected_flrgs</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
+                <span class="n">flrg</span> <span class="o">=</span> <span class="n">affected_flrgs</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+                <span class="k">if</span> <span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">:</span>
+                    <span class="n">tmp</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()]</span><span class="o">.</span><span class="n">get_midpoint</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">,</span> <span class="n">perturb2</span><span class="p">))</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">fset</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="n">flrg</span><span class="o">.</span><span class="n">LHS</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]]</span>
+                    <span class="n">ix</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">ordered_sets</span><span class="o">.</span><span class="n">index</span><span class="p">(</span><span class="n">flrg</span><span class="o">.</span><span class="n">LHS</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
+                    <span class="n">tmp</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">fset</span><span class="o">.</span><span class="n">get_midpoint</span><span class="p">(</span><span class="n">perturb</span><span class="p">[</span><span class="n">ix</span><span class="p">]))</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="k">for</span> <span class="n">ct</span><span class="p">,</span> <span class="n">aset</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">affected_flrgs</span><span class="p">):</span>
+                    <span class="k">if</span> <span class="n">aset</span><span class="o">.</span><span class="n">get_key</span><span class="p">()</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">:</span>
+
+                        <span class="n">tmp</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">aset</span><span class="o">.</span><span class="n">get_key</span><span class="p">()]</span><span class="o">.</span><span class="n">get_midpoint</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">,</span> <span class="n">perturb2</span><span class="p">)</span> <span class="o">*</span>
+                                   <span class="n">affected_flrgs_memberships</span><span class="p">[</span><span class="n">ct</span><span class="p">])</span>
+                    <span class="k">else</span><span class="p">:</span>
+                        <span class="n">fset</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="n">aset</span><span class="o">.</span><span class="n">LHS</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]]</span>
+                        <span class="n">ix</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">ordered_sets</span><span class="o">.</span><span class="n">index</span><span class="p">(</span><span class="n">aset</span><span class="o">.</span><span class="n">LHS</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
+                        <span class="n">tmp</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">fset</span><span class="o">.</span><span class="n">get_midpoint</span><span class="p">(</span><span class="n">perturb</span><span class="p">[</span><span class="n">ix</span><span class="p">])</span><span class="o">*</span><span class="n">affected_flrgs_memberships</span><span class="p">[</span><span class="n">ct</span><span class="p">])</span>
+            <span class="n">pto</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">(</span><span class="n">tmp</span><span class="p">)</span>
+
+            <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">pto</span><span class="p">)</span>
+
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">method</span> <span class="o">==</span> <span class="s1">&#39;conditional&#39;</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">no_update</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">forecasts</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">pto</span><span class="p">)</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">residuals</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">inputs</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">forecasts</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">inputs</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">sample</span><span class="p">)</span>
+
+                <span class="k">for</span> <span class="n">g</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">):</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">inputs</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">forecasts</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">residuals</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">ret</span></div>
+
+    <span class="k">def</span> <span class="fm">__str__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="n">tmp</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">+</span> <span class="s2">&quot;:</span><span class="se">\n</span><span class="s2">&quot;</span>
+        <span class="k">for</span> <span class="n">r</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">:</span>
+            <span class="n">tmp</span> <span class="o">=</span> <span class="s2">&quot;</span><span class="si">{0}{1}</span><span class="se">\n</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">tmp</span><span class="p">,</span> <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">r</span><span class="p">]))</span>
+        <span class="k">return</span> <span class="n">tmp</span></div>
+</pre></div>
+
+            <div class="clearer"></div>
+          </div>
+        </div>
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+        <li class="nav-item nav-item-this"><a href="">pyFTS.models.nonstationary.honsfts</a></li> 
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diff --git a/docs/build/html/_modules/pyFTS/models/nonstationary/nsfts.html b/docs/build/html/_modules/pyFTS/models/nonstationary/nsfts.html
new file mode 100644
index 0000000..a09da74
--- /dev/null
+++ b/docs/build/html/_modules/pyFTS/models/nonstationary/nsfts.html
@@ -0,0 +1,458 @@
+
+
+<!doctype html>
+
+<html>
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+        <li class="nav-item nav-item-this"><a href="">pyFTS.models.nonstationary.nsfts</a></li> 
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+            
+  <h1>Source code for pyFTS.models.nonstationary.nsfts</h1><div class="highlight"><pre>
+<span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">from</span> <span class="nn">pyFTS.common</span> <span class="kn">import</span> <span class="n">FLR</span><span class="p">,</span> <span class="n">fts</span>
+<span class="kn">from</span> <span class="nn">pyFTS.models.nonstationary</span> <span class="kn">import</span> <span class="n">common</span><span class="p">,</span> <span class="n">flrg</span>
+
+
+<div class="viewcode-block" id="ConventionalNonStationaryFLRG"><a class="viewcode-back" href="../../../../pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.nsfts.ConventionalNonStationaryFLRG">[docs]</a><span class="k">class</span> <span class="nc">ConventionalNonStationaryFLRG</span><span class="p">(</span><span class="n">flrg</span><span class="o">.</span><span class="n">NonStationaryFLRG</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;First Order NonStationary Fuzzy Logical Relationship Group&quot;&quot;&quot;</span>
+
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">LHS</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">(</span><span class="n">ConventionalNonStationaryFLRG</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">LHS</span> <span class="o">=</span> <span class="n">LHS</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span> <span class="o">=</span> <span class="nb">set</span><span class="p">()</span>
+
+<div class="viewcode-block" id="ConventionalNonStationaryFLRG.get_key"><a class="viewcode-back" href="../../../../pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.nsfts.ConventionalNonStationaryFLRG.get_key">[docs]</a>    <span class="k">def</span> <span class="nf">get_key</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">LHS</span></div>
+
+<div class="viewcode-block" id="ConventionalNonStationaryFLRG.append_rhs"><a class="viewcode-back" href="../../../../pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.nsfts.ConventionalNonStationaryFLRG.append_rhs">[docs]</a>    <span class="k">def</span> <span class="nf">append_rhs</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">c</span><span class="p">)</span></div>
+
+    <span class="k">def</span> <span class="fm">__str__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="n">tmp</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">LHS</span> <span class="o">+</span> <span class="s2">&quot; -&gt; &quot;</span>
+        <span class="n">tmp2</span> <span class="o">=</span> <span class="s2">&quot;&quot;</span>
+        <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="nb">sorted</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="p">):</span>
+            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">tmp2</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
+                <span class="n">tmp2</span> <span class="o">=</span> <span class="n">tmp2</span> <span class="o">+</span> <span class="s2">&quot;,&quot;</span>
+            <span class="n">tmp2</span> <span class="o">=</span> <span class="n">tmp2</span> <span class="o">+</span> <span class="n">c</span>
+        <span class="k">return</span> <span class="n">tmp</span> <span class="o">+</span> <span class="n">tmp2</span></div>
+
+
+<div class="viewcode-block" id="WeightedNonStationaryFLRG"><a class="viewcode-back" href="../../../../pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.nsfts.WeightedNonStationaryFLRG">[docs]</a><span class="k">class</span> <span class="nc">WeightedNonStationaryFLRG</span><span class="p">(</span><span class="n">flrg</span><span class="o">.</span><span class="n">NonStationaryFLRG</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;First Order NonStationary Fuzzy Logical Relationship Group&quot;&quot;&quot;</span>
+
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">LHS</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">(</span><span class="n">WeightedNonStationaryFLRG</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">LHS</span> <span class="o">=</span> <span class="n">LHS</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span> <span class="o">=</span> <span class="p">{}</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">count</span> <span class="o">=</span> <span class="mf">0.0</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">strlhs</span> <span class="o">=</span> <span class="s2">&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">w</span> <span class="o">=</span> <span class="kc">None</span>
+
+<div class="viewcode-block" id="WeightedNonStationaryFLRG.get_key"><a class="viewcode-back" href="../../../../pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.nsfts.WeightedNonStationaryFLRG.get_key">[docs]</a>    <span class="k">def</span> <span class="nf">get_key</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">LHS</span></div>
+
+<div class="viewcode-block" id="WeightedNonStationaryFLRG.append_rhs"><a class="viewcode-back" href="../../../../pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.nsfts.WeightedNonStationaryFLRG.append_rhs">[docs]</a>    <span class="k">def</span> <span class="nf">append_rhs</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="k">if</span> <span class="n">c</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="p">[</span><span class="n">c</span><span class="p">]</span> <span class="o">=</span> <span class="mi">1</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="p">[</span><span class="n">c</span><span class="p">]</span> <span class="o">+=</span> <span class="mi">1</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">count</span> <span class="o">+=</span> <span class="mi">1</span></div>
+
+<div class="viewcode-block" id="WeightedNonStationaryFLRG.weights"><a class="viewcode-back" href="../../../../pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.nsfts.WeightedNonStationaryFLRG.weights">[docs]</a>    <span class="k">def</span> <span class="nf">weights</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">w</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">w</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="p">[</span><span class="n">c</span><span class="p">]</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">count</span> <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="o">.</span><span class="n">keys</span><span class="p">()])</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">w</span></div>
+
+<div class="viewcode-block" id="WeightedNonStationaryFLRG.get_midpoint"><a class="viewcode-back" href="../../../../pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.nsfts.WeightedNonStationaryFLRG.get_midpoint">[docs]</a>    <span class="k">def</span> <span class="nf">get_midpoint</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sets</span><span class="p">,</span> <span class="n">perturb</span><span class="p">):</span>
+        <span class="n">mp</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">sets</span><span class="p">[</span><span class="n">c</span><span class="p">]</span><span class="o">.</span><span class="n">get_midpoint</span><span class="p">(</span><span class="n">perturb</span><span class="p">)</span> <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="o">.</span><span class="n">keys</span><span class="p">()])</span>
+        <span class="n">midpoint</span> <span class="o">=</span> <span class="n">mp</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">weights</span><span class="p">())</span>
+        <span class="k">return</span> <span class="n">midpoint</span></div>
+
+    <span class="k">def</span> <span class="fm">__str__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="n">_str</span> <span class="o">=</span> <span class="s2">&quot;&quot;</span>
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
+            <span class="n">_str</span> <span class="o">+=</span> <span class="s2">&quot;, &quot;</span> <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">_str</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span> <span class="k">else</span> <span class="s2">&quot;&quot;</span>
+            <span class="n">_str</span> <span class="o">+=</span> <span class="n">k</span> <span class="o">+</span> <span class="s2">&quot; (&quot;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">count</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span> <span class="o">+</span> <span class="s2">&quot;)&quot;</span>
+
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_key</span><span class="p">()</span> <span class="o">+</span> <span class="s2">&quot; -&gt; &quot;</span> <span class="o">+</span> <span class="n">_str</span></div>
+
+
+<div class="viewcode-block" id="NonStationaryFTS"><a class="viewcode-back" href="../../../../pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.nsfts.NonStationaryFTS">[docs]</a><span class="k">class</span> <span class="nc">NonStationaryFTS</span><span class="p">(</span><span class="n">fts</span><span class="o">.</span><span class="n">FTS</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;NonStationaryFTS Fuzzy Time Series&quot;&quot;&quot;</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">(</span><span class="n">NonStationaryFTS</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="s2">&quot;Non Stationary FTS&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">shortname</span> <span class="o">=</span> <span class="s2">&quot;NSFTS&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">detail</span> <span class="o">=</span> <span class="s2">&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span> <span class="o">=</span> <span class="p">{}</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">method</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;method&#39;</span><span class="p">,</span><span class="s1">&#39;conditional&#39;</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">is_high_order</span> <span class="o">=</span> <span class="kc">False</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">append_transformation</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">transformation</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">method</span> <span class="o">==</span> <span class="s1">&#39;conditional&#39;</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">min_stack</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">max_stack</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">uod_clip</span> <span class="o">=</span> <span class="kc">False</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">order</span> <span class="o">=</span> <span class="mi">1</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">min_order</span> <span class="o">=</span> <span class="mi">1</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span> <span class="o">=</span> <span class="mi">1</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">inputs</span> <span class="o">=</span> <span class="p">[]</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">forecasts</span> <span class="o">=</span> <span class="p">[]</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">residuals</span> <span class="o">=</span> <span class="p">[]</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">variance_residual</span> <span class="o">=</span> <span class="mf">0.</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">mean_residual</span> <span class="o">=</span> <span class="mf">0.</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">memory_window</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;memory_window&quot;</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span>
+
+<div class="viewcode-block" id="NonStationaryFTS.generate_flrg"><a class="viewcode-back" href="../../../../pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.nsfts.NonStationaryFTS.generate_flrg">[docs]</a>    <span class="k">def</span> <span class="nf">generate_flrg</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">flrs</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="k">for</span> <span class="n">flr</span> <span class="ow">in</span> <span class="n">flrs</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">flr</span><span class="o">.</span><span class="n">LHS</span><span class="o">.</span><span class="n">name</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">flr</span><span class="o">.</span><span class="n">LHS</span><span class="o">.</span><span class="n">name</span><span class="p">]</span><span class="o">.</span><span class="n">append_rhs</span><span class="p">(</span><span class="n">flr</span><span class="o">.</span><span class="n">RHS</span><span class="o">.</span><span class="n">name</span><span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">flr</span><span class="o">.</span><span class="n">LHS</span><span class="o">.</span><span class="n">name</span><span class="p">]</span> <span class="o">=</span> <span class="n">ConventionalNonStationaryFLRG</span><span class="p">(</span><span class="n">flr</span><span class="o">.</span><span class="n">LHS</span><span class="o">.</span><span class="n">name</span><span class="p">)</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">flr</span><span class="o">.</span><span class="n">LHS</span><span class="o">.</span><span class="n">name</span><span class="p">]</span><span class="o">.</span><span class="n">append_rhs</span><span class="p">(</span><span class="n">flr</span><span class="o">.</span><span class="n">RHS</span><span class="o">.</span><span class="n">name</span><span class="p">)</span></div>
+
+    <span class="k">def</span> <span class="nf">_smooth</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">a</span><span class="p">):</span>
+        <span class="k">return</span> <span class="o">.</span><span class="mi">1</span> <span class="o">*</span> <span class="n">a</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="o">.</span><span class="mi">3</span> <span class="o">*</span> <span class="n">a</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="o">.</span><span class="mi">6</span> <span class="o">*</span> <span class="n">a</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span>
+
+<div class="viewcode-block" id="NonStationaryFTS.train"><a class="viewcode-back" href="../../../../pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.nsfts.NonStationaryFTS.train">[docs]</a>    <span class="k">def</span> <span class="nf">train</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">method</span> <span class="o">==</span> <span class="s1">&#39;unconditional&#39;</span><span class="p">:</span>
+            <span class="n">window_size</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;parameters&#39;</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+            <span class="n">tmpdata</span> <span class="o">=</span> <span class="n">common</span><span class="o">.</span><span class="n">fuzzySeries</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">,</span>
+                                         <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">ordered_sets</span><span class="p">,</span>
+                                         <span class="n">window_size</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s1">&#39;fuzzy&#39;</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">tmpdata</span> <span class="o">=</span> <span class="n">common</span><span class="o">.</span><span class="n">fuzzySeries</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">,</span>
+                                 <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">ordered_sets</span><span class="p">,</span>
+                                 <span class="n">method</span><span class="o">=</span><span class="s1">&#39;fuzzy&#39;</span><span class="p">,</span> <span class="n">const_t</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+
+        <span class="n">flrs</span> <span class="o">=</span> <span class="n">FLR</span><span class="o">.</span><span class="n">generate_non_recurrent_flrs</span><span class="p">(</span><span class="n">tmpdata</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">generate_flrg</span><span class="p">(</span><span class="n">flrs</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">method</span> <span class="o">==</span> <span class="s1">&#39;conditional&#39;</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">forecasts</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">forecast</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">no_update</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">residuals</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="mi">1</span><span class="p">:])</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">forecasts</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
+
+            <span class="bp">self</span><span class="o">.</span><span class="n">variance_residual</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">var</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">residuals</span><span class="p">)</span>  <span class="c1"># np.max(self.residuals</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">mean_residual</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">residuals</span><span class="p">)</span>
+
+            <span class="bp">self</span><span class="o">.</span><span class="n">residuals</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">residuals</span><span class="p">[</span><span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">memory_window</span><span class="p">:]</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">forecasts</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">forecasts</span><span class="p">[</span><span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">memory_window</span><span class="p">:]</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">inputs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">memory_window</span><span class="p">:])</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span></div>
+
+<div class="viewcode-block" id="NonStationaryFTS.conditional_perturbation_factors"><a class="viewcode-back" href="../../../../pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.nsfts.NonStationaryFTS.conditional_perturbation_factors">[docs]</a>    <span class="k">def</span> <span class="nf">conditional_perturbation_factors</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="n">npart</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">)</span>
+        <span class="n">_max</span> <span class="o">=</span> <span class="mi">0</span>
+        <span class="n">_min</span> <span class="o">=</span> <span class="mi">0</span>
+        <span class="k">if</span> <span class="n">data</span> <span class="o">&lt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">original_min</span><span class="p">:</span>
+            <span class="n">_min</span> <span class="o">=</span> <span class="n">data</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">original_min</span> <span class="k">if</span> <span class="n">data</span> <span class="o">&lt;</span> <span class="mi">0</span> <span class="k">else</span> <span class="bp">self</span><span class="o">.</span><span class="n">original_min</span> <span class="o">-</span> <span class="n">data</span>
+        <span class="k">elif</span> <span class="n">data</span> <span class="o">&gt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">original_max</span><span class="p">:</span>
+            <span class="n">_max</span> <span class="o">=</span> <span class="n">data</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">original_max</span> <span class="k">if</span> <span class="n">data</span> <span class="o">&gt;</span> <span class="mi">0</span> <span class="k">else</span> <span class="bp">self</span><span class="o">.</span><span class="n">original_max</span> <span class="o">-</span> <span class="n">data</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">min_stack</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">min_stack</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">_min</span><span class="p">)</span>
+        <span class="n">_min</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">min_stack</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">max_stack</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">max_stack</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">_max</span><span class="p">)</span>
+        <span class="n">_max</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">max_stack</span><span class="p">)</span>
+
+        <span class="n">_range</span> <span class="o">=</span> <span class="p">(</span><span class="n">_max</span> <span class="o">-</span> <span class="n">_min</span><span class="p">)</span><span class="o">/</span><span class="mi">2</span>
+
+        <span class="n">translate</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="n">_min</span><span class="p">,</span> <span class="n">_max</span><span class="p">,</span> <span class="n">npart</span><span class="p">)</span>
+
+        <span class="n">var</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">std</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">residuals</span><span class="p">)</span>
+
+        <span class="n">var</span> <span class="o">=</span> <span class="mi">0</span> <span class="k">if</span> <span class="n">var</span> <span class="o">&lt;</span> <span class="mi">1</span> <span class="k">else</span> <span class="n">var</span>
+
+        <span class="n">loc</span> <span class="o">=</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">mean_residual</span> <span class="o">+</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">residuals</span><span class="p">))</span>
+
+        <span class="n">location</span> <span class="o">=</span> <span class="p">[</span><span class="n">_range</span> <span class="o">+</span> <span class="n">w</span> <span class="o">+</span> <span class="n">loc</span> <span class="o">+</span> <span class="n">k</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="o">-</span><span class="n">var</span><span class="p">,</span><span class="n">var</span><span class="p">,</span> <span class="n">npart</span><span class="p">)</span> <span class="k">for</span> <span class="n">w</span> <span class="ow">in</span> <span class="n">translate</span><span class="p">]</span>
+
+        <span class="n">scale</span> <span class="o">=</span> <span class="p">[</span><span class="nb">abs</span><span class="p">(</span><span class="n">location</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="n">location</span><span class="p">[</span><span class="mi">2</span><span class="p">])]</span>
+        <span class="n">scale</span><span class="o">.</span><span class="n">extend</span><span class="p">([</span><span class="nb">abs</span><span class="p">(</span><span class="n">location</span><span class="p">[</span><span class="n">k</span> <span class="o">-</span> <span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">location</span><span class="p">[</span><span class="n">k</span> <span class="o">+</span> <span class="mi">1</span><span class="p">])</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">npart</span><span class="p">)])</span>
+        <span class="n">scale</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">abs</span><span class="p">(</span><span class="n">location</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">location</span><span class="p">[</span><span class="o">-</span><span class="mi">3</span><span class="p">]))</span>
+
+        <span class="n">perturb</span> <span class="o">=</span> <span class="p">[[</span><span class="n">location</span><span class="p">[</span><span class="n">k</span><span class="p">],</span> <span class="n">scale</span><span class="p">[</span><span class="n">k</span><span class="p">]]</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">npart</span><span class="p">)]</span>
+
+        <span class="k">return</span> <span class="n">perturb</span></div>
+
+    <span class="k">def</span> <span class="nf">_affected_sets</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sample</span><span class="p">,</span> <span class="n">perturb</span><span class="p">):</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">method</span> <span class="o">==</span> <span class="s1">&#39;conditional&#39;</span><span class="p">:</span>
+
+            <span class="n">affected_sets</span> <span class="o">=</span> <span class="p">[[</span><span class="n">ct</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="n">key</span><span class="p">]</span><span class="o">.</span><span class="n">membership</span><span class="p">(</span><span class="n">sample</span><span class="p">,</span> <span class="n">perturb</span><span class="p">[</span><span class="n">ct</span><span class="p">])]</span>
+                             <span class="k">for</span> <span class="n">ct</span><span class="p">,</span> <span class="n">key</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">ordered_sets</span><span class="p">)</span>
+                             <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="n">key</span><span class="p">]</span><span class="o">.</span><span class="n">membership</span><span class="p">(</span><span class="n">sample</span><span class="p">,</span> <span class="n">perturb</span><span class="p">[</span><span class="n">ct</span><span class="p">])</span> <span class="o">&gt;</span> <span class="mf">0.0</span><span class="p">]</span>
+
+            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">affected_sets</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+                <span class="k">if</span> <span class="n">sample</span> <span class="o">&lt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">lower_set</span><span class="p">()</span><span class="o">.</span><span class="n">get_lower</span><span class="p">(</span><span class="n">perturb</span><span class="p">[</span><span class="mi">0</span><span class="p">]):</span>
+                    <span class="n">affected_sets</span><span class="o">.</span><span class="n">append</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">])</span>
+                <span class="k">elif</span> <span class="n">sample</span> <span class="o">&gt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">upper_set</span><span class="p">()</span><span class="o">.</span><span class="n">get_upper</span><span class="p">(</span><span class="n">perturb</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]):</span>
+                    <span class="n">affected_sets</span><span class="o">.</span><span class="n">append</span><span class="p">([</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">sets</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">])</span>
+
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">affected_sets</span> <span class="o">=</span> <span class="p">[[</span><span class="n">ct</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="n">key</span><span class="p">]</span><span class="o">.</span><span class="n">membership</span><span class="p">(</span><span class="n">sample</span><span class="p">,</span> <span class="n">perturb</span><span class="p">)]</span>
+                             <span class="k">for</span> <span class="n">ct</span><span class="p">,</span> <span class="n">key</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">ordered_sets</span><span class="p">)</span>
+                             <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="n">key</span><span class="p">]</span><span class="o">.</span><span class="n">membership</span><span class="p">(</span><span class="n">sample</span><span class="p">,</span> <span class="n">perturb</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mf">0.0</span><span class="p">]</span>
+
+            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">affected_sets</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+
+                <span class="k">if</span> <span class="n">sample</span> <span class="o">&lt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">lower_set</span><span class="p">()</span><span class="o">.</span><span class="n">get_lower</span><span class="p">(</span><span class="n">perturb</span><span class="p">):</span>
+                    <span class="n">affected_sets</span><span class="o">.</span><span class="n">append</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">])</span>
+                <span class="k">elif</span> <span class="n">sample</span> <span class="o">&gt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">upper_set</span><span class="p">()</span><span class="o">.</span><span class="n">get_upper</span><span class="p">(</span><span class="n">perturb</span><span class="p">):</span>
+                    <span class="n">affected_sets</span><span class="o">.</span><span class="n">append</span><span class="p">([</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">sets</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">])</span>
+
+        <span class="k">return</span> <span class="n">affected_sets</span>
+
+<div class="viewcode-block" id="NonStationaryFTS.forecast"><a class="viewcode-back" href="../../../../pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.nsfts.NonStationaryFTS.forecast">[docs]</a>    <span class="k">def</span> <span class="nf">forecast</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ndata</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+
+        <span class="n">time_displacement</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;time_displacement&quot;</span><span class="p">,</span><span class="mi">0</span><span class="p">)</span>
+
+        <span class="n">window_size</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;window_size&quot;</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+
+        <span class="n">no_update</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;no_update&quot;</span><span class="p">,</span> <span class="kc">False</span><span class="p">)</span>
+
+        <span class="n">l</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">ndata</span><span class="p">)</span>
+
+        <span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
+
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">l</span><span class="p">):</span>
+
+            <span class="n">sample</span> <span class="o">=</span> <span class="n">ndata</span><span class="p">[</span><span class="n">k</span><span class="p">]</span>
+
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">method</span> <span class="o">==</span> <span class="s1">&#39;unconditional&#39;</span><span class="p">:</span>
+                <span class="n">perturb</span> <span class="o">=</span> <span class="n">common</span><span class="o">.</span><span class="n">window_index</span><span class="p">(</span><span class="n">k</span> <span class="o">+</span> <span class="n">time_displacement</span><span class="p">,</span> <span class="n">window_size</span><span class="p">)</span>
+            <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">method</span> <span class="o">==</span> <span class="s1">&#39;conditional&#39;</span><span class="p">:</span>
+                <span class="k">if</span> <span class="ow">not</span> <span class="n">no_update</span><span class="p">:</span>
+                    <span class="n">perturb</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">conditional_perturbation_factors</span><span class="p">(</span><span class="n">sample</span><span class="p">)</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">perturb</span> <span class="o">=</span> <span class="p">[[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">))]</span>
+
+            <span class="n">affected_sets</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_affected_sets</span><span class="p">(</span><span class="n">sample</span><span class="p">,</span> <span class="n">perturb</span><span class="p">)</span>
+
+            <span class="n">numerator</span> <span class="o">=</span> <span class="p">[]</span>
+            <span class="n">denominator</span> <span class="o">=</span> <span class="p">[]</span>
+
+            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">affected_sets</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
+                <span class="n">ix</span> <span class="o">=</span> <span class="n">affected_sets</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span>
+                <span class="n">aset</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">ordered_sets</span><span class="p">[</span><span class="n">ix</span><span class="p">]</span>
+                <span class="k">if</span> <span class="n">aset</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">:</span>
+                    <span class="n">numerator</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">aset</span><span class="p">]</span><span class="o">.</span><span class="n">get_midpoint</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">,</span> <span class="n">perturb</span><span class="p">[</span><span class="n">ix</span><span class="p">]))</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">fuzzy_set</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="n">aset</span><span class="p">]</span>
+                    <span class="n">numerator</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">fuzzy_set</span><span class="o">.</span><span class="n">get_midpoint</span><span class="p">(</span><span class="n">perturb</span><span class="p">[</span><span class="n">ix</span><span class="p">]))</span>
+                <span class="n">denominator</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="k">for</span> <span class="n">aset</span> <span class="ow">in</span> <span class="n">affected_sets</span><span class="p">:</span>
+                    <span class="n">ix</span> <span class="o">=</span> <span class="n">aset</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+                    <span class="n">fs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">ordered_sets</span><span class="p">[</span><span class="n">ix</span><span class="p">]</span>
+                    <span class="n">tdisp</span> <span class="o">=</span> <span class="n">perturb</span><span class="p">[</span><span class="n">ix</span><span class="p">]</span>
+                    <span class="k">if</span> <span class="n">fs</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">:</span>
+                        <span class="n">numerator</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">fs</span><span class="p">]</span><span class="o">.</span><span class="n">get_midpoint</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">,</span> <span class="n">tdisp</span><span class="p">)</span> <span class="o">*</span> <span class="n">aset</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
+                    <span class="k">else</span><span class="p">:</span>
+                        <span class="n">fuzzy_set</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="n">fs</span><span class="p">]</span>
+                        <span class="n">numerator</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">fuzzy_set</span><span class="o">.</span><span class="n">get_midpoint</span><span class="p">(</span><span class="n">tdisp</span><span class="p">)</span> <span class="o">*</span> <span class="n">aset</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
+                    <span class="n">denominator</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">aset</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
+
+            <span class="k">if</span> <span class="nb">sum</span><span class="p">(</span><span class="n">denominator</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
+                <span class="n">pto</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">(</span><span class="n">numerator</span><span class="p">)</span> <span class="o">/</span> <span class="nb">sum</span><span class="p">(</span><span class="n">denominator</span><span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">pto</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">(</span><span class="n">numerator</span><span class="p">)</span>
+
+            <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">pto</span><span class="p">)</span>
+
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">method</span> <span class="o">==</span> <span class="s1">&#39;conditional&#39;</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">no_update</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">forecasts</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">pto</span><span class="p">)</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">residuals</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">inputs</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">forecasts</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">inputs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">sample</span><span class="p">)</span>
+
+                <span class="bp">self</span><span class="o">.</span><span class="n">inputs</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">forecasts</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">residuals</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">ret</span></div>
+
+<div class="viewcode-block" id="NonStationaryFTS.forecast_interval"><a class="viewcode-back" href="../../../../pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.nsfts.NonStationaryFTS.forecast_interval">[docs]</a>    <span class="k">def</span> <span class="nf">forecast_interval</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ndata</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+
+        <span class="n">time_displacement</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;time_displacement&quot;</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
+
+        <span class="n">window_size</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;window_size&quot;</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+
+        <span class="n">l</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">ndata</span><span class="p">)</span>
+
+        <span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
+
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">l</span><span class="p">):</span>
+
+            <span class="c1"># print(&quot;input: &quot; + str(ndata[k]))</span>
+
+            <span class="n">tdisp</span> <span class="o">=</span> <span class="n">common</span><span class="o">.</span><span class="n">window_index</span><span class="p">(</span><span class="n">k</span> <span class="o">+</span> <span class="n">time_displacement</span><span class="p">,</span> <span class="n">window_size</span><span class="p">)</span>
+
+            <span class="n">affected_sets</span> <span class="o">=</span> <span class="p">[[</span><span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="n">key</span><span class="p">],</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="n">key</span><span class="p">]</span><span class="o">.</span><span class="n">membership</span><span class="p">(</span><span class="n">ndata</span><span class="p">[</span><span class="n">k</span><span class="p">],</span> <span class="n">tdisp</span><span class="p">)]</span>
+                             <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">ordered_sets</span>
+                             <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="n">key</span><span class="p">]</span><span class="o">.</span><span class="n">membership</span><span class="p">(</span><span class="n">ndata</span><span class="p">[</span><span class="n">k</span><span class="p">],</span> <span class="n">tdisp</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mf">0.0</span><span class="p">]</span>
+
+            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">affected_sets</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+                <span class="n">affected_sets</span><span class="o">.</span><span class="n">append</span><span class="p">([</span><span class="n">common</span><span class="o">.</span><span class="n">check_bounds</span><span class="p">(</span><span class="n">ndata</span><span class="p">[</span><span class="n">k</span><span class="p">],</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="p">,</span> <span class="n">tdisp</span><span class="p">),</span> <span class="mf">1.0</span><span class="p">])</span>
+
+            <span class="n">upper</span> <span class="o">=</span> <span class="p">[]</span>
+            <span class="n">lower</span> <span class="o">=</span> <span class="p">[]</span>
+
+            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">affected_sets</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
+                <span class="n">aset</span> <span class="o">=</span> <span class="n">affected_sets</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span>
+                <span class="k">if</span> <span class="n">aset</span><span class="o">.</span><span class="n">name</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">:</span>
+                    <span class="n">lower</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">aset</span><span class="o">.</span><span class="n">name</span><span class="p">]</span><span class="o">.</span><span class="n">get_lower</span><span class="p">(</span><span class="n">tdisp</span><span class="p">))</span>
+                    <span class="n">upper</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">aset</span><span class="o">.</span><span class="n">name</span><span class="p">]</span><span class="o">.</span><span class="n">get_upper</span><span class="p">(</span><span class="n">tdisp</span><span class="p">))</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">lower</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">aset</span><span class="o">.</span><span class="n">get_lower</span><span class="p">(</span><span class="n">tdisp</span><span class="p">))</span>
+                    <span class="n">upper</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">aset</span><span class="o">.</span><span class="n">get_upper</span><span class="p">(</span><span class="n">tdisp</span><span class="p">))</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="k">for</span> <span class="n">aset</span> <span class="ow">in</span> <span class="n">affected_sets</span><span class="p">:</span>
+                    <span class="k">if</span> <span class="n">aset</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">name</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">:</span>
+                        <span class="n">lower</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">aset</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">name</span><span class="p">]</span><span class="o">.</span><span class="n">get_lower</span><span class="p">(</span><span class="n">tdisp</span><span class="p">)</span> <span class="o">*</span> <span class="n">aset</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
+                        <span class="n">upper</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">aset</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">name</span><span class="p">]</span><span class="o">.</span><span class="n">get_upper</span><span class="p">(</span><span class="n">tdisp</span><span class="p">)</span> <span class="o">*</span> <span class="n">aset</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
+                    <span class="k">else</span><span class="p">:</span>
+                        <span class="n">lower</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">aset</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">get_lower</span><span class="p">(</span><span class="n">tdisp</span><span class="p">)</span> <span class="o">*</span> <span class="n">aset</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
+                        <span class="n">upper</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">aset</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">get_upper</span><span class="p">(</span><span class="n">tdisp</span><span class="p">)</span> <span class="o">*</span> <span class="n">aset</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
+
+
+            <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">([</span><span class="nb">sum</span><span class="p">(</span><span class="n">lower</span><span class="p">),</span> <span class="nb">sum</span><span class="p">(</span><span class="n">upper</span><span class="p">)])</span>
+
+        <span class="k">return</span> <span class="n">ret</span></div>
+
+    <span class="k">def</span> <span class="fm">__str__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="n">tmp</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">+</span> <span class="s2">&quot;:</span><span class="se">\n</span><span class="s2">&quot;</span>
+        <span class="k">for</span> <span class="n">r</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">:</span>
+            <span class="n">tmp</span> <span class="o">=</span> <span class="s2">&quot;</span><span class="si">{0}{1}</span><span class="se">\n</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">tmp</span><span class="p">,</span> <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">r</span><span class="p">]))</span>
+        <span class="k">return</span> <span class="n">tmp</span></div>
+
+
+<div class="viewcode-block" id="WeightedNonStationaryFTS"><a class="viewcode-back" href="../../../../pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.nsfts.WeightedNonStationaryFTS">[docs]</a><span class="k">class</span> <span class="nc">WeightedNonStationaryFTS</span><span class="p">(</span><span class="n">NonStationaryFTS</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;Weighted NonStationaryFTS Fuzzy Time Series&quot;&quot;&quot;</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">(</span><span class="n">WeightedNonStationaryFTS</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="s2">&quot;Weighted Non Stationary FTS&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">shortname</span> <span class="o">=</span> <span class="s2">&quot;WNSFTS&quot;</span>
+
+<div class="viewcode-block" id="WeightedNonStationaryFTS.train"><a class="viewcode-back" href="../../../../pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.nsfts.WeightedNonStationaryFTS.train">[docs]</a>    <span class="k">def</span> <span class="nf">train</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">method</span> <span class="o">==</span> <span class="s1">&#39;unconditional&#39;</span><span class="p">:</span>
+            <span class="n">window_size</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;parameters&#39;</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+            <span class="n">tmpdata</span> <span class="o">=</span> <span class="n">common</span><span class="o">.</span><span class="n">fuzzySeries</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">,</span>
+                                         <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">ordered_sets</span><span class="p">,</span>
+                                         <span class="n">window_size</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s1">&#39;fuzzy&#39;</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">tmpdata</span> <span class="o">=</span> <span class="n">common</span><span class="o">.</span><span class="n">fuzzySeries</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">,</span>
+                                 <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">ordered_sets</span><span class="p">,</span>
+                                 <span class="n">method</span><span class="o">=</span><span class="s1">&#39;fuzzy&#39;</span><span class="p">,</span> <span class="n">const_t</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+
+        <span class="n">flrs</span> <span class="o">=</span> <span class="n">FLR</span><span class="o">.</span><span class="n">generate_recurrent_flrs</span><span class="p">(</span><span class="n">tmpdata</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">generate_flrg</span><span class="p">(</span><span class="n">flrs</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">method</span> <span class="o">==</span> <span class="s1">&#39;conditional&#39;</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">forecasts</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">forecast</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">no_update</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">residuals</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="mi">1</span><span class="p">:])</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">forecasts</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
+
+            <span class="bp">self</span><span class="o">.</span><span class="n">variance_residual</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">var</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">residuals</span><span class="p">)</span>  <span class="c1"># np.max(self.residuals</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">mean_residual</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">residuals</span><span class="p">)</span>
+
+            <span class="bp">self</span><span class="o">.</span><span class="n">residuals</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">residuals</span><span class="p">[</span><span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">memory_window</span><span class="p">:]</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">forecasts</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">forecasts</span><span class="p">[</span><span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">memory_window</span><span class="p">:]</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">inputs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">memory_window</span><span class="p">:])</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span></div>
+
+<div class="viewcode-block" id="WeightedNonStationaryFTS.generate_flrg"><a class="viewcode-back" href="../../../../pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.nsfts.WeightedNonStationaryFTS.generate_flrg">[docs]</a>    <span class="k">def</span> <span class="nf">generate_flrg</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">flrs</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="k">for</span> <span class="n">flr</span> <span class="ow">in</span> <span class="n">flrs</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">flr</span><span class="o">.</span><span class="n">LHS</span><span class="o">.</span><span class="n">name</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">flr</span><span class="o">.</span><span class="n">LHS</span><span class="o">.</span><span class="n">name</span><span class="p">]</span><span class="o">.</span><span class="n">append_rhs</span><span class="p">(</span><span class="n">flr</span><span class="o">.</span><span class="n">RHS</span><span class="o">.</span><span class="n">name</span><span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">flr</span><span class="o">.</span><span class="n">LHS</span><span class="o">.</span><span class="n">name</span><span class="p">]</span> <span class="o">=</span> <span class="n">WeightedNonStationaryFLRG</span><span class="p">(</span><span class="n">flr</span><span class="o">.</span><span class="n">LHS</span><span class="o">.</span><span class="n">name</span><span class="p">)</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">flr</span><span class="o">.</span><span class="n">LHS</span><span class="o">.</span><span class="n">name</span><span class="p">]</span><span class="o">.</span><span class="n">append_rhs</span><span class="p">(</span><span class="n">flr</span><span class="o">.</span><span class="n">RHS</span><span class="o">.</span><span class="n">name</span><span class="p">)</span></div></div>
+</pre></div>
+
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@@ -0,0 +1,194 @@
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+            
+  <h1>Source code for pyFTS.models.nonstationary.util</h1><div class="highlight"><pre>
+<span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+<span class="kn">import</span> <span class="nn">matplotlib</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="kn">import</span> <span class="nn">matplotlib.colors</span> <span class="k">as</span> <span class="nn">pltcolors</span>
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="kn">from</span> <span class="nn">pyFTS.common</span> <span class="kn">import</span> <span class="n">Membership</span><span class="p">,</span> <span class="n">Util</span>
+
+
+<div class="viewcode-block" id="plot_sets"><a class="viewcode-back" href="../../../../pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.util.plot_sets">[docs]</a><span class="k">def</span> <span class="nf">plot_sets</span><span class="p">(</span><span class="n">partitioner</span><span class="p">,</span> <span class="n">start</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">end</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">step</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">tam</span><span class="o">=</span><span class="p">[</span><span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">],</span> <span class="n">colors</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+              <span class="n">save</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">file</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">axes</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">window_size</span> <span class="o">=</span> <span class="mi">1</span><span class="p">,</span> <span class="n">only_lines</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">legend</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
+
+    <span class="nb">range</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">start</span><span class="p">,</span><span class="n">end</span><span class="p">,</span><span class="n">step</span><span class="p">)</span>
+    <span class="n">ticks</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="k">if</span> <span class="n">axes</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="n">fig</span><span class="p">,</span> <span class="n">axes</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">nrows</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">ncols</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="n">tam</span><span class="p">)</span>
+
+    <span class="k">for</span> <span class="n">ct</span><span class="p">,</span> <span class="n">key</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">partitioner</span><span class="o">.</span><span class="n">ordered_sets</span><span class="p">):</span>
+        <span class="n">fset</span> <span class="o">=</span> <span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="n">key</span><span class="p">]</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="n">only_lines</span><span class="p">:</span>
+            <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">:</span>
+                <span class="n">tdisp</span> <span class="o">=</span> <span class="n">t</span> <span class="o">-</span> <span class="p">(</span><span class="n">t</span> <span class="o">%</span> <span class="n">window_size</span><span class="p">)</span>
+                <span class="n">fset</span><span class="o">.</span><span class="n">membership</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">tdisp</span><span class="p">)</span>
+                <span class="n">param</span> <span class="o">=</span> <span class="n">fset</span><span class="o">.</span><span class="n">perturbated_parameters</span><span class="p">[</span><span class="nb">str</span><span class="p">(</span><span class="n">tdisp</span><span class="p">)]</span>
+
+                <span class="k">if</span> <span class="n">fset</span><span class="o">.</span><span class="n">mf</span> <span class="o">==</span> <span class="n">Membership</span><span class="o">.</span><span class="n">trimf</span><span class="p">:</span>
+                    <span class="k">if</span> <span class="n">t</span> <span class="o">==</span> <span class="n">start</span><span class="p">:</span>
+                        <span class="n">line</span> <span class="o">=</span> <span class="n">axes</span><span class="o">.</span><span class="n">plot</span><span class="p">([</span><span class="n">t</span><span class="p">,</span> <span class="n">t</span><span class="o">+</span><span class="mi">1</span><span class="p">,</span> <span class="n">t</span><span class="p">],</span> <span class="n">param</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="n">fset</span><span class="o">.</span><span class="n">name</span><span class="p">)</span>
+                        <span class="n">fset</span><span class="o">.</span><span class="n">metadata</span><span class="p">[</span><span class="s1">&#39;color&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">line</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">get_color</span><span class="p">()</span>
+                    <span class="k">else</span><span class="p">:</span>
+                        <span class="n">axes</span><span class="o">.</span><span class="n">plot</span><span class="p">([</span><span class="n">t</span><span class="p">,</span> <span class="n">t</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="n">t</span><span class="p">],</span> <span class="n">param</span><span class="p">,</span><span class="n">c</span><span class="o">=</span><span class="n">fset</span><span class="o">.</span><span class="n">metadata</span><span class="p">[</span><span class="s1">&#39;color&#39;</span><span class="p">])</span>
+
+                <span class="n">ticks</span><span class="o">.</span><span class="n">extend</span><span class="p">([</span><span class="s2">&quot;t+&quot;</span><span class="o">+</span><span class="nb">str</span><span class="p">(</span><span class="n">t</span><span class="p">),</span><span class="s2">&quot;&quot;</span><span class="p">])</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">tmp</span> <span class="o">=</span> <span class="p">[]</span>
+            <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">:</span>
+                <span class="n">tdisp</span> <span class="o">=</span> <span class="n">t</span> <span class="o">-</span> <span class="p">(</span><span class="n">t</span> <span class="o">%</span> <span class="n">window_size</span><span class="p">)</span>
+                <span class="n">fset</span><span class="o">.</span><span class="n">membership</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">tdisp</span><span class="p">)</span>
+                <span class="n">param</span> <span class="o">=</span> <span class="n">fset</span><span class="o">.</span><span class="n">perturbated_parameters</span><span class="p">[</span><span class="nb">str</span><span class="p">(</span><span class="n">tdisp</span><span class="p">)]</span>
+                <span class="n">tmp</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">polyval</span><span class="p">(</span><span class="n">param</span><span class="p">,</span> <span class="n">tdisp</span><span class="p">))</span>
+            <span class="n">axes</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="nb">range</span><span class="p">,</span> <span class="n">tmp</span><span class="p">,</span> <span class="n">ls</span><span class="o">=</span><span class="s2">&quot;--&quot;</span><span class="p">,</span> <span class="n">c</span><span class="o">=</span><span class="s2">&quot;blue&quot;</span><span class="p">)</span>
+
+    <span class="n">axes</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s2">&quot;Universe of Discourse&quot;</span><span class="p">)</span>
+    <span class="n">axes</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s2">&quot;Time&quot;</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">xticks</span><span class="p">([</span><span class="n">k</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">],</span> <span class="n">ticks</span><span class="p">,</span> <span class="n">rotation</span><span class="o">=</span><span class="s1">&#39;vertical&#39;</span><span class="p">)</span>
+    
+    <span class="k">if</span> <span class="n">legend</span><span class="p">:</span>
+        <span class="n">handles0</span><span class="p">,</span> <span class="n">labels0</span> <span class="o">=</span> <span class="n">axes</span><span class="o">.</span><span class="n">get_legend_handles_labels</span><span class="p">()</span>
+        <span class="n">lgd</span> <span class="o">=</span> <span class="n">axes</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">handles0</span><span class="p">,</span> <span class="n">labels0</span><span class="p">,</span> <span class="n">loc</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">bbox_to_anchor</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>
+
+    <span class="k">if</span> <span class="n">data</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="n">axes</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">start</span><span class="p">,</span> <span class="n">start</span> <span class="o">+</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">),</span> <span class="mi">1</span><span class="p">),</span> <span class="n">data</span><span class="p">,</span><span class="n">c</span><span class="o">=</span><span class="s2">&quot;black&quot;</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="n">file</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
+        <span class="n">Util</span><span class="o">.</span><span class="n">show_and_save_image</span><span class="p">(</span><span class="n">fig</span><span class="p">,</span> <span class="n">file</span><span class="p">,</span> <span class="n">save</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="plot_sets_conditional"><a class="viewcode-back" href="../../../../pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.util.plot_sets_conditional">[docs]</a><span class="k">def</span> <span class="nf">plot_sets_conditional</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">step</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="p">[</span><span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">],</span> <span class="n">colors</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+                          <span class="n">save</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">file</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">axes</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">fig</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+    <span class="nb">range</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">),</span> <span class="n">step</span><span class="p">)</span>
+    <span class="n">ticks</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="k">if</span> <span class="n">axes</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="n">fig</span><span class="p">,</span> <span class="n">axes</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">nrows</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">ncols</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="n">size</span><span class="p">)</span>
+
+    <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">:</span>
+        <span class="n">model</span><span class="o">.</span><span class="n">forecast</span><span class="p">([</span><span class="n">data</span><span class="p">[</span><span class="n">t</span><span class="p">]])</span>
+        <span class="n">perturb</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">conditional_perturbation_factors</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="n">t</span><span class="p">])</span>
+
+        <span class="k">for</span> <span class="n">ct</span><span class="p">,</span> <span class="n">key</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">ordered_sets</span><span class="p">):</span>
+            <span class="nb">set</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="n">key</span><span class="p">]</span>
+            <span class="nb">set</span><span class="o">.</span><span class="n">perturbate_parameters</span><span class="p">(</span><span class="n">perturb</span><span class="p">[</span><span class="n">ct</span><span class="p">])</span>
+            <span class="n">param</span> <span class="o">=</span> <span class="nb">set</span><span class="o">.</span><span class="n">perturbated_parameters</span><span class="p">[</span><span class="nb">str</span><span class="p">(</span><span class="n">perturb</span><span class="p">[</span><span class="n">ct</span><span class="p">])]</span>
+
+            <span class="k">if</span> <span class="nb">set</span><span class="o">.</span><span class="n">mf</span> <span class="o">==</span> <span class="n">Membership</span><span class="o">.</span><span class="n">trimf</span><span class="p">:</span>
+                <span class="k">if</span> <span class="n">t</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+                    <span class="n">line</span> <span class="o">=</span> <span class="n">axes</span><span class="o">.</span><span class="n">plot</span><span class="p">([</span><span class="n">t</span><span class="p">,</span> <span class="n">t</span><span class="o">+</span><span class="mi">1</span><span class="p">,</span> <span class="n">t</span><span class="p">],</span> <span class="n">param</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="nb">set</span><span class="o">.</span><span class="n">name</span><span class="p">)</span>
+                    <span class="nb">set</span><span class="o">.</span><span class="n">metadata</span><span class="p">[</span><span class="s1">&#39;color&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">line</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">get_color</span><span class="p">()</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">axes</span><span class="o">.</span><span class="n">plot</span><span class="p">([</span><span class="n">t</span><span class="p">,</span> <span class="n">t</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="n">t</span><span class="p">],</span> <span class="n">param</span><span class="p">,</span><span class="n">c</span><span class="o">=</span><span class="nb">set</span><span class="o">.</span><span class="n">metadata</span><span class="p">[</span><span class="s1">&#39;color&#39;</span><span class="p">])</span>
+
+            <span class="c1">#ticks.extend([&quot;t+&quot;+str(t),&quot;&quot;])</span>
+
+    <span class="n">axes</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s2">&quot;Universe of Discourse&quot;</span><span class="p">)</span>
+    <span class="n">axes</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s2">&quot;Time&quot;</span><span class="p">)</span>
+    <span class="c1">#plt.xticks([k for k in range], ticks, rotation=&#39;vertical&#39;)</span>
+
+    <span class="n">handles0</span><span class="p">,</span> <span class="n">labels0</span> <span class="o">=</span> <span class="n">axes</span><span class="o">.</span><span class="n">get_legend_handles_labels</span><span class="p">()</span>
+    <span class="n">lgd</span> <span class="o">=</span> <span class="n">axes</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">handles0</span><span class="p">,</span> <span class="n">labels0</span><span class="p">,</span> <span class="n">loc</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">bbox_to_anchor</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>
+
+    <span class="k">if</span> <span class="n">data</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="n">axes</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">),</span> <span class="mi">1</span><span class="p">),</span> <span class="n">data</span><span class="p">,</span><span class="n">c</span><span class="o">=</span><span class="s2">&quot;black&quot;</span><span class="p">)</span>
+
+    <span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
+
+    <span class="n">Util</span><span class="o">.</span><span class="n">show_and_save_image</span><span class="p">(</span><span class="n">fig</span><span class="p">,</span> <span class="n">file</span><span class="p">,</span> <span class="n">save</span><span class="p">)</span></div>
+</pre></div>
+
+            <div class="clearer"></div>
+          </div>
+        </div>
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+      <div class="sphinxsidebar" role="navigation" aria-label="main navigation">
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diff --git a/docs/build/html/_modules/pyFTS/models/pwfts.html b/docs/build/html/_modules/pyFTS/models/pwfts.html
new file mode 100644
index 0000000..f4772b9
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+++ b/docs/build/html/_modules/pyFTS/models/pwfts.html
@@ -0,0 +1,794 @@
+
+
+<!doctype html>
+
+<html>
+  <head>
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+        <li class="nav-item nav-item-this"><a href="">pyFTS.models.pwfts</a></li> 
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+            
+  <h1>Source code for pyFTS.models.pwfts</h1><div class="highlight"><pre>
+<span></span><span class="ch">#!/usr/bin/python</span>
+<span class="c1"># -*- coding: utf8 -*-</span>
+
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+<span class="kn">import</span> <span class="nn">math</span>
+<span class="kn">from</span> <span class="nn">operator</span> <span class="kn">import</span> <span class="n">itemgetter</span>
+<span class="kn">from</span> <span class="nn">pyFTS.common</span> <span class="kn">import</span> <span class="n">FLR</span><span class="p">,</span> <span class="n">FuzzySet</span>
+<span class="kn">from</span> <span class="nn">pyFTS.models</span> <span class="kn">import</span> <span class="n">hofts</span><span class="p">,</span> <span class="n">ifts</span>
+<span class="kn">from</span> <span class="nn">pyFTS.probabilistic</span> <span class="kn">import</span> <span class="n">ProbabilityDistribution</span>
+<span class="kn">from</span> <span class="nn">itertools</span> <span class="kn">import</span> <span class="n">product</span>
+
+
+<div class="viewcode-block" id="ProbabilisticWeightedFLRG"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFLRG">[docs]</a><span class="k">class</span> <span class="nc">ProbabilisticWeightedFLRG</span><span class="p">(</span><span class="n">hofts</span><span class="o">.</span><span class="n">HighOrderFLRG</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;High Order Probabilistic Weighted Fuzzy Logical Relationship Group&quot;&quot;&quot;</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">order</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">(</span><span class="n">ProbabilisticWeightedFLRG</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">order</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span> <span class="o">=</span> <span class="p">{}</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">frequency_count</span> <span class="o">=</span> <span class="mf">0.0</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">Z</span> <span class="o">=</span> <span class="kc">None</span>
+
+<div class="viewcode-block" id="ProbabilisticWeightedFLRG.get_membership"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFLRG.get_membership">[docs]</a>    <span class="k">def</span> <span class="nf">get_membership</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">sets</span><span class="p">):</span>
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span> <span class="nb">list</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">,</span> <span class="nb">set</span><span class="p">)):</span>
+            <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">nanprod</span><span class="p">([</span><span class="n">sets</span><span class="p">[</span><span class="n">key</span><span class="p">]</span><span class="o">.</span><span class="n">membership</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="n">count</span><span class="p">])</span>
+                               <span class="k">for</span> <span class="n">count</span><span class="p">,</span> <span class="n">key</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">LHS</span><span class="p">,</span> <span class="n">start</span><span class="o">=</span><span class="mi">0</span><span class="p">)])</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">sets</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">LHS</span><span class="p">[</span><span class="mi">0</span><span class="p">]]</span><span class="o">.</span><span class="n">membership</span><span class="p">(</span><span class="n">data</span><span class="p">)</span></div>
+
+<div class="viewcode-block" id="ProbabilisticWeightedFLRG.append_rhs"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFLRG.append_rhs">[docs]</a>    <span class="k">def</span> <span class="nf">append_rhs</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="n">count</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;count&#39;</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">frequency_count</span> <span class="o">+=</span> <span class="n">count</span>
+        <span class="k">if</span> <span class="n">c</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="p">[</span><span class="n">c</span><span class="p">]</span> <span class="o">+=</span> <span class="n">count</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="p">[</span><span class="n">c</span><span class="p">]</span> <span class="o">=</span> <span class="n">count</span></div>
+
+<div class="viewcode-block" id="ProbabilisticWeightedFLRG.lhs_conditional_probability"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFLRG.lhs_conditional_probability">[docs]</a>    <span class="k">def</span> <span class="nf">lhs_conditional_probability</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">sets</span><span class="p">,</span> <span class="n">norm</span><span class="p">,</span> <span class="n">uod</span><span class="p">,</span> <span class="n">nbins</span><span class="p">):</span>
+        <span class="n">pk</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">frequency_count</span> <span class="o">/</span> <span class="n">norm</span>
+
+        <span class="n">tmp</span> <span class="o">=</span> <span class="n">pk</span> <span class="o">*</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">get_membership</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">sets</span><span class="p">)</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">partition_function</span><span class="p">(</span><span class="n">sets</span><span class="p">,</span> <span class="n">uod</span><span class="p">,</span> <span class="n">nbins</span><span class="o">=</span><span class="n">nbins</span><span class="p">))</span>
+
+        <span class="k">return</span> <span class="n">tmp</span></div>
+
+<div class="viewcode-block" id="ProbabilisticWeightedFLRG.lhs_conditional_probability_fuzzyfied"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFLRG.lhs_conditional_probability_fuzzyfied">[docs]</a>    <span class="k">def</span> <span class="nf">lhs_conditional_probability_fuzzyfied</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">lhs_mv</span><span class="p">,</span> <span class="n">sets</span><span class="p">,</span> <span class="n">norm</span><span class="p">,</span> <span class="n">uod</span><span class="p">,</span> <span class="n">nbins</span><span class="p">):</span>
+        <span class="n">pk</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">frequency_count</span> <span class="o">/</span> <span class="n">norm</span>
+
+        <span class="n">tmp</span> <span class="o">=</span> <span class="n">pk</span> <span class="o">*</span> <span class="p">(</span><span class="n">lhs_mv</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">partition_function</span><span class="p">(</span><span class="n">sets</span><span class="p">,</span> <span class="n">uod</span><span class="p">,</span> <span class="n">nbins</span><span class="o">=</span><span class="n">nbins</span><span class="p">))</span>
+
+        <span class="k">return</span> <span class="n">tmp</span></div>
+
+<div class="viewcode-block" id="ProbabilisticWeightedFLRG.rhs_unconditional_probability"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFLRG.rhs_unconditional_probability">[docs]</a>    <span class="k">def</span> <span class="nf">rhs_unconditional_probability</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">c</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="p">[</span><span class="n">c</span><span class="p">]</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">frequency_count</span></div>
+
+<div class="viewcode-block" id="ProbabilisticWeightedFLRG.rhs_conditional_probability"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFLRG.rhs_conditional_probability">[docs]</a>    <span class="k">def</span> <span class="nf">rhs_conditional_probability</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">sets</span><span class="p">,</span> <span class="n">uod</span><span class="p">,</span> <span class="n">nbins</span><span class="p">):</span>
+        <span class="n">total</span> <span class="o">=</span> <span class="mf">0.0</span>
+        <span class="k">for</span> <span class="n">rhs</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
+            <span class="nb">set</span> <span class="o">=</span> <span class="n">sets</span><span class="p">[</span><span class="n">rhs</span><span class="p">]</span>
+            <span class="n">wi</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">rhs_unconditional_probability</span><span class="p">(</span><span class="n">rhs</span><span class="p">)</span>
+            <span class="n">mv</span> <span class="o">=</span> <span class="nb">set</span><span class="o">.</span><span class="n">membership</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="o">/</span> <span class="nb">set</span><span class="o">.</span><span class="n">partition_function</span><span class="p">(</span><span class="n">uod</span><span class="p">,</span> <span class="n">nbins</span><span class="o">=</span><span class="n">nbins</span><span class="p">)</span>
+            <span class="n">total</span> <span class="o">+=</span> <span class="n">wi</span> <span class="o">*</span> <span class="n">mv</span>
+
+        <span class="k">return</span> <span class="n">total</span></div>
+
+<div class="viewcode-block" id="ProbabilisticWeightedFLRG.partition_function"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFLRG.partition_function">[docs]</a>    <span class="k">def</span> <span class="nf">partition_function</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sets</span><span class="p">,</span> <span class="n">uod</span><span class="p">,</span> <span class="n">nbins</span><span class="o">=</span><span class="mi">100</span><span class="p">):</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">Z</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">Z</span> <span class="o">=</span> <span class="mf">0.0</span>
+            <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="n">uod</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">uod</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">nbins</span><span class="p">):</span>
+                <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">LHS</span><span class="p">:</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">Z</span> <span class="o">+=</span> <span class="n">sets</span><span class="p">[</span><span class="n">key</span><span class="p">]</span><span class="o">.</span><span class="n">membership</span><span class="p">(</span><span class="n">k</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">Z</span></div>
+
+<div class="viewcode-block" id="ProbabilisticWeightedFLRG.get_midpoint"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFLRG.get_midpoint">[docs]</a>    <span class="k">def</span> <span class="nf">get_midpoint</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sets</span><span class="p">):</span>
+        <span class="sd">&#39;&#39;&#39;Return the expectation of the PWFLRG, the weighted sum&#39;&#39;&#39;</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">midpoint</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">midpoint</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="bp">self</span><span class="o">.</span><span class="n">rhs_unconditional_probability</span><span class="p">(</span><span class="n">s</span><span class="p">)</span> <span class="o">*</span> <span class="n">sets</span><span class="p">[</span><span class="n">s</span><span class="p">]</span><span class="o">.</span><span class="n">centroid</span>
+                                             <span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="o">.</span><span class="n">keys</span><span class="p">()]))</span>
+
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">midpoint</span></div>
+
+<div class="viewcode-block" id="ProbabilisticWeightedFLRG.get_upper"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFLRG.get_upper">[docs]</a>    <span class="k">def</span> <span class="nf">get_upper</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sets</span><span class="p">):</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">upper</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">upper</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="bp">self</span><span class="o">.</span><span class="n">rhs_unconditional_probability</span><span class="p">(</span><span class="n">s</span><span class="p">)</span> <span class="o">*</span> <span class="n">sets</span><span class="p">[</span><span class="n">s</span><span class="p">]</span><span class="o">.</span><span class="n">upper</span>
+                                          <span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="o">.</span><span class="n">keys</span><span class="p">()]))</span>
+
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">upper</span></div>
+
+<div class="viewcode-block" id="ProbabilisticWeightedFLRG.get_lower"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFLRG.get_lower">[docs]</a>    <span class="k">def</span> <span class="nf">get_lower</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sets</span><span class="p">):</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">lower</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">lower</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="bp">self</span><span class="o">.</span><span class="n">rhs_unconditional_probability</span><span class="p">(</span><span class="n">s</span><span class="p">)</span> <span class="o">*</span> <span class="n">sets</span><span class="p">[</span><span class="n">s</span><span class="p">]</span><span class="o">.</span><span class="n">lower</span>
+                                          <span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="o">.</span><span class="n">keys</span><span class="p">()]))</span>
+
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">lower</span></div>
+
+    <span class="k">def</span> <span class="fm">__str__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="n">tmp2</span> <span class="o">=</span> <span class="s2">&quot;&quot;</span>
+        <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="nb">sorted</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="o">.</span><span class="n">keys</span><span class="p">()):</span>
+            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">tmp2</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
+                <span class="n">tmp2</span> <span class="o">=</span> <span class="n">tmp2</span> <span class="o">+</span> <span class="s2">&quot;, &quot;</span>
+            <span class="n">tmp2</span> <span class="o">=</span> <span class="n">tmp2</span> <span class="o">+</span> <span class="s2">&quot;(&quot;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="p">[</span><span class="n">c</span><span class="p">]</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">frequency_count</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span> <span class="o">+</span> <span class="s2">&quot;)&quot;</span> <span class="o">+</span> <span class="n">c</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_key</span><span class="p">()</span> <span class="o">+</span> <span class="s2">&quot; -&gt; &quot;</span> <span class="o">+</span> <span class="n">tmp2</span></div>
+
+
+<div class="viewcode-block" id="ProbabilisticWeightedFTS"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFTS">[docs]</a><span class="k">class</span> <span class="nc">ProbabilisticWeightedFTS</span><span class="p">(</span><span class="n">ifts</span><span class="o">.</span><span class="n">IntervalFTS</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;High Order Probabilistic Weighted Fuzzy Time Series&quot;&quot;&quot;</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">(</span><span class="n">ProbabilisticWeightedFTS</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">shortname</span> <span class="o">=</span> <span class="s2">&quot;PWFTS&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="s2">&quot;Probabilistic FTS&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">detail</span> <span class="o">=</span> <span class="s2">&quot;Silva, P.; Guimarães, F.; Sadaei, H.&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span> <span class="o">=</span> <span class="p">{}</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">global_frequency_count</span> <span class="o">=</span> <span class="mi">0</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">has_point_forecasting</span> <span class="o">=</span> <span class="kc">True</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">has_interval_forecasting</span> <span class="o">=</span> <span class="kc">True</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">has_probability_forecasting</span> <span class="o">=</span> <span class="kc">True</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">is_high_order</span> <span class="o">=</span> <span class="kc">True</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">min_order</span> <span class="o">=</span> <span class="mi">1</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">auto_update</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;update&#39;</span><span class="p">,</span><span class="kc">False</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">configure_lags</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+
+<div class="viewcode-block" id="ProbabilisticWeightedFTS.train"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFTS.train">[docs]</a>    <span class="k">def</span> <span class="nf">train</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">configure_lags</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="ow">not</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;fuzzyfied&#39;</span><span class="p">,</span><span class="kc">False</span><span class="p">):</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">generate_flrg2</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">generate_flrg_fuzzyfied</span><span class="p">(</span><span class="n">data</span><span class="p">)</span></div>
+
+<div class="viewcode-block" id="ProbabilisticWeightedFTS.generate_flrg2"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFTS.generate_flrg2">[docs]</a>    <span class="k">def</span> <span class="nf">generate_flrg2</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">):</span>
+        <span class="n">fuzz</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="n">l</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">l</span><span class="p">):</span>
+            <span class="n">fuzz</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">fuzzyfy</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="n">k</span><span class="p">],</span> <span class="n">mode</span><span class="o">=</span><span class="s1">&#39;both&#39;</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s1">&#39;fuzzy&#39;</span><span class="p">,</span>
+                                                 <span class="n">alpha_cut</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">alpha_cut</span><span class="p">))</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">generate_flrg_fuzzyfied</span><span class="p">(</span><span class="n">fuzz</span><span class="p">)</span></div>
+
+<div class="viewcode-block" id="ProbabilisticWeightedFTS.generate_flrg_fuzzyfied"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFTS.generate_flrg_fuzzyfied">[docs]</a>    <span class="k">def</span> <span class="nf">generate_flrg_fuzzyfied</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">):</span>
+        <span class="n">l</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">,</span> <span class="n">l</span><span class="p">):</span>
+            <span class="n">sample</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="n">k</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">:</span> <span class="n">k</span><span class="p">]</span>
+            <span class="n">set_sample</span> <span class="o">=</span> <span class="p">[]</span>
+            <span class="k">for</span> <span class="n">instance</span> <span class="ow">in</span> <span class="n">sample</span><span class="p">:</span>
+                <span class="n">set_sample</span><span class="o">.</span><span class="n">append</span><span class="p">([</span><span class="n">k</span> <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">instance</span><span class="p">])</span>
+
+            <span class="n">flrgs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">generate_lhs_flrg_fuzzyfied</span><span class="p">(</span><span class="n">set_sample</span><span class="p">)</span>
+
+            <span class="k">for</span> <span class="n">flrg</span> <span class="ow">in</span> <span class="n">flrgs</span><span class="p">:</span>
+
+                <span class="k">if</span> <span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">:</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()]</span> <span class="o">=</span> <span class="n">flrg</span><span class="p">;</span>
+
+                <span class="n">lhs_mv</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">pwflrg_lhs_memberhip_fuzzyfied</span><span class="p">(</span><span class="n">flrg</span><span class="p">,</span> <span class="n">sample</span><span class="p">)</span>
+
+                <span class="n">mvs</span> <span class="o">=</span> <span class="p">[]</span>
+                <span class="n">inst</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="n">k</span><span class="p">]</span>
+                <span class="k">for</span> <span class="nb">set</span><span class="p">,</span> <span class="n">mv</span> <span class="ow">in</span> <span class="n">inst</span><span class="p">:</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()]</span><span class="o">.</span><span class="n">append_rhs</span><span class="p">(</span><span class="nb">set</span><span class="p">,</span> <span class="n">count</span><span class="o">=</span><span class="n">lhs_mv</span> <span class="o">*</span> <span class="n">mv</span><span class="p">)</span>
+                    <span class="n">mvs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">mv</span><span class="p">)</span>
+
+                <span class="n">tmp_fq</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">([</span><span class="n">lhs_mv</span> <span class="o">*</span> <span class="n">kk</span> <span class="k">for</span> <span class="n">kk</span> <span class="ow">in</span> <span class="n">mvs</span> <span class="k">if</span> <span class="n">kk</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">])</span>
+
+                <span class="bp">self</span><span class="o">.</span><span class="n">global_frequency_count</span> <span class="o">+=</span> <span class="n">tmp_fq</span></div>
+
+<div class="viewcode-block" id="ProbabilisticWeightedFTS.pwflrg_lhs_memberhip_fuzzyfied"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFTS.pwflrg_lhs_memberhip_fuzzyfied">[docs]</a>    <span class="k">def</span> <span class="nf">pwflrg_lhs_memberhip_fuzzyfied</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">flrg</span><span class="p">,</span> <span class="n">sample</span><span class="p">):</span>
+        <span class="n">vals</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="k">for</span> <span class="n">ct</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">flrg</span><span class="o">.</span><span class="n">LHS</span><span class="p">)):</span> <span class="c1"># fuzz in enumerate(sample):</span>
+            <span class="n">vals</span><span class="o">.</span><span class="n">append</span><span class="p">([</span><span class="n">mv</span> <span class="k">for</span> <span class="n">fset</span><span class="p">,</span> <span class="n">mv</span> <span class="ow">in</span> <span class="n">sample</span><span class="p">[</span><span class="n">ct</span><span class="p">]</span> <span class="k">if</span> <span class="n">fset</span> <span class="o">==</span> <span class="n">flrg</span><span class="o">.</span><span class="n">LHS</span><span class="p">[</span><span class="n">ct</span><span class="p">]])</span>
+
+        <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">nanprod</span><span class="p">(</span><span class="n">vals</span><span class="p">)</span></div>
+
+<div class="viewcode-block" id="ProbabilisticWeightedFTS.generate_lhs_flrg"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFTS.generate_lhs_flrg">[docs]</a>    <span class="k">def</span> <span class="nf">generate_lhs_flrg</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sample</span><span class="p">,</span> <span class="n">explain</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">sample</span><span class="p">,</span> <span class="p">(</span><span class="nb">tuple</span><span class="p">,</span> <span class="nb">list</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">)):</span>
+            <span class="n">sample</span> <span class="o">=</span> <span class="p">[</span><span class="n">sample</span><span class="p">]</span>
+
+        <span class="n">nsample</span> <span class="o">=</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">fuzzyfy</span><span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s2">&quot;sets&quot;</span><span class="p">,</span> <span class="n">alpha_cut</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">alpha_cut</span><span class="p">)</span>
+                   <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">sample</span><span class="p">]</span>
+
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">generate_lhs_flrg_fuzzyfied</span><span class="p">(</span><span class="n">nsample</span><span class="p">,</span> <span class="n">explain</span><span class="p">)</span></div>
+
+<div class="viewcode-block" id="ProbabilisticWeightedFTS.generate_lhs_flrg_fuzzyfied"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFTS.generate_lhs_flrg_fuzzyfied">[docs]</a>    <span class="k">def</span> <span class="nf">generate_lhs_flrg_fuzzyfied</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sample</span><span class="p">,</span> <span class="n">explain</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
+        <span class="n">lags</span> <span class="o">=</span> <span class="p">[]</span>
+
+        <span class="n">flrgs</span> <span class="o">=</span> <span class="p">[]</span>
+
+        <span class="k">for</span> <span class="n">ct</span><span class="p">,</span> <span class="n">o</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">lags</span><span class="p">):</span>
+            <span class="n">lhs</span> <span class="o">=</span> <span class="n">sample</span><span class="p">[</span><span class="n">o</span> <span class="o">-</span> <span class="mi">1</span><span class="p">]</span>
+            <span class="n">lags</span><span class="o">.</span><span class="n">append</span><span class="p">(</span> <span class="n">lhs</span> <span class="p">)</span>
+
+            <span class="k">if</span> <span class="n">explain</span><span class="p">:</span>
+                <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\t</span><span class="s2"> (Lag </span><span class="si">{}</span><span class="s2">) </span><span class="si">{}</span><span class="s2"> -&gt; </span><span class="si">{}</span><span class="s2"> </span><span class="se">\n</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">o</span><span class="p">,</span> <span class="n">sample</span><span class="p">[</span><span class="n">o</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="n">lhs</span><span class="p">))</span>
+
+        <span class="c1"># Trace the possible paths</span>
+        <span class="k">for</span> <span class="n">path</span> <span class="ow">in</span> <span class="n">product</span><span class="p">(</span><span class="o">*</span><span class="n">lags</span><span class="p">):</span>
+            <span class="n">flrg</span> <span class="o">=</span> <span class="n">ProbabilisticWeightedFLRG</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">)</span>
+
+            <span class="k">for</span> <span class="n">lhs</span> <span class="ow">in</span> <span class="n">path</span><span class="p">:</span>
+                <span class="n">flrg</span><span class="o">.</span><span class="n">append_lhs</span><span class="p">(</span><span class="n">lhs</span><span class="p">)</span>
+
+            <span class="n">flrgs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">flrg</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">flrgs</span></div>
+
+<div class="viewcode-block" id="ProbabilisticWeightedFTS.generate_flrg"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFTS.generate_flrg">[docs]</a>    <span class="k">def</span> <span class="nf">generate_flrg</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">):</span>
+        <span class="n">l</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">,</span> <span class="n">l</span><span class="p">):</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">dump</span><span class="p">:</span> <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;FLR: &quot;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">k</span><span class="p">))</span>
+
+            <span class="n">sample</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="n">k</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">:</span> <span class="n">k</span><span class="p">]</span>
+
+            <span class="n">flrgs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">generate_lhs_flrg</span><span class="p">(</span><span class="n">sample</span><span class="p">)</span>
+
+            <span class="k">for</span> <span class="n">flrg</span> <span class="ow">in</span> <span class="n">flrgs</span><span class="p">:</span>
+
+                <span class="n">lhs_mv</span> <span class="o">=</span> <span class="n">flrg</span><span class="o">.</span><span class="n">get_membership</span><span class="p">(</span><span class="n">sample</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">)</span>
+
+                <span class="k">if</span> <span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">:</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()]</span> <span class="o">=</span> <span class="n">flrg</span><span class="p">;</span>
+
+                <span class="n">fuzzyfied</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">fuzzyfy</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="n">k</span><span class="p">],</span> <span class="n">mode</span><span class="o">=</span><span class="s1">&#39;both&#39;</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s1">&#39;fuzzy&#39;</span><span class="p">,</span>
+                                                     <span class="n">alpha_cut</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">alpha_cut</span><span class="p">)</span>
+
+                <span class="n">mvs</span> <span class="o">=</span> <span class="p">[]</span>
+                <span class="k">for</span> <span class="nb">set</span><span class="p">,</span> <span class="n">mv</span> <span class="ow">in</span> <span class="n">fuzzyfied</span><span class="p">:</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()]</span><span class="o">.</span><span class="n">append_rhs</span><span class="p">(</span><span class="nb">set</span><span class="p">,</span> <span class="n">count</span><span class="o">=</span><span class="n">lhs_mv</span> <span class="o">*</span> <span class="n">mv</span><span class="p">)</span>
+                    <span class="n">mvs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">mv</span><span class="p">)</span>
+
+                <span class="n">tmp_fq</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">([</span><span class="n">lhs_mv</span><span class="o">*</span><span class="n">kk</span> <span class="k">for</span> <span class="n">kk</span> <span class="ow">in</span> <span class="n">mvs</span> <span class="k">if</span> <span class="n">kk</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">])</span>
+
+                <span class="bp">self</span><span class="o">.</span><span class="n">global_frequency_count</span> <span class="o">+=</span> <span class="n">tmp_fq</span></div>
+
+
+<div class="viewcode-block" id="ProbabilisticWeightedFTS.update_model"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFTS.update_model">[docs]</a>    <span class="k">def</span> <span class="nf">update_model</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span><span class="n">data</span><span class="p">):</span>
+        <span class="k">pass</span></div>
+
+
+<div class="viewcode-block" id="ProbabilisticWeightedFTS.add_new_PWFLGR"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFTS.add_new_PWFLGR">[docs]</a>    <span class="k">def</span> <span class="nf">add_new_PWFLGR</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">flrg</span><span class="p">):</span>
+        <span class="k">if</span> <span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">:</span>
+            <span class="n">tmp</span> <span class="o">=</span> <span class="n">ProbabilisticWeightedFLRG</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">)</span>
+            <span class="k">for</span> <span class="n">fs</span> <span class="ow">in</span> <span class="n">flrg</span><span class="o">.</span><span class="n">LHS</span><span class="p">:</span> <span class="n">tmp</span><span class="o">.</span><span class="n">append_lhs</span><span class="p">(</span><span class="n">fs</span><span class="p">)</span>
+            <span class="n">tmp</span><span class="o">.</span><span class="n">append_rhs</span><span class="p">(</span><span class="n">flrg</span><span class="o">.</span><span class="n">LHS</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">tmp</span><span class="o">.</span><span class="n">get_key</span><span class="p">()]</span> <span class="o">=</span> <span class="n">tmp</span><span class="p">;</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">global_frequency_count</span> <span class="o">+=</span> <span class="mi">1</span></div>
+
+<div class="viewcode-block" id="ProbabilisticWeightedFTS.flrg_lhs_unconditional_probability"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFTS.flrg_lhs_unconditional_probability">[docs]</a>    <span class="k">def</span> <span class="nf">flrg_lhs_unconditional_probability</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">flrg</span><span class="p">):</span>
+        <span class="k">if</span> <span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">:</span>
+            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()]</span><span class="o">.</span><span class="n">frequency_count</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">global_frequency_count</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">return</span> <span class="mf">1.0</span></div>
+            <span class="c1">#self.add_new_PWFLGR(flrg)</span>
+            <span class="c1">#return self.flrg_lhs_unconditional_probability(flrg)</span>
+
+<div class="viewcode-block" id="ProbabilisticWeightedFTS.flrg_lhs_conditional_probability"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFTS.flrg_lhs_conditional_probability">[docs]</a>    <span class="k">def</span> <span class="nf">flrg_lhs_conditional_probability</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">flrg</span><span class="p">):</span>
+        <span class="n">mv</span> <span class="o">=</span> <span class="n">flrg</span><span class="o">.</span><span class="n">get_membership</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">)</span>
+        <span class="n">pb</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrg_lhs_unconditional_probability</span><span class="p">(</span><span class="n">flrg</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">mv</span> <span class="o">*</span> <span class="n">pb</span></div>
+
+<div class="viewcode-block" id="ProbabilisticWeightedFTS.flrg_lhs_conditional_probability_fuzzyfied"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFTS.flrg_lhs_conditional_probability_fuzzyfied">[docs]</a>    <span class="k">def</span> <span class="nf">flrg_lhs_conditional_probability_fuzzyfied</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">flrg</span><span class="p">):</span>
+        <span class="n">mv</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">pwflrg_lhs_memberhip_fuzzyfied</span><span class="p">(</span><span class="n">flrg</span><span class="p">,</span> <span class="n">x</span><span class="p">)</span>
+        <span class="n">pb</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrg_lhs_unconditional_probability</span><span class="p">(</span><span class="n">flrg</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">mv</span> <span class="o">*</span> <span class="n">pb</span></div>
+
+<div class="viewcode-block" id="ProbabilisticWeightedFTS.get_midpoint"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFTS.get_midpoint">[docs]</a>    <span class="k">def</span> <span class="nf">get_midpoint</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">flrg</span><span class="p">):</span>
+        <span class="k">if</span> <span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">:</span>
+            <span class="n">tmp</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()]</span>
+            <span class="n">ret</span> <span class="o">=</span> <span class="n">tmp</span><span class="o">.</span><span class="n">get_midpoint</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">)</span> <span class="c1">#sum(np.array([tmp.rhs_unconditional_probability(s) * self.setsDict[s].centroid for s in tmp.RHS]))</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">flrg</span><span class="o">.</span><span class="n">LHS</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
+                <span class="n">pi</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">/</span> <span class="nb">len</span><span class="p">(</span><span class="n">flrg</span><span class="o">.</span><span class="n">LHS</span><span class="p">)</span>
+                <span class="n">ret</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">pi</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="n">s</span><span class="p">]</span><span class="o">.</span><span class="n">centroid</span> <span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="n">flrg</span><span class="o">.</span><span class="n">LHS</span><span class="p">]))</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">ret</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
+        <span class="k">return</span> <span class="n">ret</span></div>
+
+<div class="viewcode-block" id="ProbabilisticWeightedFTS.flrg_rhs_conditional_probability"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFTS.flrg_rhs_conditional_probability">[docs]</a>    <span class="k">def</span> <span class="nf">flrg_rhs_conditional_probability</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">flrg</span><span class="p">):</span>
+
+        <span class="k">if</span> <span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">:</span>
+            <span class="n">_flrg</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()]</span>
+            <span class="n">cond</span> <span class="o">=</span> <span class="p">[]</span>
+            <span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="n">_flrg</span><span class="o">.</span><span class="n">RHS</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
+                <span class="n">_set</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="n">s</span><span class="p">]</span>
+                <span class="n">tmp</span> <span class="o">=</span> <span class="n">_flrg</span><span class="o">.</span><span class="n">rhs_unconditional_probability</span><span class="p">(</span><span class="n">s</span><span class="p">)</span> <span class="o">*</span> <span class="p">(</span><span class="n">_set</span><span class="o">.</span><span class="n">membership</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="o">/</span> <span class="n">_set</span><span class="o">.</span><span class="n">partition_function</span><span class="p">(</span><span class="n">uod</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">get_UoD</span><span class="p">()))</span>
+                <span class="n">cond</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">tmp</span><span class="p">)</span>
+            <span class="n">ret</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">cond</span><span class="p">))</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">pi</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">/</span> <span class="nb">len</span><span class="p">(</span><span class="n">flrg</span><span class="o">.</span><span class="n">LHS</span><span class="p">)</span>
+            <span class="n">ret</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">pi</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="n">s</span><span class="p">]</span><span class="o">.</span><span class="n">membership</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="n">flrg</span><span class="o">.</span><span class="n">LHS</span><span class="p">]))</span>
+        <span class="k">return</span> <span class="n">ret</span></div>
+
+<div class="viewcode-block" id="ProbabilisticWeightedFTS.get_upper"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFTS.get_upper">[docs]</a>    <span class="k">def</span> <span class="nf">get_upper</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">flrg</span><span class="p">):</span>
+        <span class="k">if</span> <span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">:</span>
+            <span class="n">tmp</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()]</span>
+            <span class="n">ret</span> <span class="o">=</span> <span class="n">tmp</span><span class="o">.</span><span class="n">get_upper</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">ret</span> <span class="o">=</span> <span class="mi">0</span>
+        <span class="k">return</span> <span class="n">ret</span></div>
+
+<div class="viewcode-block" id="ProbabilisticWeightedFTS.get_lower"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFTS.get_lower">[docs]</a>    <span class="k">def</span> <span class="nf">get_lower</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">flrg</span><span class="p">):</span>
+        <span class="k">if</span> <span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">:</span>
+            <span class="n">tmp</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()]</span>
+            <span class="n">ret</span> <span class="o">=</span> <span class="n">tmp</span><span class="o">.</span><span class="n">get_lower</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">ret</span> <span class="o">=</span> <span class="mi">0</span>
+        <span class="k">return</span> <span class="n">ret</span></div>
+
+<div class="viewcode-block" id="ProbabilisticWeightedFTS.forecast"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFTS.forecast">[docs]</a>    <span class="k">def</span> <span class="nf">forecast</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="n">method</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;method&#39;</span><span class="p">,</span><span class="s1">&#39;heuristic&#39;</span><span class="p">)</span>
+
+        <span class="n">l</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">)</span><span class="o">+</span><span class="mi">1</span>
+
+        <span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
+
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">,</span> <span class="n">l</span><span class="p">):</span>
+            <span class="n">sample</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="n">k</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">:</span> <span class="n">k</span><span class="p">]</span>
+
+            <span class="k">if</span> <span class="n">method</span> <span class="o">==</span> <span class="s1">&#39;heuristic&#39;</span><span class="p">:</span>
+                <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">point_heuristic</span><span class="p">(</span><span class="n">sample</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">))</span>
+            <span class="k">elif</span> <span class="n">method</span> <span class="o">==</span> <span class="s1">&#39;expected_value&#39;</span><span class="p">:</span>
+                <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">point_expected_value</span><span class="p">(</span><span class="n">sample</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">))</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Unknown point forecasting method!&quot;</span><span class="p">)</span>
+
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">auto_update</span> <span class="ow">and</span> <span class="n">k</span> <span class="o">&gt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="o">+</span><span class="mi">1</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">update_model</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="n">k</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span> <span class="o">-</span> <span class="mi">1</span> <span class="p">:</span> <span class="n">k</span><span class="p">])</span>
+
+        <span class="k">return</span> <span class="n">ret</span></div>
+
+<div class="viewcode-block" id="ProbabilisticWeightedFTS.point_heuristic"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFTS.point_heuristic">[docs]</a>    <span class="k">def</span> <span class="nf">point_heuristic</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sample</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+
+        <span class="n">explain</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;explain&#39;</span><span class="p">,</span> <span class="kc">False</span><span class="p">)</span>
+        <span class="n">fuzzyfied</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;fuzzyfied&#39;</span><span class="p">,</span> <span class="kc">False</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">explain</span><span class="p">:</span>
+            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Fuzzyfication </span><span class="se">\n</span><span class="s2">&quot;</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="ow">not</span> <span class="n">fuzzyfied</span><span class="p">:</span>
+            <span class="n">flrgs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">generate_lhs_flrg</span><span class="p">(</span><span class="n">sample</span><span class="p">,</span> <span class="n">explain</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">fsets</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_sets_from_both_fuzzyfication</span><span class="p">(</span><span class="n">sample</span><span class="p">)</span>
+            <span class="n">flrgs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">generate_lhs_flrg_fuzzyfied</span><span class="p">(</span><span class="n">fsets</span><span class="p">,</span> <span class="n">explain</span><span class="p">)</span>
+
+        <span class="n">mp</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="n">norms</span> <span class="o">=</span> <span class="p">[]</span>
+
+        <span class="k">if</span> <span class="n">explain</span><span class="p">:</span>
+            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Rules:</span><span class="se">\n</span><span class="s2">&quot;</span><span class="p">)</span>
+
+        <span class="k">for</span> <span class="n">flrg</span> <span class="ow">in</span> <span class="n">flrgs</span><span class="p">:</span>
+            <span class="k">if</span> <span class="ow">not</span> <span class="n">fuzzyfied</span><span class="p">:</span>
+                <span class="n">norm</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrg_lhs_conditional_probability</span><span class="p">(</span><span class="n">sample</span><span class="p">,</span> <span class="n">flrg</span><span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">norm</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrg_lhs_conditional_probability_fuzzyfied</span><span class="p">(</span><span class="n">sample</span><span class="p">,</span> <span class="n">flrg</span><span class="p">)</span>
+
+            <span class="k">if</span> <span class="n">norm</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+                <span class="n">norm</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrg_lhs_unconditional_probability</span><span class="p">(</span><span class="n">flrg</span><span class="p">)</span>
+
+            <span class="k">if</span> <span class="n">explain</span><span class="p">:</span>
+                <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\t</span><span class="s2"> </span><span class="si">{}</span><span class="s2"> </span><span class="se">\t</span><span class="s2"> Midpoint: </span><span class="si">{}</span><span class="se">\t</span><span class="s2"> Norm: </span><span class="si">{}</span><span class="se">\n</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()]),</span>
+                                                                  <span class="bp">self</span><span class="o">.</span><span class="n">get_midpoint</span><span class="p">(</span><span class="n">flrg</span><span class="p">),</span> <span class="n">norm</span><span class="p">))</span>
+            <span class="n">mp</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">norm</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_midpoint</span><span class="p">(</span><span class="n">flrg</span><span class="p">))</span>
+            <span class="n">norms</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">norm</span><span class="p">)</span>
+
+        <span class="n">norm</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">(</span><span class="n">norms</span><span class="p">)</span>
+
+        <span class="n">final</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">(</span><span class="n">mp</span><span class="p">)</span> <span class="o">/</span> <span class="n">norm</span> <span class="k">if</span> <span class="n">norm</span> <span class="o">!=</span> <span class="mi">0</span> <span class="k">else</span> <span class="mi">0</span>
+
+        <span class="k">if</span> <span class="n">explain</span><span class="p">:</span>
+            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Deffuzyfied value: </span><span class="si">{}</span><span class="s2"> </span><span class="se">\n</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">final</span><span class="p">))</span>
+        <span class="k">return</span> <span class="n">final</span></div>
+
+<div class="viewcode-block" id="ProbabilisticWeightedFTS.get_sets_from_both_fuzzyfication"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFTS.get_sets_from_both_fuzzyfication">[docs]</a>    <span class="k">def</span> <span class="nf">get_sets_from_both_fuzzyfication</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sample</span><span class="p">):</span>
+        <span class="k">return</span> <span class="p">[[</span><span class="n">k</span> <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">inst</span><span class="p">]</span> <span class="k">for</span> <span class="n">inst</span> <span class="ow">in</span> <span class="n">sample</span><span class="p">]</span></div>
+
+<div class="viewcode-block" id="ProbabilisticWeightedFTS.point_expected_value"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFTS.point_expected_value">[docs]</a>    <span class="k">def</span> <span class="nf">point_expected_value</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sample</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="n">explain</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;explain&#39;</span><span class="p">,</span> <span class="kc">False</span><span class="p">)</span>
+
+        <span class="n">dist</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">forecast_distribution</span><span class="p">(</span><span class="n">sample</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
+
+        <span class="n">final</span> <span class="o">=</span> <span class="n">dist</span><span class="o">.</span><span class="n">expected_value</span><span class="p">()</span>
+        <span class="k">return</span> <span class="n">final</span></div>
+
+<div class="viewcode-block" id="ProbabilisticWeightedFTS.forecast_interval"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFTS.forecast_interval">[docs]</a>    <span class="k">def</span> <span class="nf">forecast_interval</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ndata</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+
+        <span class="n">method</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;method&#39;</span><span class="p">,</span><span class="s1">&#39;heuristic&#39;</span><span class="p">)</span>
+        <span class="n">alpha</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;alpha&#39;</span><span class="p">,</span> <span class="mf">0.05</span><span class="p">)</span>
+
+        <span class="n">l</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">ndata</span><span class="p">)</span>
+
+        <span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
+
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span> <span class="o">-</span> <span class="mi">1</span><span class="p">,</span> <span class="n">l</span><span class="p">):</span>
+
+            <span class="n">sample</span> <span class="o">=</span> <span class="n">ndata</span><span class="p">[</span><span class="n">k</span> <span class="o">-</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span> <span class="o">-</span> <span class="mi">1</span><span class="p">):</span> <span class="n">k</span> <span class="o">+</span> <span class="mi">1</span><span class="p">]</span>
+
+            <span class="k">if</span> <span class="n">method</span> <span class="o">==</span> <span class="s1">&#39;heuristic&#39;</span><span class="p">:</span>
+                <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">interval_heuristic</span><span class="p">(</span><span class="n">sample</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">))</span>
+            <span class="k">elif</span> <span class="n">method</span> <span class="o">==</span> <span class="s1">&#39;quantile&#39;</span><span class="p">:</span>
+                <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">interval_quantile</span><span class="p">(</span><span class="n">sample</span><span class="p">,</span> <span class="n">alpha</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">))</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Unknown interval forecasting method!&quot;</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">ret</span></div>
+
+<div class="viewcode-block" id="ProbabilisticWeightedFTS.interval_quantile"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFTS.interval_quantile">[docs]</a>    <span class="k">def</span> <span class="nf">interval_quantile</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ndata</span><span class="p">,</span> <span class="n">alpha</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="n">dist</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">forecast_distribution</span><span class="p">(</span><span class="n">ndata</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+        <span class="n">itvl</span> <span class="o">=</span> <span class="n">dist</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">quantile</span><span class="p">([</span><span class="n">alpha</span><span class="p">,</span> <span class="mf">1.0</span> <span class="o">-</span> <span class="n">alpha</span><span class="p">])</span>
+        <span class="k">return</span> <span class="n">itvl</span></div>
+
+<div class="viewcode-block" id="ProbabilisticWeightedFTS.interval_heuristic"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFTS.interval_heuristic">[docs]</a>    <span class="k">def</span> <span class="nf">interval_heuristic</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sample</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="n">fuzzyfied</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;fuzzyfied&#39;</span><span class="p">,</span> <span class="kc">False</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="ow">not</span> <span class="n">fuzzyfied</span><span class="p">:</span>
+            <span class="n">flrgs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">generate_lhs_flrg</span><span class="p">(</span><span class="n">sample</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">fsets</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_sets_from_both_fuzzyfication</span><span class="p">(</span><span class="n">sample</span><span class="p">)</span>
+            <span class="n">flrgs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">generate_lhs_flrg_fuzzyfied</span><span class="p">(</span><span class="n">fsets</span><span class="p">)</span>
+
+        <span class="n">up</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="n">lo</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="n">norms</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="k">for</span> <span class="n">flrg</span> <span class="ow">in</span> <span class="n">flrgs</span><span class="p">:</span>
+            <span class="k">if</span> <span class="ow">not</span> <span class="n">fuzzyfied</span><span class="p">:</span>
+                <span class="n">norm</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrg_lhs_conditional_probability</span><span class="p">(</span><span class="n">sample</span><span class="p">,</span> <span class="n">flrg</span><span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">norm</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrg_lhs_conditional_probability_fuzzyfied</span><span class="p">(</span><span class="n">sample</span><span class="p">,</span> <span class="n">flrg</span><span class="p">)</span>
+
+            <span class="k">if</span> <span class="n">norm</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+                <span class="n">norm</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrg_lhs_unconditional_probability</span><span class="p">(</span><span class="n">flrg</span><span class="p">)</span>
+            <span class="n">up</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">norm</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_upper</span><span class="p">(</span><span class="n">flrg</span><span class="p">))</span>
+            <span class="n">lo</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">norm</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_lower</span><span class="p">(</span><span class="n">flrg</span><span class="p">))</span>
+            <span class="n">norms</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">norm</span><span class="p">)</span>
+
+            <span class="c1"># gerar o intervalo</span>
+        <span class="n">norm</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">(</span><span class="n">norms</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">norm</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="k">return</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">lo_</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">(</span><span class="n">lo</span><span class="p">)</span> <span class="o">/</span> <span class="n">norm</span>
+            <span class="n">up_</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">(</span><span class="n">up</span><span class="p">)</span> <span class="o">/</span> <span class="n">norm</span>
+            <span class="k">return</span> <span class="p">[</span><span class="n">lo_</span><span class="p">,</span> <span class="n">up_</span><span class="p">]</span></div>
+
+<div class="viewcode-block" id="ProbabilisticWeightedFTS.forecast_distribution"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFTS.forecast_distribution">[docs]</a>    <span class="k">def</span> <span class="nf">forecast_distribution</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ndata</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+
+        <span class="n">smooth</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;smooth&quot;</span><span class="p">,</span> <span class="s2">&quot;none&quot;</span><span class="p">)</span>
+
+        <span class="n">from_distribution</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;from_distribution&#39;</span><span class="p">,</span> <span class="kc">False</span><span class="p">)</span>
+
+        <span class="n">fuzzyfied</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;fuzzyfied&#39;</span><span class="p">,</span> <span class="kc">False</span><span class="p">)</span>
+
+        <span class="n">l</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">ndata</span><span class="p">)</span>
+        <span class="n">uod</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_UoD</span><span class="p">()</span>
+
+        <span class="k">if</span> <span class="s1">&#39;bins&#39;</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="p">:</span>
+            <span class="n">_bins</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s1">&#39;bins&#39;</span><span class="p">)</span>
+            <span class="n">nbins</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">_bins</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">nbins</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;num_bins&quot;</span><span class="p">,</span> <span class="mi">100</span><span class="p">)</span>
+            <span class="n">_bins</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="n">uod</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">uod</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">nbins</span><span class="p">)</span>
+
+        <span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
+
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span> <span class="o">-</span> <span class="mi">1</span><span class="p">,</span> <span class="n">l</span><span class="p">):</span>
+            <span class="n">sample</span> <span class="o">=</span> <span class="n">ndata</span><span class="p">[</span><span class="n">k</span> <span class="o">-</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span> <span class="o">-</span> <span class="mi">1</span><span class="p">):</span> <span class="n">k</span> <span class="o">+</span> <span class="mi">1</span><span class="p">]</span>
+
+            <span class="k">if</span> <span class="n">from_distribution</span><span class="p">:</span>
+                <span class="n">dist</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">forecast_distribution_from_distribution</span><span class="p">(</span><span class="n">sample</span><span class="p">,</span><span class="n">smooth</span><span class="p">,</span><span class="n">uod</span><span class="p">,</span><span class="n">_bins</span><span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+
+                <span class="k">if</span> <span class="ow">not</span> <span class="n">fuzzyfied</span><span class="p">:</span>
+                    <span class="n">flrgs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">generate_lhs_flrg</span><span class="p">(</span><span class="n">sample</span><span class="p">)</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">fsets</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_sets_from_both_fuzzyfication</span><span class="p">(</span><span class="n">sample</span><span class="p">)</span>
+                    <span class="n">flrgs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">generate_lhs_flrg_fuzzyfied</span><span class="p">(</span><span class="n">fsets</span><span class="p">)</span>
+
+                <span class="k">if</span> <span class="s1">&#39;type&#39;</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="p">:</span>
+                    <span class="n">kwargs</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s1">&#39;type&#39;</span><span class="p">)</span>
+
+                <span class="n">dist</span> <span class="o">=</span> <span class="n">ProbabilityDistribution</span><span class="o">.</span><span class="n">ProbabilityDistribution</span><span class="p">(</span><span class="n">smooth</span><span class="p">,</span> <span class="n">uod</span><span class="o">=</span><span class="n">uod</span><span class="p">,</span> <span class="n">bins</span><span class="o">=</span><span class="n">_bins</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+
+                <span class="k">for</span> <span class="nb">bin</span> <span class="ow">in</span> <span class="n">_bins</span><span class="p">:</span>
+                    <span class="n">num</span> <span class="o">=</span> <span class="p">[]</span>
+                    <span class="n">den</span> <span class="o">=</span> <span class="p">[]</span>
+                    <span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="n">flrgs</span><span class="p">:</span>
+                        <span class="k">if</span> <span class="n">s</span><span class="o">.</span><span class="n">get_key</span><span class="p">()</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">:</span>
+                            <span class="n">flrg</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">s</span><span class="o">.</span><span class="n">get_key</span><span class="p">()]</span>
+                            <span class="n">wi</span> <span class="o">=</span> <span class="n">flrg</span><span class="o">.</span><span class="n">rhs_conditional_probability</span><span class="p">(</span><span class="nb">bin</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">,</span> <span class="n">uod</span><span class="p">,</span> <span class="n">nbins</span><span class="p">)</span>
+                            <span class="k">if</span> <span class="ow">not</span> <span class="n">fuzzyfied</span><span class="p">:</span>
+                                <span class="n">pk</span> <span class="o">=</span> <span class="n">flrg</span><span class="o">.</span><span class="n">lhs_conditional_probability</span><span class="p">(</span><span class="n">sample</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">global_frequency_count</span><span class="p">,</span> <span class="n">uod</span><span class="p">,</span> <span class="n">nbins</span><span class="p">)</span>
+                            <span class="k">else</span><span class="p">:</span>
+                                <span class="n">lhs_mv</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">pwflrg_lhs_memberhip_fuzzyfied</span><span class="p">(</span><span class="n">flrg</span><span class="p">,</span> <span class="n">sample</span><span class="p">)</span>
+                                <span class="n">pk</span> <span class="o">=</span> <span class="n">flrg</span><span class="o">.</span><span class="n">lhs_conditional_probability_fuzzyfied</span><span class="p">(</span><span class="n">lhs_mv</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">,</span>
+                                                                      <span class="bp">self</span><span class="o">.</span><span class="n">global_frequency_count</span><span class="p">,</span> <span class="n">uod</span><span class="p">,</span> <span class="n">nbins</span><span class="p">)</span>
+
+                            <span class="n">num</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">wi</span> <span class="o">*</span> <span class="n">pk</span><span class="p">)</span>
+                            <span class="n">den</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">pk</span><span class="p">)</span>
+                        <span class="k">else</span><span class="p">:</span>
+                            <span class="n">num</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="mf">0.0</span><span class="p">)</span>
+                            <span class="n">den</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="mf">0.000000001</span><span class="p">)</span>
+                    <span class="n">pf</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">(</span><span class="n">num</span><span class="p">)</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">(</span><span class="n">den</span><span class="p">)</span>
+
+                    <span class="n">dist</span><span class="o">.</span><span class="n">set</span><span class="p">(</span><span class="nb">bin</span><span class="p">,</span> <span class="n">pf</span><span class="p">)</span>
+
+            <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">dist</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">ret</span></div>
+
+    <span class="k">def</span> <span class="nf">__check_point_bounds</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">point</span><span class="p">):</span>
+        <span class="n">lower_set</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">lower_set</span><span class="p">()</span>
+        <span class="n">upper_set</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">upper_set</span><span class="p">()</span>
+        <span class="k">return</span> <span class="n">point</span> <span class="o">&lt;=</span> <span class="n">lower_set</span><span class="o">.</span><span class="n">lower</span> <span class="ow">or</span> <span class="n">point</span> <span class="o">&gt;=</span> <span class="n">upper_set</span><span class="o">.</span><span class="n">upper</span>
+
+<div class="viewcode-block" id="ProbabilisticWeightedFTS.forecast_ahead"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFTS.forecast_ahead">[docs]</a>    <span class="k">def</span> <span class="nf">forecast_ahead</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">steps</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+
+        <span class="n">l</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+
+        <span class="n">fuzzyfied</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;fuzzyfied&#39;</span><span class="p">,</span> <span class="kc">False</span><span class="p">)</span>
+
+        <span class="n">start</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;start_at&#39;</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span>
+            <span class="n">data</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
+
+        <span class="n">ret</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="n">start</span><span class="p">:</span> <span class="n">start</span><span class="o">+</span><span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">]</span>
+
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">,</span> <span class="n">steps</span><span class="o">+</span><span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">):</span>
+
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">__check_point_bounds</span><span class="p">(</span><span class="n">ret</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">fuzzyfied</span><span class="p">:</span>
+                <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">ret</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">mp</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">forecast</span><span class="p">(</span><span class="n">ret</span><span class="p">[</span><span class="n">k</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">:</span> <span class="n">k</span><span class="p">],</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+                <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">mp</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
+
+        <span class="k">return</span> <span class="n">ret</span><span class="p">[</span><span class="o">-</span><span class="n">steps</span><span class="p">:]</span></div>
+
+    <span class="k">def</span> <span class="nf">__check_interval_bounds</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">interval</span><span class="p">):</span>
+        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">transformations</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="n">lower_set</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">lower_set</span><span class="p">()</span>
+            <span class="n">upper_set</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">upper_set</span><span class="p">()</span>
+            <span class="k">return</span> <span class="n">interval</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">&lt;=</span> <span class="n">lower_set</span><span class="o">.</span><span class="n">lower</span> <span class="ow">and</span> <span class="n">interval</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">&gt;=</span> <span class="n">upper_set</span><span class="o">.</span><span class="n">upper</span>
+        <span class="k">elif</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">transformations</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">interval</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">&lt;=</span> <span class="bp">self</span><span class="o">.</span><span class="n">original_min</span> <span class="ow">and</span> <span class="n">interval</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">&gt;=</span> <span class="bp">self</span><span class="o">.</span><span class="n">original_max</span>
+
+<div class="viewcode-block" id="ProbabilisticWeightedFTS.forecast_ahead_interval"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFTS.forecast_ahead_interval">[docs]</a>    <span class="k">def</span> <span class="nf">forecast_ahead_interval</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">steps</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+
+        <span class="n">start</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;start_at&#39;</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="s1">&#39;fuzzyfied&#39;</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="p">:</span>
+            <span class="n">fuzzyfied</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s1">&#39;fuzzyfied&#39;</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">fuzzyfied</span> <span class="o">=</span> <span class="kc">False</span>
+
+        <span class="n">sample</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="n">start</span><span class="p">:</span> <span class="n">start</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">]</span>
+
+        <span class="k">if</span> <span class="ow">not</span> <span class="n">fuzzyfied</span><span class="p">:</span>
+            <span class="n">ret</span> <span class="o">=</span> <span class="p">[[</span><span class="n">k</span><span class="p">,</span> <span class="n">k</span><span class="p">]</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">sample</span><span class="p">]</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
+            <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">sample</span><span class="p">:</span>
+                <span class="n">kv</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">defuzzyfy</span><span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s1">&#39;both&#39;</span><span class="p">)</span>
+                <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">([</span><span class="n">kv</span><span class="p">,</span> <span class="n">kv</span><span class="p">])</span>
+        
+        <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">forecast_interval</span><span class="p">(</span><span class="n">sample</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)[</span><span class="mi">0</span><span class="p">])</span>
+
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">start</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">,</span> <span class="n">steps</span> <span class="o">+</span> <span class="n">start</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">):</span>
+
+            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">ret</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">__check_interval_bounds</span><span class="p">(</span><span class="n">ret</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]):</span>
+                <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">ret</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">lower</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">forecast_interval</span><span class="p">([</span><span class="n">ret</span><span class="p">[</span><span class="n">x</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">k</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">,</span> <span class="n">k</span><span class="p">)],</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+                <span class="n">upper</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">forecast_interval</span><span class="p">([</span><span class="n">ret</span><span class="p">[</span><span class="n">x</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">k</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">,</span> <span class="n">k</span><span class="p">)],</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+
+                <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">([</span><span class="n">np</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">lower</span><span class="p">),</span> <span class="n">np</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">upper</span><span class="p">)])</span>
+
+        <span class="k">return</span> <span class="n">ret</span><span class="p">[</span><span class="o">-</span><span class="n">steps</span><span class="p">:]</span></div>
+
+<div class="viewcode-block" id="ProbabilisticWeightedFTS.forecast_ahead_distribution"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFTS.forecast_ahead_distribution">[docs]</a>    <span class="k">def</span> <span class="nf">forecast_ahead_distribution</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ndata</span><span class="p">,</span> <span class="n">steps</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+
+        <span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
+
+        <span class="k">if</span> <span class="s1">&#39;type&#39;</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="p">:</span>
+            <span class="n">kwargs</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s1">&#39;type&#39;</span><span class="p">)</span>
+
+        <span class="n">smooth</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;smooth&quot;</span><span class="p">,</span> <span class="s2">&quot;none&quot;</span><span class="p">)</span>
+
+        <span class="n">uod</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_UoD</span><span class="p">()</span>
+
+        <span class="k">if</span> <span class="s1">&#39;bins&#39;</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="p">:</span>
+            <span class="n">_bins</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s1">&#39;bins&#39;</span><span class="p">)</span>
+            <span class="n">nbins</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">_bins</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">nbins</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;num_bins&quot;</span><span class="p">,</span> <span class="mi">100</span><span class="p">)</span>
+            <span class="n">_bins</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="n">uod</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">uod</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">nbins</span><span class="p">)</span>
+
+        <span class="n">start</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;start_at&#39;</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="s1">&#39;fuzzyfied&#39;</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="p">:</span>
+            <span class="n">fuzzyfied</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s1">&#39;fuzzyfied&#39;</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">fuzzyfied</span> <span class="o">=</span> <span class="kc">False</span>
+
+        <span class="k">if</span> <span class="ow">not</span> <span class="n">fuzzyfied</span><span class="p">:</span>
+            <span class="n">sample</span> <span class="o">=</span> <span class="n">ndata</span><span class="p">[</span><span class="n">start</span><span class="p">:</span> <span class="n">start</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">]</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">sample</span> <span class="o">=</span> <span class="p">[]</span>
+            <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">ndata</span><span class="p">[</span><span class="n">start</span><span class="p">:</span> <span class="n">start</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">]:</span>
+                <span class="n">kv</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">defuzzyfy</span><span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s1">&#39;both&#39;</span><span class="p">)</span>
+                <span class="n">sample</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">kv</span><span class="p">)</span>
+
+        <span class="k">for</span> <span class="n">dat</span> <span class="ow">in</span> <span class="n">sample</span><span class="p">:</span>
+            <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">dat</span><span class="p">,</span> <span class="n">ProbabilityDistribution</span><span class="o">.</span><span class="n">ProbabilityDistribution</span><span class="p">):</span>
+                <span class="n">tmp</span> <span class="o">=</span> <span class="n">ProbabilityDistribution</span><span class="o">.</span><span class="n">ProbabilityDistribution</span><span class="p">(</span><span class="n">smooth</span><span class="p">,</span> <span class="n">uod</span><span class="o">=</span><span class="n">uod</span><span class="p">,</span> <span class="n">bins</span><span class="o">=</span><span class="n">_bins</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+                <span class="n">tmp</span><span class="o">.</span><span class="n">set</span><span class="p">(</span><span class="n">dat</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">)</span>
+                <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">tmp</span><span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">dat</span><span class="p">)</span>
+
+        <span class="n">dist</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">forecast_distribution_from_distribution</span><span class="p">(</span><span class="n">ret</span><span class="p">,</span> <span class="n">smooth</span><span class="p">,</span><span class="n">uod</span><span class="p">,</span><span class="n">_bins</span><span class="p">,</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+
+        <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">dist</span><span class="p">)</span>
+
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">start</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">,</span> <span class="n">steps</span> <span class="o">+</span> <span class="n">start</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">):</span>
+            <span class="n">dist</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">forecast_distribution_from_distribution</span><span class="p">(</span><span class="n">ret</span><span class="p">[</span><span class="n">k</span><span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">:],</span> <span class="n">smooth</span><span class="p">,</span> <span class="n">uod</span><span class="p">,</span> <span class="n">_bins</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+            <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">dist</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">ret</span><span class="p">[</span><span class="o">-</span><span class="n">steps</span><span class="p">:]</span></div>
+
+<div class="viewcode-block" id="ProbabilisticWeightedFTS.forecast_distribution_from_distribution"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFTS.forecast_distribution_from_distribution">[docs]</a>    <span class="k">def</span> <span class="nf">forecast_distribution_from_distribution</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">previous_dist</span><span class="p">,</span> <span class="n">smooth</span><span class="p">,</span> <span class="n">uod</span><span class="p">,</span> <span class="n">bins</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="n">dist</span> <span class="o">=</span> <span class="n">ProbabilityDistribution</span><span class="o">.</span><span class="n">ProbabilityDistribution</span><span class="p">(</span><span class="n">smooth</span><span class="p">,</span> <span class="n">uod</span><span class="o">=</span><span class="n">uod</span><span class="p">,</span> <span class="n">bins</span><span class="o">=</span><span class="n">bins</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+
+        <span class="n">lags</span> <span class="o">=</span> <span class="p">[]</span>
+
+        <span class="c1"># Find all bins of past distributions with probability greater than zero</span>
+
+        <span class="k">for</span> <span class="n">ct</span><span class="p">,</span> <span class="n">lag</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">lags</span><span class="p">):</span>
+            <span class="n">dd</span> <span class="o">=</span> <span class="n">previous_dist</span><span class="p">[</span><span class="o">-</span><span class="n">lag</span><span class="p">]</span>
+            <span class="n">vals</span> <span class="o">=</span> <span class="p">[</span><span class="nb">float</span><span class="p">(</span><span class="n">v</span><span class="p">)</span> <span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">dd</span><span class="o">.</span><span class="n">bins</span> <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">dd</span><span class="o">.</span><span class="n">density</span><span class="p">(</span><span class="n">v</span><span class="p">),</span> <span class="mi">4</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mf">0.0</span><span class="p">]</span>
+            <span class="n">lags</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">sorted</span><span class="p">(</span><span class="n">vals</span><span class="p">))</span>
+
+        <span class="c1"># Trace all possible combinations between the bins of past distributions</span>
+
+        <span class="k">for</span> <span class="n">path</span> <span class="ow">in</span> <span class="n">product</span><span class="p">(</span><span class="o">*</span><span class="n">lags</span><span class="p">):</span>
+
+            <span class="c1"># get the combined probabilities for this path</span>
+            <span class="n">pk</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">prod</span><span class="p">([</span><span class="n">previous_dist</span><span class="p">[</span><span class="o">-</span><span class="n">lag</span><span class="p">]</span><span class="o">.</span><span class="n">density</span><span class="p">(</span><span class="n">path</span><span class="p">[</span><span class="n">ct</span><span class="p">])</span>
+                          <span class="k">for</span> <span class="n">ct</span><span class="p">,</span> <span class="n">lag</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">lags</span><span class="p">)])</span>
+
+            <span class="n">d</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">forecast_distribution</span><span class="p">(</span><span class="n">path</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
+
+            <span class="k">for</span> <span class="nb">bin</span> <span class="ow">in</span> <span class="n">bins</span><span class="p">:</span>
+                <span class="n">dist</span><span class="o">.</span><span class="n">set</span><span class="p">(</span><span class="nb">bin</span><span class="p">,</span> <span class="n">dist</span><span class="o">.</span><span class="n">density</span><span class="p">(</span><span class="nb">bin</span><span class="p">)</span> <span class="o">+</span> <span class="n">pk</span> <span class="o">*</span> <span class="n">d</span><span class="o">.</span><span class="n">density</span><span class="p">(</span><span class="nb">bin</span><span class="p">))</span>
+
+        <span class="k">return</span> <span class="n">dist</span></div>
+
+    <span class="k">def</span> <span class="fm">__str__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="n">tmp</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">+</span> <span class="s2">&quot;:</span><span class="se">\n</span><span class="s2">&quot;</span>
+        <span class="k">for</span> <span class="n">r</span> <span class="ow">in</span> <span class="nb">sorted</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="o">.</span><span class="n">keys</span><span class="p">()):</span>
+            <span class="n">p</span> <span class="o">=</span> <span class="nb">round</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">r</span><span class="p">]</span><span class="o">.</span><span class="n">frequency_count</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">global_frequency_count</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
+            <span class="n">tmp</span> <span class="o">=</span> <span class="n">tmp</span> <span class="o">+</span> <span class="s2">&quot;(&quot;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">p</span><span class="p">)</span> <span class="o">+</span> <span class="s2">&quot;) &quot;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">r</span><span class="p">])</span> <span class="o">+</span> <span class="s2">&quot;</span><span class="se">\n</span><span class="s2">&quot;</span>
+        <span class="k">return</span> <span class="n">tmp</span></div>
+
+
+<div class="viewcode-block" id="visualize_distributions"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.pwfts.visualize_distributions">[docs]</a><span class="k">def</span> <span class="nf">visualize_distributions</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+    <span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+    <span class="kn">from</span> <span class="nn">matplotlib</span> <span class="kn">import</span> <span class="n">gridspec</span>
+    <span class="kn">import</span> <span class="nn">seaborn</span> <span class="k">as</span> <span class="nn">sns</span>
+
+    <span class="n">ordered_sets</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">ordered_sets</span>
+    <span class="n">ftpg_keys</span> <span class="o">=</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">flrgs</span><span class="o">.</span><span class="n">keys</span><span class="p">(),</span> <span class="n">key</span><span class="o">=</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">model</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">x</span><span class="p">]</span><span class="o">.</span><span class="n">get_midpoint</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">sets</span><span class="p">))</span>
+
+    <span class="n">lhs_probs</span> <span class="o">=</span> <span class="p">[</span><span class="n">model</span><span class="o">.</span><span class="n">flrg_lhs_unconditional_probability</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">k</span><span class="p">])</span>
+                 <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">ftpg_keys</span><span class="p">]</span>
+
+    <span class="n">mat</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="nb">len</span><span class="p">(</span><span class="n">ftpg_keys</span><span class="p">),</span> <span class="nb">len</span><span class="p">(</span><span class="n">ordered_sets</span><span class="p">)))</span>
+    <span class="k">for</span> <span class="n">row</span><span class="p">,</span> <span class="n">w</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">ftpg_keys</span><span class="p">):</span>
+        <span class="k">for</span> <span class="n">col</span><span class="p">,</span> <span class="n">k</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">ordered_sets</span><span class="p">):</span>
+            <span class="k">if</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">model</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">w</span><span class="p">]</span><span class="o">.</span><span class="n">RHS</span><span class="p">:</span>
+                <span class="n">mat</span><span class="p">[</span><span class="n">row</span><span class="p">,</span> <span class="n">col</span><span class="p">]</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">w</span><span class="p">]</span><span class="o">.</span><span class="n">rhs_unconditional_probability</span><span class="p">(</span><span class="n">k</span><span class="p">)</span>
+
+    <span class="n">size</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;size&#39;</span><span class="p">,</span> <span class="p">(</span><span class="mi">5</span><span class="p">,</span><span class="mi">10</span><span class="p">))</span>
+
+    <span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="n">size</span><span class="p">)</span>
+
+    <span class="n">gs</span> <span class="o">=</span> <span class="n">gridspec</span><span class="o">.</span><span class="n">GridSpec</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="n">width_ratios</span><span class="o">=</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">4</span><span class="p">])</span>
+    <span class="n">ax1</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="n">gs</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
+    <span class="n">sns</span><span class="o">.</span><span class="n">barplot</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="s1">&#39;y&#39;</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="s1">&#39;x&#39;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;darkblue&#39;</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="p">{</span><span class="s1">&#39;x&#39;</span><span class="p">:</span> <span class="n">ftpg_keys</span><span class="p">,</span> <span class="s1">&#39;y&#39;</span><span class="p">:</span> <span class="n">lhs_probs</span><span class="p">},</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax1</span><span class="p">)</span>
+    <span class="n">ax1</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s2">&quot;LHS Probabilities&quot;</span><span class="p">)</span>
+
+    <span class="n">ind_sets</span> <span class="o">=</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">ordered_sets</span><span class="p">))</span>
+    <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="n">gs</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
+    <span class="n">sns</span><span class="o">.</span><span class="n">heatmap</span><span class="p">(</span><span class="n">mat</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="s1">&#39;Blues&#39;</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span> <span class="n">yticklabels</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">&quot;RHS probabilities&quot;</span><span class="p">)</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">set_xticks</span><span class="p">(</span><span class="n">ind_sets</span><span class="p">)</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">set_xticklabels</span><span class="p">(</span><span class="n">ordered_sets</span><span class="p">)</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="kc">True</span><span class="p">)</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">xaxis</span><span class="o">.</span><span class="n">set_tick_params</span><span class="p">(</span><span class="n">rotation</span><span class="o">=</span><span class="mi">90</span><span class="p">)</span></div>
+</pre></div>
+
+            <div class="clearer"></div>
+          </div>
+        </div>
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new file mode 100644
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@@ -0,0 +1,215 @@
+
+
+<!doctype html>
+
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+        <li class="nav-item nav-item-this"><a href="">pyFTS.models.sadaei</a></li> 
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+            
+  <h1>Source code for pyFTS.models.sadaei</h1><div class="highlight"><pre>
+<span></span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">First Order Exponentialy Weighted Fuzzy Time Series by Sadaei et al. (2013)</span>
+
+<span class="sd">H. J. Sadaei, R. Enayatifar, A. H. Abdullah, and A. Gani, “Short-term load forecasting using a hybrid model with a </span>
+<span class="sd">refined exponentially weighted fuzzy time series and an improved harmony search,” Int. J. Electr. Power Energy Syst., vol. 62, no. from 2005, pp. 118–129, 2014.</span>
+<span class="sd">&quot;&quot;&quot;</span>
+
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">from</span> <span class="nn">pyFTS.common</span> <span class="kn">import</span> <span class="n">FuzzySet</span><span class="p">,</span><span class="n">FLR</span><span class="p">,</span><span class="n">fts</span><span class="p">,</span> <span class="n">flrg</span>
+
+<span class="n">default_c</span> <span class="o">=</span> <span class="mf">1.1</span>
+
+
+<div class="viewcode-block" id="ExponentialyWeightedFLRG"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.sadaei.ExponentialyWeightedFLRG">[docs]</a><span class="k">class</span> <span class="nc">ExponentialyWeightedFLRG</span><span class="p">(</span><span class="n">flrg</span><span class="o">.</span><span class="n">FLRG</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;First Order Exponentialy Weighted Fuzzy Logical Relationship Group&quot;&quot;&quot;</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">LHS</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">(</span><span class="n">ExponentialyWeightedFLRG</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">LHS</span> <span class="o">=</span> <span class="n">LHS</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">count</span> <span class="o">=</span> <span class="mf">0.0</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">c</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;c&quot;</span><span class="p">,</span><span class="n">default_c</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">w</span> <span class="o">=</span> <span class="kc">None</span>
+
+<div class="viewcode-block" id="ExponentialyWeightedFLRG.append_rhs"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.sadaei.ExponentialyWeightedFLRG.append_rhs">[docs]</a>    <span class="k">def</span> <span class="nf">append_rhs</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="n">count</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;count&#39;</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">c</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">count</span> <span class="o">+=</span> <span class="n">count</span></div>
+
+<div class="viewcode-block" id="ExponentialyWeightedFLRG.weights"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.sadaei.ExponentialyWeightedFLRG.weights">[docs]</a>    <span class="k">def</span> <span class="nf">weights</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">w</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">wei</span> <span class="o">=</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">c</span> <span class="o">**</span> <span class="n">k</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mf">0.0</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">count</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">)]</span>
+            <span class="n">tot</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">(</span><span class="n">wei</span><span class="p">)</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">w</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">k</span> <span class="o">/</span> <span class="n">tot</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">wei</span><span class="p">])</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">w</span></div>
+
+    <span class="k">def</span> <span class="fm">__str__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="n">tmp</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">LHS</span> <span class="o">+</span> <span class="s2">&quot; -&gt; &quot;</span>
+        <span class="n">tmp2</span> <span class="o">=</span> <span class="s2">&quot;&quot;</span>
+        <span class="n">cc</span> <span class="o">=</span> <span class="mi">0</span>
+        <span class="n">wei</span> <span class="o">=</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">c</span> <span class="o">**</span> <span class="n">k</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mf">0.0</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">count</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">)]</span>
+        <span class="n">tot</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">(</span><span class="n">wei</span><span class="p">)</span>
+        <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="nb">sorted</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="p">):</span>
+            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">tmp2</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
+                <span class="n">tmp2</span> <span class="o">=</span> <span class="n">tmp2</span> <span class="o">+</span> <span class="s2">&quot;,&quot;</span>
+            <span class="n">tmp2</span> <span class="o">=</span> <span class="n">tmp2</span> <span class="o">+</span> <span class="n">c</span> <span class="o">+</span> <span class="s2">&quot;(&quot;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">wei</span><span class="p">[</span><span class="n">cc</span><span class="p">]</span> <span class="o">/</span> <span class="n">tot</span><span class="p">)</span> <span class="o">+</span> <span class="s2">&quot;)&quot;</span>
+            <span class="n">cc</span> <span class="o">=</span> <span class="n">cc</span> <span class="o">+</span> <span class="mi">1</span>
+        <span class="k">return</span> <span class="n">tmp</span> <span class="o">+</span> <span class="n">tmp2</span>
+
+    <span class="k">def</span> <span class="fm">__len__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="ExponentialyWeightedFTS"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.sadaei.ExponentialyWeightedFTS">[docs]</a><span class="k">class</span> <span class="nc">ExponentialyWeightedFTS</span><span class="p">(</span><span class="n">fts</span><span class="o">.</span><span class="n">FTS</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;First Order Exponentialy Weighted Fuzzy Time Series&quot;&quot;&quot;</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">(</span><span class="n">ExponentialyWeightedFTS</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">order</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s2">&quot;EWFTS&quot;</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="s2">&quot;Exponentialy Weighted FTS&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">detail</span> <span class="o">=</span> <span class="s2">&quot;Sadaei&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">c</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;c&#39;</span><span class="p">,</span> <span class="n">default_c</span><span class="p">)</span>
+
+<div class="viewcode-block" id="ExponentialyWeightedFTS.generate_flrg"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.sadaei.ExponentialyWeightedFTS.generate_flrg">[docs]</a>    <span class="k">def</span> <span class="nf">generate_flrg</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">flrs</span><span class="p">,</span> <span class="n">c</span><span class="p">):</span>
+        <span class="k">for</span> <span class="n">flr</span> <span class="ow">in</span> <span class="n">flrs</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">flr</span><span class="o">.</span><span class="n">LHS</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">flr</span><span class="o">.</span><span class="n">LHS</span><span class="p">]</span><span class="o">.</span><span class="n">append_rhs</span><span class="p">(</span><span class="n">flr</span><span class="o">.</span><span class="n">RHS</span><span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">flr</span><span class="o">.</span><span class="n">LHS</span><span class="p">]</span> <span class="o">=</span> <span class="n">ExponentialyWeightedFLRG</span><span class="p">(</span><span class="n">flr</span><span class="o">.</span><span class="n">LHS</span><span class="p">,</span> <span class="n">c</span><span class="o">=</span><span class="n">c</span><span class="p">);</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">flr</span><span class="o">.</span><span class="n">LHS</span><span class="p">]</span><span class="o">.</span><span class="n">append_rhs</span><span class="p">(</span><span class="n">flr</span><span class="o">.</span><span class="n">RHS</span><span class="p">)</span></div>
+
+<div class="viewcode-block" id="ExponentialyWeightedFTS.train"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.sadaei.ExponentialyWeightedFTS.train">[docs]</a>    <span class="k">def</span> <span class="nf">train</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="n">tmpdata</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">fuzzyfy</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s1">&#39;maximum&#39;</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s1">&#39;sets&#39;</span><span class="p">)</span>
+        <span class="n">flrs</span> <span class="o">=</span> <span class="n">FLR</span><span class="o">.</span><span class="n">generate_recurrent_flrs</span><span class="p">(</span><span class="n">tmpdata</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">generate_flrg</span><span class="p">(</span><span class="n">flrs</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">c</span><span class="p">)</span></div>
+
+<div class="viewcode-block" id="ExponentialyWeightedFTS.forecast"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.sadaei.ExponentialyWeightedFTS.forecast">[docs]</a>    <span class="k">def</span> <span class="nf">forecast</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ndata</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+
+        <span class="n">explain</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;explain&#39;</span><span class="p">,</span> <span class="kc">False</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">ordered_sets</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">ordered_sets</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">ordered_sets</span> <span class="o">=</span> <span class="n">FuzzySet</span><span class="o">.</span><span class="n">set_ordered</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">)</span>
+
+        <span class="n">data</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">ndata</span><span class="p">)</span>
+
+        <span class="n">l</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">ndata</span><span class="p">)</span>
+
+        <span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
+
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">l</span><span class="p">):</span>
+
+            <span class="n">actual</span> <span class="o">=</span> <span class="n">FuzzySet</span><span class="o">.</span><span class="n">get_maximum_membership_fuzzyset</span><span class="p">(</span><span class="n">ndata</span><span class="p">[</span><span class="n">k</span><span class="p">],</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">,</span> <span class="n">ordered_sets</span><span class="p">)</span>
+
+            <span class="k">if</span> <span class="n">explain</span><span class="p">:</span>
+                <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Fuzzyfication:</span><span class="se">\n\n</span><span class="s2"> </span><span class="si">{}</span><span class="s2"> -&gt; </span><span class="si">{}</span><span class="s2"> </span><span class="se">\n</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">ndata</span><span class="p">[</span><span class="n">k</span><span class="p">],</span> <span class="n">actual</span><span class="o">.</span><span class="n">name</span><span class="p">))</span>
+
+            <span class="k">if</span> <span class="n">actual</span><span class="o">.</span><span class="n">name</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">:</span>
+                <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">actual</span><span class="o">.</span><span class="n">centroid</span><span class="p">)</span>
+
+                <span class="k">if</span> <span class="n">explain</span><span class="p">:</span>
+                    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Rules:</span><span class="se">\n\n</span><span class="s2"> </span><span class="si">{}</span><span class="s2"> -&gt; </span><span class="si">{}</span><span class="s2"> (Naïve)</span><span class="se">\t</span><span class="s2"> Midpoint: </span><span class="si">{}</span><span class="s2">  </span><span class="se">\n\n</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">actual</span><span class="o">.</span><span class="n">name</span><span class="p">,</span> <span class="n">actual</span><span class="o">.</span><span class="n">name</span><span class="p">,</span><span class="n">actual</span><span class="o">.</span><span class="n">centroid</span><span class="p">))</span>
+
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">flrg</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">actual</span><span class="o">.</span><span class="n">name</span><span class="p">]</span>
+                <span class="n">mp</span> <span class="o">=</span> <span class="n">flrg</span><span class="o">.</span><span class="n">get_midpoints</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">)</span>
+
+                <span class="n">final</span> <span class="o">=</span> <span class="n">mp</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">flrg</span><span class="o">.</span><span class="n">weights</span><span class="p">())</span>
+
+                <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">final</span><span class="p">)</span>
+
+                <span class="k">if</span> <span class="n">explain</span><span class="p">:</span>
+                    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Rules:</span><span class="se">\n\n</span><span class="s2"> </span><span class="si">{}</span><span class="s2"> </span><span class="se">\n\n</span><span class="s2"> &quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">flrg</span><span class="p">)))</span>
+                    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Midpoints: </span><span class="se">\n\n</span><span class="s2"> </span><span class="si">{}</span><span class="se">\n\n</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">mp</span><span class="p">))</span>
+
+                    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Deffuzyfied value: </span><span class="si">{}</span><span class="s2"> </span><span class="se">\n</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">final</span><span class="p">))</span>
+
+        <span class="k">return</span> <span class="n">ret</span></div></div>
+</pre></div>
+
+            <div class="clearer"></div>
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+
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+            
+  <h1>Source code for pyFTS.models.seasonal.cmsfts</h1><div class="highlight"><pre>
+<span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">from</span> <span class="nn">pyFTS.common</span> <span class="kn">import</span> <span class="n">FuzzySet</span><span class="p">,</span> <span class="n">FLR</span>
+<span class="kn">from</span> <span class="nn">pyFTS.models.seasonal</span> <span class="kn">import</span> <span class="n">sfts</span>
+<span class="kn">from</span> <span class="nn">pyFTS.models</span> <span class="kn">import</span> <span class="n">chen</span>
+
+
+<div class="viewcode-block" id="ContextualSeasonalFLRG"><a class="viewcode-back" href="../../../../pyFTS.models.seasonal.html#pyFTS.models.seasonal.cmsfts.ContextualSeasonalFLRG">[docs]</a><span class="k">class</span> <span class="nc">ContextualSeasonalFLRG</span><span class="p">(</span><span class="n">sfts</span><span class="o">.</span><span class="n">SeasonalFLRG</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Contextual Seasonal Fuzzy Logical Relationship Group</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">seasonality</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">(</span><span class="n">ContextualSeasonalFLRG</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">seasonality</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span> <span class="o">=</span> <span class="p">{}</span>
+
+<div class="viewcode-block" id="ContextualSeasonalFLRG.append_rhs"><a class="viewcode-back" href="../../../../pyFTS.models.seasonal.html#pyFTS.models.seasonal.cmsfts.ContextualSeasonalFLRG.append_rhs">[docs]</a>    <span class="k">def</span> <span class="nf">append_rhs</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">flr</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="k">if</span> <span class="n">flr</span><span class="o">.</span><span class="n">LHS</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="p">[</span><span class="n">flr</span><span class="o">.</span><span class="n">LHS</span><span class="p">]</span><span class="o">.</span><span class="n">append_rhs</span><span class="p">(</span><span class="n">flr</span><span class="o">.</span><span class="n">RHS</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="p">[</span><span class="n">flr</span><span class="o">.</span><span class="n">LHS</span><span class="p">]</span> <span class="o">=</span> <span class="n">chen</span><span class="o">.</span><span class="n">ConventionalFLRG</span><span class="p">(</span><span class="n">flr</span><span class="o">.</span><span class="n">LHS</span><span class="p">)</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="p">[</span><span class="n">flr</span><span class="o">.</span><span class="n">LHS</span><span class="p">]</span><span class="o">.</span><span class="n">append_rhs</span><span class="p">(</span><span class="n">flr</span><span class="o">.</span><span class="n">RHS</span><span class="p">)</span></div>
+
+    <span class="k">def</span> <span class="fm">__str__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="n">tmp</span> <span class="o">=</span> <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">LHS</span><span class="p">)</span> <span class="o">+</span> <span class="s2">&quot;: </span><span class="se">\n</span><span class="s2"> &quot;</span>
+        <span class="n">tmp2</span> <span class="o">=</span> <span class="s2">&quot;</span><span class="se">\t</span><span class="s2">&quot;</span>
+        <span class="k">for</span> <span class="n">r</span> <span class="ow">in</span> <span class="nb">sorted</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="p">):</span>
+            <span class="n">tmp2</span> <span class="o">+=</span> <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="p">[</span><span class="n">r</span><span class="p">])</span> <span class="o">+</span> <span class="s2">&quot;</span><span class="se">\n\t</span><span class="s2">&quot;</span>
+        <span class="k">return</span> <span class="n">tmp</span> <span class="o">+</span> <span class="n">tmp2</span> <span class="o">+</span> <span class="s2">&quot;</span><span class="se">\n</span><span class="s2">&quot;</span></div>
+
+
+<div class="viewcode-block" id="ContextualMultiSeasonalFTS"><a class="viewcode-back" href="../../../../pyFTS.models.seasonal.html#pyFTS.models.seasonal.cmsfts.ContextualMultiSeasonalFTS">[docs]</a><span class="k">class</span> <span class="nc">ContextualMultiSeasonalFTS</span><span class="p">(</span><span class="n">sfts</span><span class="o">.</span><span class="n">SeasonalFTS</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Contextual Multi-Seasonal Fuzzy Time Series</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">(</span><span class="n">ContextualMultiSeasonalFTS</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="s2">&quot;Contextual Multi Seasonal FTS&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">shortname</span> <span class="o">=</span> <span class="s2">&quot;CMSFTS &quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">detail</span> <span class="o">=</span> <span class="s2">&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">seasonality</span> <span class="o">=</span> <span class="mi">1</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">has_seasonality</span> <span class="o">=</span> <span class="kc">True</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">has_point_forecasting</span> <span class="o">=</span> <span class="kc">True</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">is_high_order</span> <span class="o">=</span> <span class="kc">True</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">is_multivariate</span> <span class="o">=</span> <span class="kc">True</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">order</span> <span class="o">=</span> <span class="mi">1</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span> <span class="o">=</span> <span class="p">{}</span>
+
+<div class="viewcode-block" id="ContextualMultiSeasonalFTS.generate_flrg"><a class="viewcode-back" href="../../../../pyFTS.models.seasonal.html#pyFTS.models.seasonal.cmsfts.ContextualMultiSeasonalFTS.generate_flrg">[docs]</a>    <span class="k">def</span> <span class="nf">generate_flrg</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">flrs</span><span class="p">):</span>
+        <span class="k">for</span> <span class="n">flr</span> <span class="ow">in</span> <span class="n">flrs</span><span class="p">:</span>
+
+            <span class="k">if</span> <span class="nb">str</span><span class="p">(</span><span class="n">flr</span><span class="o">.</span><span class="n">index</span><span class="p">)</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="nb">str</span><span class="p">(</span><span class="n">flr</span><span class="o">.</span><span class="n">index</span><span class="p">)]</span> <span class="o">=</span> <span class="n">ContextualSeasonalFLRG</span><span class="p">(</span><span class="n">flr</span><span class="o">.</span><span class="n">index</span><span class="p">)</span>
+
+            <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="nb">str</span><span class="p">(</span><span class="n">flr</span><span class="o">.</span><span class="n">index</span><span class="p">)]</span><span class="o">.</span><span class="n">append_rhs</span><span class="p">(</span><span class="n">flr</span><span class="p">)</span></div>
+
+<div class="viewcode-block" id="ContextualMultiSeasonalFTS.train"><a class="viewcode-back" href="../../../../pyFTS.models.seasonal.html#pyFTS.models.seasonal.cmsfts.ContextualMultiSeasonalFTS.train">[docs]</a>    <span class="k">def</span> <span class="nf">train</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span>  <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="k">if</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;sets&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">sets</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;sets&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;parameters&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">seasonality</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;parameters&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
+        <span class="n">flrs</span> <span class="o">=</span> <span class="n">FLR</span><span class="o">.</span><span class="n">generate_indexed_flrs</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">sets</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">indexer</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span>
+                                         <span class="n">transformation</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">transformation</span><span class="p">,</span>
+                                         <span class="n">alpha_cut</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">alpha_cut</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">generate_flrg</span><span class="p">(</span><span class="n">flrs</span><span class="p">)</span></div>
+
+<div class="viewcode-block" id="ContextualMultiSeasonalFTS.get_midpoints"><a class="viewcode-back" href="../../../../pyFTS.models.seasonal.html#pyFTS.models.seasonal.cmsfts.ContextualMultiSeasonalFTS.get_midpoints">[docs]</a>    <span class="k">def</span> <span class="nf">get_midpoints</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">flrg</span><span class="p">,</span> <span class="n">data</span><span class="p">):</span>
+        <span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="k">for</span> <span class="n">d</span> <span class="ow">in</span> <span class="n">data</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">d</span> <span class="ow">in</span> <span class="n">flrg</span><span class="o">.</span><span class="n">RHS</span><span class="p">:</span>
+                <span class="n">ret</span><span class="o">.</span><span class="n">extend</span><span class="p">([</span><span class="bp">self</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="n">s</span><span class="p">]</span><span class="o">.</span><span class="n">centroid</span> <span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="n">flrg</span><span class="o">.</span><span class="n">RHS</span><span class="p">[</span><span class="n">d</span><span class="p">]</span><span class="o">.</span><span class="n">RHS</span><span class="p">])</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">ret</span><span class="o">.</span><span class="n">extend</span><span class="p">([</span><span class="bp">self</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="n">d</span><span class="p">]</span><span class="o">.</span><span class="n">centroid</span><span class="p">])</span>
+
+        <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">ret</span><span class="p">)</span></div>
+
+<div class="viewcode-block" id="ContextualMultiSeasonalFTS.forecast"><a class="viewcode-back" href="../../../../pyFTS.models.seasonal.html#pyFTS.models.seasonal.cmsfts.ContextualMultiSeasonalFTS.forecast">[docs]</a>    <span class="k">def</span> <span class="nf">forecast</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="n">ordered_sets</span> <span class="o">=</span> <span class="n">FuzzySet</span><span class="o">.</span><span class="n">set_ordered</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">sets</span><span class="p">)</span>
+
+        <span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
+
+        <span class="n">index</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">indexer</span><span class="o">.</span><span class="n">get_season_of_data</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+        <span class="n">ndata</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">indexer</span><span class="o">.</span><span class="n">get_data</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">)):</span>
+            
+            <span class="k">if</span> <span class="nb">str</span><span class="p">(</span><span class="n">index</span><span class="p">[</span><span class="n">k</span><span class="p">])</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">:</span>
+
+                <span class="n">flrg</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="nb">str</span><span class="p">(</span><span class="n">index</span><span class="p">[</span><span class="n">k</span><span class="p">])]</span>
+
+                <span class="n">d</span> <span class="o">=</span> <span class="n">FuzzySet</span><span class="o">.</span><span class="n">get_fuzzysets</span><span class="p">(</span><span class="n">ndata</span><span class="p">[</span><span class="n">k</span><span class="p">],</span> <span class="bp">self</span><span class="o">.</span><span class="n">sets</span><span class="p">,</span> <span class="n">ordered_sets</span><span class="p">,</span> <span class="n">alpha_cut</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">alpha_cut</span><span class="p">)</span>
+
+                <span class="n">mp</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_midpoints</span><span class="p">(</span><span class="n">flrg</span><span class="p">,</span> <span class="n">d</span><span class="p">)</span>
+
+                <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">sum</span><span class="p">(</span><span class="n">mp</span><span class="p">)</span> <span class="o">/</span> <span class="nb">len</span><span class="p">(</span><span class="n">mp</span><span class="p">))</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">ret</span></div>
+
+<div class="viewcode-block" id="ContextualMultiSeasonalFTS.forecast_ahead"><a class="viewcode-back" href="../../../../pyFTS.models.seasonal.html#pyFTS.models.seasonal.cmsfts.ContextualMultiSeasonalFTS.forecast_ahead">[docs]</a>    <span class="k">def</span> <span class="nf">forecast_ahead</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">steps</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">steps</span><span class="p">:</span>
+            <span class="n">flrg</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="nb">str</span><span class="p">(</span><span class="n">i</span><span class="p">)]</span>
+
+            <span class="n">mp</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_midpoints</span><span class="p">(</span><span class="n">flrg</span><span class="p">)</span>
+
+            <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">sum</span><span class="p">(</span><span class="n">mp</span><span class="p">)</span> <span class="o">/</span> <span class="nb">len</span><span class="p">(</span><span class="n">mp</span><span class="p">))</span>
+
+        <span class="k">return</span> <span class="n">ret</span></div></div>
+
+
+</pre></div>
+
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+        <li class="nav-item nav-item-this"><a href="">pyFTS.models.seasonal.cmsfts</a></li> 
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diff --git a/docs/build/html/_modules/pyFTS/models/seasonal/msfts.html b/docs/build/html/_modules/pyFTS/models/seasonal/msfts.html
new file mode 100644
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@@ -0,0 +1,165 @@
+
+
+<!doctype html>
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+            
+  <h1>Source code for pyFTS.models.seasonal.msfts</h1><div class="highlight"><pre>
+<span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">from</span> <span class="nn">pyFTS.common</span> <span class="kn">import</span> <span class="n">FLR</span>
+<span class="kn">from</span> <span class="nn">pyFTS.models.seasonal</span> <span class="kn">import</span> <span class="n">sfts</span>
+
+
+<div class="viewcode-block" id="MultiSeasonalFTS"><a class="viewcode-back" href="../../../../pyFTS.models.seasonal.html#pyFTS.models.seasonal.msfts.MultiSeasonalFTS">[docs]</a><span class="k">class</span> <span class="nc">MultiSeasonalFTS</span><span class="p">(</span><span class="n">sfts</span><span class="o">.</span><span class="n">SeasonalFTS</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Multi-Seasonal Fuzzy Time Series</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">name</span><span class="p">,</span> <span class="n">indexer</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">(</span><span class="n">MultiSeasonalFTS</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="s2">&quot;MSFTS&quot;</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="s2">&quot;Multi Seasonal FTS&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">shortname</span> <span class="o">=</span> <span class="s2">&quot;MSFTS &quot;</span> <span class="o">+</span> <span class="n">name</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">detail</span> <span class="o">=</span> <span class="s2">&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">seasonality</span> <span class="o">=</span> <span class="mi">1</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">has_seasonality</span> <span class="o">=</span> <span class="kc">True</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">has_point_forecasting</span> <span class="o">=</span> <span class="kc">True</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">is_high_order</span> <span class="o">=</span> <span class="kc">False</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">is_multivariate</span> <span class="o">=</span> <span class="kc">True</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">indexer</span> <span class="o">=</span> <span class="n">indexer</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span> <span class="o">=</span> <span class="p">{}</span>
+
+<div class="viewcode-block" id="MultiSeasonalFTS.generate_flrg"><a class="viewcode-back" href="../../../../pyFTS.models.seasonal.html#pyFTS.models.seasonal.msfts.MultiSeasonalFTS.generate_flrg">[docs]</a>    <span class="k">def</span> <span class="nf">generate_flrg</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">flrs</span><span class="p">):</span>
+        <span class="k">for</span> <span class="n">flr</span> <span class="ow">in</span> <span class="n">flrs</span><span class="p">:</span>
+
+            <span class="k">if</span> <span class="nb">str</span><span class="p">(</span><span class="n">flr</span><span class="o">.</span><span class="n">index</span><span class="p">)</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="nb">str</span><span class="p">(</span><span class="n">flr</span><span class="o">.</span><span class="n">index</span><span class="p">)]</span> <span class="o">=</span> <span class="n">sfts</span><span class="o">.</span><span class="n">SeasonalFLRG</span><span class="p">(</span><span class="n">flr</span><span class="o">.</span><span class="n">index</span><span class="p">)</span>
+
+            <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="nb">str</span><span class="p">(</span><span class="n">flr</span><span class="o">.</span><span class="n">index</span><span class="p">)]</span><span class="o">.</span><span class="n">append_rhs</span><span class="p">(</span><span class="n">flr</span><span class="o">.</span><span class="n">RHS</span><span class="p">)</span></div>
+
+<div class="viewcode-block" id="MultiSeasonalFTS.train"><a class="viewcode-back" href="../../../../pyFTS.models.seasonal.html#pyFTS.models.seasonal.msfts.MultiSeasonalFTS.train">[docs]</a>    <span class="k">def</span> <span class="nf">train</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span>  <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="k">if</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;sets&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">sets</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;sets&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;parameters&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">seasonality</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;parameters&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
+        <span class="c1">#ndata = self.indexer.set_data(data,self.doTransformations(self.indexer.get_data(data)))</span>
+        <span class="n">flrs</span> <span class="o">=</span> <span class="n">FLR</span><span class="o">.</span><span class="n">generate_indexed_flrs</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">sets</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">indexer</span><span class="p">,</span> <span class="n">data</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">generate_flrg</span><span class="p">(</span><span class="n">flrs</span><span class="p">)</span></div>
+
+<div class="viewcode-block" id="MultiSeasonalFTS.forecast"><a class="viewcode-back" href="../../../../pyFTS.models.seasonal.html#pyFTS.models.seasonal.msfts.MultiSeasonalFTS.forecast">[docs]</a>    <span class="k">def</span> <span class="nf">forecast</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+
+        <span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
+
+        <span class="n">index</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">indexer</span><span class="o">.</span><span class="n">get_season_of_data</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+        <span class="n">ndata</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">indexer</span><span class="o">.</span><span class="n">get_data</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">index</span><span class="p">)):</span>
+
+            <span class="n">flrg</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="nb">str</span><span class="p">(</span><span class="n">index</span><span class="p">[</span><span class="n">k</span><span class="p">])]</span>
+
+            <span class="n">mp</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">getMidpoints</span><span class="p">(</span><span class="n">flrg</span><span class="p">)</span>
+
+            <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">sum</span><span class="p">(</span><span class="n">mp</span><span class="p">)</span> <span class="o">/</span> <span class="nb">len</span><span class="p">(</span><span class="n">mp</span><span class="p">))</span>
+
+        <span class="k">return</span> <span class="n">ret</span></div>
+
+<div class="viewcode-block" id="MultiSeasonalFTS.forecast_ahead"><a class="viewcode-back" href="../../../../pyFTS.models.seasonal.html#pyFTS.models.seasonal.msfts.MultiSeasonalFTS.forecast_ahead">[docs]</a>    <span class="k">def</span> <span class="nf">forecast_ahead</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">steps</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">steps</span><span class="p">:</span>
+            <span class="n">flrg</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="nb">str</span><span class="p">(</span><span class="n">i</span><span class="p">)]</span>
+
+            <span class="n">mp</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">getMidpoints</span><span class="p">(</span><span class="n">flrg</span><span class="p">)</span>
+
+            <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">sum</span><span class="p">(</span><span class="n">mp</span><span class="p">)</span> <span class="o">/</span> <span class="nb">len</span><span class="p">(</span><span class="n">mp</span><span class="p">))</span>
+
+        <span class="k">return</span> <span class="n">ret</span></div></div>
+</pre></div>
+
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+          <li class="nav-item nav-item-1"><a href="../../../index.html" >Module code</a> &#187;</li>
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+    <div class="footer" role="contentinfo">
+        &#169; Copyright 2018, Machine Intelligence and Data Science Laboratory - UFMG - Brazil.
+      Created using <a href="https://www.sphinx-doc.org/">Sphinx</a> 3.1.2.
+    </div>
+  </body>
+</html>
\ No newline at end of file
diff --git a/docs/build/html/_modules/pyFTS/models/seasonal/sfts.html b/docs/build/html/_modules/pyFTS/models/seasonal/sfts.html
new file mode 100644
index 0000000..798e701
--- /dev/null
+++ b/docs/build/html/_modules/pyFTS/models/seasonal/sfts.html
@@ -0,0 +1,200 @@
+
+
+<!doctype html>
+
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+        <li class="nav-item nav-item-this"><a href="">pyFTS.models.seasonal.sfts</a></li> 
+      </ul>
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+
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+          <div class="body" role="main">
+            
+  <h1>Source code for pyFTS.models.seasonal.sfts</h1><div class="highlight"><pre>
+<span></span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">Simple First Order Seasonal Fuzzy Time Series implementation of Song (1999) based of Conventional FTS by Chen (1996)</span>
+
+<span class="sd">Q. Song, “Seasonal forecasting in fuzzy time series,” Fuzzy sets Syst., vol. 107, pp. 235–236, 1999.</span>
+
+<span class="sd">S.-M. Chen, “Forecasting enrollments based on fuzzy time series,” Fuzzy Sets Syst., vol. 81, no. 3, pp. 311–319, 1996.</span>
+<span class="sd">&quot;&quot;&quot;</span>
+
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">from</span> <span class="nn">pyFTS.common</span> <span class="kn">import</span> <span class="n">FuzzySet</span><span class="p">,</span> <span class="n">FLR</span><span class="p">,</span> <span class="n">flrg</span><span class="p">,</span> <span class="n">fts</span>
+
+
+<div class="viewcode-block" id="SeasonalFLRG"><a class="viewcode-back" href="../../../../pyFTS.models.seasonal.html#pyFTS.models.seasonal.sfts.SeasonalFLRG">[docs]</a><span class="k">class</span> <span class="nc">SeasonalFLRG</span><span class="p">(</span><span class="n">flrg</span><span class="o">.</span><span class="n">FLRG</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;First Order Seasonal Fuzzy Logical Relationship Group&quot;&quot;&quot;</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">seasonality</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">(</span><span class="n">SeasonalFLRG</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">LHS</span> <span class="o">=</span> <span class="n">seasonality</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span> <span class="o">=</span> <span class="p">[]</span>
+
+<div class="viewcode-block" id="SeasonalFLRG.get_key"><a class="viewcode-back" href="../../../../pyFTS.models.seasonal.html#pyFTS.models.seasonal.sfts.SeasonalFLRG.get_key">[docs]</a>    <span class="k">def</span> <span class="nf">get_key</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">LHS</span></div>
+
+<div class="viewcode-block" id="SeasonalFLRG.append_rhs"><a class="viewcode-back" href="../../../../pyFTS.models.seasonal.html#pyFTS.models.seasonal.sfts.SeasonalFLRG.append_rhs">[docs]</a>    <span class="k">def</span> <span class="nf">append_rhs</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">c</span><span class="p">)</span></div>
+
+    <span class="k">def</span> <span class="fm">__str__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="n">tmp</span> <span class="o">=</span> <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">LHS</span><span class="p">)</span> <span class="o">+</span> <span class="s2">&quot; -&gt; &quot;</span>
+        <span class="n">tmp2</span> <span class="o">=</span> <span class="s2">&quot;&quot;</span>
+        <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="nb">sorted</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="p">,</span> <span class="n">key</span><span class="o">=</span><span class="k">lambda</span> <span class="n">s</span><span class="p">:</span> <span class="nb">str</span><span class="p">(</span><span class="n">s</span><span class="p">)):</span>
+            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">tmp2</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
+                <span class="n">tmp2</span> <span class="o">=</span> <span class="n">tmp2</span> <span class="o">+</span> <span class="s2">&quot;,&quot;</span>
+            <span class="n">tmp2</span> <span class="o">=</span> <span class="n">tmp2</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">c</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">tmp</span> <span class="o">+</span> <span class="n">tmp2</span>
+
+    <span class="k">def</span> <span class="fm">__len__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="SeasonalFTS"><a class="viewcode-back" href="../../../../pyFTS.models.seasonal.html#pyFTS.models.seasonal.sfts.SeasonalFTS">[docs]</a><span class="k">class</span> <span class="nc">SeasonalFTS</span><span class="p">(</span><span class="n">fts</span><span class="o">.</span><span class="n">FTS</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;First Order Seasonal Fuzzy Time Series&quot;&quot;&quot;</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">(</span><span class="n">SeasonalFTS</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="s2">&quot;Seasonal FTS&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">shortname</span> <span class="o">=</span> <span class="s2">&quot;SFTS&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">order</span> <span class="o">=</span> <span class="mi">1</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">seasonality</span> <span class="o">=</span> <span class="mi">1</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">has_seasonality</span> <span class="o">=</span> <span class="kc">True</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">has_point_forecasting</span> <span class="o">=</span> <span class="kc">True</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">is_high_order</span> <span class="o">=</span> <span class="kc">False</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span> <span class="o">=</span> <span class="p">{}</span>
+
+<div class="viewcode-block" id="SeasonalFTS.generate_flrg"><a class="viewcode-back" href="../../../../pyFTS.models.seasonal.html#pyFTS.models.seasonal.sfts.SeasonalFTS.generate_flrg">[docs]</a>    <span class="k">def</span> <span class="nf">generate_flrg</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">flrs</span><span class="p">):</span>
+
+        <span class="k">for</span> <span class="n">ct</span><span class="p">,</span> <span class="n">flr</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">flrs</span><span class="p">,</span> <span class="n">start</span><span class="o">=</span><span class="mi">1</span><span class="p">):</span>
+
+            <span class="n">season</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">indexer</span><span class="o">.</span><span class="n">get_season_by_index</span><span class="p">(</span><span class="n">ct</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
+
+            <span class="n">ss</span> <span class="o">=</span> <span class="nb">str</span><span class="p">(</span><span class="n">season</span><span class="p">)</span>
+
+            <span class="k">if</span> <span class="n">ss</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">ss</span><span class="p">]</span> <span class="o">=</span> <span class="n">SeasonalFLRG</span><span class="p">(</span><span class="n">season</span><span class="p">)</span>
+
+            <span class="c1">#print(season)</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">ss</span><span class="p">]</span><span class="o">.</span><span class="n">append_rhs</span><span class="p">(</span><span class="n">flr</span><span class="o">.</span><span class="n">RHS</span><span class="p">)</span></div>
+
+<div class="viewcode-block" id="SeasonalFTS.get_midpoints"><a class="viewcode-back" href="../../../../pyFTS.models.seasonal.html#pyFTS.models.seasonal.sfts.SeasonalFTS.get_midpoints">[docs]</a>    <span class="k">def</span> <span class="nf">get_midpoints</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">flrg</span><span class="p">):</span>
+        <span class="n">ret</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="bp">self</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="n">s</span><span class="p">]</span><span class="o">.</span><span class="n">centroid</span> <span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="n">flrg</span><span class="o">.</span><span class="n">RHS</span><span class="p">])</span>
+        <span class="k">return</span> <span class="n">ret</span></div>
+
+<div class="viewcode-block" id="SeasonalFTS.train"><a class="viewcode-back" href="../../../../pyFTS.models.seasonal.html#pyFTS.models.seasonal.sfts.SeasonalFTS.train">[docs]</a>    <span class="k">def</span> <span class="nf">train</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span>  <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="k">if</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;sets&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">sets</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;sets&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
+        <span class="n">tmpdata</span> <span class="o">=</span> <span class="n">FuzzySet</span><span class="o">.</span><span class="n">fuzzyfy_series_old</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">sets</span><span class="p">)</span>
+        <span class="n">flrs</span> <span class="o">=</span> <span class="n">FLR</span><span class="o">.</span><span class="n">generate_non_recurrent_flrs</span><span class="p">(</span><span class="n">tmpdata</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">generate_flrg</span><span class="p">(</span><span class="n">flrs</span><span class="p">)</span></div>
+
+<div class="viewcode-block" id="SeasonalFTS.forecast"><a class="viewcode-back" href="../../../../pyFTS.models.seasonal.html#pyFTS.models.seasonal.sfts.SeasonalFTS.forecast">[docs]</a>    <span class="k">def</span> <span class="nf">forecast</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+
+        <span class="n">l</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+
+        <span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
+
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">l</span><span class="p">):</span>
+
+            <span class="n">season</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">indexer</span><span class="o">.</span><span class="n">get_season_by_index</span><span class="p">(</span><span class="n">k</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
+
+            <span class="n">flrg</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="nb">str</span><span class="p">(</span><span class="n">season</span><span class="p">)]</span>
+
+            <span class="n">mp</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_midpoints</span><span class="p">(</span><span class="n">flrg</span><span class="p">)</span>
+
+            <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">percentile</span><span class="p">(</span><span class="n">mp</span><span class="p">,</span> <span class="mi">50</span><span class="p">))</span>
+
+        <span class="k">return</span> <span class="n">ret</span></div>
+
+    <span class="k">def</span> <span class="fm">__str__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="sd">&quot;&quot;&quot;String representation of the model&quot;&quot;&quot;</span>
+
+        <span class="n">tmp</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">+</span> <span class="s2">&quot;:</span><span class="se">\n</span><span class="s2">&quot;</span>
+        <span class="k">for</span> <span class="n">r</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">:</span>
+            <span class="n">tmp</span> <span class="o">=</span> <span class="n">tmp</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">r</span><span class="p">])</span> <span class="o">+</span> <span class="s2">&quot;</span><span class="se">\n</span><span class="s2">&quot;</span>
+        <span class="k">return</span> <span class="n">tmp</span></div>
+</pre></div>
+
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+            
+  <h1>Source code for pyFTS.models.song</h1><div class="highlight"><pre>
+<span></span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">First Order Traditional Fuzzy Time Series method by Song &amp; Chissom (1993)</span>
+
+<span class="sd">Q. Song and B. S. Chissom, “Fuzzy time series and its models,” Fuzzy Sets Syst., vol. 54, no. 3, pp. 269–277, 1993.</span>
+<span class="sd">&quot;&quot;&quot;</span>
+
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">from</span> <span class="nn">pyFTS.common</span> <span class="kn">import</span> <span class="n">FuzzySet</span><span class="p">,</span> <span class="n">FLR</span><span class="p">,</span> <span class="n">fts</span>
+
+
+<div class="viewcode-block" id="ConventionalFTS"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.song.ConventionalFTS">[docs]</a><span class="k">class</span> <span class="nc">ConventionalFTS</span><span class="p">(</span><span class="n">fts</span><span class="o">.</span><span class="n">FTS</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;Traditional Fuzzy Time Series&quot;&quot;&quot;</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">(</span><span class="n">ConventionalFTS</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">order</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s2">&quot;FTS&quot;</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="s2">&quot;Traditional FTS&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">detail</span> <span class="o">=</span> <span class="s2">&quot;Song &amp; Chissom&quot;</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">sets</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">sets</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">R</span> <span class="o">=</span> <span class="kc">None</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">sets</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">l</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">sets</span><span class="p">)</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">R</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">l</span><span class="p">,</span><span class="n">l</span><span class="p">))</span>
+
+<div class="viewcode-block" id="ConventionalFTS.flr_membership_matrix"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.song.ConventionalFTS.flr_membership_matrix">[docs]</a>    <span class="k">def</span> <span class="nf">flr_membership_matrix</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">flr</span><span class="p">):</span>
+        <span class="n">ordered_set</span> <span class="o">=</span> <span class="n">FuzzySet</span><span class="o">.</span><span class="n">set_ordered</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">sets</span><span class="p">)</span>
+        <span class="n">centroids</span> <span class="o">=</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="n">k</span><span class="p">]</span><span class="o">.</span><span class="n">centroid</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">ordered_set</span><span class="p">]</span>
+        <span class="n">lm</span> <span class="o">=</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="n">flr</span><span class="o">.</span><span class="n">LHS</span><span class="p">]</span><span class="o">.</span><span class="n">membership</span><span class="p">(</span><span class="n">k</span><span class="p">)</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">centroids</span><span class="p">]</span>
+        <span class="n">rm</span> <span class="o">=</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="n">flr</span><span class="o">.</span><span class="n">RHS</span><span class="p">]</span><span class="o">.</span><span class="n">membership</span><span class="p">(</span><span class="n">k</span><span class="p">)</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">centroids</span><span class="p">]</span>
+
+        <span class="n">l</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">ordered_set</span><span class="p">)</span>
+        <span class="n">r</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">l</span><span class="p">,</span> <span class="n">l</span><span class="p">))</span>
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="n">l</span><span class="p">):</span>
+            <span class="k">for</span> <span class="n">l</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">l</span><span class="p">):</span>
+                <span class="n">r</span><span class="p">[</span><span class="n">k</span><span class="p">][</span><span class="n">l</span><span class="p">]</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="n">lm</span><span class="p">[</span><span class="n">k</span><span class="p">],</span> <span class="n">rm</span><span class="p">[</span><span class="n">l</span><span class="p">])</span>
+
+        <span class="k">return</span> <span class="n">r</span></div>
+
+<div class="viewcode-block" id="ConventionalFTS.operation_matrix"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.song.ConventionalFTS.operation_matrix">[docs]</a>    <span class="k">def</span> <span class="nf">operation_matrix</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">flrs</span><span class="p">):</span>
+        <span class="n">l</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">sets</span><span class="p">)</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">R</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">or</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">R</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span> <span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">R</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">l</span><span class="p">,</span> <span class="n">l</span><span class="p">))</span>
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">flrs</span><span class="p">:</span>
+            <span class="n">mm</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">flr_membership_matrix</span><span class="p">(</span><span class="n">k</span><span class="p">)</span>
+            <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">l</span><span class="p">):</span>
+                <span class="k">for</span> <span class="n">l</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">l</span><span class="p">):</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">R</span><span class="p">[</span><span class="n">k</span><span class="p">][</span><span class="n">l</span><span class="p">]</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">R</span><span class="p">[</span><span class="n">k</span><span class="p">][</span><span class="n">l</span><span class="p">],</span> <span class="n">mm</span><span class="p">[</span><span class="n">k</span><span class="p">][</span><span class="n">l</span><span class="p">])</span></div>
+
+
+<div class="viewcode-block" id="ConventionalFTS.train"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.song.ConventionalFTS.train">[docs]</a>    <span class="k">def</span> <span class="nf">train</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+
+        <span class="n">tmpdata</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">fuzzyfy</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s1">&#39;maximum&#39;</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s1">&#39;sets&#39;</span><span class="p">)</span>
+        <span class="n">flrs</span> <span class="o">=</span> <span class="n">FLR</span><span class="o">.</span><span class="n">generate_non_recurrent_flrs</span><span class="p">(</span><span class="n">tmpdata</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">operation_matrix</span><span class="p">(</span><span class="n">flrs</span><span class="p">)</span></div>
+
+<div class="viewcode-block" id="ConventionalFTS.forecast"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.song.ConventionalFTS.forecast">[docs]</a>    <span class="k">def</span> <span class="nf">forecast</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ndata</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">ordered_sets</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">ordered_sets</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">ordered_sets</span> <span class="o">=</span> <span class="n">FuzzySet</span><span class="o">.</span><span class="n">set_ordered</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">sets</span><span class="p">)</span>
+
+        <span class="n">l</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">ndata</span><span class="p">)</span>
+        <span class="n">npart</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">sets</span><span class="p">)</span>
+
+        <span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
+
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">l</span><span class="p">):</span>
+            <span class="n">mv</span> <span class="o">=</span> <span class="n">FuzzySet</span><span class="o">.</span><span class="n">fuzzyfy_instance</span><span class="p">(</span><span class="n">ndata</span><span class="p">[</span><span class="n">k</span><span class="p">],</span> <span class="bp">self</span><span class="o">.</span><span class="n">sets</span><span class="p">)</span>
+
+            <span class="n">r</span> <span class="o">=</span> <span class="p">[</span><span class="nb">max</span><span class="p">([</span> <span class="nb">min</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">R</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="n">j</span><span class="p">],</span> <span class="n">mv</span><span class="p">[</span><span class="n">j</span><span class="p">])</span> <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="n">npart</span><span class="p">)</span> <span class="p">])</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="n">npart</span><span class="p">)]</span>
+
+            <span class="n">fs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ravel</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">argwhere</span><span class="p">(</span><span class="n">r</span> <span class="o">==</span> <span class="nb">max</span><span class="p">(</span><span class="n">r</span><span class="p">)))</span>
+
+            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">fs</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
+                <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="n">ordered_sets</span><span class="p">[</span><span class="n">fs</span><span class="p">[</span><span class="mi">0</span><span class="p">]]]</span><span class="o">.</span><span class="n">centroid</span><span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">mp</span> <span class="o">=</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="n">ordered_sets</span><span class="p">[</span><span class="n">s</span><span class="p">]]</span><span class="o">.</span><span class="n">centroid</span> <span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="n">fs</span><span class="p">]</span>
+
+                <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span> <span class="nb">sum</span><span class="p">(</span><span class="n">mp</span><span class="p">)</span><span class="o">/</span><span class="nb">len</span><span class="p">(</span><span class="n">mp</span><span class="p">))</span>
+
+        <span class="k">return</span> <span class="n">ret</span></div>
+
+    <span class="k">def</span> <span class="fm">__str__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="n">tmp</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">+</span> <span class="s2">&quot;:</span><span class="se">\n</span><span class="s2">&quot;</span>
+        <span class="k">return</span> <span class="n">tmp</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">R</span><span class="p">)</span></div>
+</pre></div>
+
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new file mode 100644
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+
+
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+  <h1>Source code for pyFTS.models.yu</h1><div class="highlight"><pre>
+<span></span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">First Order Weighted Fuzzy Time Series by Yu(2005)</span>
+
+<span class="sd">H.-K. Yu, “Weighted fuzzy time series models for TAIEX forecasting,” </span>
+<span class="sd">Phys. A Stat. Mech. its Appl., vol. 349, no. 3, pp. 609–624, 2005.</span>
+<span class="sd">&quot;&quot;&quot;</span>
+
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">from</span> <span class="nn">pyFTS.common</span> <span class="kn">import</span> <span class="n">FuzzySet</span><span class="p">,</span> <span class="n">FLR</span><span class="p">,</span> <span class="n">fts</span><span class="p">,</span> <span class="n">flrg</span>
+<span class="kn">from</span> <span class="nn">pyFTS.models</span> <span class="kn">import</span> <span class="n">chen</span>
+
+
+<div class="viewcode-block" id="WeightedFLRG"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.yu.WeightedFLRG">[docs]</a><span class="k">class</span> <span class="nc">WeightedFLRG</span><span class="p">(</span><span class="n">flrg</span><span class="o">.</span><span class="n">FLRG</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;First Order Weighted Fuzzy Logical Relationship Group&quot;&quot;&quot;</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">LHS</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">(</span><span class="n">WeightedFLRG</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">LHS</span> <span class="o">=</span> <span class="n">LHS</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">count</span> <span class="o">=</span> <span class="mf">1.0</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">w</span> <span class="o">=</span> <span class="kc">None</span>
+
+<div class="viewcode-block" id="WeightedFLRG.append_rhs"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.yu.WeightedFLRG.append_rhs">[docs]</a>    <span class="k">def</span> <span class="nf">append_rhs</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="n">count</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;count&#39;</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">c</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">count</span> <span class="o">+=</span> <span class="n">count</span></div>
+
+<div class="viewcode-block" id="WeightedFLRG.weights"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.yu.WeightedFLRG.weights">[docs]</a>    <span class="k">def</span> <span class="nf">weights</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sets</span><span class="p">):</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">w</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">tot</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mf">1.0</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">count</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">))</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">w</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">k</span> <span class="o">/</span> <span class="n">tot</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mf">1.0</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">count</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">)])</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">w</span></div>
+
+    <span class="k">def</span> <span class="fm">__str__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="n">tmp</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">LHS</span> <span class="o">+</span> <span class="s2">&quot; -&gt; &quot;</span>
+        <span class="n">tmp2</span> <span class="o">=</span> <span class="s2">&quot;&quot;</span>
+        <span class="n">cc</span> <span class="o">=</span> <span class="mf">1.0</span>
+        <span class="n">tot</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mf">1.0</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">count</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">))</span>
+        <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="nb">sorted</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="p">):</span>
+            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">tmp2</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
+                <span class="n">tmp2</span> <span class="o">=</span> <span class="n">tmp2</span> <span class="o">+</span> <span class="s2">&quot;,&quot;</span>
+            <span class="n">tmp2</span> <span class="o">=</span> <span class="n">tmp2</span> <span class="o">+</span> <span class="n">c</span> <span class="o">+</span> <span class="s2">&quot;(&quot;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">cc</span> <span class="o">/</span> <span class="n">tot</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span> <span class="o">+</span> <span class="s2">&quot;)&quot;</span>
+            <span class="n">cc</span> <span class="o">=</span> <span class="n">cc</span> <span class="o">+</span> <span class="mf">1.0</span>
+        <span class="k">return</span> <span class="n">tmp</span> <span class="o">+</span> <span class="n">tmp2</span></div>
+
+
+<div class="viewcode-block" id="WeightedFTS"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.yu.WeightedFTS">[docs]</a><span class="k">class</span> <span class="nc">WeightedFTS</span><span class="p">(</span><span class="n">fts</span><span class="o">.</span><span class="n">FTS</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;First Order Weighted Fuzzy Time Series&quot;&quot;&quot;</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">(</span><span class="n">WeightedFTS</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">order</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s2">&quot;WFTS&quot;</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="s2">&quot;Weighted FTS&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">detail</span> <span class="o">=</span> <span class="s2">&quot;Yu&quot;</span>
+
+<div class="viewcode-block" id="WeightedFTS.generate_FLRG"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.yu.WeightedFTS.generate_FLRG">[docs]</a>    <span class="k">def</span> <span class="nf">generate_FLRG</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">flrs</span><span class="p">):</span>
+        <span class="k">for</span> <span class="n">flr</span> <span class="ow">in</span> <span class="n">flrs</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">flr</span><span class="o">.</span><span class="n">LHS</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">flr</span><span class="o">.</span><span class="n">LHS</span><span class="p">]</span><span class="o">.</span><span class="n">append_rhs</span><span class="p">(</span><span class="n">flr</span><span class="o">.</span><span class="n">RHS</span><span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">flr</span><span class="o">.</span><span class="n">LHS</span><span class="p">]</span> <span class="o">=</span> <span class="n">WeightedFLRG</span><span class="p">(</span><span class="n">flr</span><span class="o">.</span><span class="n">LHS</span><span class="p">);</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">flr</span><span class="o">.</span><span class="n">LHS</span><span class="p">]</span><span class="o">.</span><span class="n">append_rhs</span><span class="p">(</span><span class="n">flr</span><span class="o">.</span><span class="n">RHS</span><span class="p">)</span></div>
+
+<div class="viewcode-block" id="WeightedFTS.train"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.yu.WeightedFTS.train">[docs]</a>    <span class="k">def</span> <span class="nf">train</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ndata</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="n">tmpdata</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">fuzzyfy</span><span class="p">(</span><span class="n">ndata</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s1">&#39;maximum&#39;</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s1">&#39;sets&#39;</span><span class="p">)</span>
+        <span class="n">flrs</span> <span class="o">=</span> <span class="n">FLR</span><span class="o">.</span><span class="n">generate_recurrent_flrs</span><span class="p">(</span><span class="n">tmpdata</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">generate_FLRG</span><span class="p">(</span><span class="n">flrs</span><span class="p">)</span></div>
+
+<div class="viewcode-block" id="WeightedFTS.forecast"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.yu.WeightedFTS.forecast">[docs]</a>    <span class="k">def</span> <span class="nf">forecast</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ndata</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+
+        <span class="n">explain</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;explain&#39;</span><span class="p">,</span> <span class="kc">False</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">ordered_sets</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">ordered_sets</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">ordered_sets</span> <span class="o">=</span> <span class="n">FuzzySet</span><span class="o">.</span><span class="n">set_ordered</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">)</span>
+
+        <span class="n">ndata</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">ndata</span><span class="p">)</span>
+
+        <span class="n">l</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">ndata</span><span class="p">)</span> <span class="k">if</span> <span class="ow">not</span> <span class="n">explain</span> <span class="k">else</span> <span class="mi">1</span>
+
+        <span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
+
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">l</span><span class="p">):</span>
+
+            <span class="n">actual</span> <span class="o">=</span> <span class="n">FuzzySet</span><span class="o">.</span><span class="n">get_maximum_membership_fuzzyset</span><span class="p">(</span><span class="n">ndata</span><span class="p">[</span><span class="n">k</span><span class="p">],</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">,</span> <span class="n">ordered_sets</span><span class="p">)</span>
+
+            <span class="k">if</span> <span class="n">explain</span><span class="p">:</span>
+                <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Fuzzyfication:</span><span class="se">\n\n</span><span class="s2"> </span><span class="si">{}</span><span class="s2"> -&gt; </span><span class="si">{}</span><span class="s2"> </span><span class="se">\n\n</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">ndata</span><span class="p">[</span><span class="n">k</span><span class="p">],</span> <span class="n">actual</span><span class="o">.</span><span class="n">name</span><span class="p">))</span>
+
+            <span class="k">if</span> <span class="n">actual</span><span class="o">.</span><span class="n">name</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">:</span>
+                <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">actual</span><span class="o">.</span><span class="n">centroid</span><span class="p">)</span>
+
+                <span class="k">if</span> <span class="n">explain</span><span class="p">:</span>
+                    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Rules:</span><span class="se">\n\n</span><span class="s2"> </span><span class="si">{}</span><span class="s2"> -&gt; </span><span class="si">{}</span><span class="s2"> (Naïve)</span><span class="se">\t</span><span class="s2"> Midpoint: </span><span class="si">{}</span><span class="s2">  </span><span class="se">\n\n</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">actual</span><span class="o">.</span><span class="n">name</span><span class="p">,</span> <span class="n">actual</span><span class="o">.</span><span class="n">name</span><span class="p">,</span><span class="n">actual</span><span class="o">.</span><span class="n">centroid</span><span class="p">))</span>
+
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">flrg</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">actual</span><span class="o">.</span><span class="n">name</span><span class="p">]</span>
+                <span class="n">mp</span> <span class="o">=</span> <span class="n">flrg</span><span class="o">.</span><span class="n">get_midpoints</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">)</span>
+
+                <span class="n">final</span> <span class="o">=</span> <span class="n">mp</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">flrg</span><span class="o">.</span><span class="n">weights</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">))</span>
+
+                <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">final</span><span class="p">)</span>
+
+                <span class="k">if</span> <span class="n">explain</span><span class="p">:</span>
+                    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Rules:</span><span class="se">\n\n</span><span class="s2"> </span><span class="si">{}</span><span class="s2"> </span><span class="se">\n\n</span><span class="s2"> &quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">flrg</span><span class="p">)))</span>
+                    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Midpoints: </span><span class="se">\n\n</span><span class="s2"> </span><span class="si">{}</span><span class="se">\n\n</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">mp</span><span class="p">))</span>
+
+                    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Deffuzyfied value: </span><span class="si">{}</span><span class="s2"> </span><span class="se">\n</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">final</span><span class="p">))</span>
+
+        <span class="k">return</span> <span class="n">ret</span></div></div>
+</pre></div>
+
+            <div class="clearer"></div>
+          </div>
+        </div>
+      </div>
+      <div class="sphinxsidebar" role="navigation" aria-label="main navigation">
+        <div class="sphinxsidebarwrapper">
+<div id="searchbox" style="display: none" role="search">
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+<script>$('#searchbox').show(0);</script>
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+      <h3>Navigation</h3>
+      <ul>
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+        <li class="right" >
+          <a href="../../../py-modindex.html" title="Python Module Index"
+             >modules</a> |</li>
+        <li class="nav-item nav-item-0"><a href="../../../index.html">pyFTS 1.6 documentation</a> &#187;</li>
+          <li class="nav-item nav-item-1"><a href="../../index.html" >Module code</a> &#187;</li>
+        <li class="nav-item nav-item-this"><a href="">pyFTS.models.yu</a></li> 
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+        &#169; Copyright 2018, Machine Intelligence and Data Science Laboratory - UFMG - Brazil.
+      Created using <a href="https://www.sphinx-doc.org/">Sphinx</a> 3.1.2.
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\ No newline at end of file
diff --git a/docs/build/html/_modules/pyFTS/partitioners/Huarng.html b/docs/build/html/_modules/pyFTS/partitioners/Huarng.html
new file mode 100644
index 0000000..acf2a78
--- /dev/null
+++ b/docs/build/html/_modules/pyFTS/partitioners/Huarng.html
@@ -0,0 +1,154 @@
+
+
+<!doctype html>
+
+<html>
+  <head>
+    <meta charset="utf-8" />
+    <meta name="viewport" content="width=device-width, initial-scale=1.0"><script type="text/javascript">
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+  <h1>Source code for pyFTS.partitioners.Huarng</h1><div class="highlight"><pre>
+<span></span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">K. H. Huarng, “Effective lengths of intervals to improve forecasting in fuzzy time series,”</span>
+<span class="sd">Fuzzy Sets Syst., vol. 123, no. 3, pp. 387–394, Nov. 2001.</span>
+<span class="sd">&quot;&quot;&quot;</span>
+
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">math</span>
+<span class="kn">import</span> <span class="nn">random</span> <span class="k">as</span> <span class="nn">rnd</span>
+<span class="kn">import</span> <span class="nn">functools</span><span class="o">,</span> <span class="nn">operator</span>
+<span class="kn">from</span> <span class="nn">pyFTS.common</span> <span class="kn">import</span> <span class="n">FuzzySet</span><span class="p">,</span> <span class="n">Membership</span><span class="p">,</span> <span class="n">Transformations</span>
+
+<span class="kn">from</span> <span class="nn">pyFTS.partitioners</span> <span class="kn">import</span> <span class="n">partitioner</span>
+
+<div class="viewcode-block" id="HuarngPartitioner"><a class="viewcode-back" href="../../../pyFTS.partitioners.html#pyFTS.partitioners.Huarng.HuarngPartitioner">[docs]</a><span class="k">class</span> <span class="nc">HuarngPartitioner</span><span class="p">(</span><span class="n">partitioner</span><span class="o">.</span><span class="n">Partitioner</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;Huarng Empirical Partitioner&quot;&quot;&quot;</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">(</span><span class="n">HuarngPartitioner</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s2">&quot;Huarng&quot;</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+
+<div class="viewcode-block" id="HuarngPartitioner.build"><a class="viewcode-back" href="../../../pyFTS.partitioners.html#pyFTS.partitioners.Huarng.HuarngPartitioner.build">[docs]</a>    <span class="k">def</span> <span class="nf">build</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">):</span>
+        <span class="n">diff</span> <span class="o">=</span> <span class="n">Transformations</span><span class="o">.</span><span class="n">Differential</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
+        <span class="n">data2</span> <span class="o">=</span> <span class="n">diff</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+        <span class="n">davg</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">data2</span><span class="p">)</span> <span class="o">/</span> <span class="mi">2</span> <span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">davg</span> <span class="o">&lt;=</span> <span class="mf">1.0</span><span class="p">:</span>
+            <span class="n">base</span> <span class="o">=</span> <span class="mf">0.1</span>
+        <span class="k">elif</span> <span class="mi">1</span> <span class="o">&lt;</span> <span class="n">davg</span> <span class="o">&lt;=</span> <span class="mi">10</span><span class="p">:</span>
+            <span class="n">base</span> <span class="o">=</span> <span class="mf">1.0</span>
+        <span class="k">elif</span> <span class="mi">10</span> <span class="o">&lt;</span> <span class="n">davg</span> <span class="o">&lt;=</span> <span class="mi">100</span><span class="p">:</span>
+            <span class="n">base</span> <span class="o">=</span> <span class="mi">10</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">base</span> <span class="o">=</span> <span class="mi">100</span>
+
+        <span class="n">sets</span> <span class="o">=</span> <span class="p">{}</span>
+
+        <span class="n">kwargs</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;type&#39;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">type</span><span class="p">,</span> <span class="s1">&#39;variable&#39;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">variable</span><span class="p">}</span>
+
+        <span class="n">dlen</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">max</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">min</span>
+        <span class="n">npart</span> <span class="o">=</span> <span class="n">math</span><span class="o">.</span><span class="n">ceil</span><span class="p">(</span><span class="n">dlen</span> <span class="o">/</span> <span class="n">base</span><span class="p">)</span>
+        <span class="n">partition</span> <span class="o">=</span> <span class="n">math</span><span class="o">.</span><span class="n">ceil</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">min</span><span class="p">)</span>
+        <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">npart</span><span class="p">):</span>
+            <span class="n">_name</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_name</span><span class="p">(</span><span class="n">c</span><span class="p">)</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">membership_function</span> <span class="o">==</span> <span class="n">Membership</span><span class="o">.</span><span class="n">trimf</span><span class="p">:</span>
+                <span class="n">sets</span><span class="p">[</span><span class="n">_name</span><span class="p">]</span> <span class="o">=</span> <span class="n">FuzzySet</span><span class="o">.</span><span class="n">FuzzySet</span><span class="p">(</span><span class="n">_name</span><span class="p">,</span> <span class="n">Membership</span><span class="o">.</span><span class="n">trimf</span><span class="p">,</span>
+                                               <span class="p">[</span><span class="n">partition</span> <span class="o">-</span> <span class="n">base</span><span class="p">,</span> <span class="n">partition</span><span class="p">,</span> <span class="n">partition</span> <span class="o">+</span> <span class="n">base</span><span class="p">],</span> <span class="n">partition</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+            <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">membership_function</span> <span class="o">==</span> <span class="n">Membership</span><span class="o">.</span><span class="n">gaussmf</span><span class="p">:</span>
+                <span class="n">sets</span><span class="p">[</span><span class="n">_name</span><span class="p">]</span> <span class="o">=</span> <span class="n">FuzzySet</span><span class="o">.</span><span class="n">FuzzySet</span><span class="p">(</span><span class="n">_name</span><span class="p">,</span> <span class="n">Membership</span><span class="o">.</span><span class="n">gaussmf</span><span class="p">,</span>
+                                              <span class="p">[</span><span class="n">partition</span><span class="p">,</span> <span class="n">base</span><span class="o">/</span><span class="mi">2</span><span class="p">],</span> <span class="n">partition</span><span class="p">)</span>
+            <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">membership_function</span> <span class="o">==</span> <span class="n">Membership</span><span class="o">.</span><span class="n">trapmf</span><span class="p">:</span>
+                <span class="n">sets</span><span class="p">[</span><span class="n">_name</span><span class="p">]</span> <span class="o">=</span> <span class="n">FuzzySet</span><span class="o">.</span><span class="n">FuzzySet</span><span class="p">(</span><span class="n">_name</span><span class="p">,</span> <span class="n">Membership</span><span class="o">.</span><span class="n">trapmf</span><span class="p">,</span>
+                                              <span class="p">[</span><span class="n">partition</span> <span class="o">-</span> <span class="n">base</span><span class="p">,</span> <span class="n">partition</span> <span class="o">-</span> <span class="p">(</span><span class="n">base</span><span class="o">/</span><span class="mi">2</span><span class="p">),</span>
+                                               <span class="n">partition</span> <span class="o">+</span> <span class="p">(</span><span class="n">base</span> <span class="o">/</span> <span class="mi">2</span><span class="p">),</span> <span class="n">partition</span> <span class="o">+</span> <span class="n">base</span><span class="p">],</span> <span class="n">partition</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+
+            <span class="n">partition</span> <span class="o">+=</span> <span class="n">base</span>
+
+        <span class="k">return</span> <span class="n">sets</span></div></div>
+</pre></div>
+
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+  <h1>Source code for pyFTS.partitioners.Util</h1><div class="highlight"><pre>
+<span></span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">Facility methods for pyFTS partitioners module</span>
+<span class="sd">&quot;&quot;&quot;</span>
+
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+<span class="kn">import</span> <span class="nn">matplotlib</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="kn">import</span> <span class="nn">matplotlib.colors</span> <span class="k">as</span> <span class="nn">pltcolors</span>
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="c1">#from mpl_toolkits.mplot3d import Axes3D</span>
+
+<span class="kn">from</span> <span class="nn">pyFTS.benchmarks</span> <span class="kn">import</span> <span class="n">Measures</span>
+<span class="kn">from</span> <span class="nn">pyFTS.common</span> <span class="kn">import</span> <span class="n">Membership</span><span class="p">,</span> <span class="n">Util</span>
+<span class="kn">from</span> <span class="nn">pyFTS.partitioners</span> <span class="kn">import</span> <span class="n">Grid</span><span class="p">,</span><span class="n">Huarng</span><span class="p">,</span><span class="n">FCM</span><span class="p">,</span><span class="n">Entropy</span>
+
+<span class="n">all_methods</span> <span class="o">=</span> <span class="p">[</span><span class="n">Grid</span><span class="o">.</span><span class="n">GridPartitioner</span><span class="p">,</span> <span class="n">Entropy</span><span class="o">.</span><span class="n">EntropyPartitioner</span><span class="p">,</span> <span class="n">FCM</span><span class="o">.</span><span class="n">FCMPartitioner</span><span class="p">,</span> <span class="n">Huarng</span><span class="o">.</span><span class="n">HuarngPartitioner</span><span class="p">]</span>
+
+<span class="n">mfs</span> <span class="o">=</span> <span class="p">[</span><span class="n">Membership</span><span class="o">.</span><span class="n">trimf</span><span class="p">,</span> <span class="n">Membership</span><span class="o">.</span><span class="n">gaussmf</span><span class="p">,</span> <span class="n">Membership</span><span class="o">.</span><span class="n">trapmf</span><span class="p">]</span>
+
+
+<div class="viewcode-block" id="plot_sets"><a class="viewcode-back" href="../../../pyFTS.partitioners.html#pyFTS.partitioners.Util.plot_sets">[docs]</a><span class="k">def</span> <span class="nf">plot_sets</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">sets</span><span class="p">,</span> <span class="n">titles</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="p">[</span><span class="mi">12</span><span class="p">,</span> <span class="mi">10</span><span class="p">],</span> <span class="n">save</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">file</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+    <span class="n">num</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">sets</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="n">axis</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="n">fig</span><span class="p">,</span> <span class="n">axes</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">nrows</span><span class="o">=</span><span class="n">num</span><span class="p">,</span> <span class="n">ncols</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span><span class="n">figsize</span><span class="o">=</span><span class="n">size</span><span class="p">)</span>
+    <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="n">num</span><span class="p">):</span>
+        <span class="n">ticks</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="n">x</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="n">ax</span> <span class="o">=</span> <span class="n">axes</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="k">if</span> <span class="n">axis</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">axis</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="n">titles</span><span class="p">[</span><span class="n">k</span><span class="p">])</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mf">1.1</span><span class="p">])</span>
+        <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">sets</span><span class="p">[</span><span class="n">k</span><span class="p">]</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
+            <span class="n">s</span> <span class="o">=</span> <span class="n">sets</span><span class="p">[</span><span class="n">k</span><span class="p">][</span><span class="n">key</span><span class="p">]</span>
+            <span class="k">if</span> <span class="n">s</span><span class="o">.</span><span class="n">mf</span> <span class="o">==</span> <span class="n">Membership</span><span class="o">.</span><span class="n">trimf</span><span class="p">:</span>
+                <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">s</span><span class="o">.</span><span class="n">parameters</span><span class="p">,[</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">0</span><span class="p">])</span>
+            <span class="k">elif</span> <span class="n">s</span><span class="o">.</span><span class="n">mf</span> <span class="o">==</span> <span class="n">Membership</span><span class="o">.</span><span class="n">gaussmf</span><span class="p">:</span>
+                <span class="n">tmpx</span> <span class="o">=</span> <span class="p">[</span> <span class="n">kk</span> <span class="k">for</span> <span class="n">kk</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">s</span><span class="o">.</span><span class="n">lower</span><span class="p">,</span> <span class="n">s</span><span class="o">.</span><span class="n">upper</span><span class="p">)]</span>
+                <span class="n">tmpy</span> <span class="o">=</span> <span class="p">[</span><span class="n">s</span><span class="o">.</span><span class="n">membership</span><span class="p">(</span><span class="n">kk</span><span class="p">)</span> <span class="k">for</span> <span class="n">kk</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">s</span><span class="o">.</span><span class="n">lower</span><span class="p">,</span> <span class="n">s</span><span class="o">.</span><span class="n">upper</span><span class="p">)]</span>
+                <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">tmpx</span><span class="p">,</span> <span class="n">tmpy</span><span class="p">)</span>
+            <span class="k">elif</span> <span class="n">s</span><span class="o">.</span><span class="n">mf</span> <span class="o">==</span> <span class="n">Membership</span><span class="o">.</span><span class="n">trapmf</span><span class="p">:</span>
+                <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">s</span><span class="o">.</span><span class="n">parameters</span><span class="p">,</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">])</span>
+            <span class="n">ticks</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">s</span><span class="o">.</span><span class="n">centroid</span><span class="p">,</span> <span class="mi">0</span><span class="p">))</span> <span class="o">+</span> <span class="s1">&#39;</span><span class="se">\n</span><span class="s1">&#39;</span> <span class="o">+</span> <span class="n">s</span><span class="o">.</span><span class="n">name</span><span class="p">)</span>
+            <span class="n">x</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">s</span><span class="o">.</span><span class="n">centroid</span><span class="p">)</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">xaxis</span><span class="o">.</span><span class="n">set_ticklabels</span><span class="p">(</span><span class="n">ticks</span><span class="p">)</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">xaxis</span><span class="o">.</span><span class="n">set_ticks</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="n">axis</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
+
+        <span class="n">Util</span><span class="o">.</span><span class="n">show_and_save_image</span><span class="p">(</span><span class="n">fig</span><span class="p">,</span> <span class="n">file</span><span class="p">,</span> <span class="n">save</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="plot_partitioners"><a class="viewcode-back" href="../../../pyFTS.partitioners.html#pyFTS.partitioners.Util.plot_partitioners">[docs]</a><span class="k">def</span> <span class="nf">plot_partitioners</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">objs</span><span class="p">,</span> <span class="n">tam</span><span class="o">=</span><span class="p">[</span><span class="mi">12</span><span class="p">,</span> <span class="mi">10</span><span class="p">],</span> <span class="n">save</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">file</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+    <span class="n">sets</span> <span class="o">=</span> <span class="p">[</span><span class="n">k</span><span class="o">.</span><span class="n">sets</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">objs</span><span class="p">]</span>
+    <span class="n">titles</span> <span class="o">=</span> <span class="p">[</span><span class="n">k</span><span class="o">.</span><span class="n">name</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">objs</span><span class="p">]</span>
+    <span class="n">plot_sets</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">sets</span><span class="p">,</span> <span class="n">titles</span><span class="p">,</span> <span class="n">tam</span><span class="p">,</span> <span class="n">save</span><span class="p">,</span> <span class="n">file</span><span class="p">,</span> <span class="n">axis</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="explore_partitioners"><a class="viewcode-back" href="../../../pyFTS.partitioners.html#pyFTS.partitioners.Util.explore_partitioners">[docs]</a><span class="k">def</span> <span class="nf">explore_partitioners</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">npart</span><span class="p">,</span> <span class="n">methods</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">mf</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">transformation</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+                         <span class="n">size</span><span class="o">=</span><span class="p">[</span><span class="mi">12</span><span class="p">,</span> <span class="mi">10</span><span class="p">],</span> <span class="n">save</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">file</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Create partitioners for the mf membership functions and npart partitions and show the partitioning images.</span>
+<span class="sd">    :data: Time series data</span>
+<span class="sd">    :npart: Maximum number of partitions of the universe of discourse</span>
+<span class="sd">    :methods: A list with the partitioning methods to be used</span>
+<span class="sd">    :mf: A list with the membership functions to be used</span>
+<span class="sd">    :transformation: a transformation to be used in partitioner</span>
+<span class="sd">    :size: list, the size of the output image [width, height]</span>
+<span class="sd">    :save: boolean, if the image will be saved on disk</span>
+<span class="sd">    :file: string, the file path to save the image</span>
+<span class="sd">    :return: the list of the built partitioners</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">if</span> <span class="n">methods</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="n">methods</span> <span class="o">=</span> <span class="n">all_methods</span>
+
+    <span class="k">if</span> <span class="n">mf</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="n">mf</span> <span class="o">=</span> <span class="n">mfs</span>
+
+    <span class="n">objs</span> <span class="o">=</span> <span class="p">[]</span>
+
+    <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">methods</span><span class="p">:</span>
+        <span class="k">for</span> <span class="n">m</span> <span class="ow">in</span> <span class="n">mf</span><span class="p">:</span>
+            <span class="n">obj</span> <span class="o">=</span> <span class="n">p</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">data</span><span class="p">,</span> <span class="n">npart</span><span class="o">=</span><span class="n">npart</span><span class="p">,</span> <span class="n">func</span><span class="o">=</span><span class="n">m</span><span class="p">,</span> <span class="n">transformation</span><span class="o">=</span><span class="n">transformation</span><span class="p">)</span>
+            <span class="n">obj</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="n">obj</span><span class="o">.</span><span class="n">name</span>  <span class="o">+</span> <span class="s2">&quot; - &quot;</span> <span class="o">+</span> <span class="n">obj</span><span class="o">.</span><span class="n">membership_function</span><span class="o">.</span><span class="vm">__name__</span>
+            <span class="n">objs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">obj</span><span class="p">)</span>
+
+    <span class="n">plot_partitioners</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">objs</span><span class="p">,</span> <span class="n">size</span><span class="p">,</span> <span class="n">save</span><span class="p">,</span> <span class="n">file</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">objs</span></div>
+</pre></div>
+
+            <div class="clearer"></div>
+          </div>
+        </div>
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+          <li class="nav-item nav-item-1"><a href="../../index.html" >Module code</a> &#187;</li>
+        <li class="nav-item nav-item-this"><a href="">pyFTS.partitioners.Util</a></li> 
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+  </body>
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diff --git a/docs/build/html/_modules/pyFTS/partitioners/parallel_util.html b/docs/build/html/_modules/pyFTS/partitioners/parallel_util.html
new file mode 100644
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--- /dev/null
+++ b/docs/build/html/_modules/pyFTS/partitioners/parallel_util.html
@@ -0,0 +1,131 @@
+
+
+<!doctype html>
+
+<html>
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+        <li class="nav-item nav-item-this"><a href="">pyFTS.partitioners.parallel_util</a></li> 
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+            
+  <h1>Source code for pyFTS.partitioners.parallel_util</h1><div class="highlight"><pre>
+<span></span><span class="kn">from</span> <span class="nn">copy</span> <span class="kn">import</span> <span class="n">deepcopy</span>
+<span class="kn">from</span> <span class="nn">joblib</span> <span class="kn">import</span> <span class="n">Parallel</span><span class="p">,</span> <span class="n">delayed</span>
+<span class="kn">import</span> <span class="nn">multiprocessing</span>
+<span class="kn">import</span>  <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+
+<span class="kn">from</span> <span class="nn">pyFTS.common</span> <span class="kn">import</span> <span class="n">Membership</span><span class="p">,</span> <span class="n">Util</span>
+<span class="kn">from</span> <span class="nn">pyFTS.partitioners</span> <span class="kn">import</span> <span class="n">Grid</span><span class="p">,</span><span class="n">Huarng</span><span class="p">,</span><span class="n">FCM</span><span class="p">,</span><span class="n">Entropy</span>
+<span class="kn">from</span> <span class="nn">pyFTS.partitioners</span> <span class="kn">import</span> <span class="n">Util</span>
+
+
+<div class="viewcode-block" id="explore_partitioners"><a class="viewcode-back" href="../../../pyFTS.partitioners.html#pyFTS.partitioners.parallel_util.explore_partitioners">[docs]</a><span class="k">def</span> <span class="nf">explore_partitioners</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">npart</span><span class="p">,</span> <span class="n">methods</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">mf</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">tam</span><span class="o">=</span><span class="p">[</span><span class="mi">12</span><span class="p">,</span> <span class="mi">10</span><span class="p">],</span> <span class="n">save</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">file</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+    <span class="n">all_methods</span> <span class="o">=</span> <span class="p">[</span><span class="n">Grid</span><span class="o">.</span><span class="n">GridPartitioner</span><span class="p">,</span> <span class="n">Entropy</span><span class="o">.</span><span class="n">EntropyPartitioner</span><span class="p">,</span> <span class="n">FCM</span><span class="o">.</span><span class="n">FCMPartitioner</span><span class="p">]</span>
+    <span class="n">mfs</span> <span class="o">=</span> <span class="p">[</span><span class="n">Membership</span><span class="o">.</span><span class="n">trimf</span><span class="p">,</span> <span class="n">Membership</span><span class="o">.</span><span class="n">gaussmf</span><span class="p">,</span> <span class="n">Membership</span><span class="o">.</span><span class="n">trapmf</span><span class="p">]</span>
+
+    <span class="k">if</span> <span class="n">methods</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="n">methods</span> <span class="o">=</span> <span class="n">all_methods</span>
+
+    <span class="k">if</span> <span class="n">mf</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="n">mf</span> <span class="o">=</span> <span class="n">mfs</span>
+
+    <span class="n">num_cores</span> <span class="o">=</span> <span class="n">multiprocessing</span><span class="o">.</span><span class="n">cpu_count</span><span class="p">()</span>
+
+    <span class="n">objs</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="k">for</span> <span class="n">method</span> <span class="ow">in</span> <span class="n">methods</span><span class="p">:</span>
+        <span class="nb">print</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">method</span><span class="p">))</span>
+        <span class="n">tmp</span> <span class="o">=</span> <span class="n">Parallel</span><span class="p">(</span><span class="n">n_jobs</span><span class="o">=</span><span class="n">num_cores</span><span class="p">)(</span><span class="n">delayed</span><span class="p">(</span><span class="n">method</span><span class="p">)(</span><span class="n">deepcopy</span><span class="p">(</span><span class="n">data</span><span class="p">),</span> <span class="n">npart</span><span class="p">,</span> <span class="n">m</span><span class="p">)</span> <span class="k">for</span> <span class="n">m</span> <span class="ow">in</span> <span class="n">mf</span><span class="p">)</span>
+        <span class="n">objs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">tmp</span><span class="p">)</span>
+
+    <span class="n">objs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ravel</span><span class="p">(</span><span class="n">objs</span><span class="p">)</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
+
+    <span class="n">Util</span><span class="o">.</span><span class="n">plot_partitioners</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">objs</span><span class="p">,</span> <span class="n">tam</span><span class="p">,</span> <span class="n">save</span><span class="p">,</span> <span class="n">file</span><span class="p">)</span></div>
+
+</pre></div>
+
+            <div class="clearer"></div>
+          </div>
+        </div>
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+        <li class="nav-item nav-item-this"><a href="">pyFTS.probabilistic.ProbabilityDistribution</a></li> 
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+            
+  <h1>Source code for pyFTS.probabilistic.ProbabilityDistribution</h1><div class="highlight"><pre>
+<span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="kn">from</span> <span class="nn">pyFTS.common</span> <span class="kn">import</span> <span class="n">FuzzySet</span><span class="p">,</span><span class="n">SortedCollection</span><span class="p">,</span><span class="n">tree</span>
+<span class="kn">from</span> <span class="nn">pyFTS.probabilistic</span> <span class="kn">import</span> <span class="n">kde</span>
+
+
+<div class="viewcode-block" id="from_point"><a class="viewcode-back" href="../../../pyFTS.probabilistic.html#pyFTS.probabilistic.ProbabilityDistribution.from_point">[docs]</a><span class="k">def</span> <span class="nf">from_point</span><span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Create a probability distribution from a scalar value</span>
+
+<span class="sd">    :param x: scalar value</span>
+<span class="sd">    :param kwargs: common parameters of the distribution</span>
+<span class="sd">    :return: the ProbabilityDistribution object</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">tmp</span> <span class="o">=</span> <span class="n">ProbabilityDistribution</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+    <span class="n">tmp</span><span class="o">.</span><span class="n">set</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">tmp</span></div>
+
+
+<div class="viewcode-block" id="ProbabilityDistribution"><a class="viewcode-back" href="../../../pyFTS.probabilistic.html#pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution">[docs]</a><span class="k">class</span> <span class="nc">ProbabilityDistribution</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Represents a discrete or continous probability distribution</span>
+<span class="sd">    If type is histogram, the PDF is discrete</span>
+<span class="sd">    If type is KDE the PDF is continuous</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="nb">type</span> <span class="o">=</span> <span class="s2">&quot;KDE&quot;</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">uod</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;uod&quot;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
+        <span class="sd">&quot;&quot;&quot;Universe of discourse&quot;&quot;&quot;</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">data</span> <span class="o">=</span> <span class="p">[]</span>
+
+        <span class="n">data</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;data&quot;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">type</span> <span class="o">=</span> <span class="nb">type</span>
+        <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        If type is histogram, the PDF is discrete</span>
+<span class="sd">        If type is KDE the PDF is continuous</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">bins</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;bins&quot;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
+        <span class="sd">&quot;&quot;&quot;Number of bins on a discrete PDF&quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">labels</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;bins_labels&quot;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
+        <span class="sd">&quot;&quot;&quot;Bins labels on a discrete PDF&quot;&quot;&quot;</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">type</span> <span class="o">==</span> <span class="s2">&quot;KDE&quot;</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">kde</span> <span class="o">=</span> <span class="n">kde</span><span class="o">.</span><span class="n">KernelSmoothing</span><span class="p">(</span><span class="n">h</span><span class="o">=</span><span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;h&quot;</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">),</span> <span class="n">kernel</span><span class="o">=</span><span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;kernel&quot;</span><span class="p">,</span> <span class="s2">&quot;epanechnikov&quot;</span><span class="p">))</span>
+
+        <span class="k">if</span> <span class="n">data</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">uod</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">_min</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nanmin</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+            <span class="n">_min</span> <span class="o">=</span> <span class="n">_min</span> <span class="o">*</span> <span class="o">.</span><span class="mi">7</span> <span class="k">if</span> <span class="n">_min</span> <span class="o">&gt;</span> <span class="mi">0</span> <span class="k">else</span> <span class="n">_min</span> <span class="o">*</span> <span class="mf">1.3</span>
+            <span class="n">_max</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nanmax</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+            <span class="n">_max</span> <span class="o">=</span> <span class="n">_max</span> <span class="o">*</span> <span class="mf">1.3</span> <span class="k">if</span> <span class="n">_max</span> <span class="o">&gt;</span> <span class="mi">0</span> <span class="k">else</span> <span class="n">_max</span> <span class="o">*</span> <span class="o">.</span><span class="mi">7</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">uod</span> <span class="o">=</span> <span class="p">[</span><span class="n">_min</span><span class="p">,</span> <span class="n">_max</span><span class="p">]</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">nbins</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;num_bins&quot;</span><span class="p">,</span> <span class="mi">100</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">bins</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">bins</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">uod</span><span class="p">[</span><span class="mi">0</span><span class="p">]),</span> <span class="nb">int</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">uod</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span> <span class="nb">int</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">nbins</span><span class="p">))</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">labels</span> <span class="o">=</span> <span class="p">[</span><span class="nb">str</span><span class="p">(</span><span class="n">k</span><span class="p">)</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">bins</span><span class="p">]</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">uod</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">resolution</span> <span class="o">=</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">uod</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">uod</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">nbins</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">bin_index</span> <span class="o">=</span> <span class="n">SortedCollection</span><span class="o">.</span><span class="n">SortedCollection</span><span class="p">(</span><span class="n">iterable</span><span class="o">=</span><span class="nb">sorted</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">bins</span><span class="p">))</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">quantile_index</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">distribution</span> <span class="o">=</span> <span class="p">{}</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">cdf</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">qtl</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">count</span> <span class="o">=</span> <span class="mi">0</span>
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">bins</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">distribution</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
+
+        <span class="k">if</span> <span class="n">data</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;name&quot;</span><span class="p">,</span> <span class="s2">&quot;&quot;</span><span class="p">)</span>
+
+<div class="viewcode-block" id="ProbabilityDistribution.set"><a class="viewcode-back" href="../../../pyFTS.probabilistic.html#pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution.set">[docs]</a>    <span class="k">def</span> <span class="nf">set</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">value</span><span class="p">,</span> <span class="n">density</span><span class="p">):</span>
+        <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Assert a probability &#39;density&#39; for a certain value &#39;value&#39;, such that P(value) = density</span>
+
+<span class="sd">        :param value: A value in the universe of discourse from the distribution</span>
+<span class="sd">        :param density: The probability density to assign to the value</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">k</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">bin_index</span><span class="o">.</span><span class="n">find_ge</span><span class="p">(</span><span class="n">value</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">distribution</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="n">density</span></div>
+
+<div class="viewcode-block" id="ProbabilityDistribution.append"><a class="viewcode-back" href="../../../pyFTS.probabilistic.html#pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution.append">[docs]</a>    <span class="k">def</span> <span class="nf">append</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">values</span><span class="p">):</span>
+        <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Increment the frequency count for the values</span>
+
+<span class="sd">        :param values: A list of values to account the frequency</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">type</span> <span class="o">==</span> <span class="s2">&quot;histogram&quot;</span><span class="p">:</span>
+            <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">values</span><span class="p">:</span>
+                <span class="n">v</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">bin_index</span><span class="o">.</span><span class="n">find_ge</span><span class="p">(</span><span class="n">k</span><span class="p">)</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">distribution</span><span class="p">[</span><span class="n">v</span><span class="p">]</span> <span class="o">+=</span> <span class="mi">1</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">count</span> <span class="o">+=</span> <span class="mi">1</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">values</span><span class="p">)</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">distribution</span> <span class="o">=</span> <span class="p">{}</span>
+            <span class="n">dens</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">density</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">bins</span><span class="p">)</span>
+            <span class="k">for</span> <span class="n">v</span><span class="p">,</span><span class="n">d</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">dens</span><span class="p">):</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">distribution</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">bins</span><span class="p">[</span><span class="n">v</span><span class="p">]]</span> <span class="o">=</span> <span class="n">d</span></div>
+
+<div class="viewcode-block" id="ProbabilityDistribution.append_interval"><a class="viewcode-back" href="../../../pyFTS.probabilistic.html#pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution.append_interval">[docs]</a>    <span class="k">def</span> <span class="nf">append_interval</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">intervals</span><span class="p">):</span>
+        <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Increment the frequency count for all values inside an interval</span>
+
+<span class="sd">        :param intervals: A list of intervals do increment the frequency</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">type</span> <span class="o">==</span> <span class="s2">&quot;histogram&quot;</span><span class="p">:</span>
+            <span class="k">for</span> <span class="n">interval</span> <span class="ow">in</span> <span class="n">intervals</span><span class="p">:</span>
+                <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">bin_index</span><span class="o">.</span><span class="n">inside</span><span class="p">(</span><span class="n">interval</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">interval</span><span class="p">[</span><span class="mi">1</span><span class="p">]):</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">distribution</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">+=</span> <span class="mi">1</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">count</span> <span class="o">+=</span> <span class="mi">1</span></div>
+
+<div class="viewcode-block" id="ProbabilityDistribution.density"><a class="viewcode-back" href="../../../pyFTS.probabilistic.html#pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution.density">[docs]</a>    <span class="k">def</span> <span class="nf">density</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">values</span><span class="p">):</span>
+        <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Return the probability densities for the input values</span>
+
+<span class="sd">        :param values: List of values to return the densities</span>
+<span class="sd">        :return: List of probability densities for the input values</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="n">scalar</span> <span class="o">=</span> <span class="kc">False</span>
+
+        <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">values</span><span class="p">,</span> <span class="nb">list</span><span class="p">):</span>
+            <span class="n">values</span> <span class="o">=</span> <span class="p">[</span><span class="n">values</span><span class="p">]</span>
+            <span class="n">scalar</span> <span class="o">=</span> <span class="kc">True</span>
+
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">values</span><span class="p">:</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">type</span> <span class="o">==</span> <span class="s2">&quot;histogram&quot;</span><span class="p">:</span>
+                <span class="n">v</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">bin_index</span><span class="o">.</span><span class="n">find_ge</span><span class="p">(</span><span class="n">k</span><span class="p">)</span>
+                <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">distribution</span><span class="p">[</span><span class="n">v</span><span class="p">]</span> <span class="o">/</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">count</span> <span class="o">+</span> <span class="mf">1e-5</span><span class="p">))</span>
+            <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">type</span> <span class="o">==</span> <span class="s2">&quot;KDE&quot;</span><span class="p">:</span>
+                <span class="n">v</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">kde</span><span class="o">.</span><span class="n">probability</span><span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="p">)</span>
+                <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">v</span><span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">v</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">bin_index</span><span class="o">.</span><span class="n">find_ge</span><span class="p">(</span><span class="n">k</span><span class="p">)</span>
+                <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">distribution</span><span class="p">[</span><span class="n">v</span><span class="p">])</span>
+
+        <span class="k">if</span> <span class="n">scalar</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">ret</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+
+        <span class="k">return</span> <span class="n">ret</span></div>
+
+<div class="viewcode-block" id="ProbabilityDistribution.differential_offset"><a class="viewcode-back" href="../../../pyFTS.probabilistic.html#pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution.differential_offset">[docs]</a>    <span class="k">def</span> <span class="nf">differential_offset</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">value</span><span class="p">):</span>
+        <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Auxiliary function for probability distributions of differentiated data</span>
+
+<span class="sd">        :param value:</span>
+<span class="sd">        :return:</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">nbins</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="n">dist</span> <span class="o">=</span> <span class="p">{}</span>
+
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">bins</span><span class="p">:</span>
+            <span class="n">nk</span> <span class="o">=</span> <span class="n">k</span><span class="o">+</span><span class="n">value</span>
+            <span class="n">nbins</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">nk</span><span class="p">)</span>
+            <span class="n">dist</span><span class="p">[</span><span class="n">nk</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">distribution</span><span class="p">[</span><span class="n">k</span><span class="p">]</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">bins</span> <span class="o">=</span> <span class="n">nbins</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">distribution</span> <span class="o">=</span> <span class="n">dist</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">labels</span> <span class="o">=</span> <span class="p">[</span><span class="nb">str</span><span class="p">(</span><span class="n">k</span><span class="p">)</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">bins</span><span class="p">]</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">bin_index</span> <span class="o">=</span> <span class="n">SortedCollection</span><span class="o">.</span><span class="n">SortedCollection</span><span class="p">(</span><span class="n">iterable</span><span class="o">=</span><span class="nb">sorted</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">bins</span><span class="p">))</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">quantile_index</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">cdf</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">qtl</span> <span class="o">=</span> <span class="kc">None</span></div>
+
+<div class="viewcode-block" id="ProbabilityDistribution.expected_value"><a class="viewcode-back" href="../../../pyFTS.probabilistic.html#pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution.expected_value">[docs]</a>    <span class="k">def</span> <span class="nf">expected_value</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Return the expected value of the distribution, as E[X] = ∑ x * P(x)</span>
+
+<span class="sd">        :return: The expected value of the distribution</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">([</span><span class="n">v</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">distribution</span><span class="p">[</span><span class="n">v</span><span class="p">]</span> <span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">bins</span><span class="p">])</span></div>
+
+<div class="viewcode-block" id="ProbabilityDistribution.build_cdf_qtl"><a class="viewcode-back" href="../../../pyFTS.probabilistic.html#pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution.build_cdf_qtl">[docs]</a>    <span class="k">def</span> <span class="nf">build_cdf_qtl</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="n">ret</span> <span class="o">=</span> <span class="mf">0.0</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">cdf</span> <span class="o">=</span> <span class="p">{}</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">qtl</span> <span class="o">=</span> <span class="p">{}</span>
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="nb">sorted</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">bins</span><span class="p">):</span>
+            <span class="n">ret</span> <span class="o">+=</span> <span class="bp">self</span><span class="o">.</span><span class="n">density</span><span class="p">(</span><span class="n">k</span><span class="p">)</span>
+            <span class="k">if</span> <span class="n">k</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">cdf</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">cdf</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="n">ret</span>
+
+            <span class="k">if</span> <span class="nb">str</span><span class="p">(</span><span class="n">ret</span><span class="p">)</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">qtl</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">qtl</span><span class="p">[</span><span class="nb">str</span><span class="p">(</span><span class="n">ret</span><span class="p">)]</span> <span class="o">=</span> <span class="p">[]</span>
+
+            <span class="bp">self</span><span class="o">.</span><span class="n">qtl</span><span class="p">[</span><span class="nb">str</span><span class="p">(</span><span class="n">ret</span><span class="p">)]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">k</span><span class="p">)</span>
+
+        <span class="n">_keys</span> <span class="o">=</span> <span class="p">[</span><span class="nb">float</span><span class="p">(</span><span class="n">k</span><span class="p">)</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="nb">sorted</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">qtl</span><span class="o">.</span><span class="n">keys</span><span class="p">())]</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">quantile_index</span> <span class="o">=</span> <span class="n">SortedCollection</span><span class="o">.</span><span class="n">SortedCollection</span><span class="p">(</span><span class="n">iterable</span><span class="o">=</span><span class="n">_keys</span><span class="p">)</span></div>
+
+<div class="viewcode-block" id="ProbabilityDistribution.cumulative"><a class="viewcode-back" href="../../../pyFTS.probabilistic.html#pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution.cumulative">[docs]</a>    <span class="k">def</span> <span class="nf">cumulative</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">values</span><span class="p">):</span>
+        <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Return the cumulative probability densities for the input values,</span>
+<span class="sd">        such that F(x) = P(X &lt;= x)</span>
+
+<span class="sd">        :param values: A list of input values</span>
+<span class="sd">        :return: The cumulative probability densities for the input values</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">cdf</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">build_cdf_qtl</span><span class="p">()</span>
+
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">values</span><span class="p">,</span> <span class="nb">list</span><span class="p">):</span>
+            <span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
+            <span class="k">for</span> <span class="n">val</span> <span class="ow">in</span> <span class="n">values</span><span class="p">:</span>
+                <span class="k">try</span><span class="p">:</span>
+                    <span class="n">k</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">bin_index</span><span class="o">.</span><span class="n">find_ge</span><span class="p">(</span><span class="n">val</span><span class="p">)</span>
+                    <span class="c1">#ret.append(self.cdf[k])</span>
+                    <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">cdf</span><span class="p">[</span><span class="n">val</span><span class="p">])</span>
+                <span class="k">except</span><span class="p">:</span>
+                    <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">try</span><span class="p">:</span>
+                <span class="n">k</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">bin_index</span><span class="o">.</span><span class="n">find_ge</span><span class="p">(</span><span class="n">values</span><span class="p">)</span>
+                <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">cdf</span><span class="p">[</span><span class="n">k</span><span class="p">]</span>
+            <span class="k">except</span><span class="p">:</span>
+                <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span></div>
+
+<div class="viewcode-block" id="ProbabilityDistribution.quantile"><a class="viewcode-back" href="../../../pyFTS.probabilistic.html#pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution.quantile">[docs]</a>    <span class="k">def</span> <span class="nf">quantile</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">values</span><span class="p">):</span>
+        <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Return the Universe of Discourse values in relation to the quantile input values,</span>
+<span class="sd">        such that Q(tau) = min( {x | F(x) &gt;= tau })</span>
+
+<span class="sd">        :param values: input values</span>
+<span class="sd">        :return: The list of the quantile values for the input values</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">qtl</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">build_cdf_qtl</span><span class="p">()</span>
+
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">values</span><span class="p">,</span> <span class="nb">list</span><span class="p">):</span>
+            <span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
+            <span class="k">for</span> <span class="n">val</span> <span class="ow">in</span> <span class="n">values</span><span class="p">:</span>
+                <span class="k">try</span><span class="p">:</span>
+                    <span class="n">k</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">quantile_index</span><span class="o">.</span><span class="n">find_ge</span><span class="p">(</span><span class="n">val</span><span class="p">)</span>
+                    <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">qtl</span><span class="p">[</span><span class="nb">str</span><span class="p">(</span><span class="n">k</span><span class="p">)][</span><span class="mi">0</span><span class="p">])</span>
+                <span class="k">except</span><span class="p">:</span>
+                    <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">try</span><span class="p">:</span>
+                <span class="n">k</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">quantile_index</span><span class="o">.</span><span class="n">find_ge</span><span class="p">(</span><span class="n">values</span><span class="p">)</span>
+                <span class="n">ret</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">qtl</span><span class="p">[</span><span class="nb">str</span><span class="p">(</span><span class="n">k</span><span class="p">)]</span>
+            <span class="k">except</span><span class="p">:</span>
+                <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
+
+        <span class="k">return</span> <span class="n">ret</span></div>
+
+<div class="viewcode-block" id="ProbabilityDistribution.entropy"><a class="viewcode-back" href="../../../pyFTS.probabilistic.html#pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution.entropy">[docs]</a>    <span class="k">def</span> <span class="nf">entropy</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Return the entropy of the probability distribution, H(P) = E[ -ln P(X) ] = - ∑ P(x) log ( P(x) )</span>
+
+<span class="sd">        :return:the entropy of the probability distribution</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">h</span> <span class="o">=</span> <span class="o">-</span><span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">([</span><span class="bp">self</span><span class="o">.</span><span class="n">distribution</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">distribution</span><span class="p">[</span><span class="n">k</span><span class="p">])</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">distribution</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">&gt;</span> <span class="mi">0</span> <span class="k">else</span> <span class="mi">0</span>
+                  <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">bins</span><span class="p">])</span>
+        <span class="k">return</span> <span class="n">h</span></div>
+
+<div class="viewcode-block" id="ProbabilityDistribution.crossentropy"><a class="viewcode-back" href="../../../pyFTS.probabilistic.html#pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution.crossentropy">[docs]</a>    <span class="k">def</span> <span class="nf">crossentropy</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span><span class="n">q</span><span class="p">):</span>
+        <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Cross entropy between the actual probability distribution and the informed one, </span>
+<span class="sd">        H(P,Q) = - ∑ P(x) log ( Q(x) )</span>
+
+<span class="sd">        :param q: a probabilistic.ProbabilityDistribution object</span>
+<span class="sd">        :return: Cross entropy between this probability distribution and the given distribution</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">h</span> <span class="o">=</span> <span class="o">-</span><span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">([</span><span class="bp">self</span><span class="o">.</span><span class="n">distribution</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">q</span><span class="o">.</span><span class="n">distribution</span><span class="p">[</span><span class="n">k</span><span class="p">])</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">distribution</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">&gt;</span> <span class="mi">0</span> <span class="k">else</span> <span class="mi">0</span>
+                  <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">bins</span><span class="p">])</span>
+        <span class="k">return</span> <span class="n">h</span></div>
+
+<div class="viewcode-block" id="ProbabilityDistribution.kullbackleiblerdivergence"><a class="viewcode-back" href="../../../pyFTS.probabilistic.html#pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution.kullbackleiblerdivergence">[docs]</a>    <span class="k">def</span> <span class="nf">kullbackleiblerdivergence</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span><span class="n">q</span><span class="p">):</span>
+        <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Kullback-Leibler divergence between the actual probability distribution and the informed one.</span>
+<span class="sd">        DKL(P || Q) = - ∑ P(x) log( P(X) / Q(x) )</span>
+
+<span class="sd">        :param q:  a probabilistic.ProbabilityDistribution object</span>
+<span class="sd">        :return: Kullback-Leibler divergence</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">h</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">([</span><span class="bp">self</span><span class="o">.</span><span class="n">distribution</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">distribution</span><span class="p">[</span><span class="n">k</span><span class="p">]</span><span class="o">/</span><span class="n">q</span><span class="o">.</span><span class="n">distribution</span><span class="p">[</span><span class="n">k</span><span class="p">])</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">distribution</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">&gt;</span> <span class="mi">0</span> <span class="k">else</span> <span class="mi">0</span>
+                  <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">bins</span><span class="p">])</span>
+        <span class="k">return</span> <span class="n">h</span></div>
+
+<div class="viewcode-block" id="ProbabilityDistribution.empiricalloglikelihood"><a class="viewcode-back" href="../../../pyFTS.probabilistic.html#pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution.empiricalloglikelihood">[docs]</a>    <span class="k">def</span> <span class="nf">empiricalloglikelihood</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Empirical Log Likelihood of the probability distribution, L(P) = ∑ log( P(x) )</span>
+
+<span class="sd">        :return:</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">_s</span> <span class="o">=</span> <span class="mi">0</span>
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">bins</span><span class="p">:</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">distribution</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
+                <span class="n">_s</span> <span class="o">+=</span> <span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">distribution</span><span class="p">[</span><span class="n">k</span><span class="p">])</span>
+        <span class="k">return</span> <span class="n">_s</span></div>
+
+<div class="viewcode-block" id="ProbabilityDistribution.pseudologlikelihood"><a class="viewcode-back" href="../../../pyFTS.probabilistic.html#pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution.pseudologlikelihood">[docs]</a>    <span class="k">def</span> <span class="nf">pseudologlikelihood</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">):</span>
+        <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Pseudo log likelihood of the probability distribution with respect to data</span>
+
+<span class="sd">        :param data:</span>
+<span class="sd">        :return:</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="n">densities</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">density</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+
+        <span class="n">_s</span> <span class="o">=</span> <span class="mi">0</span>
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">densities</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">k</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
+                <span class="n">_s</span> <span class="o">+=</span> <span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">k</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">_s</span></div>
+
+<div class="viewcode-block" id="ProbabilityDistribution.averageloglikelihood"><a class="viewcode-back" href="../../../pyFTS.probabilistic.html#pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution.averageloglikelihood">[docs]</a>    <span class="k">def</span> <span class="nf">averageloglikelihood</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">):</span>
+        <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Average log likelihood of the probability distribution with respect to data</span>
+
+<span class="sd">        :param data:</span>
+<span class="sd">        :return:</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="n">densities</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">density</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+
+        <span class="n">_s</span> <span class="o">=</span> <span class="mi">0</span>
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">densities</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">k</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
+                <span class="n">_s</span> <span class="o">+=</span> <span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">k</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">_s</span> <span class="o">/</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">)</span></div>
+
+<div class="viewcode-block" id="ProbabilityDistribution.plot"><a class="viewcode-back" href="../../../pyFTS.probabilistic.html#pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution.plot">[docs]</a>    <span class="k">def</span> <span class="nf">plot</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span><span class="n">axis</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span><span class="n">color</span><span class="o">=</span><span class="s2">&quot;black&quot;</span><span class="p">,</span><span class="n">tam</span><span class="o">=</span><span class="p">[</span><span class="mi">10</span><span class="p">,</span> <span class="mi">6</span><span class="p">],</span> <span class="n">title</span> <span class="o">=</span> <span class="kc">None</span><span class="p">):</span>
+
+        <span class="k">if</span> <span class="n">axis</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="n">tam</span><span class="p">)</span>
+            <span class="n">axis</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">add_subplot</span><span class="p">(</span><span class="mi">111</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">type</span> <span class="o">==</span> <span class="s2">&quot;histogram&quot;</span><span class="p">:</span>
+            <span class="n">ys</span> <span class="o">=</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">distribution</span><span class="p">[</span><span class="n">k</span><span class="p">]</span><span class="o">/</span><span class="bp">self</span><span class="o">.</span><span class="n">count</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">bins</span><span class="p">]</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">ys</span> <span class="o">=</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">distribution</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">bins</span><span class="p">]</span>
+            <span class="n">yp</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="p">]</span>
+            <span class="n">axis</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="p">,</span> <span class="n">yp</span><span class="p">,</span> <span class="n">c</span><span class="o">=</span><span class="s2">&quot;red&quot;</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">title</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">title</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">name</span>
+        <span class="n">axis</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">bins</span><span class="p">,</span> <span class="n">ys</span><span class="p">,</span> <span class="n">c</span><span class="o">=</span><span class="n">color</span><span class="p">)</span>
+        <span class="n">axis</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="n">title</span><span class="p">)</span>
+
+        <span class="n">axis</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;Universe of Discourse&#39;</span><span class="p">)</span>
+        <span class="n">axis</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;Probability&#39;</span><span class="p">)</span></div>
+
+    <span class="k">def</span> <span class="fm">__str__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="n">ret</span> <span class="o">=</span> <span class="s2">&quot;&quot;</span>
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="nb">sorted</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">bins</span><span class="p">):</span>
+            <span class="n">ret</span> <span class="o">+=</span> <span class="nb">str</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">k</span><span class="p">,</span><span class="mi">2</span><span class="p">))</span> <span class="o">+</span> <span class="s1">&#39;:</span><span class="se">\t</span><span class="s1">&#39;</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">type</span> <span class="o">==</span> <span class="s2">&quot;histogram&quot;</span><span class="p">:</span>
+                <span class="n">ret</span> <span class="o">+=</span>  <span class="nb">str</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">distribution</span><span class="p">[</span><span class="n">k</span><span class="p">]</span>  <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">count</span><span class="p">,</span><span class="mi">3</span><span class="p">))</span>
+            <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">type</span> <span class="o">==</span> <span class="s2">&quot;KDE&quot;</span><span class="p">:</span>
+                <span class="n">ret</span> <span class="o">+=</span>  <span class="nb">str</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">density</span><span class="p">(</span><span class="n">k</span><span class="p">),</span><span class="mi">3</span><span class="p">))</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">ret</span> <span class="o">+=</span> <span class="nb">str</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">distribution</span><span class="p">[</span><span class="n">k</span><span class="p">],</span> <span class="mi">6</span><span class="p">))</span>
+            <span class="n">ret</span> <span class="o">+=</span> <span class="s1">&#39;</span><span class="se">\n</span><span class="s1">&#39;</span>
+        <span class="k">return</span> <span class="n">ret</span></div>
+</pre></div>
+
+            <div class="clearer"></div>
+          </div>
+        </div>
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+      <div class="sphinxsidebar" role="navigation" aria-label="main navigation">
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+             >modules</a> |</li>
+        <li class="nav-item nav-item-0"><a href="../../../index.html">pyFTS 1.6 documentation</a> &#187;</li>
+          <li class="nav-item nav-item-1"><a href="../../index.html" >Module code</a> &#187;</li>
+        <li class="nav-item nav-item-this"><a href="">pyFTS.probabilistic.ProbabilityDistribution</a></li> 
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+        &#169; Copyright 2018, Machine Intelligence and Data Science Laboratory - UFMG - Brazil.
+      Created using <a href="https://www.sphinx-doc.org/">Sphinx</a> 3.1.2.
+    </div>
+  </body>
+</html>
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diff --git a/docs/build/html/_modules/pyFTS/probabilistic/kde.html b/docs/build/html/_modules/pyFTS/probabilistic/kde.html
new file mode 100644
index 0000000..d8971b3
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+++ b/docs/build/html/_modules/pyFTS/probabilistic/kde.html
@@ -0,0 +1,169 @@
+
+
+<!doctype html>
+
+<html>
+  <head>
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+          <li class="nav-item nav-item-1"><a href="../../index.html" accesskey="U">Module code</a> &#187;</li>
+        <li class="nav-item nav-item-this"><a href="">pyFTS.probabilistic.kde</a></li> 
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+            
+  <h1>Source code for pyFTS.probabilistic.kde</h1><div class="highlight"><pre>
+<span></span><span class="c1"># -*- coding: utf8 -*-</span>
+
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">Kernel Density Estimation </span>
+<span class="sd">&quot;&quot;&quot;</span>
+
+<span class="kn">from</span> <span class="nn">pyFTS.common</span> <span class="kn">import</span> <span class="n">Transformations</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+
+
+<div class="viewcode-block" id="KernelSmoothing"><a class="viewcode-back" href="../../../pyFTS.probabilistic.html#pyFTS.probabilistic.kde.KernelSmoothing">[docs]</a><span class="k">class</span> <span class="nc">KernelSmoothing</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;Kernel Density Estimation&quot;&quot;&quot;</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">h</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;h&#39;</span><span class="p">,</span><span class="o">.</span><span class="mi">5</span><span class="p">)</span>
+        <span class="sd">&quot;&quot;&quot;Width parameter&quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">kernel</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;kernel&quot;</span><span class="p">,</span> <span class="s2">&quot;epanechnikov&quot;</span><span class="p">)</span>
+        <span class="sd">&quot;&quot;&quot;Kernel function&quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">data</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;data&quot;</span><span class="p">,</span><span class="kc">None</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">transf</span> <span class="o">=</span> <span class="n">Transformations</span><span class="o">.</span><span class="n">Scale</span><span class="p">(</span><span class="nb">min</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span><span class="nb">max</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">data</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">data</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">transf</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="p">)</span>
+
+<div class="viewcode-block" id="KernelSmoothing.kernel_function"><a class="viewcode-back" href="../../../pyFTS.probabilistic.html#pyFTS.probabilistic.kde.KernelSmoothing.kernel_function">[docs]</a>    <span class="k">def</span> <span class="nf">kernel_function</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">u</span><span class="p">):</span>
+        <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Apply the kernel</span>
+
+<span class="sd">        :param u:</span>
+<span class="sd">        :return:</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">kernel</span> <span class="o">==</span> <span class="s2">&quot;epanechnikov&quot;</span><span class="p">:</span>
+            <span class="n">tmp</span> <span class="o">=</span> <span class="p">(</span><span class="mi">3</span><span class="o">/</span><span class="mi">4</span><span class="p">)</span><span class="o">*</span><span class="p">(</span><span class="mf">1.0</span> <span class="o">-</span> <span class="n">u</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span>
+            <span class="k">return</span> <span class="n">tmp</span> <span class="k">if</span> <span class="n">tmp</span> <span class="o">&gt;</span> <span class="mi">0</span> <span class="k">else</span> <span class="mi">0</span>
+        <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">kernel</span> <span class="o">==</span> <span class="s2">&quot;gaussian&quot;</span><span class="p">:</span>
+            <span class="k">return</span> <span class="p">(</span><span class="mf">1.0</span><span class="o">/</span><span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="mi">2</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">))</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="mf">0.5</span><span class="o">*</span><span class="n">u</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span>
+        <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">kernel</span> <span class="o">==</span> <span class="s2">&quot;uniform&quot;</span><span class="p">:</span>
+            <span class="k">return</span> <span class="mf">0.5</span>
+        <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">kernel</span> <span class="o">==</span> <span class="s2">&quot;triangular&quot;</span><span class="p">:</span>
+            <span class="n">tmp</span> <span class="o">=</span> <span class="mf">1.0</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">u</span><span class="p">)</span>
+            <span class="k">return</span> <span class="n">tmp</span> <span class="k">if</span> <span class="n">tmp</span> <span class="o">&gt;</span> <span class="mi">0</span> <span class="k">else</span> <span class="mi">0</span>
+        <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">kernel</span> <span class="o">==</span> <span class="s2">&quot;logistic&quot;</span><span class="p">:</span>
+            <span class="k">return</span> <span class="mf">1.0</span><span class="o">/</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="n">u</span><span class="p">)</span><span class="o">+</span><span class="mi">2</span><span class="o">+</span><span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">u</span><span class="p">))</span>
+        <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">kernel</span> <span class="o">==</span> <span class="s2">&quot;cosine&quot;</span><span class="p">:</span>
+            <span class="k">return</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="o">/</span><span class="mf">4.0</span><span class="p">)</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">cos</span><span class="p">((</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="o">/</span><span class="mf">2.0</span><span class="p">)</span><span class="o">*</span><span class="n">u</span><span class="p">)</span>
+        <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">kernel</span> <span class="o">==</span> <span class="s2">&quot;sigmoid&quot;</span><span class="p">:</span>
+            <span class="k">return</span> <span class="p">(</span><span class="mf">2.0</span><span class="o">/</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">)</span><span class="o">*</span><span class="p">(</span><span class="mf">1.0</span><span class="o">/</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="n">u</span><span class="p">)</span><span class="o">+</span><span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">u</span><span class="p">)))</span>
+        <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">kernel</span> <span class="o">==</span> <span class="s2">&quot;tophat&quot;</span><span class="p">:</span>
+            <span class="k">return</span> <span class="mi">1</span> <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">u</span><span class="p">)</span> <span class="o">&lt;</span> <span class="mf">0.5</span> <span class="k">else</span> <span class="mi">0</span>
+        <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">kernel</span> <span class="o">==</span> <span class="s2">&quot;exponential&quot;</span><span class="p">:</span>
+            <span class="k">return</span> <span class="mf">0.5</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">u</span><span class="p">))</span></div>
+
+<div class="viewcode-block" id="KernelSmoothing.probability"><a class="viewcode-back" href="../../../pyFTS.probabilistic.html#pyFTS.probabilistic.kde.KernelSmoothing.probability">[docs]</a>    <span class="k">def</span> <span class="nf">probability</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Probability of the point x on data</span>
+
+<span class="sd">        :param x:</span>
+<span class="sd">        :param data:</span>
+<span class="sd">        :return:</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">data</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">data</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;data&#39;</span><span class="p">,</span><span class="kc">None</span><span class="p">)</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">data</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">transf</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="p">)</span>
+
+        <span class="n">l</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="p">)</span>
+
+        <span class="n">nx</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">transf</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+        <span class="n">p</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">([</span><span class="bp">self</span><span class="o">.</span><span class="n">kernel_function</span><span class="p">((</span><span class="n">nx</span> <span class="o">-</span> <span class="n">k</span><span class="p">)</span><span class="o">/</span><span class="bp">self</span><span class="o">.</span><span class="n">h</span><span class="p">)</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="p">])</span> <span class="o">/</span> <span class="n">l</span><span class="o">*</span><span class="bp">self</span><span class="o">.</span><span class="n">h</span>
+
+        <span class="k">return</span> <span class="n">p</span></div></div>
+</pre></div>
+
+            <div class="clearer"></div>
+          </div>
+        </div>
+      </div>
+      <div class="sphinxsidebar" role="navigation" aria-label="main navigation">
+        <div class="sphinxsidebarwrapper">
+<div id="searchbox" style="display: none" role="search">
+  <h3 id="searchlabel">Quick search</h3>
+    <div class="searchformwrapper">
+    <form class="search" action="../../../search.html" method="get">
+      <input type="text" name="q" aria-labelledby="searchlabel" />
+      <input type="submit" value="Go" />
+    </form>
+    </div>
+</div>
+<script>$('#searchbox').show(0);</script>
+        </div>
+      </div>
+      <div class="clearer"></div>
+    </div>
+    <div class="related" role="navigation" aria-label="related navigation">
+      <h3>Navigation</h3>
+      <ul>
+        <li class="right" style="margin-right: 10px">
+          <a href="../../../genindex.html" title="General Index"
+             >index</a></li>
+        <li class="right" >
+          <a href="../../../py-modindex.html" title="Python Module Index"
+             >modules</a> |</li>
+        <li class="nav-item nav-item-0"><a href="../../../index.html">pyFTS 1.6 documentation</a> &#187;</li>
+          <li class="nav-item nav-item-1"><a href="../../index.html" >Module code</a> &#187;</li>
+        <li class="nav-item nav-item-this"><a href="">pyFTS.probabilistic.kde</a></li> 
+      </ul>
+    </div>
+    <div class="footer" role="contentinfo">
+        &#169; Copyright 2018, Machine Intelligence and Data Science Laboratory - UFMG - Brazil.
+      Created using <a href="https://www.sphinx-doc.org/">Sphinx</a> 3.1.2.
+    </div>
+  </body>
+</html>
\ No newline at end of file
diff --git a/docs/build/html/_sources/pyFTS.common.rst.txt b/docs/build/html/_sources/pyFTS.common.rst.txt
index 1e6db4b..412bd44 100644
--- a/docs/build/html/_sources/pyFTS.common.rst.txt
+++ b/docs/build/html/_sources/pyFTS.common.rst.txt
@@ -52,14 +52,6 @@ pyFTS.common.SortedCollection module
     :undoc-members:
     :show-inheritance:
 
-pyFTS.common.transformations module
------------------------------------
-
-.. automodule:: pyFTS.common.Transformations
-    :members:
-    :undoc-members:
-    :show-inheritance:
-
 pyFTS.common.Util module
 ------------------------
 
diff --git a/docs/build/html/_sources/pyFTS.common.transformations.rst.txt b/docs/build/html/_sources/pyFTS.common.transformations.rst.txt
index a604d7d..089f361 100644
--- a/docs/build/html/_sources/pyFTS.common.transformations.rst.txt
+++ b/docs/build/html/_sources/pyFTS.common.transformations.rst.txt
@@ -60,6 +60,14 @@ pyFTS.common.transformations.scale module
     :undoc-members:
     :show-inheritance:
 
+pyFTS.common.transformations.smoothing module
+---------------------------------------------
+
+.. automodule:: pyFTS.common.transformations.smoothing
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
 pyFTS.common.transformations.som module
 ---------------------------------------
 
diff --git a/docs/build/html/genindex.html b/docs/build/html/genindex.html
index d9425f3..88bf84d 100644
--- a/docs/build/html/genindex.html
+++ b/docs/build/html/genindex.html
@@ -86,7 +86,29 @@
 <h2 id="A">A</h2>
 <table style="width: 100%" class="indextable genindextable"><tr>
   <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.Measures.acf">acf() (in module pyFTS.benchmarks.Measures)</a>
+</li>
+      <li><a href="pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFTS.add_new_PWFLGR">add_new_PWFLGR() (pyFTS.models.pwfts.ProbabilisticWeightedFTS method)</a>
+</li>
+      <li><a href="pyFTS.models.multivariate.html#pyFTS.models.multivariate.variable.Variable.alias">alias (pyFTS.models.multivariate.variable.Variable attribute)</a>
+</li>
+      <li><a href="pyFTS.models.ensemble.html#pyFTS.models.ensemble.ensemble.AllMethodEnsembleFTS">AllMethodEnsembleFTS (class in pyFTS.models.ensemble.ensemble)</a>
+</li>
       <li><a href="pyFTS.common.html#pyFTS.common.FuzzySet.FuzzySet.alpha">alpha (pyFTS.common.FuzzySet.FuzzySet attribute)</a>
+
+      <ul>
+        <li><a href="pyFTS.models.ensemble.html#pyFTS.models.ensemble.ensemble.EnsembleFTS.alpha">(pyFTS.models.ensemble.ensemble.EnsembleFTS attribute)</a>
+</li>
+      </ul></li>
+      <li><a href="pyFTS.common.html#pyFTS.common.fts.FTS.alpha_cut">alpha_cut (pyFTS.common.fts.FTS attribute)</a>
+
+      <ul>
+        <li><a href="pyFTS.models.multivariate.html#pyFTS.models.multivariate.variable.Variable.alpha_cut">(pyFTS.models.multivariate.variable.Variable attribute)</a>
+</li>
+      </ul></li>
+      <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.Util.analytic_tabular_dataframe">analytic_tabular_dataframe() (in module pyFTS.benchmarks.Util)</a>
+</li>
+      <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.Util.analytical_data_columns">analytical_data_columns() (in module pyFTS.benchmarks.Util)</a>
 </li>
       <li><a href="pyFTS.common.html#pyFTS.common.Composite.FuzzySet.append">append() (pyFTS.common.Composite.FuzzySet method)</a>
 
@@ -94,27 +116,113 @@
         <li><a href="pyFTS.models.multivariate.html#pyFTS.models.multivariate.partitioner.MultivariatePartitioner.append">(pyFTS.models.multivariate.partitioner.MultivariatePartitioner method)</a>
 </li>
         <li><a href="pyFTS.partitioners.html#pyFTS.partitioners.Simple.SimplePartitioner.append">(pyFTS.partitioners.Simple.SimplePartitioner method)</a>
+</li>
+        <li><a href="pyFTS.probabilistic.html#pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution.append">(pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution method)</a>
 </li>
       </ul></li>
       <li><a href="pyFTS.partitioners.html#pyFTS.partitioners.Simple.SimplePartitioner.append_complex">append_complex() (pyFTS.partitioners.Simple.SimplePartitioner method)</a>
 </li>
-  </ul></td>
-  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="pyFTS.probabilistic.html#pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution.append_interval">append_interval() (pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution method)</a>
+</li>
+      <li><a href="pyFTS.models.html#pyFTS.models.hofts.HighOrderFLRG.append_lhs">append_lhs() (pyFTS.models.hofts.HighOrderFLRG method)</a>
+
+      <ul>
+        <li><a href="pyFTS.models.html#pyFTS.models.hofts.WeightedHighOrderFLRG.append_lhs">(pyFTS.models.hofts.WeightedHighOrderFLRG method)</a>
+</li>
+        <li><a href="pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.cvfts.HighOrderNonstationaryFLRG.append_lhs">(pyFTS.models.nonstationary.cvfts.HighOrderNonstationaryFLRG method)</a>
+</li>
+        <li><a href="pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.honsfts.HighOrderNonStationaryFLRG.append_lhs">(pyFTS.models.nonstationary.honsfts.HighOrderNonStationaryFLRG method)</a>
+</li>
+      </ul></li>
+      <li><a href="pyFTS.common.html#pyFTS.common.fts.FTS.append_log">append_log() (pyFTS.common.fts.FTS method)</a>
+</li>
+      <li><a href="pyFTS.models.ensemble.html#pyFTS.models.ensemble.ensemble.EnsembleFTS.append_model">append_model() (pyFTS.models.ensemble.ensemble.EnsembleFTS method)</a>
+</li>
       <li><a href="pyFTS.common.html#pyFTS.common.flrg.FLRG.append_rhs">append_rhs() (pyFTS.common.flrg.FLRG method)</a>
 
       <ul>
+        <li><a href="pyFTS.models.html#pyFTS.models.chen.ConventionalFLRG.append_rhs">(pyFTS.models.chen.ConventionalFLRG method)</a>
+</li>
+        <li><a href="pyFTS.models.html#pyFTS.models.hofts.HighOrderFLRG.append_rhs">(pyFTS.models.hofts.HighOrderFLRG method)</a>
+</li>
+        <li><a href="pyFTS.models.html#pyFTS.models.hofts.WeightedHighOrderFLRG.append_rhs">(pyFTS.models.hofts.WeightedHighOrderFLRG method)</a>
+</li>
+        <li><a href="pyFTS.models.html#pyFTS.models.ismailefendi.ImprovedWeightedFLRG.append_rhs">(pyFTS.models.ismailefendi.ImprovedWeightedFLRG method)</a>
+</li>
         <li><a href="pyFTS.models.multivariate.html#pyFTS.models.multivariate.flrg.FLRG.append_rhs">(pyFTS.models.multivariate.flrg.FLRG method)</a>
+</li>
+        <li><a href="pyFTS.models.multivariate.html#pyFTS.models.multivariate.wmvfts.WeightedFLRG.append_rhs">(pyFTS.models.multivariate.wmvfts.WeightedFLRG method)</a>
+</li>
+        <li><a href="pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.cvfts.HighOrderNonstationaryFLRG.append_rhs">(pyFTS.models.nonstationary.cvfts.HighOrderNonstationaryFLRG method)</a>
+</li>
+        <li><a href="pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.honsfts.HighOrderNonStationaryFLRG.append_rhs">(pyFTS.models.nonstationary.honsfts.HighOrderNonStationaryFLRG method)</a>
+</li>
+        <li><a href="pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.nsfts.ConventionalNonStationaryFLRG.append_rhs">(pyFTS.models.nonstationary.nsfts.ConventionalNonStationaryFLRG method)</a>
+</li>
+        <li><a href="pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.nsfts.WeightedNonStationaryFLRG.append_rhs">(pyFTS.models.nonstationary.nsfts.WeightedNonStationaryFLRG method)</a>
+</li>
+        <li><a href="pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFLRG.append_rhs">(pyFTS.models.pwfts.ProbabilisticWeightedFLRG method)</a>
+</li>
+        <li><a href="pyFTS.models.html#pyFTS.models.sadaei.ExponentialyWeightedFLRG.append_rhs">(pyFTS.models.sadaei.ExponentialyWeightedFLRG method)</a>
+</li>
+        <li><a href="pyFTS.models.seasonal.html#pyFTS.models.seasonal.cmsfts.ContextualSeasonalFLRG.append_rhs">(pyFTS.models.seasonal.cmsfts.ContextualSeasonalFLRG method)</a>
+</li>
+        <li><a href="pyFTS.models.seasonal.html#pyFTS.models.seasonal.sfts.SeasonalFLRG.append_rhs">(pyFTS.models.seasonal.sfts.SeasonalFLRG method)</a>
+</li>
+        <li><a href="pyFTS.models.html#pyFTS.models.yu.WeightedFLRG.append_rhs">(pyFTS.models.yu.WeightedFLRG method)</a>
 </li>
       </ul></li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="pyFTS.common.html#pyFTS.common.fts.FTS.append_rule">append_rule() (pyFTS.common.fts.FTS method)</a>
+</li>
       <li><a href="pyFTS.common.html#pyFTS.common.Composite.FuzzySet.append_set">append_set() (pyFTS.common.Composite.FuzzySet method)</a>
 
       <ul>
         <li><a href="pyFTS.models.multivariate.html#pyFTS.models.multivariate.common.MultivariateFuzzySet.append_set">(pyFTS.models.multivariate.common.MultivariateFuzzySet method)</a>
 </li>
       </ul></li>
+      <li><a href="pyFTS.common.html#pyFTS.common.fts.FTS.append_transformation">append_transformation() (pyFTS.common.fts.FTS method)</a>
+
+      <ul>
+        <li><a href="pyFTS.models.multivariate.html#pyFTS.models.multivariate.mvfts.MVFTS.append_transformation">(pyFTS.models.multivariate.mvfts.MVFTS method)</a>
+</li>
+      </ul></li>
+      <li><a href="pyFTS.models.multivariate.html#pyFTS.models.multivariate.mvfts.MVFTS.append_variable">append_variable() (pyFTS.models.multivariate.mvfts.MVFTS method)</a>
+</li>
       <li><a href="pyFTS.common.html#pyFTS.common.tree.FLRGTreeNode.appendChild">appendChild() (pyFTS.common.tree.FLRGTreeNode method)</a>
 </li>
+      <li><a href="pyFTS.common.html#pyFTS.common.fts.FTS.apply_inverse_transformations">apply_inverse_transformations() (pyFTS.common.fts.FTS method)</a>
+
+      <ul>
+        <li><a href="pyFTS.models.multivariate.html#pyFTS.models.multivariate.variable.Variable.apply_inverse_transformations">(pyFTS.models.multivariate.variable.Variable method)</a>
+</li>
+      </ul></li>
+      <li><a href="pyFTS.common.html#pyFTS.common.fts.FTS.apply_transformations">apply_transformations() (pyFTS.common.fts.FTS method)</a>
+
+      <ul>
+        <li><a href="pyFTS.models.multivariate.html#pyFTS.models.multivariate.mvfts.MVFTS.apply_transformations">(pyFTS.models.multivariate.mvfts.MVFTS method)</a>
+</li>
+        <li><a href="pyFTS.models.multivariate.html#pyFTS.models.multivariate.variable.Variable.apply_transformations">(pyFTS.models.multivariate.variable.Variable method)</a>
+</li>
+      </ul></li>
+      <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.arima.ARIMA.ar">ar() (pyFTS.benchmarks.arima.ARIMA method)</a>
+</li>
+      <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.arima.ARIMA">ARIMA (class in pyFTS.benchmarks.arima)</a>
+
+      <ul>
+        <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.BSTS.ARIMA">(class in pyFTS.benchmarks.BSTS)</a>
+</li>
+      </ul></li>
       <li><a href="pyFTS.common.html#pyFTS.common.SortedCollection.SortedCollection.around">around() (pyFTS.common.SortedCollection.SortedCollection method)</a>
+</li>
+      <li><a href="pyFTS.common.html#pyFTS.common.fts.FTS.auto_update">auto_update (pyFTS.common.fts.FTS attribute)</a>
+
+      <ul>
+        <li><a href="pyFTS.models.incremental.html#pyFTS.models.incremental.TimeVariant.Retrainer.auto_update">(pyFTS.models.incremental.TimeVariant.Retrainer attribute)</a>
+</li>
+      </ul></li>
+      <li><a href="pyFTS.probabilistic.html#pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution.averageloglikelihood">averageloglikelihood() (pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution method)</a>
 </li>
   </ul></td>
 </tr></table>
@@ -122,18 +230,38 @@
 <h2 id="B">B</h2>
 <table style="width: 100%" class="indextable genindextable"><tr>
   <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.Util.base_dataframe_columns">base_dataframe_columns() (in module pyFTS.benchmarks.Util)</a>
+</li>
+      <li><a href="pyFTS.models.incremental.html#pyFTS.models.incremental.IncrementalEnsemble.IncrementalEnsembleFTS.batch_size">batch_size (pyFTS.models.incremental.IncrementalEnsemble.IncrementalEnsembleFTS attribute)</a>
+
+      <ul>
+        <li><a href="pyFTS.models.incremental.html#pyFTS.models.incremental.TimeVariant.Retrainer.batch_size">(pyFTS.models.incremental.TimeVariant.Retrainer attribute)</a>
+</li>
+      </ul></li>
       <li><a href="pyFTS.common.html#pyFTS.common.Membership.bellmf">bellmf() (in module pyFTS.common.Membership)</a>
+</li>
+      <li><a href="pyFTS.common.html#pyFTS.common.fts.FTS.benchmark_only">benchmark_only (pyFTS.common.fts.FTS attribute)</a>
 </li>
       <li><a href="pyFTS.partitioners.html#pyFTS.partitioners.Entropy.bestSplit">bestSplit() (in module pyFTS.partitioners.Entropy)</a>
 </li>
       <li><a href="pyFTS.common.html#pyFTS.common.SortedCollection.SortedCollection.between">between() (pyFTS.common.SortedCollection.SortedCollection method)</a>
+</li>
+      <li><a href="pyFTS.probabilistic.html#pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution.bins">bins (pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution attribute)</a>
 </li>
       <li><a href="pyFTS.data.html#pyFTS.data.artificial.SignalEmulator.blip">blip() (pyFTS.data.artificial.SignalEmulator method)</a>
+</li>
+      <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.Tests.BoxLjungStatistic">BoxLjungStatistic() (in module pyFTS.benchmarks.Tests)</a>
+</li>
+      <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.Tests.BoxPierceStatistic">BoxPierceStatistic() (in module pyFTS.benchmarks.Tests)</a>
+</li>
+      <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.Measures.brier_score">brier_score() (in module pyFTS.benchmarks.Measures)</a>
 </li>
       <li><a href="pyFTS.models.multivariate.html#pyFTS.models.multivariate.grid.GridCluster.build">build() (pyFTS.models.multivariate.grid.GridCluster method)</a>
 
       <ul>
         <li><a href="pyFTS.models.multivariate.html#pyFTS.models.multivariate.partitioner.MultivariatePartitioner.build">(pyFTS.models.multivariate.partitioner.MultivariatePartitioner method)</a>
+</li>
+        <li><a href="pyFTS.models.multivariate.html#pyFTS.models.multivariate.variable.Variable.build">(pyFTS.models.multivariate.variable.Variable method)</a>
 </li>
         <li><a href="pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.partitioners.PolynomialNonStationaryPartitioner.build">(pyFTS.models.nonstationary.partitioners.PolynomialNonStationaryPartitioner method)</a>
 </li>
@@ -148,6 +276,8 @@
         <li><a href="pyFTS.partitioners.html#pyFTS.partitioners.FCM.FCMPartitioner.build">(pyFTS.partitioners.FCM.FCMPartitioner method)</a>
 </li>
         <li><a href="pyFTS.partitioners.html#pyFTS.partitioners.Grid.GridPartitioner.build">(pyFTS.partitioners.Grid.GridPartitioner method)</a>
+</li>
+        <li><a href="pyFTS.partitioners.html#pyFTS.partitioners.Huarng.HuarngPartitioner.build">(pyFTS.partitioners.Huarng.HuarngPartitioner method)</a>
 </li>
         <li><a href="pyFTS.partitioners.html#pyFTS.partitioners.partitioner.Partitioner.build">(pyFTS.partitioners.partitioner.Partitioner method)</a>
 </li>
@@ -158,6 +288,8 @@
       </ul></li>
   </ul></td>
   <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="pyFTS.probabilistic.html#pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution.build_cdf_qtl">build_cdf_qtl() (pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution method)</a>
+</li>
       <li><a href="pyFTS.models.multivariate.html#pyFTS.models.multivariate.partitioner.MultivariatePartitioner.build_index">build_index() (pyFTS.models.multivariate.partitioner.MultivariatePartitioner method)</a>
 
       <ul>
@@ -175,6 +307,14 @@
 <table style="width: 100%" class="indextable genindextable"><tr>
   <td style="width: 33%; vertical-align: top;"><ul>
       <li><a href="pyFTS.partitioners.html#pyFTS.partitioners.CMeans.c_means">c_means() (in module pyFTS.partitioners.CMeans)</a>
+</li>
+      <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.Util.cast_dataframe_to_synthetic">cast_dataframe_to_synthetic() (in module pyFTS.benchmarks.Util)</a>
+</li>
+      <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.Util.cast_dataframe_to_synthetic_interval">cast_dataframe_to_synthetic_interval() (in module pyFTS.benchmarks.Util)</a>
+</li>
+      <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.Util.cast_dataframe_to_synthetic_point">cast_dataframe_to_synthetic_point() (in module pyFTS.benchmarks.Util)</a>
+</li>
+      <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.Util.cast_dataframe_to_synthetic_probabilistic">cast_dataframe_to_synthetic_probabilistic() (in module pyFTS.benchmarks.Util)</a>
 </li>
       <li><a href="pyFTS.common.html#pyFTS.common.FuzzySet.FuzzySet.centroid">centroid (pyFTS.common.FuzzySet.FuzzySet attribute)</a>
 </li>
@@ -194,19 +334,91 @@
         <li><a href="pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.common.check_bounds_index">(in module pyFTS.models.nonstationary.common)</a>
 </li>
       </ul></li>
-  </ul></td>
-  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="pyFTS.models.multivariate.html#pyFTS.models.multivariate.cmvfts.ClusteredMVFTS.check_data">check_data() (pyFTS.models.multivariate.cmvfts.ClusteredMVFTS method)</a>
+</li>
+      <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.Util.check_ignore_list">check_ignore_list() (in module pyFTS.benchmarks.Util)</a>
+</li>
+      <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.Util.check_replace_list">check_replace_list() (in module pyFTS.benchmarks.Util)</a>
+</li>
       <li><a href="pyFTS.common.html#pyFTS.common.SortedCollection.SortedCollection.clear">clear() (pyFTS.common.SortedCollection.SortedCollection method)</a>
+</li>
+      <li><a href="pyFTS.common.html#pyFTS.common.fts.FTS.clip_uod">clip_uod() (pyFTS.common.fts.FTS method)</a>
+</li>
+      <li><a href="pyFTS.common.html#pyFTS.common.fts.FTS.clone_parameters">clone_parameters() (pyFTS.common.fts.FTS method)</a>
+
+      <ul>
+        <li><a href="pyFTS.models.multivariate.html#pyFTS.models.multivariate.mvfts.MVFTS.clone_parameters">(pyFTS.models.multivariate.mvfts.MVFTS method)</a>
+</li>
+      </ul></li>
+      <li><a href="pyFTS.hyperparam.html#pyFTS.hyperparam.GridSearch.cluster_method">cluster_method() (in module pyFTS.hyperparam.GridSearch)</a>
+</li>
+      <li><a href="pyFTS.models.multivariate.html#pyFTS.models.multivariate.cmvfts.ClusteredMVFTS">ClusteredMVFTS (class in pyFTS.models.multivariate.cmvfts)</a>
 </li>
       <li><a href="pyFTS.partitioners.html#pyFTS.partitioners.CMeans.CMeansPartitioner">CMeansPartitioner (class in pyFTS.partitioners.CMeans)</a>
+</li>
+      <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.benchmarks.common_process_interval_jobs">common_process_interval_jobs() (in module pyFTS.benchmarks.benchmarks)</a>
+</li>
+      <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.benchmarks.common_process_point_jobs">common_process_point_jobs() (in module pyFTS.benchmarks.benchmarks)</a>
+</li>
+      <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.benchmarks.common_process_probabilistic_jobs">common_process_probabilistic_jobs() (in module pyFTS.benchmarks.benchmarks)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.benchmarks.common_process_time_jobs">common_process_time_jobs() (in module pyFTS.benchmarks.benchmarks)</a>
+</li>
+      <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.ResidualAnalysis.compare_residuals">compare_residuals() (in module pyFTS.benchmarks.ResidualAnalysis)</a>
+</li>
+      <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.benchmarks.compareModelsPlot">compareModelsPlot() (in module pyFTS.benchmarks.benchmarks)</a>
+</li>
+      <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.benchmarks.compareModelsTable">compareModelsTable() (in module pyFTS.benchmarks.benchmarks)</a>
 </li>
       <li><a href="pyFTS.data.html#pyFTS.data.artificial.SignalEmulator.components">components (pyFTS.data.artificial.SignalEmulator attribute)</a>
+</li>
+      <li><a href="pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.nsfts.NonStationaryFTS.conditional_perturbation_factors">conditional_perturbation_factors() (pyFTS.models.nonstationary.nsfts.NonStationaryFTS method)</a>
+</li>
+      <li><a href="pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.cvfts.ConditionalVarianceFTS">ConditionalVarianceFTS (class in pyFTS.models.nonstationary.cvfts)</a>
+</li>
+      <li><a href="pyFTS.models.html#pyFTS.models.hofts.HighOrderFTS.configure_lags">configure_lags() (pyFTS.models.hofts.HighOrderFTS method)</a>
+
+      <ul>
+        <li><a href="pyFTS.models.html#pyFTS.models.hwang.HighOrderFTS.configure_lags">(pyFTS.models.hwang.HighOrderFTS method)</a>
+</li>
+        <li><a href="pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.honsfts.HighOrderNonStationaryFTS.configure_lags">(pyFTS.models.nonstationary.honsfts.HighOrderNonStationaryFTS method)</a>
+</li>
+      </ul></li>
+      <li><a href="pyFTS.models.seasonal.html#pyFTS.models.seasonal.cmsfts.ContextualMultiSeasonalFTS">ContextualMultiSeasonalFTS (class in pyFTS.models.seasonal.cmsfts)</a>
+</li>
+      <li><a href="pyFTS.models.seasonal.html#pyFTS.models.seasonal.cmsfts.ContextualSeasonalFLRG">ContextualSeasonalFLRG (class in pyFTS.models.seasonal.cmsfts)</a>
+</li>
+      <li><a href="pyFTS.models.html#pyFTS.models.chen.ConventionalFLRG">ConventionalFLRG (class in pyFTS.models.chen)</a>
+</li>
+      <li><a href="pyFTS.models.html#pyFTS.models.chen.ConventionalFTS">ConventionalFTS (class in pyFTS.models.chen)</a>
+
+      <ul>
+        <li><a href="pyFTS.models.html#pyFTS.models.song.ConventionalFTS">(class in pyFTS.models.song)</a>
+</li>
+      </ul></li>
+      <li><a href="pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.nsfts.ConventionalNonStationaryFLRG">ConventionalNonStationaryFLRG (class in pyFTS.models.nonstationary.nsfts)</a>
 </li>
       <li><a href="pyFTS.common.html#pyFTS.common.SortedCollection.SortedCollection.copy">copy() (pyFTS.common.SortedCollection.SortedCollection method)</a>
 </li>
       <li><a href="pyFTS.common.html#pyFTS.common.SortedCollection.SortedCollection.count">count() (pyFTS.common.SortedCollection.SortedCollection method)</a>
+</li>
+      <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.Measures.coverage">coverage() (in module pyFTS.benchmarks.Measures)</a>
+</li>
+      <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.Util.create_benchmark_tables">create_benchmark_tables() (in module pyFTS.benchmarks.Util)</a>
 </li>
       <li><a href="pyFTS.hyperparam.html#pyFTS.hyperparam.Util.create_hyperparam_tables">create_hyperparam_tables() (in module pyFTS.hyperparam.Util)</a>
+</li>
+      <li><a href="pyFTS.probabilistic.html#pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution.crossentropy">crossentropy() (pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution method)</a>
+</li>
+      <li><a href="pyFTS.hyperparam.html#pyFTS.hyperparam.Evolutionary.crossover">crossover() (in module pyFTS.hyperparam.Evolutionary)</a>
+</li>
+      <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.Measures.crps">crps() (in module pyFTS.benchmarks.Measures)</a>
+</li>
+      <li><a href="pyFTS.probabilistic.html#pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution.cumulative">cumulative() (pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution method)</a>
+</li>
+      <li><a href="pyFTS.common.html#pyFTS.common.Util.current_milli_time">current_milli_time() (in module pyFTS.common.Util)</a>
 </li>
   </ul></td>
 </tr></table>
@@ -214,6 +426,10 @@
 <h2 id="D">D</h2>
 <table style="width: 100%" class="indextable genindextable"><tr>
   <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="pyFTS.models.multivariate.html#pyFTS.models.multivariate.variable.Variable.data_label">data_label (pyFTS.models.multivariate.variable.Variable attribute)</a>
+</li>
+      <li><a href="pyFTS.models.multivariate.html#pyFTS.models.multivariate.variable.Variable.data_type">data_type (pyFTS.models.multivariate.variable.Variable attribute)</a>
+</li>
       <li><a href="pyFTS.models.seasonal.html#pyFTS.models.seasonal.SeasonalIndexer.DataFrameSeasonalIndexer">DataFrameSeasonalIndexer (class in pyFTS.models.seasonal.SeasonalIndexer)</a>
 </li>
       <li><a href="pyFTS.models.seasonal.html#pyFTS.models.seasonal.common.DateTime">DateTime (class in pyFTS.models.seasonal.common)</a>
@@ -222,8 +438,6 @@
 </li>
       <li><a href="pyFTS.models.seasonal.html#pyFTS.models.seasonal.common.DateTime.day_of_month">day_of_month (pyFTS.models.seasonal.common.DateTime attribute)</a>
 </li>
-  </ul></td>
-  <td style="width: 33%; vertical-align: top;"><ul>
       <li><a href="pyFTS.models.seasonal.html#pyFTS.models.seasonal.common.DateTime.day_of_week">day_of_week (pyFTS.models.seasonal.common.DateTime attribute)</a>
 </li>
       <li><a href="pyFTS.models.seasonal.html#pyFTS.models.seasonal.common.DateTime.day_of_year">day_of_year (pyFTS.models.seasonal.common.DateTime attribute)</a>
@@ -234,7 +448,27 @@
         <li><a href="pyFTS.partitioners.html#pyFTS.partitioners.partitioner.Partitioner.defuzzyfy">(pyFTS.partitioners.partitioner.Partitioner method)</a>
 </li>
       </ul></li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="pyFTS.probabilistic.html#pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution.density">density() (pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution method)</a>
+</li>
+      <li><a href="pyFTS.common.html#pyFTS.common.fts.FTS.detail">detail (pyFTS.common.fts.FTS attribute)</a>
+</li>
+      <li><a href="pyFTS.hyperparam.html#pyFTS.hyperparam.GridSearch.dict_individual">dict_individual() (in module pyFTS.hyperparam.GridSearch)</a>
+</li>
+      <li><a href="pyFTS.probabilistic.html#pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution.differential_offset">differential_offset() (pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution method)</a>
+</li>
       <li><a href="pyFTS.partitioners.html#pyFTS.partitioners.CMeans.distance">distance() (in module pyFTS.partitioners.CMeans)</a>
+</li>
+      <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.benchmarks.distributed_model_train_test_time">distributed_model_train_test_time() (in module pyFTS.benchmarks.benchmarks)</a>
+</li>
+      <li><a href="pyFTS.distributed.html#pyFTS.distributed.dispy.distributed_predict">distributed_predict() (in module pyFTS.distributed.dispy)</a>
+</li>
+      <li><a href="pyFTS.distributed.html#pyFTS.distributed.dispy.distributed_train">distributed_train() (in module pyFTS.distributed.dispy)</a>
+</li>
+      <li><a href="pyFTS.hyperparam.html#pyFTS.hyperparam.Evolutionary.double_tournament">double_tournament() (in module pyFTS.hyperparam.Evolutionary)</a>
+</li>
+      <li><a href="pyFTS.common.html#pyFTS.common.Util.draw_sets_on_axis">draw_sets_on_axis() (in module pyFTS.common.Util)</a>
 </li>
   </ul></td>
 </tr></table>
@@ -242,13 +476,47 @@
 <h2 id="E">E</h2>
 <table style="width: 100%" class="indextable genindextable"><tr>
   <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="pyFTS.partitioners.html#pyFTS.partitioners.Entropy.entropy">entropy() (in module pyFTS.partitioners.Entropy)</a>
+      <li><a href="pyFTS.hyperparam.html#pyFTS.hyperparam.Evolutionary.elitism">elitism() (in module pyFTS.hyperparam.Evolutionary)</a>
 </li>
+      <li><a href="pyFTS.probabilistic.html#pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution.empiricalloglikelihood">empiricalloglikelihood() (pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution method)</a>
+</li>
+      <li><a href="pyFTS.models.ensemble.html#pyFTS.models.ensemble.ensemble.EnsembleFTS">EnsembleFTS (class in pyFTS.models.ensemble.ensemble)</a>
+</li>
+      <li><a href="pyFTS.partitioners.html#pyFTS.partitioners.Entropy.entropy">entropy() (in module pyFTS.partitioners.Entropy)</a>
+
+      <ul>
+        <li><a href="pyFTS.probabilistic.html#pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution.entropy">(pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution method)</a>
+</li>
+      </ul></li>
       <li><a href="pyFTS.partitioners.html#pyFTS.partitioners.Entropy.EntropyPartitioner">EntropyPartitioner (class in pyFTS.partitioners.Entropy)</a>
 </li>
+      <li><a href="pyFTS.common.html#pyFTS.common.Util.enumerate2">enumerate2() (in module pyFTS.common.Util)</a>
+</li>
+      <li><a href="pyFTS.hyperparam.html#pyFTS.hyperparam.Evolutionary.evaluate">evaluate() (in module pyFTS.hyperparam.Evolutionary)</a>
+</li>
+      <li><a href="pyFTS.hyperparam.html#pyFTS.hyperparam.Evolutionary.execute">execute() (in module pyFTS.hyperparam.Evolutionary)</a>
+
+      <ul>
+        <li><a href="pyFTS.hyperparam.html#pyFTS.hyperparam.GridSearch.execute">(in module pyFTS.hyperparam.GridSearch)</a>
+</li>
+      </ul></li>
   </ul></td>
   <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="pyFTS.probabilistic.html#pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution.expected_value">expected_value() (pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution method)</a>
+</li>
+      <li><a href="pyFTS.partitioners.html#pyFTS.partitioners.parallel_util.explore_partitioners">explore_partitioners() (in module pyFTS.partitioners.parallel_util)</a>
+
+      <ul>
+        <li><a href="pyFTS.partitioners.html#pyFTS.partitioners.Util.explore_partitioners">(in module pyFTS.partitioners.Util)</a>
+</li>
+      </ul></li>
       <li><a href="pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.perturbation.exponential">exponential() (in module pyFTS.models.nonstationary.perturbation)</a>
+</li>
+      <li><a href="pyFTS.models.html#pyFTS.models.sadaei.ExponentialyWeightedFLRG">ExponentialyWeightedFLRG (class in pyFTS.models.sadaei)</a>
+</li>
+      <li><a href="pyFTS.models.html#pyFTS.models.sadaei.ExponentialyWeightedFTS">ExponentialyWeightedFTS (class in pyFTS.models.sadaei)</a>
+</li>
+      <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.Util.extract_measure">extract_measure() (in module pyFTS.benchmarks.Util)</a>
 </li>
       <li><a href="pyFTS.models.seasonal.html#pyFTS.models.seasonal.partitioner.TimeGridPartitioner.extractor">extractor() (pyFTS.models.seasonal.partitioner.TimeGridPartitioner method)</a>
 
@@ -265,6 +533,8 @@
       <li><a href="pyFTS.partitioners.html#pyFTS.partitioners.FCM.FCMPartitioner">FCMPartitioner (class in pyFTS.partitioners.FCM)</a>
 </li>
       <li><a href="pyFTS.common.html#pyFTS.common.SortedCollection.SortedCollection.find">find() (pyFTS.common.SortedCollection.SortedCollection method)</a>
+</li>
+      <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.Util.find_best">find_best() (in module pyFTS.benchmarks.Util)</a>
 </li>
       <li><a href="pyFTS.common.html#pyFTS.common.SortedCollection.SortedCollection.find_ge">find_ge() (pyFTS.common.SortedCollection.SortedCollection method)</a>
 </li>
@@ -273,6 +543,8 @@
       <li><a href="pyFTS.common.html#pyFTS.common.SortedCollection.SortedCollection.find_le">find_le() (pyFTS.common.SortedCollection.SortedCollection method)</a>
 </li>
       <li><a href="pyFTS.common.html#pyFTS.common.SortedCollection.SortedCollection.find_lt">find_lt() (pyFTS.common.SortedCollection.SortedCollection method)</a>
+</li>
+      <li><a href="pyFTS.common.html#pyFTS.common.fts.FTS.fit">fit() (pyFTS.common.fts.FTS method)</a>
 </li>
       <li><a href="pyFTS.common.html#pyFTS.common.tree.flat">flat() (in module pyFTS.common.tree)</a>
 </li>
@@ -282,29 +554,245 @@
         <li><a href="pyFTS.models.multivariate.html#pyFTS.models.multivariate.FLR.FLR">(class in pyFTS.models.multivariate.FLR)</a>
 </li>
       </ul></li>
+      <li><a href="pyFTS.models.html#pyFTS.models.song.ConventionalFTS.flr_membership_matrix">flr_membership_matrix() (pyFTS.models.song.ConventionalFTS method)</a>
+</li>
       <li><a href="pyFTS.common.html#pyFTS.common.flrg.FLRG">FLRG (class in pyFTS.common.flrg)</a>
 
       <ul>
         <li><a href="pyFTS.models.multivariate.html#pyFTS.models.multivariate.flrg.FLRG">(class in pyFTS.models.multivariate.flrg)</a>
 </li>
       </ul></li>
+      <li><a href="pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFTS.flrg_lhs_conditional_probability">flrg_lhs_conditional_probability() (pyFTS.models.pwfts.ProbabilisticWeightedFTS method)</a>
+</li>
+      <li><a href="pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFTS.flrg_lhs_conditional_probability_fuzzyfied">flrg_lhs_conditional_probability_fuzzyfied() (pyFTS.models.pwfts.ProbabilisticWeightedFTS method)</a>
+</li>
+      <li><a href="pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFTS.flrg_lhs_unconditional_probability">flrg_lhs_unconditional_probability() (pyFTS.models.pwfts.ProbabilisticWeightedFTS method)</a>
+</li>
+      <li><a href="pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFTS.flrg_rhs_conditional_probability">flrg_rhs_conditional_probability() (pyFTS.models.pwfts.ProbabilisticWeightedFTS method)</a>
+</li>
+      <li><a href="pyFTS.common.html#pyFTS.common.fts.FTS.flrgs">flrgs (pyFTS.common.fts.FTS attribute)</a>
+</li>
       <li><a href="pyFTS.common.html#pyFTS.common.tree.FLRGTree">FLRGTree (class in pyFTS.common.tree)</a>
 </li>
       <li><a href="pyFTS.common.html#pyFTS.common.tree.FLRGTreeNode">FLRGTreeNode (class in pyFTS.common.tree)</a>
 </li>
-      <li><a href="pyFTS.models.multivariate.html#pyFTS.models.multivariate.partitioner.MultivariatePartitioner.format_data">format_data() (pyFTS.models.multivariate.partitioner.MultivariatePartitioner method)</a>
+      <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.arima.ARIMA.forecast">forecast() (pyFTS.benchmarks.arima.ARIMA method)</a>
+
+      <ul>
+        <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.BSTS.ARIMA.forecast">(pyFTS.benchmarks.BSTS.ARIMA method)</a>
 </li>
+        <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.knn.KNearestNeighbors.forecast">(pyFTS.benchmarks.knn.KNearestNeighbors method)</a>
+</li>
+        <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.naive.Naive.forecast">(pyFTS.benchmarks.naive.Naive method)</a>
+</li>
+        <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.quantreg.QuantileRegression.forecast">(pyFTS.benchmarks.quantreg.QuantileRegression method)</a>
+</li>
+        <li><a href="pyFTS.common.html#pyFTS.common.fts.FTS.forecast">(pyFTS.common.fts.FTS method)</a>
+</li>
+        <li><a href="pyFTS.models.html#pyFTS.models.chen.ConventionalFTS.forecast">(pyFTS.models.chen.ConventionalFTS method)</a>
+</li>
+        <li><a href="pyFTS.models.ensemble.html#pyFTS.models.ensemble.ensemble.EnsembleFTS.forecast">(pyFTS.models.ensemble.ensemble.EnsembleFTS method)</a>
+</li>
+        <li><a href="pyFTS.models.html#pyFTS.models.hofts.HighOrderFTS.forecast">(pyFTS.models.hofts.HighOrderFTS method)</a>
+</li>
+        <li><a href="pyFTS.models.html#pyFTS.models.hwang.HighOrderFTS.forecast">(pyFTS.models.hwang.HighOrderFTS method)</a>
+</li>
+        <li><a href="pyFTS.models.incremental.html#pyFTS.models.incremental.IncrementalEnsemble.IncrementalEnsembleFTS.forecast">(pyFTS.models.incremental.IncrementalEnsemble.IncrementalEnsembleFTS method)</a>
+</li>
+        <li><a href="pyFTS.models.incremental.html#pyFTS.models.incremental.TimeVariant.Retrainer.forecast">(pyFTS.models.incremental.TimeVariant.Retrainer method)</a>
+</li>
+        <li><a href="pyFTS.models.html#pyFTS.models.ismailefendi.ImprovedWeightedFTS.forecast">(pyFTS.models.ismailefendi.ImprovedWeightedFTS method)</a>
+</li>
+        <li><a href="pyFTS.models.multivariate.html#pyFTS.models.multivariate.cmvfts.ClusteredMVFTS.forecast">(pyFTS.models.multivariate.cmvfts.ClusteredMVFTS method)</a>
+</li>
+        <li><a href="pyFTS.models.multivariate.html#pyFTS.models.multivariate.mvfts.MVFTS.forecast">(pyFTS.models.multivariate.mvfts.MVFTS method)</a>
+</li>
+        <li><a href="pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.cvfts.ConditionalVarianceFTS.forecast">(pyFTS.models.nonstationary.cvfts.ConditionalVarianceFTS method)</a>
+</li>
+        <li><a href="pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.honsfts.HighOrderNonStationaryFTS.forecast">(pyFTS.models.nonstationary.honsfts.HighOrderNonStationaryFTS method)</a>
+</li>
+        <li><a href="pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.nsfts.NonStationaryFTS.forecast">(pyFTS.models.nonstationary.nsfts.NonStationaryFTS method)</a>
+</li>
+        <li><a href="pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFTS.forecast">(pyFTS.models.pwfts.ProbabilisticWeightedFTS method)</a>
+</li>
+        <li><a href="pyFTS.models.html#pyFTS.models.sadaei.ExponentialyWeightedFTS.forecast">(pyFTS.models.sadaei.ExponentialyWeightedFTS method)</a>
+</li>
+        <li><a href="pyFTS.models.seasonal.html#pyFTS.models.seasonal.cmsfts.ContextualMultiSeasonalFTS.forecast">(pyFTS.models.seasonal.cmsfts.ContextualMultiSeasonalFTS method)</a>
+</li>
+        <li><a href="pyFTS.models.seasonal.html#pyFTS.models.seasonal.msfts.MultiSeasonalFTS.forecast">(pyFTS.models.seasonal.msfts.MultiSeasonalFTS method)</a>
+</li>
+        <li><a href="pyFTS.models.seasonal.html#pyFTS.models.seasonal.sfts.SeasonalFTS.forecast">(pyFTS.models.seasonal.sfts.SeasonalFTS method)</a>
+</li>
+        <li><a href="pyFTS.models.html#pyFTS.models.song.ConventionalFTS.forecast">(pyFTS.models.song.ConventionalFTS method)</a>
+</li>
+        <li><a href="pyFTS.models.html#pyFTS.models.yu.WeightedFTS.forecast">(pyFTS.models.yu.WeightedFTS method)</a>
+</li>
+      </ul></li>
+      <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.BSTS.ARIMA.forecast_ahead">forecast_ahead() (pyFTS.benchmarks.BSTS.ARIMA method)</a>
+
+      <ul>
+        <li><a href="pyFTS.common.html#pyFTS.common.fts.FTS.forecast_ahead">(pyFTS.common.fts.FTS method)</a>
+</li>
+        <li><a href="pyFTS.models.incremental.html#pyFTS.models.incremental.IncrementalEnsemble.IncrementalEnsembleFTS.forecast_ahead">(pyFTS.models.incremental.IncrementalEnsemble.IncrementalEnsembleFTS method)</a>
+</li>
+        <li><a href="pyFTS.models.incremental.html#pyFTS.models.incremental.TimeVariant.Retrainer.forecast_ahead">(pyFTS.models.incremental.TimeVariant.Retrainer method)</a>
+</li>
+        <li><a href="pyFTS.models.multivariate.html#pyFTS.models.multivariate.mvfts.MVFTS.forecast_ahead">(pyFTS.models.multivariate.mvfts.MVFTS method)</a>
+</li>
+        <li><a href="pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFTS.forecast_ahead">(pyFTS.models.pwfts.ProbabilisticWeightedFTS method)</a>
+</li>
+        <li><a href="pyFTS.models.seasonal.html#pyFTS.models.seasonal.cmsfts.ContextualMultiSeasonalFTS.forecast_ahead">(pyFTS.models.seasonal.cmsfts.ContextualMultiSeasonalFTS method)</a>
+</li>
+        <li><a href="pyFTS.models.seasonal.html#pyFTS.models.seasonal.msfts.MultiSeasonalFTS.forecast_ahead">(pyFTS.models.seasonal.msfts.MultiSeasonalFTS method)</a>
+</li>
+      </ul></li>
+      <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.arima.ARIMA.forecast_ahead_distribution">forecast_ahead_distribution() (pyFTS.benchmarks.arima.ARIMA method)</a>
+
+      <ul>
+        <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.BSTS.ARIMA.forecast_ahead_distribution">(pyFTS.benchmarks.BSTS.ARIMA method)</a>
+</li>
+        <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.knn.KNearestNeighbors.forecast_ahead_distribution">(pyFTS.benchmarks.knn.KNearestNeighbors method)</a>
+</li>
+        <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.quantreg.QuantileRegression.forecast_ahead_distribution">(pyFTS.benchmarks.quantreg.QuantileRegression method)</a>
+</li>
+        <li><a href="pyFTS.common.html#pyFTS.common.fts.FTS.forecast_ahead_distribution">(pyFTS.common.fts.FTS method)</a>
+</li>
+        <li><a href="pyFTS.models.ensemble.html#pyFTS.models.ensemble.ensemble.EnsembleFTS.forecast_ahead_distribution">(pyFTS.models.ensemble.ensemble.EnsembleFTS method)</a>
+</li>
+        <li><a href="pyFTS.models.multivariate.html#pyFTS.models.multivariate.cmvfts.ClusteredMVFTS.forecast_ahead_distribution">(pyFTS.models.multivariate.cmvfts.ClusteredMVFTS method)</a>
+</li>
+        <li><a href="pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFTS.forecast_ahead_distribution">(pyFTS.models.pwfts.ProbabilisticWeightedFTS method)</a>
+</li>
+      </ul></li>
+      <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.arima.ARIMA.forecast_ahead_interval">forecast_ahead_interval() (pyFTS.benchmarks.arima.ARIMA method)</a>
+
+      <ul>
+        <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.BSTS.ARIMA.forecast_ahead_interval">(pyFTS.benchmarks.BSTS.ARIMA method)</a>
+</li>
+        <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.knn.KNearestNeighbors.forecast_ahead_interval">(pyFTS.benchmarks.knn.KNearestNeighbors method)</a>
+</li>
+        <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.quantreg.QuantileRegression.forecast_ahead_interval">(pyFTS.benchmarks.quantreg.QuantileRegression method)</a>
+</li>
+        <li><a href="pyFTS.common.html#pyFTS.common.fts.FTS.forecast_ahead_interval">(pyFTS.common.fts.FTS method)</a>
+</li>
+        <li><a href="pyFTS.models.ensemble.html#pyFTS.models.ensemble.ensemble.EnsembleFTS.forecast_ahead_interval">(pyFTS.models.ensemble.ensemble.EnsembleFTS method)</a>
+</li>
+        <li><a href="pyFTS.models.html#pyFTS.models.ifts.IntervalFTS.forecast_ahead_interval">(pyFTS.models.ifts.IntervalFTS method)</a>
+</li>
+        <li><a href="pyFTS.models.html#pyFTS.models.ifts.WeightedIntervalFTS.forecast_ahead_interval">(pyFTS.models.ifts.WeightedIntervalFTS method)</a>
+</li>
+        <li><a href="pyFTS.models.multivariate.html#pyFTS.models.multivariate.mvfts.MVFTS.forecast_ahead_interval">(pyFTS.models.multivariate.mvfts.MVFTS method)</a>
+</li>
+        <li><a href="pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFTS.forecast_ahead_interval">(pyFTS.models.pwfts.ProbabilisticWeightedFTS method)</a>
+</li>
+      </ul></li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="pyFTS.common.html#pyFTS.common.fts.FTS.forecast_ahead_multivariate">forecast_ahead_multivariate() (pyFTS.common.fts.FTS method)</a>
+
+      <ul>
+        <li><a href="pyFTS.models.multivariate.html#pyFTS.models.multivariate.cmvfts.ClusteredMVFTS.forecast_ahead_multivariate">(pyFTS.models.multivariate.cmvfts.ClusteredMVFTS method)</a>
+</li>
+      </ul></li>
+      <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.arima.ARIMA.forecast_distribution">forecast_distribution() (pyFTS.benchmarks.arima.ARIMA method)</a>
+
+      <ul>
+        <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.BSTS.ARIMA.forecast_distribution">(pyFTS.benchmarks.BSTS.ARIMA method)</a>
+</li>
+        <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.knn.KNearestNeighbors.forecast_distribution">(pyFTS.benchmarks.knn.KNearestNeighbors method)</a>
+</li>
+        <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.quantreg.QuantileRegression.forecast_distribution">(pyFTS.benchmarks.quantreg.QuantileRegression method)</a>
+</li>
+        <li><a href="pyFTS.common.html#pyFTS.common.fts.FTS.forecast_distribution">(pyFTS.common.fts.FTS method)</a>
+</li>
+        <li><a href="pyFTS.models.ensemble.html#pyFTS.models.ensemble.ensemble.EnsembleFTS.forecast_distribution">(pyFTS.models.ensemble.ensemble.EnsembleFTS method)</a>
+</li>
+        <li><a href="pyFTS.models.ensemble.html#pyFTS.models.ensemble.multiseasonal.SeasonalEnsembleFTS.forecast_distribution">(pyFTS.models.ensemble.multiseasonal.SeasonalEnsembleFTS method)</a>
+</li>
+        <li><a href="pyFTS.models.multivariate.html#pyFTS.models.multivariate.cmvfts.ClusteredMVFTS.forecast_distribution">(pyFTS.models.multivariate.cmvfts.ClusteredMVFTS method)</a>
+</li>
+        <li><a href="pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFTS.forecast_distribution">(pyFTS.models.pwfts.ProbabilisticWeightedFTS method)</a>
+</li>
+      </ul></li>
+      <li><a href="pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFTS.forecast_distribution_from_distribution">forecast_distribution_from_distribution() (pyFTS.models.pwfts.ProbabilisticWeightedFTS method)</a>
+</li>
+      <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.arima.ARIMA.forecast_interval">forecast_interval() (pyFTS.benchmarks.arima.ARIMA method)</a>
+
+      <ul>
+        <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.BSTS.ARIMA.forecast_interval">(pyFTS.benchmarks.BSTS.ARIMA method)</a>
+</li>
+        <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.knn.KNearestNeighbors.forecast_interval">(pyFTS.benchmarks.knn.KNearestNeighbors method)</a>
+</li>
+        <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.quantreg.QuantileRegression.forecast_interval">(pyFTS.benchmarks.quantreg.QuantileRegression method)</a>
+</li>
+        <li><a href="pyFTS.common.html#pyFTS.common.fts.FTS.forecast_interval">(pyFTS.common.fts.FTS method)</a>
+</li>
+        <li><a href="pyFTS.models.ensemble.html#pyFTS.models.ensemble.ensemble.EnsembleFTS.forecast_interval">(pyFTS.models.ensemble.ensemble.EnsembleFTS method)</a>
+</li>
+        <li><a href="pyFTS.models.html#pyFTS.models.ifts.IntervalFTS.forecast_interval">(pyFTS.models.ifts.IntervalFTS method)</a>
+</li>
+        <li><a href="pyFTS.models.html#pyFTS.models.ifts.WeightedIntervalFTS.forecast_interval">(pyFTS.models.ifts.WeightedIntervalFTS method)</a>
+</li>
+        <li><a href="pyFTS.models.multivariate.html#pyFTS.models.multivariate.cmvfts.ClusteredMVFTS.forecast_interval">(pyFTS.models.multivariate.cmvfts.ClusteredMVFTS method)</a>
+</li>
+        <li><a href="pyFTS.models.multivariate.html#pyFTS.models.multivariate.mvfts.MVFTS.forecast_interval">(pyFTS.models.multivariate.mvfts.MVFTS method)</a>
+</li>
+        <li><a href="pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.cvfts.ConditionalVarianceFTS.forecast_interval">(pyFTS.models.nonstationary.cvfts.ConditionalVarianceFTS method)</a>
+</li>
+        <li><a href="pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.nsfts.NonStationaryFTS.forecast_interval">(pyFTS.models.nonstationary.nsfts.NonStationaryFTS method)</a>
+</li>
+        <li><a href="pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFTS.forecast_interval">(pyFTS.models.pwfts.ProbabilisticWeightedFTS method)</a>
+</li>
+      </ul></li>
+      <li><a href="pyFTS.common.html#pyFTS.common.fts.FTS.forecast_multivariate">forecast_multivariate() (pyFTS.common.fts.FTS method)</a>
+
+      <ul>
+        <li><a href="pyFTS.models.multivariate.html#pyFTS.models.multivariate.cmvfts.ClusteredMVFTS.forecast_multivariate">(pyFTS.models.multivariate.cmvfts.ClusteredMVFTS method)</a>
+</li>
+      </ul></li>
+      <li><a href="pyFTS.common.html#pyFTS.common.fts.FTS.forecast_step">forecast_step() (pyFTS.common.fts.FTS method)</a>
+</li>
+      <li><a href="pyFTS.models.multivariate.html#pyFTS.models.multivariate.mvfts.MVFTS.format_data">format_data() (pyFTS.models.multivariate.mvfts.MVFTS method)</a>
+
+      <ul>
+        <li><a href="pyFTS.models.multivariate.html#pyFTS.models.multivariate.partitioner.MultivariatePartitioner.format_data">(pyFTS.models.multivariate.partitioner.MultivariatePartitioner method)</a>
+</li>
+      </ul></li>
+      <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.Tests.format_experiment_table">format_experiment_table() (in module pyFTS.benchmarks.Tests)</a>
+</li>
+      <li><a href="pyFTS.probabilistic.html#pyFTS.probabilistic.ProbabilityDistribution.from_point">from_point() (in module pyFTS.probabilistic.ProbabilityDistribution)</a>
+</li>
+      <li><a href="pyFTS.common.html#pyFTS.common.fts.FTS">FTS (class in pyFTS.common.fts)</a>
+</li>
+      <li><a href="pyFTS.models.incremental.html#pyFTS.models.incremental.IncrementalEnsemble.IncrementalEnsembleFTS.fts_method">fts_method (pyFTS.models.incremental.IncrementalEnsemble.IncrementalEnsembleFTS attribute)</a>
+
+      <ul>
+        <li><a href="pyFTS.models.incremental.html#pyFTS.models.incremental.TimeVariant.Retrainer.fts_method">(pyFTS.models.incremental.TimeVariant.Retrainer attribute)</a>
+</li>
+        <li><a href="pyFTS.models.multivariate.html#pyFTS.models.multivariate.cmvfts.ClusteredMVFTS.fts_method">(pyFTS.models.multivariate.cmvfts.ClusteredMVFTS attribute)</a>
+</li>
+      </ul></li>
+      <li><a href="pyFTS.models.incremental.html#pyFTS.models.incremental.IncrementalEnsemble.IncrementalEnsembleFTS.fts_params">fts_params (pyFTS.models.incremental.IncrementalEnsemble.IncrementalEnsembleFTS attribute)</a>
+
+      <ul>
+        <li><a href="pyFTS.models.incremental.html#pyFTS.models.incremental.TimeVariant.Retrainer.fts_params">(pyFTS.models.incremental.TimeVariant.Retrainer attribute)</a>
+</li>
+        <li><a href="pyFTS.models.multivariate.html#pyFTS.models.multivariate.cmvfts.ClusteredMVFTS.fts_params">(pyFTS.models.multivariate.cmvfts.ClusteredMVFTS attribute)</a>
+</li>
+      </ul></li>
       <li><a href="pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.common.fuzzify">fuzzify() (in module pyFTS.models.nonstationary.common)</a>
+</li>
+      <li><a href="pyFTS.common.html#pyFTS.common.fts.FTS.fuzzy">fuzzy() (pyFTS.common.fts.FTS method)</a>
 </li>
       <li><a href="pyFTS.partitioners.html#pyFTS.partitioners.FCM.fuzzy_cmeans">fuzzy_cmeans() (in module pyFTS.partitioners.FCM)</a>
 </li>
-  </ul></td>
-  <td style="width: 33%; vertical-align: top;"><ul>
       <li><a href="pyFTS.partitioners.html#pyFTS.partitioners.FCM.fuzzy_distance">fuzzy_distance() (in module pyFTS.partitioners.FCM)</a>
 </li>
       <li><a href="pyFTS.common.html#pyFTS.common.FuzzySet.fuzzyfy">fuzzyfy() (in module pyFTS.common.FuzzySet)</a>
 
       <ul>
+        <li><a href="pyFTS.models.multivariate.html#pyFTS.models.multivariate.cmvfts.ClusteredMVFTS.fuzzyfy">(pyFTS.models.multivariate.cmvfts.ClusteredMVFTS method)</a>
+</li>
         <li><a href="pyFTS.models.multivariate.html#pyFTS.models.multivariate.grid.IncrementalGridCluster.fuzzyfy">(pyFTS.models.multivariate.grid.IncrementalGridCluster method)</a>
 </li>
         <li><a href="pyFTS.models.multivariate.html#pyFTS.models.multivariate.partitioner.MultivariatePartitioner.fuzzyfy">(pyFTS.models.multivariate.partitioner.MultivariatePartitioner method)</a>
@@ -345,12 +833,72 @@
 <table style="width: 100%" class="indextable genindextable"><tr>
   <td style="width: 33%; vertical-align: top;"><ul>
       <li><a href="pyFTS.common.html#pyFTS.common.Membership.gaussmf">gaussmf() (in module pyFTS.common.Membership)</a>
+</li>
+      <li><a href="pyFTS.models.html#pyFTS.models.chen.ConventionalFTS.generate_flrg">generate_flrg() (pyFTS.models.chen.ConventionalFTS method)</a>
+</li>
+      <li><a href="pyFTS.models.html#pyFTS.models.cheng.TrendWeightedFTS.generate_FLRG">generate_FLRG() (pyFTS.models.cheng.TrendWeightedFTS method)</a>
+</li>
+      <li><a href="pyFTS.models.html#pyFTS.models.hofts.HighOrderFTS.generate_flrg">generate_flrg() (pyFTS.models.hofts.HighOrderFTS method)</a>
+
+      <ul>
+        <li><a href="pyFTS.models.html#pyFTS.models.ismailefendi.ImprovedWeightedFTS.generate_flrg">(pyFTS.models.ismailefendi.ImprovedWeightedFTS method)</a>
+</li>
+        <li><a href="pyFTS.models.multivariate.html#pyFTS.models.multivariate.mvfts.MVFTS.generate_flrg">(pyFTS.models.multivariate.mvfts.MVFTS method)</a>
+</li>
+        <li><a href="pyFTS.models.multivariate.html#pyFTS.models.multivariate.wmvfts.WeightedMVFTS.generate_flrg">(pyFTS.models.multivariate.wmvfts.WeightedMVFTS method)</a>
+</li>
+        <li><a href="pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.cvfts.ConditionalVarianceFTS.generate_flrg">(pyFTS.models.nonstationary.cvfts.ConditionalVarianceFTS method)</a>
+</li>
+        <li><a href="pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.honsfts.HighOrderNonStationaryFTS.generate_flrg">(pyFTS.models.nonstationary.honsfts.HighOrderNonStationaryFTS method)</a>
+</li>
+        <li><a href="pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.nsfts.NonStationaryFTS.generate_flrg">(pyFTS.models.nonstationary.nsfts.NonStationaryFTS method)</a>
+</li>
+        <li><a href="pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.nsfts.WeightedNonStationaryFTS.generate_flrg">(pyFTS.models.nonstationary.nsfts.WeightedNonStationaryFTS method)</a>
+</li>
+        <li><a href="pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFTS.generate_flrg">(pyFTS.models.pwfts.ProbabilisticWeightedFTS method)</a>
+</li>
+        <li><a href="pyFTS.models.html#pyFTS.models.sadaei.ExponentialyWeightedFTS.generate_flrg">(pyFTS.models.sadaei.ExponentialyWeightedFTS method)</a>
+</li>
+        <li><a href="pyFTS.models.seasonal.html#pyFTS.models.seasonal.cmsfts.ContextualMultiSeasonalFTS.generate_flrg">(pyFTS.models.seasonal.cmsfts.ContextualMultiSeasonalFTS method)</a>
+</li>
+        <li><a href="pyFTS.models.seasonal.html#pyFTS.models.seasonal.msfts.MultiSeasonalFTS.generate_flrg">(pyFTS.models.seasonal.msfts.MultiSeasonalFTS method)</a>
+</li>
+        <li><a href="pyFTS.models.seasonal.html#pyFTS.models.seasonal.sfts.SeasonalFTS.generate_flrg">(pyFTS.models.seasonal.sfts.SeasonalFTS method)</a>
+</li>
+      </ul></li>
+      <li><a href="pyFTS.models.html#pyFTS.models.yu.WeightedFTS.generate_FLRG">generate_FLRG() (pyFTS.models.yu.WeightedFTS method)</a>
+</li>
+      <li><a href="pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFTS.generate_flrg2">generate_flrg2() (pyFTS.models.pwfts.ProbabilisticWeightedFTS method)</a>
+</li>
+      <li><a href="pyFTS.models.html#pyFTS.models.hofts.HighOrderFTS.generate_flrg_fuzzyfied">generate_flrg_fuzzyfied() (pyFTS.models.hofts.HighOrderFTS method)</a>
+
+      <ul>
+        <li><a href="pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFTS.generate_flrg_fuzzyfied">(pyFTS.models.pwfts.ProbabilisticWeightedFTS method)</a>
+</li>
+      </ul></li>
+      <li><a href="pyFTS.models.multivariate.html#pyFTS.models.multivariate.mvfts.MVFTS.generate_flrs">generate_flrs() (pyFTS.models.multivariate.mvfts.MVFTS method)</a>
 </li>
       <li><a href="pyFTS.data.html#pyFTS.data.artificial.generate_gaussian_linear">generate_gaussian_linear() (in module pyFTS.data.artificial)</a>
 </li>
       <li><a href="pyFTS.common.html#pyFTS.common.FLR.generate_high_order_recurrent_flr">generate_high_order_recurrent_flr() (in module pyFTS.common.FLR)</a>
 </li>
       <li><a href="pyFTS.common.html#pyFTS.common.FLR.generate_indexed_flrs">generate_indexed_flrs() (in module pyFTS.common.FLR)</a>
+</li>
+      <li><a href="pyFTS.models.html#pyFTS.models.hofts.HighOrderFTS.generate_lhs_flrg">generate_lhs_flrg() (pyFTS.models.hofts.HighOrderFTS method)</a>
+
+      <ul>
+        <li><a href="pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFTS.generate_lhs_flrg">(pyFTS.models.pwfts.ProbabilisticWeightedFTS method)</a>
+</li>
+      </ul></li>
+      <li><a href="pyFTS.models.html#pyFTS.models.hofts.HighOrderFTS.generate_lhs_flrg_fuzzyfied">generate_lhs_flrg_fuzzyfied() (pyFTS.models.hofts.HighOrderFTS method)</a>
+
+      <ul>
+        <li><a href="pyFTS.models.html#pyFTS.models.hofts.WeightedHighOrderFTS.generate_lhs_flrg_fuzzyfied">(pyFTS.models.hofts.WeightedHighOrderFTS method)</a>
+</li>
+        <li><a href="pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFTS.generate_lhs_flrg_fuzzyfied">(pyFTS.models.pwfts.ProbabilisticWeightedFTS method)</a>
+</li>
+      </ul></li>
+      <li><a href="pyFTS.models.multivariate.html#pyFTS.models.multivariate.mvfts.MVFTS.generate_lhs_flrs">generate_lhs_flrs() (pyFTS.models.multivariate.mvfts.MVFTS method)</a>
 </li>
       <li><a href="pyFTS.data.html#pyFTS.data.artificial.generate_linear_periodic_gaussian">generate_linear_periodic_gaussian() (in module pyFTS.data.artificial)</a>
 </li>
@@ -361,6 +909,16 @@
       <li><a href="pyFTS.data.html#pyFTS.data.artificial.generate_sinoidal_periodic_gaussian">generate_sinoidal_periodic_gaussian() (in module pyFTS.data.artificial)</a>
 </li>
       <li><a href="pyFTS.data.html#pyFTS.data.artificial.generate_uniform_linear">generate_uniform_linear() (in module pyFTS.data.artificial)</a>
+</li>
+      <li><a href="pyFTS.hyperparam.html#pyFTS.hyperparam.Evolutionary.GeneticAlgorithm">GeneticAlgorithm() (in module pyFTS.hyperparam.Evolutionary)</a>
+</li>
+      <li><a href="pyFTS.hyperparam.html#pyFTS.hyperparam.Evolutionary.genotype">genotype() (in module pyFTS.hyperparam.Evolutionary)</a>
+</li>
+      <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.benchmarks.get_benchmark_interval_methods">get_benchmark_interval_methods() (in module pyFTS.benchmarks.benchmarks)</a>
+</li>
+      <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.benchmarks.get_benchmark_point_methods">get_benchmark_point_methods() (in module pyFTS.benchmarks.benchmarks)</a>
+</li>
+      <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.benchmarks.get_benchmark_probabilistic_methods">get_benchmark_probabilistic_methods() (in module pyFTS.benchmarks.benchmarks)</a>
 </li>
       <li><a href="pyFTS.data.html#pyFTS.data.AirPassengers.get_data">get_data() (in module pyFTS.data.AirPassengers)</a>
 
@@ -458,8 +1016,16 @@
         <li><a href="pyFTS.data.html#pyFTS.data.TAIEX.get_dataframe">(in module pyFTS.data.TAIEX)</a>
 </li>
       </ul></li>
+      <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.Util.get_dataframe_from_bd">get_dataframe_from_bd() (in module pyFTS.benchmarks.Util)</a>
+</li>
+      <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.Measures.get_distribution_ahead_statistics">get_distribution_ahead_statistics() (in module pyFTS.benchmarks.Measures)</a>
+</li>
   </ul></td>
   <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="pyFTS.models.ensemble.html#pyFTS.models.ensemble.ensemble.EnsembleFTS.get_distribution_interquantile">get_distribution_interquantile() (pyFTS.models.ensemble.ensemble.EnsembleFTS method)</a>
+</li>
+      <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.Measures.get_distribution_statistics">get_distribution_statistics() (in module pyFTS.benchmarks.Measures)</a>
+</li>
       <li><a href="pyFTS.common.html#pyFTS.common.FuzzySet.get_fuzzysets">get_fuzzysets() (in module pyFTS.common.FuzzySet)</a>
 </li>
       <li><a href="pyFTS.models.seasonal.html#pyFTS.models.seasonal.SeasonalIndexer.DateTimeSeasonalIndexer.get_index">get_index() (pyFTS.models.seasonal.SeasonalIndexer.DateTimeSeasonalIndexer method)</a>
@@ -478,20 +1044,50 @@
         <li><a href="pyFTS.models.seasonal.html#pyFTS.models.seasonal.SeasonalIndexer.SeasonalIndexer.get_index_by_season">(pyFTS.models.seasonal.SeasonalIndexer.SeasonalIndexer method)</a>
 </li>
       </ul></li>
+      <li><a href="pyFTS.models.ensemble.html#pyFTS.models.ensemble.ensemble.EnsembleFTS.get_interval">get_interval() (pyFTS.models.ensemble.ensemble.EnsembleFTS method)</a>
+</li>
+      <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.Measures.get_interval_ahead_statistics">get_interval_ahead_statistics() (in module pyFTS.benchmarks.Measures)</a>
+</li>
+      <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.benchmarks.get_interval_methods">get_interval_methods() (in module pyFTS.benchmarks.benchmarks)</a>
+</li>
+      <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.Measures.get_interval_statistics">get_interval_statistics() (in module pyFTS.benchmarks.Measures)</a>
+</li>
       <li><a href="pyFTS.common.html#pyFTS.common.flrg.FLRG.get_key">get_key() (pyFTS.common.flrg.FLRG method)</a>
 
       <ul>
+        <li><a href="pyFTS.models.html#pyFTS.models.chen.ConventionalFLRG.get_key">(pyFTS.models.chen.ConventionalFLRG method)</a>
+</li>
         <li><a href="pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.flrg.NonStationaryFLRG.get_key">(pyFTS.models.nonstationary.flrg.NonStationaryFLRG method)</a>
+</li>
+        <li><a href="pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.nsfts.ConventionalNonStationaryFLRG.get_key">(pyFTS.models.nonstationary.nsfts.ConventionalNonStationaryFLRG method)</a>
+</li>
+        <li><a href="pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.nsfts.WeightedNonStationaryFLRG.get_key">(pyFTS.models.nonstationary.nsfts.WeightedNonStationaryFLRG method)</a>
+</li>
+        <li><a href="pyFTS.models.seasonal.html#pyFTS.models.seasonal.sfts.SeasonalFLRG.get_key">(pyFTS.models.seasonal.sfts.SeasonalFLRG method)</a>
 </li>
       </ul></li>
       <li><a href="pyFTS.common.html#pyFTS.common.flrg.FLRG.get_lower">get_lower() (pyFTS.common.flrg.FLRG method)</a>
 
       <ul>
+        <li><a href="pyFTS.models.html#pyFTS.models.hofts.WeightedHighOrderFLRG.get_lower">(pyFTS.models.hofts.WeightedHighOrderFLRG method)</a>
+</li>
+        <li><a href="pyFTS.models.html#pyFTS.models.ifts.IntervalFTS.get_lower">(pyFTS.models.ifts.IntervalFTS method)</a>
+</li>
+        <li><a href="pyFTS.models.html#pyFTS.models.ifts.WeightedIntervalFTS.get_lower">(pyFTS.models.ifts.WeightedIntervalFTS method)</a>
+</li>
         <li><a href="pyFTS.models.multivariate.html#pyFTS.models.multivariate.flrg.FLRG.get_lower">(pyFTS.models.multivariate.flrg.FLRG method)</a>
+</li>
+        <li><a href="pyFTS.models.multivariate.html#pyFTS.models.multivariate.wmvfts.WeightedFLRG.get_lower">(pyFTS.models.multivariate.wmvfts.WeightedFLRG method)</a>
 </li>
         <li><a href="pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.common.FuzzySet.get_lower">(pyFTS.models.nonstationary.common.FuzzySet method)</a>
 </li>
         <li><a href="pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.flrg.NonStationaryFLRG.get_lower">(pyFTS.models.nonstationary.flrg.NonStationaryFLRG method)</a>
+</li>
+        <li><a href="pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.honsfts.HighOrderNonStationaryFLRG.get_lower">(pyFTS.models.nonstationary.honsfts.HighOrderNonStationaryFLRG method)</a>
+</li>
+        <li><a href="pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFLRG.get_lower">(pyFTS.models.pwfts.ProbabilisticWeightedFLRG method)</a>
+</li>
+        <li><a href="pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFTS.get_lower">(pyFTS.models.pwfts.ProbabilisticWeightedFTS method)</a>
 </li>
       </ul></li>
       <li><a href="pyFTS.common.html#pyFTS.common.FuzzySet.get_maximum_membership_fuzzyset">get_maximum_membership_fuzzyset() (in module pyFTS.common.FuzzySet)</a>
@@ -504,21 +1100,57 @@
         <li><a href="pyFTS.models.multivariate.html#pyFTS.models.multivariate.flrg.FLRG.get_membership">(pyFTS.models.multivariate.flrg.FLRG method)</a>
 </li>
         <li><a href="pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.flrg.NonStationaryFLRG.get_membership">(pyFTS.models.nonstationary.flrg.NonStationaryFLRG method)</a>
+</li>
+        <li><a href="pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFLRG.get_membership">(pyFTS.models.pwfts.ProbabilisticWeightedFLRG method)</a>
 </li>
       </ul></li>
       <li><a href="pyFTS.common.html#pyFTS.common.flrg.FLRG.get_midpoint">get_midpoint() (pyFTS.common.flrg.FLRG method)</a>
 
       <ul>
+        <li><a href="pyFTS.models.html#pyFTS.models.hofts.WeightedHighOrderFLRG.get_midpoint">(pyFTS.models.hofts.WeightedHighOrderFLRG method)</a>
+</li>
+        <li><a href="pyFTS.models.multivariate.html#pyFTS.models.multivariate.wmvfts.WeightedFLRG.get_midpoint">(pyFTS.models.multivariate.wmvfts.WeightedFLRG method)</a>
+</li>
         <li><a href="pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.common.FuzzySet.get_midpoint">(pyFTS.models.nonstationary.common.FuzzySet method)</a>
 </li>
         <li><a href="pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.flrg.NonStationaryFLRG.get_midpoint">(pyFTS.models.nonstationary.flrg.NonStationaryFLRG method)</a>
+</li>
+        <li><a href="pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.honsfts.HighOrderNonStationaryFLRG.get_midpoint">(pyFTS.models.nonstationary.honsfts.HighOrderNonStationaryFLRG method)</a>
+</li>
+        <li><a href="pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.nsfts.WeightedNonStationaryFLRG.get_midpoint">(pyFTS.models.nonstationary.nsfts.WeightedNonStationaryFLRG method)</a>
+</li>
+        <li><a href="pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFLRG.get_midpoint">(pyFTS.models.pwfts.ProbabilisticWeightedFLRG method)</a>
+</li>
+        <li><a href="pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFTS.get_midpoint">(pyFTS.models.pwfts.ProbabilisticWeightedFTS method)</a>
 </li>
       </ul></li>
       <li><a href="pyFTS.common.html#pyFTS.common.flrg.FLRG.get_midpoints">get_midpoints() (pyFTS.common.flrg.FLRG method)</a>
+
+      <ul>
+        <li><a href="pyFTS.models.seasonal.html#pyFTS.models.seasonal.cmsfts.ContextualMultiSeasonalFTS.get_midpoints">(pyFTS.models.seasonal.cmsfts.ContextualMultiSeasonalFTS method)</a>
+</li>
+        <li><a href="pyFTS.models.seasonal.html#pyFTS.models.seasonal.sfts.SeasonalFTS.get_midpoints">(pyFTS.models.seasonal.sfts.SeasonalFTS method)</a>
+</li>
+      </ul></li>
+      <li><a href="pyFTS.models.ensemble.html#pyFTS.models.ensemble.ensemble.EnsembleFTS.get_models_forecasts">get_models_forecasts() (pyFTS.models.ensemble.ensemble.EnsembleFTS method)</a>
 </li>
       <li><a href="pyFTS.partitioners.html#pyFTS.partitioners.partitioner.Partitioner.get_name">get_name() (pyFTS.partitioners.partitioner.Partitioner method)</a>
+</li>
+      <li><a href="pyFTS.distributed.html#pyFTS.distributed.dispy.get_number_of_cpus">get_number_of_cpus() (in module pyFTS.distributed.dispy)</a>
+</li>
+      <li><a href="pyFTS.models.ensemble.html#pyFTS.models.ensemble.ensemble.EnsembleFTS.get_point">get_point() (pyFTS.models.ensemble.ensemble.EnsembleFTS method)</a>
+</li>
+      <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.Measures.get_point_ahead_statistics">get_point_ahead_statistics() (in module pyFTS.benchmarks.Measures)</a>
+</li>
+      <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.benchmarks.get_point_methods">get_point_methods() (in module pyFTS.benchmarks.benchmarks)</a>
+</li>
+      <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.benchmarks.get_point_multivariate_methods">get_point_multivariate_methods() (in module pyFTS.benchmarks.benchmarks)</a>
+</li>
+      <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.Measures.get_point_statistics">get_point_statistics() (in module pyFTS.benchmarks.Measures)</a>
 </li>
       <li><a href="pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.partitioners.PolynomialNonStationaryPartitioner.get_polynomial_perturbations">get_polynomial_perturbations() (pyFTS.models.nonstationary.partitioners.PolynomialNonStationaryPartitioner method)</a>
+</li>
+      <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.benchmarks.get_probabilistic_methods">get_probabilistic_methods() (in module pyFTS.benchmarks.benchmarks)</a>
 </li>
       <li><a href="pyFTS.models.seasonal.html#pyFTS.models.seasonal.SeasonalIndexer.DataFrameSeasonalIndexer.get_season_by_index">get_season_by_index() (pyFTS.models.seasonal.SeasonalIndexer.DataFrameSeasonalIndexer method)</a>
 
@@ -538,16 +1170,44 @@
         <li><a href="pyFTS.models.seasonal.html#pyFTS.models.seasonal.SeasonalIndexer.LinearSeasonalIndexer.get_season_of_data">(pyFTS.models.seasonal.SeasonalIndexer.LinearSeasonalIndexer method)</a>
 </li>
         <li><a href="pyFTS.models.seasonal.html#pyFTS.models.seasonal.SeasonalIndexer.SeasonalIndexer.get_season_of_data">(pyFTS.models.seasonal.SeasonalIndexer.SeasonalIndexer method)</a>
+</li>
+      </ul></li>
+      <li><a href="pyFTS.models.html#pyFTS.models.ifts.IntervalFTS.get_sequence_membership">get_sequence_membership() (pyFTS.models.ifts.IntervalFTS method)</a>
+
+      <ul>
+        <li><a href="pyFTS.models.html#pyFTS.models.ifts.WeightedIntervalFTS.get_sequence_membership">(pyFTS.models.ifts.WeightedIntervalFTS method)</a>
+</li>
+      </ul></li>
+      <li><a href="pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFTS.get_sets_from_both_fuzzyfication">get_sets_from_both_fuzzyfication() (pyFTS.models.pwfts.ProbabilisticWeightedFTS method)</a>
+</li>
+      <li><a href="pyFTS.common.html#pyFTS.common.fts.FTS.get_UoD">get_UoD() (pyFTS.common.fts.FTS method)</a>
+
+      <ul>
+        <li><a href="pyFTS.models.ensemble.html#pyFTS.models.ensemble.ensemble.EnsembleFTS.get_UoD">(pyFTS.models.ensemble.ensemble.EnsembleFTS method)</a>
 </li>
       </ul></li>
       <li><a href="pyFTS.common.html#pyFTS.common.flrg.FLRG.get_upper">get_upper() (pyFTS.common.flrg.FLRG method)</a>
 
       <ul>
+        <li><a href="pyFTS.models.html#pyFTS.models.hofts.WeightedHighOrderFLRG.get_upper">(pyFTS.models.hofts.WeightedHighOrderFLRG method)</a>
+</li>
+        <li><a href="pyFTS.models.html#pyFTS.models.ifts.IntervalFTS.get_upper">(pyFTS.models.ifts.IntervalFTS method)</a>
+</li>
+        <li><a href="pyFTS.models.html#pyFTS.models.ifts.WeightedIntervalFTS.get_upper">(pyFTS.models.ifts.WeightedIntervalFTS method)</a>
+</li>
         <li><a href="pyFTS.models.multivariate.html#pyFTS.models.multivariate.flrg.FLRG.get_upper">(pyFTS.models.multivariate.flrg.FLRG method)</a>
+</li>
+        <li><a href="pyFTS.models.multivariate.html#pyFTS.models.multivariate.wmvfts.WeightedFLRG.get_upper">(pyFTS.models.multivariate.wmvfts.WeightedFLRG method)</a>
 </li>
         <li><a href="pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.common.FuzzySet.get_upper">(pyFTS.models.nonstationary.common.FuzzySet method)</a>
 </li>
         <li><a href="pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.flrg.NonStationaryFLRG.get_upper">(pyFTS.models.nonstationary.flrg.NonStationaryFLRG method)</a>
+</li>
+        <li><a href="pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.honsfts.HighOrderNonStationaryFLRG.get_upper">(pyFTS.models.nonstationary.honsfts.HighOrderNonStationaryFLRG method)</a>
+</li>
+        <li><a href="pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFLRG.get_upper">(pyFTS.models.pwfts.ProbabilisticWeightedFLRG method)</a>
+</li>
+        <li><a href="pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFTS.get_upper">(pyFTS.models.pwfts.ProbabilisticWeightedFTS method)</a>
 </li>
       </ul></li>
       <li><a href="pyFTS.common.html#pyFTS.common.tree.FLRGTreeNode.getChildren">getChildren() (pyFTS.common.tree.FLRGTreeNode method)</a>
@@ -555,6 +1215,8 @@
       <li><a href="pyFTS.common.html#pyFTS.common.tree.FLRGTreeNode.getStr">getStr() (pyFTS.common.tree.FLRGTreeNode method)</a>
 </li>
       <li><a href="pyFTS.common.html#pyFTS.common.FuzzySet.grant_bounds">grant_bounds() (in module pyFTS.common.FuzzySet)</a>
+</li>
+      <li><a href="pyFTS.models.multivariate.html#pyFTS.models.multivariate.granular.GranularWMVFTS">GranularWMVFTS (class in pyFTS.models.multivariate.granular)</a>
 </li>
       <li><a href="pyFTS.models.multivariate.html#pyFTS.models.multivariate.grid.GridCluster">GridCluster (class in pyFTS.models.multivariate.grid)</a>
 </li>
@@ -566,19 +1228,45 @@
 <h2 id="H">H</h2>
 <table style="width: 100%" class="indextable genindextable"><tr>
   <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="pyFTS.probabilistic.html#pyFTS.probabilistic.kde.KernelSmoothing.h">h (pyFTS.probabilistic.kde.KernelSmoothing attribute)</a>
+</li>
       <li><a href="pyFTS.models.seasonal.html#pyFTS.models.seasonal.common.DateTime.half">half (pyFTS.models.seasonal.common.DateTime attribute)</a>
+</li>
+      <li><a href="pyFTS.common.html#pyFTS.common.fts.FTS.has_interval_forecasting">has_interval_forecasting (pyFTS.common.fts.FTS attribute)</a>
+</li>
+      <li><a href="pyFTS.common.html#pyFTS.common.fts.FTS.has_point_forecasting">has_point_forecasting (pyFTS.common.fts.FTS attribute)</a>
+</li>
+      <li><a href="pyFTS.common.html#pyFTS.common.fts.FTS.has_probability_forecasting">has_probability_forecasting (pyFTS.common.fts.FTS attribute)</a>
+</li>
+      <li><a href="pyFTS.common.html#pyFTS.common.fts.FTS.has_seasonality">has_seasonality (pyFTS.common.fts.FTS attribute)</a>
+</li>
+      <li><a href="pyFTS.models.html#pyFTS.models.hofts.HighOrderFLRG">HighOrderFLRG (class in pyFTS.models.hofts)</a>
+</li>
+      <li><a href="pyFTS.models.html#pyFTS.models.hofts.HighOrderFTS">HighOrderFTS (class in pyFTS.models.hofts)</a>
+
+      <ul>
+        <li><a href="pyFTS.models.html#pyFTS.models.hwang.HighOrderFTS">(class in pyFTS.models.hwang)</a>
+</li>
+      </ul></li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.cvfts.HighOrderNonstationaryFLRG">HighOrderNonstationaryFLRG (class in pyFTS.models.nonstationary.cvfts)</a>
+</li>
+      <li><a href="pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.honsfts.HighOrderNonStationaryFLRG">HighOrderNonStationaryFLRG (class in pyFTS.models.nonstationary.honsfts)</a>
+</li>
+      <li><a href="pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.honsfts.HighOrderNonStationaryFTS">HighOrderNonStationaryFTS (class in pyFTS.models.nonstationary.honsfts)</a>
 </li>
       <li><a href="pyFTS.models.seasonal.html#pyFTS.models.seasonal.common.DateTime.hour">hour (pyFTS.models.seasonal.common.DateTime attribute)</a>
 </li>
       <li><a href="pyFTS.models.seasonal.html#pyFTS.models.seasonal.common.DateTime.hour_of_day">hour_of_day (pyFTS.models.seasonal.common.DateTime attribute)</a>
 </li>
-  </ul></td>
-  <td style="width: 33%; vertical-align: top;"><ul>
       <li><a href="pyFTS.models.seasonal.html#pyFTS.models.seasonal.common.DateTime.hour_of_month">hour_of_month (pyFTS.models.seasonal.common.DateTime attribute)</a>
 </li>
       <li><a href="pyFTS.models.seasonal.html#pyFTS.models.seasonal.common.DateTime.hour_of_week">hour_of_week (pyFTS.models.seasonal.common.DateTime attribute)</a>
 </li>
       <li><a href="pyFTS.models.seasonal.html#pyFTS.models.seasonal.common.DateTime.hour_of_year">hour_of_year (pyFTS.models.seasonal.common.DateTime attribute)</a>
+</li>
+      <li><a href="pyFTS.partitioners.html#pyFTS.partitioners.Huarng.HuarngPartitioner">HuarngPartitioner (class in pyFTS.partitioners.Huarng)</a>
 </li>
   </ul></td>
 </tr></table>
@@ -587,10 +1275,16 @@
 <table style="width: 100%" class="indextable genindextable"><tr>
   <td style="width: 33%; vertical-align: top;"><ul>
       <li><a href="pyFTS.partitioners.html#pyFTS.partitioners.SubClust.imax">imax() (in module pyFTS.partitioners.SubClust)</a>
+</li>
+      <li><a href="pyFTS.models.html#pyFTS.models.ismailefendi.ImprovedWeightedFLRG">ImprovedWeightedFLRG (class in pyFTS.models.ismailefendi)</a>
+</li>
+      <li><a href="pyFTS.models.html#pyFTS.models.ismailefendi.ImprovedWeightedFTS">ImprovedWeightedFTS (class in pyFTS.models.ismailefendi)</a>
 </li>
       <li><a href="pyFTS.data.html#pyFTS.data.artificial.SignalEmulator.incremental_gaussian">incremental_gaussian() (pyFTS.data.artificial.SignalEmulator method)</a>
 </li>
       <li><a href="pyFTS.models.multivariate.html#pyFTS.models.multivariate.grid.IncrementalGridCluster.incremental_search">incremental_search() (pyFTS.models.multivariate.grid.IncrementalGridCluster method)</a>
+</li>
+      <li><a href="pyFTS.models.incremental.html#pyFTS.models.incremental.IncrementalEnsemble.IncrementalEnsembleFTS">IncrementalEnsembleFTS (class in pyFTS.models.incremental.IncrementalEnsemble)</a>
 </li>
       <li><a href="pyFTS.models.multivariate.html#pyFTS.models.multivariate.grid.IncrementalGridCluster">IncrementalGridCluster (class in pyFTS.models.multivariate.grid)</a>
 </li>
@@ -598,19 +1292,51 @@
 </li>
       <li><a href="pyFTS.common.html#pyFTS.common.SortedCollection.SortedCollection.index">index() (pyFTS.common.SortedCollection.SortedCollection method)</a>
 </li>
-  </ul></td>
-  <td style="width: 33%; vertical-align: top;"><ul>
       <li><a href="pyFTS.common.html#pyFTS.common.FLR.IndexedFLR">IndexedFLR (class in pyFTS.common.FLR)</a>
+</li>
+      <li><a href="pyFTS.common.html#pyFTS.common.fts.FTS.indexer">indexer (pyFTS.common.fts.FTS attribute)</a>
+</li>
+      <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.BSTS.ARIMA.inference">inference() (pyFTS.benchmarks.BSTS.ARIMA method)</a>
 </li>
       <li><a href="pyFTS.partitioners.html#pyFTS.partitioners.Entropy.informationGain">informationGain() (in module pyFTS.partitioners.Entropy)</a>
+</li>
+      <li><a href="pyFTS.hyperparam.html#pyFTS.hyperparam.Evolutionary.initial_population">initial_population() (in module pyFTS.hyperparam.Evolutionary)</a>
 </li>
       <li><a href="pyFTS.common.html#pyFTS.common.SortedCollection.SortedCollection.insert">insert() (pyFTS.common.SortedCollection.SortedCollection method)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.Util.insert_benchmark">insert_benchmark() (in module pyFTS.benchmarks.Util)</a>
 </li>
       <li><a href="pyFTS.hyperparam.html#pyFTS.hyperparam.Util.insert_hyperparam">insert_hyperparam() (in module pyFTS.hyperparam.Util)</a>
 </li>
       <li><a href="pyFTS.common.html#pyFTS.common.SortedCollection.SortedCollection.insert_right">insert_right() (pyFTS.common.SortedCollection.SortedCollection method)</a>
 </li>
       <li><a href="pyFTS.common.html#pyFTS.common.SortedCollection.SortedCollection.inside">inside() (pyFTS.common.SortedCollection.SortedCollection method)</a>
+</li>
+      <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.Util.interval_dataframe_analytic_columns">interval_dataframe_analytic_columns() (in module pyFTS.benchmarks.Util)</a>
+</li>
+      <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.Util.interval_dataframe_synthetic_columns">interval_dataframe_synthetic_columns() (in module pyFTS.benchmarks.Util)</a>
+</li>
+      <li><a href="pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFTS.interval_heuristic">interval_heuristic() (pyFTS.models.pwfts.ProbabilisticWeightedFTS method)</a>
+</li>
+      <li><a href="pyFTS.models.ensemble.html#pyFTS.models.ensemble.ensemble.EnsembleFTS.interval_method">interval_method (pyFTS.models.ensemble.ensemble.EnsembleFTS attribute)</a>
+</li>
+      <li><a href="pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFTS.interval_quantile">interval_quantile() (pyFTS.models.pwfts.ProbabilisticWeightedFTS method)</a>
+</li>
+      <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.quantreg.QuantileRegression.interval_to_interval">interval_to_interval() (pyFTS.benchmarks.quantreg.QuantileRegression method)</a>
+</li>
+      <li><a href="pyFTS.models.html#pyFTS.models.ifts.IntervalFTS">IntervalFTS (class in pyFTS.models.ifts)</a>
+</li>
+      <li><a href="pyFTS.common.html#pyFTS.common.fts.FTS.is_clustered">is_clustered (pyFTS.common.fts.FTS attribute)</a>
+</li>
+      <li><a href="pyFTS.common.html#pyFTS.common.fts.FTS.is_high_order">is_high_order (pyFTS.common.fts.FTS attribute)</a>
+</li>
+      <li><a href="pyFTS.common.html#pyFTS.common.fts.FTS.is_multivariate">is_multivariate (pyFTS.common.fts.FTS attribute)</a>
+</li>
+      <li><a href="pyFTS.common.html#pyFTS.common.fts.FTS.is_time_variant">is_time_variant (pyFTS.common.fts.FTS attribute)</a>
+</li>
+      <li><a href="pyFTS.common.html#pyFTS.common.fts.FTS.is_wrapper">is_wrapper (pyFTS.common.fts.FTS attribute)</a>
 </li>
   </ul></td>
 </tr></table>
@@ -619,10 +1345,22 @@
 <table style="width: 100%" class="indextable genindextable"><tr>
   <td style="width: 33%; vertical-align: top;"><ul>
       <li><a href="pyFTS.partitioners.html#pyFTS.partitioners.partitioner.Partitioner.kdtree">kdtree (pyFTS.partitioners.partitioner.Partitioner attribute)</a>
+</li>
+      <li><a href="pyFTS.probabilistic.html#pyFTS.probabilistic.kde.KernelSmoothing.kernel">kernel (pyFTS.probabilistic.kde.KernelSmoothing attribute)</a>
+</li>
+      <li><a href="pyFTS.probabilistic.html#pyFTS.probabilistic.kde.KernelSmoothing.kernel_function">kernel_function() (pyFTS.probabilistic.kde.KernelSmoothing method)</a>
+</li>
+      <li><a href="pyFTS.probabilistic.html#pyFTS.probabilistic.kde.KernelSmoothing">KernelSmoothing (class in pyFTS.probabilistic.kde)</a>
 </li>
   </ul></td>
   <td style="width: 33%; vertical-align: top;"><ul>
       <li><a href="pyFTS.common.html#pyFTS.common.SortedCollection.SortedCollection.key">key() (pyFTS.common.SortedCollection.SortedCollection property)</a>
+</li>
+      <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.knn.KNearestNeighbors">KNearestNeighbors (class in pyFTS.benchmarks.knn)</a>
+</li>
+      <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.knn.KNearestNeighbors.knn">knn() (pyFTS.benchmarks.knn.KNearestNeighbors method)</a>
+</li>
+      <li><a href="pyFTS.probabilistic.html#pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution.kullbackleiblerdivergence">kullbackleiblerdivergence() (pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution method)</a>
 </li>
   </ul></td>
 </tr></table>
@@ -630,21 +1368,47 @@
 <h2 id="L">L</h2>
 <table style="width: 100%" class="indextable genindextable"><tr>
   <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="pyFTS.probabilistic.html#pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution.labels">labels (pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution attribute)</a>
+</li>
+      <li><a href="pyFTS.hyperparam.html#pyFTS.hyperparam.Evolutionary.lag_crossover2">lag_crossover2() (in module pyFTS.hyperparam.Evolutionary)</a>
+</li>
+      <li><a href="pyFTS.common.html#pyFTS.common.fts.FTS.lags">lags (pyFTS.common.fts.FTS attribute)</a>
+</li>
+      <li><a href="pyFTS.common.html#pyFTS.common.fts.FTS.len_total">len_total() (pyFTS.common.fts.FTS method)</a>
+</li>
       <li><a href="pyFTS.common.html#pyFTS.common.FLR.FLR.LHS">LHS (pyFTS.common.FLR.FLR attribute)</a>
 
       <ul>
         <li><a href="pyFTS.common.html#pyFTS.common.flrg.FLRG.LHS">(pyFTS.common.flrg.FLRG attribute)</a>
 </li>
       </ul></li>
+      <li><a href="pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFLRG.lhs_conditional_probability">lhs_conditional_probability() (pyFTS.models.pwfts.ProbabilisticWeightedFLRG method)</a>
+</li>
+      <li><a href="pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFLRG.lhs_conditional_probability_fuzzyfied">lhs_conditional_probability_fuzzyfied() (pyFTS.models.pwfts.ProbabilisticWeightedFLRG method)</a>
+</li>
       <li><a href="pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.perturbation.linear">linear() (in module pyFTS.models.nonstationary.perturbation)</a>
 </li>
-      <li><a href="pyFTS.models.seasonal.html#pyFTS.models.seasonal.SeasonalIndexer.LinearSeasonalIndexer">LinearSeasonalIndexer (class in pyFTS.models.seasonal.SeasonalIndexer)</a>
+      <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.quantreg.QuantileRegression.linearmodel">linearmodel() (pyFTS.benchmarks.quantreg.QuantileRegression method)</a>
 </li>
   </ul></td>
   <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="pyFTS.models.seasonal.html#pyFTS.models.seasonal.SeasonalIndexer.LinearSeasonalIndexer">LinearSeasonalIndexer (class in pyFTS.models.seasonal.SeasonalIndexer)</a>
+</li>
+      <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.ResidualAnalysis.ljung_box_test">ljung_box_test() (in module pyFTS.benchmarks.ResidualAnalysis)</a>
+</li>
+      <li><a href="pyFTS.common.html#pyFTS.common.Util.load_env">load_env() (in module pyFTS.common.Util)</a>
+</li>
+      <li><a href="pyFTS.common.html#pyFTS.common.Util.load_obj">load_obj() (in module pyFTS.common.Util)</a>
+</li>
       <li><a href="pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.common.FuzzySet.location">location (pyFTS.models.nonstationary.common.FuzzySet attribute)</a>
 </li>
       <li><a href="pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.common.FuzzySet.location_params">location_params (pyFTS.models.nonstationary.common.FuzzySet attribute)</a>
+</li>
+      <li><a href="pyFTS.common.html#pyFTS.common.fts.FTS.log">log (pyFTS.common.fts.FTS attribute)</a>
+</li>
+      <li><a href="pyFTS.hyperparam.html#pyFTS.hyperparam.Evolutionary.log_result">log_result() (in module pyFTS.hyperparam.Evolutionary)</a>
+</li>
+      <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.Measures.logarithm_score">logarithm_score() (in module pyFTS.benchmarks.Measures)</a>
 </li>
       <li><a href="pyFTS.partitioners.html#pyFTS.partitioners.partitioner.Partitioner.lower_margin">lower_margin (pyFTS.partitioners.partitioner.Partitioner attribute)</a>
 </li>
@@ -656,9 +1420,21 @@
 <h2 id="M">M</h2>
 <table style="width: 100%" class="indextable genindextable"><tr>
   <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.arima.ARIMA.ma">ma() (pyFTS.benchmarks.arima.ARIMA method)</a>
+</li>
+      <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.Measures.mape">mape() (in module pyFTS.benchmarks.Measures)</a>
+</li>
+      <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.Measures.mape_interval">mape_interval() (in module pyFTS.benchmarks.Measures)</a>
+</li>
       <li><a href="pyFTS.partitioners.html#pyFTS.partitioners.partitioner.Partitioner.margin">margin (pyFTS.partitioners.partitioner.Partitioner attribute)</a>
 </li>
-      <li><a href="pyFTS.models.seasonal.html#pyFTS.models.seasonal.partitioner.TimeGridPartitioner.mask">mask (pyFTS.models.seasonal.partitioner.TimeGridPartitioner attribute)</a>
+      <li><a href="pyFTS.models.multivariate.html#pyFTS.models.multivariate.variable.Variable.mask">mask (pyFTS.models.multivariate.variable.Variable attribute)</a>
+
+      <ul>
+        <li><a href="pyFTS.models.seasonal.html#pyFTS.models.seasonal.partitioner.TimeGridPartitioner.mask">(pyFTS.models.seasonal.partitioner.TimeGridPartitioner attribute)</a>
+</li>
+      </ul></li>
+      <li><a href="pyFTS.common.html#pyFTS.common.fts.FTS.max_lag">max_lag (pyFTS.common.fts.FTS attribute)</a>
 </li>
       <li><a href="pyFTS.partitioners.html#pyFTS.partitioners.FCM.membership">membership() (in module pyFTS.partitioners.FCM)</a>
 
@@ -673,8 +1449,14 @@
 </li>
       </ul></li>
       <li><a href="pyFTS.partitioners.html#pyFTS.partitioners.partitioner.Partitioner.membership_function">membership_function (pyFTS.partitioners.partitioner.Partitioner attribute)</a>
+</li>
+      <li><a href="pyFTS.common.html#pyFTS.common.fts.FTS.merge">merge() (pyFTS.common.fts.FTS method)</a>
+</li>
+      <li><a href="pyFTS.models.ensemble.html#pyFTS.models.ensemble.ensemble.SimpleEnsembleFTS.method">method (pyFTS.models.ensemble.ensemble.SimpleEnsembleFTS attribute)</a>
 </li>
       <li><a href="pyFTS.common.html#pyFTS.common.FuzzySet.FuzzySet.mf">mf (pyFTS.common.FuzzySet.FuzzySet attribute)</a>
+</li>
+      <li><a href="pyFTS.common.html#pyFTS.common.fts.FTS.min_order">min_order (pyFTS.common.fts.FTS attribute)</a>
 </li>
       <li><a href="pyFTS.models.seasonal.html#pyFTS.models.seasonal.common.DateTime.minute">minute (pyFTS.models.seasonal.common.DateTime attribute)</a>
 </li>
@@ -687,6 +1469,16 @@
       <li><a href="pyFTS.models.seasonal.html#pyFTS.models.seasonal.common.DateTime.minute_of_week">minute_of_week (pyFTS.models.seasonal.common.DateTime attribute)</a>
 </li>
       <li><a href="pyFTS.models.seasonal.html#pyFTS.models.seasonal.common.DateTime.minute_of_year">minute_of_year (pyFTS.models.seasonal.common.DateTime attribute)</a>
+</li>
+      <li><a href="pyFTS.models.incremental.html#pyFTS.models.incremental.TimeVariant.Retrainer.model">model (pyFTS.models.incremental.TimeVariant.Retrainer attribute)</a>
+
+      <ul>
+        <li><a href="pyFTS.models.multivariate.html#pyFTS.models.multivariate.cmvfts.ClusteredMVFTS.model">(pyFTS.models.multivariate.cmvfts.ClusteredMVFTS attribute)</a>
+</li>
+        <li><a href="pyFTS.models.multivariate.html#pyFTS.models.multivariate.granular.GranularWMVFTS.model">(pyFTS.models.multivariate.granular.GranularWMVFTS attribute)</a>
+</li>
+      </ul></li>
+      <li><a href="pyFTS.models.ensemble.html#pyFTS.models.ensemble.ensemble.EnsembleFTS.models">models (pyFTS.models.ensemble.ensemble.EnsembleFTS attribute)</a>
 </li>
       <li>
     module
@@ -695,6 +1487,26 @@
         <li><a href="pyFTS.html#module-pyFTS">pyFTS</a>
 </li>
         <li><a href="pyFTS.benchmarks.html#module-pyFTS.benchmarks">pyFTS.benchmarks</a>
+</li>
+        <li><a href="pyFTS.benchmarks.html#module-pyFTS.benchmarks.arima">pyFTS.benchmarks.arima</a>
+</li>
+        <li><a href="pyFTS.benchmarks.html#module-pyFTS.benchmarks.benchmarks">pyFTS.benchmarks.benchmarks</a>
+</li>
+        <li><a href="pyFTS.benchmarks.html#module-pyFTS.benchmarks.BSTS">pyFTS.benchmarks.BSTS</a>
+</li>
+        <li><a href="pyFTS.benchmarks.html#module-pyFTS.benchmarks.knn">pyFTS.benchmarks.knn</a>
+</li>
+        <li><a href="pyFTS.benchmarks.html#module-pyFTS.benchmarks.Measures">pyFTS.benchmarks.Measures</a>
+</li>
+        <li><a href="pyFTS.benchmarks.html#module-pyFTS.benchmarks.naive">pyFTS.benchmarks.naive</a>
+</li>
+        <li><a href="pyFTS.benchmarks.html#module-pyFTS.benchmarks.quantreg">pyFTS.benchmarks.quantreg</a>
+</li>
+        <li><a href="pyFTS.benchmarks.html#module-pyFTS.benchmarks.ResidualAnalysis">pyFTS.benchmarks.ResidualAnalysis</a>
+</li>
+        <li><a href="pyFTS.benchmarks.html#module-pyFTS.benchmarks.Tests">pyFTS.benchmarks.Tests</a>
+</li>
+        <li><a href="pyFTS.benchmarks.html#module-pyFTS.benchmarks.Util">pyFTS.benchmarks.Util</a>
 </li>
         <li><a href="pyFTS.common.html#module-pyFTS.common">pyFTS.common</a>
 </li>
@@ -703,6 +1515,8 @@
         <li><a href="pyFTS.common.html#module-pyFTS.common.FLR">pyFTS.common.FLR</a>
 </li>
         <li><a href="pyFTS.common.html#module-pyFTS.common.flrg">pyFTS.common.flrg</a>
+</li>
+        <li><a href="pyFTS.common.html#module-pyFTS.common.fts">pyFTS.common.fts</a>
 </li>
         <li><a href="pyFTS.common.html#module-pyFTS.common.FuzzySet">pyFTS.common.FuzzySet</a>
 </li>
@@ -711,6 +1525,8 @@
         <li><a href="pyFTS.common.html#module-pyFTS.common.SortedCollection">pyFTS.common.SortedCollection</a>
 </li>
         <li><a href="pyFTS.common.html#module-pyFTS.common.tree">pyFTS.common.tree</a>
+</li>
+        <li><a href="pyFTS.common.html#module-pyFTS.common.Util">pyFTS.common.Util</a>
 </li>
         <li><a href="pyFTS.html#module-pyFTS.conf">pyFTS.conf</a>
 </li>
@@ -761,46 +1577,104 @@
         <li><a href="pyFTS.data.html#module-pyFTS.data.TAIEX">pyFTS.data.TAIEX</a>
 </li>
         <li><a href="pyFTS.distributed.html#module-pyFTS.distributed">pyFTS.distributed</a>
+</li>
+        <li><a href="pyFTS.distributed.html#module-pyFTS.distributed.dispy">pyFTS.distributed.dispy</a>
 </li>
         <li><a href="pyFTS.hyperparam.html#module-pyFTS.hyperparam">pyFTS.hyperparam</a>
+</li>
+        <li><a href="pyFTS.hyperparam.html#module-pyFTS.hyperparam.Evolutionary">pyFTS.hyperparam.Evolutionary</a>
+</li>
+        <li><a href="pyFTS.hyperparam.html#module-pyFTS.hyperparam.GridSearch">pyFTS.hyperparam.GridSearch</a>
 </li>
         <li><a href="pyFTS.hyperparam.html#module-pyFTS.hyperparam.Util">pyFTS.hyperparam.Util</a>
 </li>
         <li><a href="pyFTS.models.html#module-pyFTS.models">pyFTS.models</a>
+</li>
+        <li><a href="pyFTS.models.html#module-pyFTS.models.chen">pyFTS.models.chen</a>
+</li>
+        <li><a href="pyFTS.models.html#module-pyFTS.models.cheng">pyFTS.models.cheng</a>
 </li>
         <li><a href="pyFTS.models.ensemble.html#module-pyFTS.models.ensemble">pyFTS.models.ensemble</a>
+</li>
+        <li><a href="pyFTS.models.ensemble.html#module-pyFTS.models.ensemble.ensemble">pyFTS.models.ensemble.ensemble</a>
+</li>
+        <li><a href="pyFTS.models.ensemble.html#module-pyFTS.models.ensemble.multiseasonal">pyFTS.models.ensemble.multiseasonal</a>
+</li>
+        <li><a href="pyFTS.models.html#module-pyFTS.models.hofts">pyFTS.models.hofts</a>
+</li>
+        <li><a href="pyFTS.models.html#module-pyFTS.models.hwang">pyFTS.models.hwang</a>
+</li>
+        <li><a href="pyFTS.models.html#module-pyFTS.models.ifts">pyFTS.models.ifts</a>
 </li>
         <li><a href="pyFTS.models.incremental.html#module-pyFTS.models.incremental">pyFTS.models.incremental</a>
+</li>
+        <li><a href="pyFTS.models.incremental.html#module-pyFTS.models.incremental.IncrementalEnsemble">pyFTS.models.incremental.IncrementalEnsemble</a>
+</li>
+        <li><a href="pyFTS.models.incremental.html#module-pyFTS.models.incremental.TimeVariant">pyFTS.models.incremental.TimeVariant</a>
+</li>
+        <li><a href="pyFTS.models.html#module-pyFTS.models.ismailefendi">pyFTS.models.ismailefendi</a>
 </li>
         <li><a href="pyFTS.models.multivariate.html#module-pyFTS.models.multivariate">pyFTS.models.multivariate</a>
+</li>
+        <li><a href="pyFTS.models.multivariate.html#module-pyFTS.models.multivariate.cmvfts">pyFTS.models.multivariate.cmvfts</a>
 </li>
         <li><a href="pyFTS.models.multivariate.html#module-pyFTS.models.multivariate.common">pyFTS.models.multivariate.common</a>
 </li>
         <li><a href="pyFTS.models.multivariate.html#module-pyFTS.models.multivariate.FLR">pyFTS.models.multivariate.FLR</a>
 </li>
         <li><a href="pyFTS.models.multivariate.html#module-pyFTS.models.multivariate.flrg">pyFTS.models.multivariate.flrg</a>
+</li>
+        <li><a href="pyFTS.models.multivariate.html#module-pyFTS.models.multivariate.granular">pyFTS.models.multivariate.granular</a>
 </li>
         <li><a href="pyFTS.models.multivariate.html#module-pyFTS.models.multivariate.grid">pyFTS.models.multivariate.grid</a>
+</li>
+        <li><a href="pyFTS.models.multivariate.html#module-pyFTS.models.multivariate.mvfts">pyFTS.models.multivariate.mvfts</a>
 </li>
         <li><a href="pyFTS.models.multivariate.html#module-pyFTS.models.multivariate.partitioner">pyFTS.models.multivariate.partitioner</a>
+</li>
+        <li><a href="pyFTS.models.multivariate.html#module-pyFTS.models.multivariate.variable">pyFTS.models.multivariate.variable</a>
+</li>
+        <li><a href="pyFTS.models.multivariate.html#module-pyFTS.models.multivariate.wmvfts">pyFTS.models.multivariate.wmvfts</a>
 </li>
         <li><a href="pyFTS.models.nonstationary.html#module-pyFTS.models.nonstationary">pyFTS.models.nonstationary</a>
 </li>
         <li><a href="pyFTS.models.nonstationary.html#module-pyFTS.models.nonstationary.common">pyFTS.models.nonstationary.common</a>
+</li>
+        <li><a href="pyFTS.models.nonstationary.html#module-pyFTS.models.nonstationary.cvfts">pyFTS.models.nonstationary.cvfts</a>
 </li>
         <li><a href="pyFTS.models.nonstationary.html#module-pyFTS.models.nonstationary.flrg">pyFTS.models.nonstationary.flrg</a>
+</li>
+        <li><a href="pyFTS.models.nonstationary.html#module-pyFTS.models.nonstationary.honsfts">pyFTS.models.nonstationary.honsfts</a>
+</li>
+        <li><a href="pyFTS.models.nonstationary.html#module-pyFTS.models.nonstationary.nsfts">pyFTS.models.nonstationary.nsfts</a>
 </li>
         <li><a href="pyFTS.models.nonstationary.html#module-pyFTS.models.nonstationary.partitioners">pyFTS.models.nonstationary.partitioners</a>
 </li>
         <li><a href="pyFTS.models.nonstationary.html#module-pyFTS.models.nonstationary.perturbation">pyFTS.models.nonstationary.perturbation</a>
+</li>
+        <li><a href="pyFTS.models.nonstationary.html#module-pyFTS.models.nonstationary.util">pyFTS.models.nonstationary.util</a>
+</li>
+        <li><a href="pyFTS.models.html#module-pyFTS.models.pwfts">pyFTS.models.pwfts</a>
+</li>
+        <li><a href="pyFTS.models.html#module-pyFTS.models.sadaei">pyFTS.models.sadaei</a>
 </li>
         <li><a href="pyFTS.models.seasonal.html#module-pyFTS.models.seasonal">pyFTS.models.seasonal</a>
+</li>
+        <li><a href="pyFTS.models.seasonal.html#module-pyFTS.models.seasonal.cmsfts">pyFTS.models.seasonal.cmsfts</a>
 </li>
         <li><a href="pyFTS.models.seasonal.html#module-pyFTS.models.seasonal.common">pyFTS.models.seasonal.common</a>
+</li>
+        <li><a href="pyFTS.models.seasonal.html#module-pyFTS.models.seasonal.msfts">pyFTS.models.seasonal.msfts</a>
 </li>
         <li><a href="pyFTS.models.seasonal.html#module-pyFTS.models.seasonal.partitioner">pyFTS.models.seasonal.partitioner</a>
 </li>
         <li><a href="pyFTS.models.seasonal.html#module-pyFTS.models.seasonal.SeasonalIndexer">pyFTS.models.seasonal.SeasonalIndexer</a>
+</li>
+        <li><a href="pyFTS.models.seasonal.html#module-pyFTS.models.seasonal.sfts">pyFTS.models.seasonal.sfts</a>
+</li>
+        <li><a href="pyFTS.models.html#module-pyFTS.models.song">pyFTS.models.song</a>
+</li>
+        <li><a href="pyFTS.models.html#module-pyFTS.models.yu">pyFTS.models.yu</a>
 </li>
         <li><a href="pyFTS.partitioners.html#module-pyFTS.partitioners">pyFTS.partitioners</a>
 </li>
@@ -811,6 +1685,10 @@
         <li><a href="pyFTS.partitioners.html#module-pyFTS.partitioners.FCM">pyFTS.partitioners.FCM</a>
 </li>
         <li><a href="pyFTS.partitioners.html#module-pyFTS.partitioners.Grid">pyFTS.partitioners.Grid</a>
+</li>
+        <li><a href="pyFTS.partitioners.html#module-pyFTS.partitioners.Huarng">pyFTS.partitioners.Huarng</a>
+</li>
+        <li><a href="pyFTS.partitioners.html#module-pyFTS.partitioners.parallel_util">pyFTS.partitioners.parallel_util</a>
 </li>
         <li><a href="pyFTS.partitioners.html#module-pyFTS.partitioners.partitioner">pyFTS.partitioners.partitioner</a>
 </li>
@@ -819,17 +1697,39 @@
         <li><a href="pyFTS.partitioners.html#module-pyFTS.partitioners.Singleton">pyFTS.partitioners.Singleton</a>
 </li>
         <li><a href="pyFTS.partitioners.html#module-pyFTS.partitioners.SubClust">pyFTS.partitioners.SubClust</a>
+</li>
+        <li><a href="pyFTS.partitioners.html#module-pyFTS.partitioners.Util">pyFTS.partitioners.Util</a>
 </li>
         <li><a href="pyFTS.probabilistic.html#module-pyFTS.probabilistic">pyFTS.probabilistic</a>
+</li>
+        <li><a href="pyFTS.probabilistic.html#module-pyFTS.probabilistic.kde">pyFTS.probabilistic.kde</a>
+</li>
+        <li><a href="pyFTS.probabilistic.html#module-pyFTS.probabilistic.ProbabilityDistribution">pyFTS.probabilistic.ProbabilityDistribution</a>
 </li>
       </ul></li>
   </ul></td>
   <td style="width: 33%; vertical-align: top;"><ul>
       <li><a href="pyFTS.models.seasonal.html#pyFTS.models.seasonal.common.DateTime.month">month (pyFTS.models.seasonal.common.DateTime attribute)</a>
+</li>
+      <li><a href="pyFTS.models.seasonal.html#pyFTS.models.seasonal.msfts.MultiSeasonalFTS">MultiSeasonalFTS (class in pyFTS.models.seasonal.msfts)</a>
+</li>
+      <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.benchmarks.multivariate_sliding_window_benchmarks2">multivariate_sliding_window_benchmarks2() (in module pyFTS.benchmarks.benchmarks)</a>
 </li>
       <li><a href="pyFTS.models.multivariate.html#pyFTS.models.multivariate.common.MultivariateFuzzySet">MultivariateFuzzySet (class in pyFTS.models.multivariate.common)</a>
 </li>
       <li><a href="pyFTS.models.multivariate.html#pyFTS.models.multivariate.partitioner.MultivariatePartitioner">MultivariatePartitioner (class in pyFTS.models.multivariate.partitioner)</a>
+</li>
+      <li><a href="pyFTS.hyperparam.html#pyFTS.hyperparam.Evolutionary.mutation">mutation() (in module pyFTS.hyperparam.Evolutionary)</a>
+</li>
+      <li><a href="pyFTS.hyperparam.html#pyFTS.hyperparam.Evolutionary.mutation_lags">mutation_lags() (in module pyFTS.hyperparam.Evolutionary)</a>
+</li>
+      <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.benchmarks.mv_run_interval2">mv_run_interval2() (in module pyFTS.benchmarks.benchmarks)</a>
+</li>
+      <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.benchmarks.mv_run_point2">mv_run_point2() (in module pyFTS.benchmarks.benchmarks)</a>
+</li>
+      <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.benchmarks.mv_run_probabilistic2">mv_run_probabilistic2() (in module pyFTS.benchmarks.benchmarks)</a>
+</li>
+      <li><a href="pyFTS.models.multivariate.html#pyFTS.models.multivariate.mvfts.MVFTS">MVFTS (class in pyFTS.models.multivariate.mvfts)</a>
 </li>
   </ul></td>
 </tr></table>
@@ -837,9 +1737,15 @@
 <h2 id="N">N</h2>
 <table style="width: 100%" class="indextable genindextable"><tr>
   <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="pyFTS.common.html#pyFTS.common.FuzzySet.FuzzySet.name">name (pyFTS.common.FuzzySet.FuzzySet attribute)</a>
+      <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.naive.Naive">Naive (class in pyFTS.benchmarks.naive)</a>
+</li>
+      <li><a href="pyFTS.common.html#pyFTS.common.fts.FTS.name">name (pyFTS.common.fts.FTS attribute)</a>
 
       <ul>
+        <li><a href="pyFTS.common.html#pyFTS.common.FuzzySet.FuzzySet.name">(pyFTS.common.FuzzySet.FuzzySet attribute)</a>
+</li>
+        <li><a href="pyFTS.models.multivariate.html#pyFTS.models.multivariate.variable.Variable.name">(pyFTS.models.multivariate.variable.Variable attribute)</a>
+</li>
         <li><a href="pyFTS.partitioners.html#pyFTS.partitioners.partitioner.Partitioner.name">(pyFTS.partitioners.partitioner.Partitioner attribute)</a>
 </li>
       </ul></li>
@@ -850,6 +1756,10 @@
       <li><a href="pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.common.FuzzySet.noise_params">noise_params (pyFTS.models.nonstationary.common.FuzzySet attribute)</a>
 </li>
       <li><a href="pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.flrg.NonStationaryFLRG">NonStationaryFLRG (class in pyFTS.models.nonstationary.flrg)</a>
+</li>
+      <li><a href="pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.nsfts.NonStationaryFTS">NonStationaryFTS (class in pyFTS.models.nonstationary.nsfts)</a>
+</li>
+      <li><a href="pyFTS.models.incremental.html#pyFTS.models.incremental.IncrementalEnsemble.IncrementalEnsembleFTS.num_models">num_models (pyFTS.models.incremental.IncrementalEnsemble.IncrementalEnsembleFTS attribute)</a>
 </li>
   </ul></td>
 </tr></table>
@@ -857,13 +1767,35 @@
 <h2 id="O">O</h2>
 <table style="width: 100%" class="indextable genindextable"><tr>
   <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="pyFTS.common.html#pyFTS.common.fts.FTS.offset">offset() (pyFTS.common.fts.FTS method)</a>
+
+      <ul>
+        <li><a href="pyFTS.models.incremental.html#pyFTS.models.incremental.IncrementalEnsemble.IncrementalEnsembleFTS.offset">(pyFTS.models.incremental.IncrementalEnsemble.IncrementalEnsembleFTS method)</a>
+</li>
+        <li><a href="pyFTS.models.incremental.html#pyFTS.models.incremental.TimeVariant.Retrainer.offset">(pyFTS.models.incremental.TimeVariant.Retrainer method)</a>
+</li>
+      </ul></li>
+      <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.Util.open_benchmark_db">open_benchmark_db() (in module pyFTS.benchmarks.Util)</a>
+</li>
       <li><a href="pyFTS.hyperparam.html#pyFTS.hyperparam.Util.open_hyperparam_db">open_hyperparam_db() (in module pyFTS.hyperparam.Util)</a>
+</li>
+      <li><a href="pyFTS.models.html#pyFTS.models.song.ConventionalFTS.operation_matrix">operation_matrix() (pyFTS.models.song.ConventionalFTS method)</a>
 </li>
   </ul></td>
   <td style="width: 33%; vertical-align: top;"><ul>
       <li><a href="pyFTS.common.html#pyFTS.common.flrg.FLRG.order">order (pyFTS.common.flrg.FLRG attribute)</a>
+
+      <ul>
+        <li><a href="pyFTS.common.html#pyFTS.common.fts.FTS.order">(pyFTS.common.fts.FTS attribute)</a>
 </li>
+      </ul></li>
       <li><a href="pyFTS.partitioners.html#pyFTS.partitioners.partitioner.Partitioner.ordered_sets">ordered_sets (pyFTS.partitioners.partitioner.Partitioner attribute)</a>
+</li>
+      <li><a href="pyFTS.models.ensemble.html#pyFTS.models.ensemble.ensemble.SimpleEnsembleFTS.orders">orders (pyFTS.models.ensemble.ensemble.SimpleEnsembleFTS attribute)</a>
+</li>
+      <li><a href="pyFTS.common.html#pyFTS.common.fts.FTS.original_max">original_max (pyFTS.common.fts.FTS attribute)</a>
+</li>
+      <li><a href="pyFTS.common.html#pyFTS.common.fts.FTS.original_min">original_min (pyFTS.common.fts.FTS attribute)</a>
 </li>
   </ul></td>
 </tr></table>
@@ -872,13 +1804,47 @@
 <table style="width: 100%" class="indextable genindextable"><tr>
   <td style="width: 33%; vertical-align: top;"><ul>
       <li><a href="pyFTS.common.html#pyFTS.common.FuzzySet.FuzzySet.parameters">parameters (pyFTS.common.FuzzySet.FuzzySet attribute)</a>
+
+      <ul>
+        <li><a href="pyFTS.models.ensemble.html#pyFTS.models.ensemble.ensemble.EnsembleFTS.parameters">(pyFTS.models.ensemble.ensemble.EnsembleFTS attribute)</a>
 </li>
+      </ul></li>
       <li><a href="pyFTS.common.html#pyFTS.common.FuzzySet.FuzzySet.partition_function">partition_function() (pyFTS.common.FuzzySet.FuzzySet method)</a>
+
+      <ul>
+        <li><a href="pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFLRG.partition_function">(pyFTS.models.pwfts.ProbabilisticWeightedFLRG method)</a>
 </li>
+      </ul></li>
       <li><a href="pyFTS.partitioners.html#pyFTS.partitioners.partitioner.Partitioner">Partitioner (class in pyFTS.partitioners.partitioner)</a>
 </li>
-      <li><a href="pyFTS.partitioners.html#pyFTS.partitioners.partitioner.Partitioner.partitions">partitions (pyFTS.partitioners.partitioner.Partitioner attribute)</a>
+      <li><a href="pyFTS.common.html#pyFTS.common.fts.FTS.partitioner">partitioner (pyFTS.common.fts.FTS attribute)</a>
+
+      <ul>
+        <li><a href="pyFTS.models.incremental.html#pyFTS.models.incremental.TimeVariant.Retrainer.partitioner">(pyFTS.models.incremental.TimeVariant.Retrainer attribute)</a>
 </li>
+        <li><a href="pyFTS.models.multivariate.html#pyFTS.models.multivariate.variable.Variable.partitioner">(pyFTS.models.multivariate.variable.Variable attribute)</a>
+</li>
+      </ul></li>
+      <li><a href="pyFTS.models.ensemble.html#pyFTS.models.ensemble.ensemble.SimpleEnsembleFTS.partitioner_method">partitioner_method (pyFTS.models.ensemble.ensemble.SimpleEnsembleFTS attribute)</a>
+
+      <ul>
+        <li><a href="pyFTS.models.incremental.html#pyFTS.models.incremental.IncrementalEnsemble.IncrementalEnsembleFTS.partitioner_method">(pyFTS.models.incremental.IncrementalEnsemble.IncrementalEnsembleFTS attribute)</a>
+</li>
+        <li><a href="pyFTS.models.incremental.html#pyFTS.models.incremental.TimeVariant.Retrainer.partitioner_method">(pyFTS.models.incremental.TimeVariant.Retrainer attribute)</a>
+</li>
+      </ul></li>
+      <li><a href="pyFTS.models.incremental.html#pyFTS.models.incremental.IncrementalEnsemble.IncrementalEnsembleFTS.partitioner_params">partitioner_params (pyFTS.models.incremental.IncrementalEnsemble.IncrementalEnsembleFTS attribute)</a>
+
+      <ul>
+        <li><a href="pyFTS.models.incremental.html#pyFTS.models.incremental.TimeVariant.Retrainer.partitioner_params">(pyFTS.models.incremental.TimeVariant.Retrainer attribute)</a>
+</li>
+      </ul></li>
+      <li><a href="pyFTS.models.ensemble.html#pyFTS.models.ensemble.ensemble.SimpleEnsembleFTS.partitions">partitions (pyFTS.models.ensemble.ensemble.SimpleEnsembleFTS attribute)</a>
+
+      <ul>
+        <li><a href="pyFTS.partitioners.html#pyFTS.partitioners.partitioner.Partitioner.partitions">(pyFTS.partitioners.partitioner.Partitioner attribute)</a>
+</li>
+      </ul></li>
       <li><a href="pyFTS.common.html#pyFTS.common.tree.FLRGTreeNode.paths">paths() (pyFTS.common.tree.FLRGTreeNode method)</a>
 </li>
       <li><a href="pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.common.FuzzySet.perform_location">perform_location() (pyFTS.models.nonstationary.common.FuzzySet method)</a>
@@ -888,28 +1854,148 @@
       <li><a href="pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.perturbation.periodic">periodic() (in module pyFTS.models.nonstationary.perturbation)</a>
 </li>
       <li><a href="pyFTS.data.html#pyFTS.data.artificial.SignalEmulator.periodic_gaussian">periodic_gaussian() (pyFTS.data.artificial.SignalEmulator method)</a>
+</li>
+      <li><a href="pyFTS.common.html#pyFTS.common.Util.persist_env">persist_env() (in module pyFTS.common.Util)</a>
+</li>
+      <li><a href="pyFTS.common.html#pyFTS.common.Util.persist_obj">persist_obj() (in module pyFTS.common.Util)</a>
+</li>
+      <li><a href="pyFTS.hyperparam.html#pyFTS.hyperparam.Evolutionary.persist_statistics">persist_statistics() (in module pyFTS.hyperparam.Evolutionary)</a>
 </li>
       <li><a href="pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.common.FuzzySet.perturbate_parameters">perturbate_parameters() (pyFTS.models.nonstationary.common.FuzzySet method)</a>
+</li>
+      <li><a href="pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.cvfts.ConditionalVarianceFTS.perturbation_factors">perturbation_factors() (pyFTS.models.nonstationary.cvfts.ConditionalVarianceFTS method)</a>
+</li>
+      <li><a href="pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.cvfts.ConditionalVarianceFTS.perturbation_factors__old">perturbation_factors__old() (pyFTS.models.nonstationary.cvfts.ConditionalVarianceFTS method)</a>
+</li>
+      <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.benchmarks.pftsExploreOrderAndPartitions">pftsExploreOrderAndPartitions() (in module pyFTS.benchmarks.benchmarks)</a>
+</li>
+      <li><a href="pyFTS.hyperparam.html#pyFTS.hyperparam.Evolutionary.phenotype">phenotype() (in module pyFTS.hyperparam.Evolutionary)</a>
+</li>
+      <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.Measures.pinball">pinball() (in module pyFTS.benchmarks.Measures)</a>
+</li>
+      <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.Measures.pinball_mean">pinball_mean() (in module pyFTS.benchmarks.Measures)</a>
 </li>
       <li><a href="pyFTS.models.seasonal.html#pyFTS.models.seasonal.partitioner.TimeGridPartitioner.plot">plot() (pyFTS.models.seasonal.partitioner.TimeGridPartitioner method)</a>
 
       <ul>
         <li><a href="pyFTS.partitioners.html#pyFTS.partitioners.partitioner.Partitioner.plot">(pyFTS.partitioners.partitioner.Partitioner method)</a>
+</li>
+        <li><a href="pyFTS.probabilistic.html#pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution.plot">(pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution method)</a>
 </li>
       </ul></li>
+      <li><a href="pyFTS.common.html#pyFTS.common.Util.plot_compared_intervals_ahead">plot_compared_intervals_ahead() (in module pyFTS.common.Util)</a>
+</li>
+      <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.benchmarks.plot_compared_series">plot_compared_series() (in module pyFTS.benchmarks.benchmarks)</a>
+</li>
+      <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.Util.plot_dataframe_interval">plot_dataframe_interval() (in module pyFTS.benchmarks.Util)</a>
+</li>
+      <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.Util.plot_dataframe_interval_pinball">plot_dataframe_interval_pinball() (in module pyFTS.benchmarks.Util)</a>
+</li>
+      <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.Util.plot_dataframe_point">plot_dataframe_point() (in module pyFTS.benchmarks.Util)</a>
+</li>
+      <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.Util.plot_dataframe_probabilistic">plot_dataframe_probabilistic() (in module pyFTS.benchmarks.Util)</a>
+</li>
+      <li><a href="pyFTS.common.html#pyFTS.common.Util.plot_density_rectange">plot_density_rectange() (in module pyFTS.common.Util)</a>
+</li>
+      <li><a href="pyFTS.common.html#pyFTS.common.Util.plot_distribution">plot_distribution() (in module pyFTS.common.Util)</a>
+</li>
+      <li><a href="pyFTS.common.html#pyFTS.common.Util.plot_distribution2">plot_distribution2() (in module pyFTS.common.Util)</a>
+</li>
+      <li><a href="pyFTS.common.html#pyFTS.common.Util.plot_distribution_tiled">plot_distribution_tiled() (in module pyFTS.common.Util)</a>
+</li>
+      <li><a href="pyFTS.common.html#pyFTS.common.Util.plot_interval">plot_interval() (in module pyFTS.common.Util)</a>
+</li>
+      <li><a href="pyFTS.common.html#pyFTS.common.Util.plot_interval2">plot_interval2() (in module pyFTS.common.Util)</a>
+</li>
+      <li><a href="pyFTS.partitioners.html#pyFTS.partitioners.Util.plot_partitioners">plot_partitioners() (in module pyFTS.partitioners.Util)</a>
+</li>
+      <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.benchmarks.plot_point">plot_point() (in module pyFTS.benchmarks.benchmarks)</a>
+</li>
+      <li><a href="pyFTS.common.html#pyFTS.common.Util.plot_probability_distributions">plot_probability_distributions() (in module pyFTS.common.Util)</a>
+</li>
+      <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.ResidualAnalysis.plot_residuals_by_model">plot_residuals_by_model() (in module pyFTS.benchmarks.ResidualAnalysis)</a>
+</li>
+      <li><a href="pyFTS.common.html#pyFTS.common.Util.plot_rules">plot_rules() (in module pyFTS.common.Util)</a>
+</li>
       <li><a href="pyFTS.partitioners.html#pyFTS.partitioners.partitioner.Partitioner.plot_set">plot_set() (pyFTS.partitioners.partitioner.Partitioner method)</a>
+</li>
+      <li><a href="pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.util.plot_sets">plot_sets() (in module pyFTS.models.nonstationary.util)</a>
+
+      <ul>
+        <li><a href="pyFTS.partitioners.html#pyFTS.partitioners.Util.plot_sets">(in module pyFTS.partitioners.Util)</a>
+</li>
+      </ul></li>
+      <li><a href="pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.util.plot_sets_conditional">plot_sets_conditional() (in module pyFTS.models.nonstationary.util)</a>
+</li>
+      <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.benchmarks.plotCompared">plotCompared() (in module pyFTS.benchmarks.benchmarks)</a>
 </li>
       <li><a href="pyFTS.partitioners.html#pyFTS.partitioners.Entropy.PMF">PMF() (in module pyFTS.partitioners.Entropy)</a>
+</li>
+      <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.Util.point_dataframe_analytic_columns">point_dataframe_analytic_columns() (in module pyFTS.benchmarks.Util)</a>
+</li>
+      <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.Util.point_dataframe_synthetic_columns">point_dataframe_synthetic_columns() (in module pyFTS.benchmarks.Util)</a>
+</li>
+      <li><a href="pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFTS.point_expected_value">point_expected_value() (pyFTS.models.pwfts.ProbabilisticWeightedFTS method)</a>
+</li>
+      <li><a href="pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFTS.point_heuristic">point_heuristic() (pyFTS.models.pwfts.ProbabilisticWeightedFTS method)</a>
+</li>
+      <li><a href="pyFTS.models.ensemble.html#pyFTS.models.ensemble.ensemble.EnsembleFTS.point_method">point_method (pyFTS.models.ensemble.ensemble.EnsembleFTS attribute)</a>
+</li>
+      <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.quantreg.QuantileRegression.point_to_interval">point_to_interval() (pyFTS.benchmarks.quantreg.QuantileRegression method)</a>
 </li>
       <li><a href="pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.partitioners.PolynomialNonStationaryPartitioner.poly_width">poly_width() (pyFTS.models.nonstationary.partitioners.PolynomialNonStationaryPartitioner method)</a>
 </li>
       <li><a href="pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.perturbation.polynomial">polynomial() (in module pyFTS.models.nonstationary.perturbation)</a>
 </li>
       <li><a href="pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.partitioners.PolynomialNonStationaryPartitioner">PolynomialNonStationaryPartitioner (class in pyFTS.models.nonstationary.partitioners)</a>
+</li>
+      <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.Tests.post_hoc_tests">post_hoc_tests() (in module pyFTS.benchmarks.Tests)</a>
+</li>
+      <li><a href="pyFTS.common.html#pyFTS.common.fts.FTS.predict">predict() (pyFTS.common.fts.FTS method)</a>
 </li>
       <li><a href="pyFTS.partitioners.html#pyFTS.partitioners.partitioner.Partitioner.prefix">prefix (pyFTS.partitioners.partitioner.Partitioner attribute)</a>
 </li>
       <li><a href="pyFTS.partitioners.html#pyFTS.partitioners.Grid.PreFixedGridPartitioner">PreFixedGridPartitioner (class in pyFTS.partitioners.Grid)</a>
+</li>
+      <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.benchmarks.print_distribution_statistics">print_distribution_statistics() (in module pyFTS.benchmarks.benchmarks)</a>
+</li>
+      <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.benchmarks.print_interval_statistics">print_interval_statistics() (in module pyFTS.benchmarks.benchmarks)</a>
+</li>
+      <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.benchmarks.print_point_statistics">print_point_statistics() (in module pyFTS.benchmarks.benchmarks)</a>
+</li>
+      <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.Util.probabilistic_dataframe_analytic_columns">probabilistic_dataframe_analytic_columns() (in module pyFTS.benchmarks.Util)</a>
+</li>
+      <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.Util.probabilistic_dataframe_synthetic_columns">probabilistic_dataframe_synthetic_columns() (in module pyFTS.benchmarks.Util)</a>
+</li>
+      <li><a href="pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFLRG">ProbabilisticWeightedFLRG (class in pyFTS.models.pwfts)</a>
+</li>
+      <li><a href="pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFTS">ProbabilisticWeightedFTS (class in pyFTS.models.pwfts)</a>
+</li>
+      <li><a href="pyFTS.probabilistic.html#pyFTS.probabilistic.kde.KernelSmoothing.probability">probability() (pyFTS.probabilistic.kde.KernelSmoothing method)</a>
+</li>
+      <li><a href="pyFTS.probabilistic.html#pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution">ProbabilityDistribution (class in pyFTS.probabilistic.ProbabilityDistribution)</a>
+</li>
+      <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.Util.process_common_data">process_common_data() (in module pyFTS.benchmarks.Util)</a>
+</li>
+      <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.Util.process_common_data2">process_common_data2() (in module pyFTS.benchmarks.Util)</a>
+</li>
+      <li><a href="pyFTS.hyperparam.html#pyFTS.hyperparam.Evolutionary.process_experiment">process_experiment() (in module pyFTS.hyperparam.Evolutionary)</a>
+</li>
+      <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.benchmarks.process_interval_jobs">process_interval_jobs() (in module pyFTS.benchmarks.benchmarks)</a>
+</li>
+      <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.benchmarks.process_interval_jobs2">process_interval_jobs2() (in module pyFTS.benchmarks.benchmarks)</a>
+</li>
+      <li><a href="pyFTS.hyperparam.html#pyFTS.hyperparam.GridSearch.process_jobs">process_jobs() (in module pyFTS.hyperparam.GridSearch)</a>
+</li>
+      <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.benchmarks.process_point_jobs">process_point_jobs() (in module pyFTS.benchmarks.benchmarks)</a>
+</li>
+      <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.benchmarks.process_point_jobs2">process_point_jobs2() (in module pyFTS.benchmarks.benchmarks)</a>
+</li>
+      <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.benchmarks.process_probabilistic_jobs">process_probabilistic_jobs() (in module pyFTS.benchmarks.benchmarks)</a>
+</li>
+      <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.benchmarks.process_probabilistic_jobs2">process_probabilistic_jobs2() (in module pyFTS.benchmarks.benchmarks)</a>
+</li>
+      <li><a href="pyFTS.models.multivariate.html#pyFTS.models.multivariate.mvfts.product_dict">product_dict() (in module pyFTS.models.multivariate.mvfts)</a>
 </li>
       <li><a href="pyFTS.models.multivariate.html#pyFTS.models.multivariate.grid.IncrementalGridCluster.prune">prune() (pyFTS.models.multivariate.grid.IncrementalGridCluster method)</a>
 
@@ -917,6 +2003,10 @@
         <li><a href="pyFTS.models.multivariate.html#pyFTS.models.multivariate.partitioner.MultivariatePartitioner.prune">(pyFTS.models.multivariate.partitioner.MultivariatePartitioner method)</a>
 </li>
       </ul></li>
+      <li><a href="pyFTS.probabilistic.html#pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution.pseudologlikelihood">pseudologlikelihood() (pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution method)</a>
+</li>
+      <li><a href="pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFTS.pwflrg_lhs_memberhip_fuzzyfied">pwflrg_lhs_memberhip_fuzzyfied() (pyFTS.models.pwfts.ProbabilisticWeightedFTS method)</a>
+</li>
       <li>
     pyFTS
 
@@ -929,6 +2019,76 @@
 
       <ul>
         <li><a href="pyFTS.benchmarks.html#module-pyFTS.benchmarks">module</a>
+</li>
+      </ul></li>
+      <li>
+    pyFTS.benchmarks.arima
+
+      <ul>
+        <li><a href="pyFTS.benchmarks.html#module-pyFTS.benchmarks.arima">module</a>
+</li>
+      </ul></li>
+      <li>
+    pyFTS.benchmarks.benchmarks
+
+      <ul>
+        <li><a href="pyFTS.benchmarks.html#module-pyFTS.benchmarks.benchmarks">module</a>
+</li>
+      </ul></li>
+      <li>
+    pyFTS.benchmarks.BSTS
+
+      <ul>
+        <li><a href="pyFTS.benchmarks.html#module-pyFTS.benchmarks.BSTS">module</a>
+</li>
+      </ul></li>
+      <li>
+    pyFTS.benchmarks.knn
+
+      <ul>
+        <li><a href="pyFTS.benchmarks.html#module-pyFTS.benchmarks.knn">module</a>
+</li>
+      </ul></li>
+      <li>
+    pyFTS.benchmarks.Measures
+
+      <ul>
+        <li><a href="pyFTS.benchmarks.html#module-pyFTS.benchmarks.Measures">module</a>
+</li>
+      </ul></li>
+      <li>
+    pyFTS.benchmarks.naive
+
+      <ul>
+        <li><a href="pyFTS.benchmarks.html#module-pyFTS.benchmarks.naive">module</a>
+</li>
+      </ul></li>
+      <li>
+    pyFTS.benchmarks.quantreg
+
+      <ul>
+        <li><a href="pyFTS.benchmarks.html#module-pyFTS.benchmarks.quantreg">module</a>
+</li>
+      </ul></li>
+      <li>
+    pyFTS.benchmarks.ResidualAnalysis
+
+      <ul>
+        <li><a href="pyFTS.benchmarks.html#module-pyFTS.benchmarks.ResidualAnalysis">module</a>
+</li>
+      </ul></li>
+      <li>
+    pyFTS.benchmarks.Tests
+
+      <ul>
+        <li><a href="pyFTS.benchmarks.html#module-pyFTS.benchmarks.Tests">module</a>
+</li>
+      </ul></li>
+      <li>
+    pyFTS.benchmarks.Util
+
+      <ul>
+        <li><a href="pyFTS.benchmarks.html#module-pyFTS.benchmarks.Util">module</a>
 </li>
       </ul></li>
       <li>
@@ -957,6 +2117,13 @@
 
       <ul>
         <li><a href="pyFTS.common.html#module-pyFTS.common.flrg">module</a>
+</li>
+      </ul></li>
+      <li>
+    pyFTS.common.fts
+
+      <ul>
+        <li><a href="pyFTS.common.html#module-pyFTS.common.fts">module</a>
 </li>
       </ul></li>
       <li>
@@ -985,6 +2152,13 @@
 
       <ul>
         <li><a href="pyFTS.common.html#module-pyFTS.common.tree">module</a>
+</li>
+      </ul></li>
+      <li>
+    pyFTS.common.Util
+
+      <ul>
+        <li><a href="pyFTS.common.html#module-pyFTS.common.Util">module</a>
 </li>
       </ul></li>
       <li>
@@ -1057,6 +2231,8 @@
         <li><a href="pyFTS.data.html#module-pyFTS.data.EURGBP">module</a>
 </li>
       </ul></li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
       <li>
     pyFTS.data.EURUSD
 
@@ -1106,8 +2282,6 @@
         <li><a href="pyFTS.data.html#module-pyFTS.data.mackey_glass">module</a>
 </li>
       </ul></li>
-  </ul></td>
-  <td style="width: 33%; vertical-align: top;"><ul>
       <li>
     pyFTS.data.Malaysia
 
@@ -1162,6 +2336,13 @@
 
       <ul>
         <li><a href="pyFTS.distributed.html#module-pyFTS.distributed">module</a>
+</li>
+      </ul></li>
+      <li>
+    pyFTS.distributed.dispy
+
+      <ul>
+        <li><a href="pyFTS.distributed.html#module-pyFTS.distributed.dispy">module</a>
 </li>
       </ul></li>
       <li>
@@ -1169,6 +2350,20 @@
 
       <ul>
         <li><a href="pyFTS.hyperparam.html#module-pyFTS.hyperparam">module</a>
+</li>
+      </ul></li>
+      <li>
+    pyFTS.hyperparam.Evolutionary
+
+      <ul>
+        <li><a href="pyFTS.hyperparam.html#module-pyFTS.hyperparam.Evolutionary">module</a>
+</li>
+      </ul></li>
+      <li>
+    pyFTS.hyperparam.GridSearch
+
+      <ul>
+        <li><a href="pyFTS.hyperparam.html#module-pyFTS.hyperparam.GridSearch">module</a>
 </li>
       </ul></li>
       <li>
@@ -1183,6 +2378,20 @@
 
       <ul>
         <li><a href="pyFTS.models.html#module-pyFTS.models">module</a>
+</li>
+      </ul></li>
+      <li>
+    pyFTS.models.chen
+
+      <ul>
+        <li><a href="pyFTS.models.html#module-pyFTS.models.chen">module</a>
+</li>
+      </ul></li>
+      <li>
+    pyFTS.models.cheng
+
+      <ul>
+        <li><a href="pyFTS.models.html#module-pyFTS.models.cheng">module</a>
 </li>
       </ul></li>
       <li>
@@ -1190,6 +2399,41 @@
 
       <ul>
         <li><a href="pyFTS.models.ensemble.html#module-pyFTS.models.ensemble">module</a>
+</li>
+      </ul></li>
+      <li>
+    pyFTS.models.ensemble.ensemble
+
+      <ul>
+        <li><a href="pyFTS.models.ensemble.html#module-pyFTS.models.ensemble.ensemble">module</a>
+</li>
+      </ul></li>
+      <li>
+    pyFTS.models.ensemble.multiseasonal
+
+      <ul>
+        <li><a href="pyFTS.models.ensemble.html#module-pyFTS.models.ensemble.multiseasonal">module</a>
+</li>
+      </ul></li>
+      <li>
+    pyFTS.models.hofts
+
+      <ul>
+        <li><a href="pyFTS.models.html#module-pyFTS.models.hofts">module</a>
+</li>
+      </ul></li>
+      <li>
+    pyFTS.models.hwang
+
+      <ul>
+        <li><a href="pyFTS.models.html#module-pyFTS.models.hwang">module</a>
+</li>
+      </ul></li>
+      <li>
+    pyFTS.models.ifts
+
+      <ul>
+        <li><a href="pyFTS.models.html#module-pyFTS.models.ifts">module</a>
 </li>
       </ul></li>
       <li>
@@ -1197,6 +2441,27 @@
 
       <ul>
         <li><a href="pyFTS.models.incremental.html#module-pyFTS.models.incremental">module</a>
+</li>
+      </ul></li>
+      <li>
+    pyFTS.models.incremental.IncrementalEnsemble
+
+      <ul>
+        <li><a href="pyFTS.models.incremental.html#module-pyFTS.models.incremental.IncrementalEnsemble">module</a>
+</li>
+      </ul></li>
+      <li>
+    pyFTS.models.incremental.TimeVariant
+
+      <ul>
+        <li><a href="pyFTS.models.incremental.html#module-pyFTS.models.incremental.TimeVariant">module</a>
+</li>
+      </ul></li>
+      <li>
+    pyFTS.models.ismailefendi
+
+      <ul>
+        <li><a href="pyFTS.models.html#module-pyFTS.models.ismailefendi">module</a>
 </li>
       </ul></li>
       <li>
@@ -1204,6 +2469,13 @@
 
       <ul>
         <li><a href="pyFTS.models.multivariate.html#module-pyFTS.models.multivariate">module</a>
+</li>
+      </ul></li>
+      <li>
+    pyFTS.models.multivariate.cmvfts
+
+      <ul>
+        <li><a href="pyFTS.models.multivariate.html#module-pyFTS.models.multivariate.cmvfts">module</a>
 </li>
       </ul></li>
       <li>
@@ -1225,6 +2497,13 @@
 
       <ul>
         <li><a href="pyFTS.models.multivariate.html#module-pyFTS.models.multivariate.flrg">module</a>
+</li>
+      </ul></li>
+      <li>
+    pyFTS.models.multivariate.granular
+
+      <ul>
+        <li><a href="pyFTS.models.multivariate.html#module-pyFTS.models.multivariate.granular">module</a>
 </li>
       </ul></li>
       <li>
@@ -1232,6 +2511,13 @@
 
       <ul>
         <li><a href="pyFTS.models.multivariate.html#module-pyFTS.models.multivariate.grid">module</a>
+</li>
+      </ul></li>
+      <li>
+    pyFTS.models.multivariate.mvfts
+
+      <ul>
+        <li><a href="pyFTS.models.multivariate.html#module-pyFTS.models.multivariate.mvfts">module</a>
 </li>
       </ul></li>
       <li>
@@ -1239,6 +2525,20 @@
 
       <ul>
         <li><a href="pyFTS.models.multivariate.html#module-pyFTS.models.multivariate.partitioner">module</a>
+</li>
+      </ul></li>
+      <li>
+    pyFTS.models.multivariate.variable
+
+      <ul>
+        <li><a href="pyFTS.models.multivariate.html#module-pyFTS.models.multivariate.variable">module</a>
+</li>
+      </ul></li>
+      <li>
+    pyFTS.models.multivariate.wmvfts
+
+      <ul>
+        <li><a href="pyFTS.models.multivariate.html#module-pyFTS.models.multivariate.wmvfts">module</a>
 </li>
       </ul></li>
       <li>
@@ -1253,6 +2553,13 @@
 
       <ul>
         <li><a href="pyFTS.models.nonstationary.html#module-pyFTS.models.nonstationary.common">module</a>
+</li>
+      </ul></li>
+      <li>
+    pyFTS.models.nonstationary.cvfts
+
+      <ul>
+        <li><a href="pyFTS.models.nonstationary.html#module-pyFTS.models.nonstationary.cvfts">module</a>
 </li>
       </ul></li>
       <li>
@@ -1260,6 +2567,20 @@
 
       <ul>
         <li><a href="pyFTS.models.nonstationary.html#module-pyFTS.models.nonstationary.flrg">module</a>
+</li>
+      </ul></li>
+      <li>
+    pyFTS.models.nonstationary.honsfts
+
+      <ul>
+        <li><a href="pyFTS.models.nonstationary.html#module-pyFTS.models.nonstationary.honsfts">module</a>
+</li>
+      </ul></li>
+      <li>
+    pyFTS.models.nonstationary.nsfts
+
+      <ul>
+        <li><a href="pyFTS.models.nonstationary.html#module-pyFTS.models.nonstationary.nsfts">module</a>
 </li>
       </ul></li>
       <li>
@@ -1274,6 +2595,27 @@
 
       <ul>
         <li><a href="pyFTS.models.nonstationary.html#module-pyFTS.models.nonstationary.perturbation">module</a>
+</li>
+      </ul></li>
+      <li>
+    pyFTS.models.nonstationary.util
+
+      <ul>
+        <li><a href="pyFTS.models.nonstationary.html#module-pyFTS.models.nonstationary.util">module</a>
+</li>
+      </ul></li>
+      <li>
+    pyFTS.models.pwfts
+
+      <ul>
+        <li><a href="pyFTS.models.html#module-pyFTS.models.pwfts">module</a>
+</li>
+      </ul></li>
+      <li>
+    pyFTS.models.sadaei
+
+      <ul>
+        <li><a href="pyFTS.models.html#module-pyFTS.models.sadaei">module</a>
 </li>
       </ul></li>
       <li>
@@ -1281,6 +2623,13 @@
 
       <ul>
         <li><a href="pyFTS.models.seasonal.html#module-pyFTS.models.seasonal">module</a>
+</li>
+      </ul></li>
+      <li>
+    pyFTS.models.seasonal.cmsfts
+
+      <ul>
+        <li><a href="pyFTS.models.seasonal.html#module-pyFTS.models.seasonal.cmsfts">module</a>
 </li>
       </ul></li>
       <li>
@@ -1288,6 +2637,13 @@
 
       <ul>
         <li><a href="pyFTS.models.seasonal.html#module-pyFTS.models.seasonal.common">module</a>
+</li>
+      </ul></li>
+      <li>
+    pyFTS.models.seasonal.msfts
+
+      <ul>
+        <li><a href="pyFTS.models.seasonal.html#module-pyFTS.models.seasonal.msfts">module</a>
 </li>
       </ul></li>
       <li>
@@ -1302,6 +2658,27 @@
 
       <ul>
         <li><a href="pyFTS.models.seasonal.html#module-pyFTS.models.seasonal.SeasonalIndexer">module</a>
+</li>
+      </ul></li>
+      <li>
+    pyFTS.models.seasonal.sfts
+
+      <ul>
+        <li><a href="pyFTS.models.seasonal.html#module-pyFTS.models.seasonal.sfts">module</a>
+</li>
+      </ul></li>
+      <li>
+    pyFTS.models.song
+
+      <ul>
+        <li><a href="pyFTS.models.html#module-pyFTS.models.song">module</a>
+</li>
+      </ul></li>
+      <li>
+    pyFTS.models.yu
+
+      <ul>
+        <li><a href="pyFTS.models.html#module-pyFTS.models.yu">module</a>
 </li>
       </ul></li>
       <li>
@@ -1337,6 +2714,20 @@
 
       <ul>
         <li><a href="pyFTS.partitioners.html#module-pyFTS.partitioners.Grid">module</a>
+</li>
+      </ul></li>
+      <li>
+    pyFTS.partitioners.Huarng
+
+      <ul>
+        <li><a href="pyFTS.partitioners.html#module-pyFTS.partitioners.Huarng">module</a>
+</li>
+      </ul></li>
+      <li>
+    pyFTS.partitioners.parallel_util
+
+      <ul>
+        <li><a href="pyFTS.partitioners.html#module-pyFTS.partitioners.parallel_util">module</a>
 </li>
       </ul></li>
       <li>
@@ -1365,6 +2756,13 @@
 
       <ul>
         <li><a href="pyFTS.partitioners.html#module-pyFTS.partitioners.SubClust">module</a>
+</li>
+      </ul></li>
+      <li>
+    pyFTS.partitioners.Util
+
+      <ul>
+        <li><a href="pyFTS.partitioners.html#module-pyFTS.partitioners.Util">module</a>
 </li>
       </ul></li>
       <li>
@@ -1372,6 +2770,20 @@
 
       <ul>
         <li><a href="pyFTS.probabilistic.html#module-pyFTS.probabilistic">module</a>
+</li>
+      </ul></li>
+      <li>
+    pyFTS.probabilistic.kde
+
+      <ul>
+        <li><a href="pyFTS.probabilistic.html#module-pyFTS.probabilistic.kde">module</a>
+</li>
+      </ul></li>
+      <li>
+    pyFTS.probabilistic.ProbabilityDistribution
+
+      <ul>
+        <li><a href="pyFTS.probabilistic.html#module-pyFTS.probabilistic.ProbabilityDistribution">module</a>
 </li>
       </ul></li>
   </ul></td>
@@ -1380,6 +2792,12 @@
 <h2 id="Q">Q</h2>
 <table style="width: 100%" class="indextable genindextable"><tr>
   <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="pyFTS.probabilistic.html#pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution.quantile">quantile() (pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution method)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.quantreg.QuantileRegression">QuantileRegression (class in pyFTS.benchmarks.quantreg)</a>
+</li>
       <li><a href="pyFTS.models.seasonal.html#pyFTS.models.seasonal.common.DateTime.quarter">quarter (pyFTS.models.seasonal.common.DateTime attribute)</a>
 </li>
   </ul></td>
@@ -1388,21 +2806,53 @@
 <h2 id="R">R</h2>
 <table style="width: 100%" class="indextable genindextable"><tr>
   <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="pyFTS.hyperparam.html#pyFTS.hyperparam.Evolutionary.random_genotype">random_genotype() (in module pyFTS.hyperparam.Evolutionary)</a>
+</li>
       <li><a href="pyFTS.data.html#pyFTS.data.artificial.random_walk">random_walk() (in module pyFTS.data.artificial)</a>
 </li>
       <li><a href="pyFTS.common.html#pyFTS.common.SortedCollection.SortedCollection.remove">remove() (pyFTS.common.SortedCollection.SortedCollection method)</a>
 </li>
       <li><a href="pyFTS.common.html#pyFTS.common.flrg.FLRG.reset_calculated_values">reset_calculated_values() (pyFTS.common.flrg.FLRG method)</a>
+
+      <ul>
+        <li><a href="pyFTS.common.html#pyFTS.common.fts.FTS.reset_calculated_values">(pyFTS.common.fts.FTS method)</a>
+</li>
+      </ul></li>
+      <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.ResidualAnalysis.residuals">residuals() (in module pyFTS.benchmarks.ResidualAnalysis)</a>
+</li>
+      <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.Measures.resolution">resolution() (in module pyFTS.benchmarks.Measures)</a>
+</li>
+      <li><a href="pyFTS.models.incremental.html#pyFTS.models.incremental.TimeVariant.Retrainer">Retrainer (class in pyFTS.models.incremental.TimeVariant)</a>
 </li>
-  </ul></td>
-  <td style="width: 33%; vertical-align: top;"><ul>
       <li><a href="pyFTS.common.html#pyFTS.common.FLR.FLR.RHS">RHS (pyFTS.common.FLR.FLR attribute)</a>
 
       <ul>
         <li><a href="pyFTS.common.html#pyFTS.common.flrg.FLRG.RHS">(pyFTS.common.flrg.FLRG attribute)</a>
 </li>
       </ul></li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFLRG.rhs_conditional_probability">rhs_conditional_probability() (pyFTS.models.pwfts.ProbabilisticWeightedFLRG method)</a>
+</li>
+      <li><a href="pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFLRG.rhs_unconditional_probability">rhs_unconditional_probability() (pyFTS.models.pwfts.ProbabilisticWeightedFLRG method)</a>
+</li>
+      <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.Measures.rmse">rmse() (in module pyFTS.benchmarks.Measures)</a>
+</li>
+      <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.Measures.rmse_interval">rmse_interval() (in module pyFTS.benchmarks.Measures)</a>
+</li>
       <li><a href="pyFTS.data.html#pyFTS.data.artificial.SignalEmulator.run">run() (pyFTS.data.artificial.SignalEmulator method)</a>
+</li>
+      <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.benchmarks.run_interval">run_interval() (in module pyFTS.benchmarks.benchmarks)</a>
+</li>
+      <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.benchmarks.run_interval2">run_interval2() (in module pyFTS.benchmarks.benchmarks)</a>
+</li>
+      <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.benchmarks.run_point">run_point() (in module pyFTS.benchmarks.benchmarks)</a>
+</li>
+      <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.benchmarks.run_point2">run_point2() (in module pyFTS.benchmarks.benchmarks)</a>
+</li>
+      <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.benchmarks.run_probabilistic">run_probabilistic() (in module pyFTS.benchmarks.benchmarks)</a>
+</li>
+      <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.benchmarks.run_probabilistic2">run_probabilistic2() (in module pyFTS.benchmarks.benchmarks)</a>
 </li>
   </ul></td>
 </tr></table>
@@ -1410,7 +2860,19 @@
 <h2 id="S">S</h2>
 <table style="width: 100%" class="indextable genindextable"><tr>
   <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="pyFTS.models.ensemble.html#pyFTS.models.ensemble.ensemble.sampler">sampler() (in module pyFTS.models.ensemble.ensemble)</a>
+</li>
+      <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.Util.save_dataframe_interval">save_dataframe_interval() (in module pyFTS.benchmarks.Util)</a>
+</li>
+      <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.Util.save_dataframe_point">save_dataframe_point() (in module pyFTS.benchmarks.Util)</a>
+</li>
+      <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.Util.save_dataframe_probabilistic">save_dataframe_probabilistic() (in module pyFTS.benchmarks.Util)</a>
+</li>
+      <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.Util.scale">scale() (in module pyFTS.benchmarks.Util)</a>
+</li>
       <li><a href="pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.partitioners.PolynomialNonStationaryPartitioner.scale_down">scale_down() (pyFTS.models.nonstationary.partitioners.PolynomialNonStationaryPartitioner method)</a>
+</li>
+      <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.Util.scale_params">scale_params() (in module pyFTS.benchmarks.Util)</a>
 </li>
       <li><a href="pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.partitioners.PolynomialNonStationaryPartitioner.scale_up">scale_up() (pyFTS.models.nonstationary.partitioners.PolynomialNonStationaryPartitioner method)</a>
 </li>
@@ -1423,6 +2885,12 @@
 </li>
       </ul></li>
       <li><a href="pyFTS.models.seasonal.html#pyFTS.models.seasonal.partitioner.TimeGridPartitioner.season">season (pyFTS.models.seasonal.partitioner.TimeGridPartitioner attribute)</a>
+</li>
+      <li><a href="pyFTS.models.ensemble.html#pyFTS.models.ensemble.multiseasonal.SeasonalEnsembleFTS">SeasonalEnsembleFTS (class in pyFTS.models.ensemble.multiseasonal)</a>
+</li>
+      <li><a href="pyFTS.models.seasonal.html#pyFTS.models.seasonal.sfts.SeasonalFLRG">SeasonalFLRG (class in pyFTS.models.seasonal.sfts)</a>
+</li>
+      <li><a href="pyFTS.models.seasonal.html#pyFTS.models.seasonal.sfts.SeasonalFTS">SeasonalFTS (class in pyFTS.models.seasonal.sfts)</a>
 </li>
       <li><a href="pyFTS.models.seasonal.html#pyFTS.models.seasonal.SeasonalIndexer.SeasonalIndexer">SeasonalIndexer (class in pyFTS.models.seasonal.SeasonalIndexer)</a>
 </li>
@@ -1433,6 +2901,10 @@
       <li><a href="pyFTS.models.seasonal.html#pyFTS.models.seasonal.common.DateTime.second_of_hour">second_of_hour (pyFTS.models.seasonal.common.DateTime attribute)</a>
 </li>
       <li><a href="pyFTS.models.seasonal.html#pyFTS.models.seasonal.common.DateTime.second_of_minute">second_of_minute (pyFTS.models.seasonal.common.DateTime attribute)</a>
+</li>
+      <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.benchmarks.SelecaoSimples_MenorRMSE">SelecaoSimples_MenorRMSE() (in module pyFTS.benchmarks.benchmarks)</a>
+</li>
+      <li><a href="pyFTS.probabilistic.html#pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution.set">set() (pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution method)</a>
 </li>
       <li><a href="pyFTS.models.seasonal.html#pyFTS.models.seasonal.SeasonalIndexer.DataFrameSeasonalIndexer.set_data">set_data() (pyFTS.models.seasonal.SeasonalIndexer.DataFrameSeasonalIndexer method)</a>
 
@@ -1450,35 +2922,71 @@
 </li>
       <li><a href="pyFTS.models.multivariate.html#pyFTS.models.multivariate.FLR.FLR.set_rhs">set_rhs() (pyFTS.models.multivariate.FLR.FLR method)</a>
 </li>
-  </ul></td>
-  <td style="width: 33%; vertical-align: top;"><ul>
       <li><a href="pyFTS.models.multivariate.html#pyFTS.models.multivariate.common.MultivariateFuzzySet.set_target_variable">set_target_variable() (pyFTS.models.multivariate.common.MultivariateFuzzySet method)</a>
+</li>
+      <li><a href="pyFTS.models.ensemble.html#pyFTS.models.ensemble.ensemble.AllMethodEnsembleFTS.set_transformations">set_transformations() (pyFTS.models.ensemble.ensemble.AllMethodEnsembleFTS method)</a>
 </li>
       <li><a href="pyFTS.partitioners.html#pyFTS.partitioners.partitioner.Partitioner.setnames">setnames (pyFTS.partitioners.partitioner.Partitioner attribute)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.Measures.sharpness">sharpness() (in module pyFTS.benchmarks.Measures)</a>
+</li>
+      <li><a href="pyFTS.common.html#pyFTS.common.fts.FTS.shortname">shortname (pyFTS.common.fts.FTS attribute)</a>
+</li>
+      <li><a href="pyFTS.common.html#pyFTS.common.Util.show_and_save_image">show_and_save_image() (in module pyFTS.common.Util)</a>
 </li>
       <li><a href="pyFTS.common.html#pyFTS.common.Membership.sigmf">sigmf() (in module pyFTS.common.Membership)</a>
 </li>
       <li><a href="pyFTS.data.html#pyFTS.data.artificial.SignalEmulator">SignalEmulator (class in pyFTS.data.artificial)</a>
+</li>
+      <li><a href="pyFTS.distributed.html#pyFTS.distributed.dispy.simple_model_predict">simple_model_predict() (in module pyFTS.distributed.dispy)</a>
+</li>
+      <li><a href="pyFTS.distributed.html#pyFTS.distributed.dispy.simple_model_train">simple_model_train() (in module pyFTS.distributed.dispy)</a>
+</li>
+      <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.Util.simple_synthetic_dataframe">simple_synthetic_dataframe() (in module pyFTS.benchmarks.Util)</a>
+</li>
+      <li><a href="pyFTS.models.ensemble.html#pyFTS.models.ensemble.ensemble.SimpleEnsembleFTS">SimpleEnsembleFTS (class in pyFTS.models.ensemble.ensemble)</a>
 </li>
       <li><a href="pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.partitioners.simplenonstationary_gridpartitioner_builder">simplenonstationary_gridpartitioner_builder() (in module pyFTS.models.nonstationary.partitioners)</a>
 </li>
       <li><a href="pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.partitioners.SimpleNonStationaryPartitioner">SimpleNonStationaryPartitioner (class in pyFTS.models.nonstationary.partitioners)</a>
 </li>
       <li><a href="pyFTS.partitioners.html#pyFTS.partitioners.Simple.SimplePartitioner">SimplePartitioner (class in pyFTS.partitioners.Simple)</a>
+</li>
+      <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.benchmarks.simpleSearch_RMSE">simpleSearch_RMSE() (in module pyFTS.benchmarks.benchmarks)</a>
+</li>
+      <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.ResidualAnalysis.single_plot_residuals">single_plot_residuals() (in module pyFTS.benchmarks.ResidualAnalysis)</a>
 </li>
       <li><a href="pyFTS.common.html#pyFTS.common.Membership.singleton">singleton() (in module pyFTS.common.Membership)</a>
 </li>
       <li><a href="pyFTS.partitioners.html#pyFTS.partitioners.Singleton.SingletonPartitioner">SingletonPartitioner (class in pyFTS.partitioners.Singleton)</a>
 </li>
       <li><a href="pyFTS.models.seasonal.html#pyFTS.models.seasonal.common.DateTime.sixth">sixth (pyFTS.models.seasonal.common.DateTime attribute)</a>
+</li>
+      <li><a href="pyFTS.common.html#pyFTS.common.Util.sliding_window">sliding_window() (in module pyFTS.common.Util)</a>
+</li>
+      <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.benchmarks.sliding_window_benchmarks">sliding_window_benchmarks() (in module pyFTS.benchmarks.benchmarks)</a>
+</li>
+      <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.benchmarks.sliding_window_benchmarks2">sliding_window_benchmarks2() (in module pyFTS.benchmarks.benchmarks)</a>
+</li>
+      <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.Measures.smape">smape() (in module pyFTS.benchmarks.Measures)</a>
 </li>
       <li><a href="pyFTS.common.html#pyFTS.common.SortedCollection.SortedCollection">SortedCollection (class in pyFTS.common.SortedCollection)</a>
 </li>
       <li><a href="pyFTS.partitioners.html#pyFTS.partitioners.Entropy.splitAbove">splitAbove() (in module pyFTS.partitioners.Entropy)</a>
 </li>
       <li><a href="pyFTS.partitioners.html#pyFTS.partitioners.Entropy.splitBelow">splitBelow() (in module pyFTS.partitioners.Entropy)</a>
+</li>
+      <li><a href="pyFTS.common.html#pyFTS.common.fts.FTS.standard_horizon">standard_horizon (pyFTS.common.fts.FTS attribute)</a>
+</li>
+      <li><a href="pyFTS.distributed.html#pyFTS.distributed.dispy.start_dispy_cluster">start_dispy_cluster() (in module pyFTS.distributed.dispy)</a>
 </li>
       <li><a href="pyFTS.data.html#pyFTS.data.artificial.SignalEmulator.stationary_gaussian">stationary_gaussian() (pyFTS.data.artificial.SignalEmulator method)</a>
+</li>
+      <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.Util.stats">stats() (in module pyFTS.benchmarks.Util)</a>
+</li>
+      <li><a href="pyFTS.distributed.html#pyFTS.distributed.dispy.stop_dispy_cluster">stop_dispy_cluster() (in module pyFTS.distributed.dispy)</a>
 </li>
       <li><a href="pyFTS.models.seasonal.html#pyFTS.models.seasonal.common.strip_datepart">strip_datepart() (in module pyFTS.models.seasonal.common)</a>
 </li>
@@ -1492,9 +3000,83 @@
 <h2 id="T">T</h2>
 <table style="width: 100%" class="indextable genindextable"><tr>
   <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.Util.tabular_dataframe_columns">tabular_dataframe_columns() (in module pyFTS.benchmarks.Util)</a>
+</li>
+      <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.Tests.test_mean_equality">test_mean_equality() (in module pyFTS.benchmarks.Tests)</a>
+</li>
+      <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.Measures.TheilsInequality">TheilsInequality() (in module pyFTS.benchmarks.Measures)</a>
+</li>
       <li><a href="pyFTS.models.seasonal.html#pyFTS.models.seasonal.common.DateTime.third">third (pyFTS.models.seasonal.common.DateTime attribute)</a>
 </li>
       <li><a href="pyFTS.models.seasonal.html#pyFTS.models.seasonal.partitioner.TimeGridPartitioner">TimeGridPartitioner (class in pyFTS.models.seasonal.partitioner)</a>
+</li>
+      <li><a href="pyFTS.hyperparam.html#pyFTS.hyperparam.Evolutionary.tournament">tournament() (in module pyFTS.hyperparam.Evolutionary)</a>
+</li>
+      <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.arima.ARIMA.train">train() (pyFTS.benchmarks.arima.ARIMA method)</a>
+
+      <ul>
+        <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.BSTS.ARIMA.train">(pyFTS.benchmarks.BSTS.ARIMA method)</a>
+</li>
+        <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.knn.KNearestNeighbors.train">(pyFTS.benchmarks.knn.KNearestNeighbors method)</a>
+</li>
+        <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.quantreg.QuantileRegression.train">(pyFTS.benchmarks.quantreg.QuantileRegression method)</a>
+</li>
+        <li><a href="pyFTS.common.html#pyFTS.common.fts.FTS.train">(pyFTS.common.fts.FTS method)</a>
+</li>
+        <li><a href="pyFTS.models.html#pyFTS.models.chen.ConventionalFTS.train">(pyFTS.models.chen.ConventionalFTS method)</a>
+</li>
+        <li><a href="pyFTS.models.ensemble.html#pyFTS.models.ensemble.ensemble.AllMethodEnsembleFTS.train">(pyFTS.models.ensemble.ensemble.AllMethodEnsembleFTS method)</a>
+</li>
+        <li><a href="pyFTS.models.ensemble.html#pyFTS.models.ensemble.ensemble.EnsembleFTS.train">(pyFTS.models.ensemble.ensemble.EnsembleFTS method)</a>
+</li>
+        <li><a href="pyFTS.models.ensemble.html#pyFTS.models.ensemble.ensemble.SimpleEnsembleFTS.train">(pyFTS.models.ensemble.ensemble.SimpleEnsembleFTS method)</a>
+</li>
+        <li><a href="pyFTS.models.ensemble.html#pyFTS.models.ensemble.multiseasonal.SeasonalEnsembleFTS.train">(pyFTS.models.ensemble.multiseasonal.SeasonalEnsembleFTS method)</a>
+</li>
+        <li><a href="pyFTS.models.html#pyFTS.models.hofts.HighOrderFTS.train">(pyFTS.models.hofts.HighOrderFTS method)</a>
+</li>
+        <li><a href="pyFTS.models.html#pyFTS.models.hwang.HighOrderFTS.train">(pyFTS.models.hwang.HighOrderFTS method)</a>
+</li>
+        <li><a href="pyFTS.models.incremental.html#pyFTS.models.incremental.IncrementalEnsemble.IncrementalEnsembleFTS.train">(pyFTS.models.incremental.IncrementalEnsemble.IncrementalEnsembleFTS method)</a>
+</li>
+        <li><a href="pyFTS.models.incremental.html#pyFTS.models.incremental.TimeVariant.Retrainer.train">(pyFTS.models.incremental.TimeVariant.Retrainer method)</a>
+</li>
+        <li><a href="pyFTS.models.html#pyFTS.models.ismailefendi.ImprovedWeightedFTS.train">(pyFTS.models.ismailefendi.ImprovedWeightedFTS method)</a>
+</li>
+        <li><a href="pyFTS.models.multivariate.html#pyFTS.models.multivariate.cmvfts.ClusteredMVFTS.train">(pyFTS.models.multivariate.cmvfts.ClusteredMVFTS method)</a>
+</li>
+        <li><a href="pyFTS.models.multivariate.html#pyFTS.models.multivariate.granular.GranularWMVFTS.train">(pyFTS.models.multivariate.granular.GranularWMVFTS method)</a>
+</li>
+        <li><a href="pyFTS.models.multivariate.html#pyFTS.models.multivariate.mvfts.MVFTS.train">(pyFTS.models.multivariate.mvfts.MVFTS method)</a>
+</li>
+        <li><a href="pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.cvfts.ConditionalVarianceFTS.train">(pyFTS.models.nonstationary.cvfts.ConditionalVarianceFTS method)</a>
+</li>
+        <li><a href="pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.honsfts.HighOrderNonStationaryFTS.train">(pyFTS.models.nonstationary.honsfts.HighOrderNonStationaryFTS method)</a>
+</li>
+        <li><a href="pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.nsfts.NonStationaryFTS.train">(pyFTS.models.nonstationary.nsfts.NonStationaryFTS method)</a>
+</li>
+        <li><a href="pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.nsfts.WeightedNonStationaryFTS.train">(pyFTS.models.nonstationary.nsfts.WeightedNonStationaryFTS method)</a>
+</li>
+        <li><a href="pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFTS.train">(pyFTS.models.pwfts.ProbabilisticWeightedFTS method)</a>
+</li>
+        <li><a href="pyFTS.models.html#pyFTS.models.sadaei.ExponentialyWeightedFTS.train">(pyFTS.models.sadaei.ExponentialyWeightedFTS method)</a>
+</li>
+        <li><a href="pyFTS.models.seasonal.html#pyFTS.models.seasonal.cmsfts.ContextualMultiSeasonalFTS.train">(pyFTS.models.seasonal.cmsfts.ContextualMultiSeasonalFTS method)</a>
+</li>
+        <li><a href="pyFTS.models.seasonal.html#pyFTS.models.seasonal.msfts.MultiSeasonalFTS.train">(pyFTS.models.seasonal.msfts.MultiSeasonalFTS method)</a>
+</li>
+        <li><a href="pyFTS.models.seasonal.html#pyFTS.models.seasonal.sfts.SeasonalFTS.train">(pyFTS.models.seasonal.sfts.SeasonalFTS method)</a>
+</li>
+        <li><a href="pyFTS.models.html#pyFTS.models.song.ConventionalFTS.train">(pyFTS.models.song.ConventionalFTS method)</a>
+</li>
+        <li><a href="pyFTS.models.html#pyFTS.models.yu.WeightedFTS.train">(pyFTS.models.yu.WeightedFTS method)</a>
+</li>
+      </ul></li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="pyFTS.models.ensemble.html#pyFTS.models.ensemble.multiseasonal.train_individual_model">train_individual_model() (in module pyFTS.models.ensemble.multiseasonal)</a>
+</li>
+      <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.benchmarks.train_test_time">train_test_time() (in module pyFTS.benchmarks.benchmarks)</a>
 </li>
       <li><a href="pyFTS.common.html#pyFTS.common.Composite.FuzzySet.transform">transform() (pyFTS.common.Composite.FuzzySet method)</a>
 
@@ -1504,11 +3086,21 @@
         <li><a href="pyFTS.models.seasonal.html#pyFTS.models.seasonal.common.FuzzySet.transform">(pyFTS.models.seasonal.common.FuzzySet method)</a>
 </li>
       </ul></li>
-  </ul></td>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="pyFTS.partitioners.html#pyFTS.partitioners.partitioner.Partitioner.transformation">transformation (pyFTS.partitioners.partitioner.Partitioner attribute)</a>
+      <li><a href="pyFTS.models.multivariate.html#pyFTS.models.multivariate.variable.Variable.transformation">transformation (pyFTS.models.multivariate.variable.Variable attribute)</a>
+
+      <ul>
+        <li><a href="pyFTS.partitioners.html#pyFTS.partitioners.partitioner.Partitioner.transformation">(pyFTS.partitioners.partitioner.Partitioner attribute)</a>
+</li>
+      </ul></li>
+      <li><a href="pyFTS.common.html#pyFTS.common.fts.FTS.transformations">transformations (pyFTS.common.fts.FTS attribute)</a>
+</li>
+      <li><a href="pyFTS.common.html#pyFTS.common.fts.FTS.transformations_param">transformations_param (pyFTS.common.fts.FTS attribute)</a>
 </li>
       <li><a href="pyFTS.common.html#pyFTS.common.Membership.trapmf">trapmf() (in module pyFTS.common.Membership)</a>
+</li>
+      <li><a href="pyFTS.models.html#pyFTS.models.cheng.TrendWeightedFLRG">TrendWeightedFLRG (class in pyFTS.models.cheng)</a>
+</li>
+      <li><a href="pyFTS.models.html#pyFTS.models.cheng.TrendWeightedFTS">TrendWeightedFTS (class in pyFTS.models.cheng)</a>
 </li>
       <li><a href="pyFTS.common.html#pyFTS.common.Membership.trimf">trimf() (in module pyFTS.common.Membership)</a>
 </li>
@@ -1516,6 +3108,8 @@
 
       <ul>
         <li><a href="pyFTS.partitioners.html#pyFTS.partitioners.partitioner.Partitioner.type">(pyFTS.partitioners.partitioner.Partitioner attribute)</a>
+</li>
+        <li><a href="pyFTS.probabilistic.html#pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution.type">(pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution attribute)</a>
 </li>
       </ul></li>
   </ul></td>
@@ -1524,13 +3118,33 @@
 <h2 id="U">U</h2>
 <table style="width: 100%" class="indextable genindextable"><tr>
   <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.Util.unified_scaled_interval">unified_scaled_interval() (in module pyFTS.benchmarks.Util)</a>
+</li>
+      <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.Util.unified_scaled_interval_pinball">unified_scaled_interval_pinball() (in module pyFTS.benchmarks.Util)</a>
+</li>
+      <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.Util.unified_scaled_point">unified_scaled_point() (in module pyFTS.benchmarks.Util)</a>
+</li>
+      <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.Util.unified_scaled_probabilistic">unified_scaled_probabilistic() (in module pyFTS.benchmarks.Util)</a>
+</li>
+      <li><a href="pyFTS.common.html#pyFTS.common.Util.uniquefilename">uniquefilename() (in module pyFTS.common.Util)</a>
+</li>
       <li><a href="pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.flrg.NonStationaryFLRG.unpack_args">unpack_args() (pyFTS.models.nonstationary.flrg.NonStationaryFLRG method)</a>
 </li>
   </ul></td>
   <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="pyFTS.probabilistic.html#pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution.uod">uod (pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution attribute)</a>
+</li>
+      <li><a href="pyFTS.common.html#pyFTS.common.fts.FTS.uod_clip">uod_clip (pyFTS.common.fts.FTS attribute)</a>
+</li>
+      <li><a href="pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFTS.update_model">update_model() (pyFTS.models.pwfts.ProbabilisticWeightedFTS method)</a>
+</li>
+      <li><a href="pyFTS.models.ensemble.html#pyFTS.models.ensemble.multiseasonal.SeasonalEnsembleFTS.update_uod">update_uod() (pyFTS.models.ensemble.multiseasonal.SeasonalEnsembleFTS method)</a>
+</li>
       <li><a href="pyFTS.partitioners.html#pyFTS.partitioners.partitioner.Partitioner.upper_margin">upper_margin (pyFTS.partitioners.partitioner.Partitioner attribute)</a>
 </li>
       <li><a href="pyFTS.partitioners.html#pyFTS.partitioners.partitioner.Partitioner.upper_set">upper_set() (pyFTS.partitioners.partitioner.Partitioner method)</a>
+</li>
+      <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.Measures.UStatistic">UStatistic() (in module pyFTS.benchmarks.Measures)</a>
 </li>
   </ul></td>
 </tr></table>
@@ -1538,6 +3152,8 @@
 <h2 id="V">V</h2>
 <table style="width: 100%" class="indextable genindextable"><tr>
   <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="pyFTS.models.multivariate.html#pyFTS.models.multivariate.variable.Variable">Variable (class in pyFTS.models.multivariate.variable)</a>
+</li>
       <li><a href="pyFTS.common.html#pyFTS.common.FuzzySet.FuzzySet.variable">variable (pyFTS.common.FuzzySet.FuzzySet attribute)</a>
 
       <ul>
@@ -1545,20 +3161,72 @@
 </li>
       </ul></li>
   </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="pyFTS.models.html#pyFTS.models.pwfts.visualize_distributions">visualize_distributions() (in module pyFTS.models.pwfts)</a>
+</li>
+  </ul></td>
 </tr></table>
 
 <h2 id="W">W</h2>
 <table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="pyFTS.models.multivariate.html#pyFTS.models.multivariate.wmvfts.WeightedFLRG">WeightedFLRG (class in pyFTS.models.multivariate.wmvfts)</a>
+
+      <ul>
+        <li><a href="pyFTS.models.html#pyFTS.models.yu.WeightedFLRG">(class in pyFTS.models.yu)</a>
+</li>
+      </ul></li>
+      <li><a href="pyFTS.models.html#pyFTS.models.yu.WeightedFTS">WeightedFTS (class in pyFTS.models.yu)</a>
+</li>
+      <li><a href="pyFTS.models.html#pyFTS.models.hofts.WeightedHighOrderFLRG">WeightedHighOrderFLRG (class in pyFTS.models.hofts)</a>
+</li>
+      <li><a href="pyFTS.models.html#pyFTS.models.hofts.WeightedHighOrderFTS">WeightedHighOrderFTS (class in pyFTS.models.hofts)</a>
+</li>
+      <li><a href="pyFTS.models.html#pyFTS.models.ifts.WeightedIntervalFTS">WeightedIntervalFTS (class in pyFTS.models.ifts)</a>
+</li>
+      <li><a href="pyFTS.models.multivariate.html#pyFTS.models.multivariate.wmvfts.WeightedMVFTS">WeightedMVFTS (class in pyFTS.models.multivariate.wmvfts)</a>
+</li>
+      <li><a href="pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.nsfts.WeightedNonStationaryFLRG">WeightedNonStationaryFLRG (class in pyFTS.models.nonstationary.nsfts)</a>
+</li>
+      <li><a href="pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.nsfts.WeightedNonStationaryFTS">WeightedNonStationaryFTS (class in pyFTS.models.nonstationary.nsfts)</a>
+</li>
+      <li><a href="pyFTS.models.html#pyFTS.models.cheng.TrendWeightedFLRG.weights">weights() (pyFTS.models.cheng.TrendWeightedFLRG method)</a>
+
+      <ul>
+        <li><a href="pyFTS.models.html#pyFTS.models.hofts.WeightedHighOrderFLRG.weights">(pyFTS.models.hofts.WeightedHighOrderFLRG method)</a>
+</li>
+        <li><a href="pyFTS.models.html#pyFTS.models.ismailefendi.ImprovedWeightedFLRG.weights">(pyFTS.models.ismailefendi.ImprovedWeightedFLRG method)</a>
+</li>
+        <li><a href="pyFTS.models.multivariate.html#pyFTS.models.multivariate.wmvfts.WeightedFLRG.weights">(pyFTS.models.multivariate.wmvfts.WeightedFLRG method)</a>
+</li>
+        <li><a href="pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.honsfts.HighOrderNonStationaryFLRG.weights">(pyFTS.models.nonstationary.honsfts.HighOrderNonStationaryFLRG method)</a>
+</li>
+        <li><a href="pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.nsfts.WeightedNonStationaryFLRG.weights">(pyFTS.models.nonstationary.nsfts.WeightedNonStationaryFLRG method)</a>
+</li>
+        <li><a href="pyFTS.models.html#pyFTS.models.sadaei.ExponentialyWeightedFLRG.weights">(pyFTS.models.sadaei.ExponentialyWeightedFLRG method)</a>
+</li>
+        <li><a href="pyFTS.models.html#pyFTS.models.yu.WeightedFLRG.weights">(pyFTS.models.yu.WeightedFLRG method)</a>
+</li>
+      </ul></li>
+  </ul></td>
   <td style="width: 33%; vertical-align: top;"><ul>
       <li><a href="pyFTS.data.html#pyFTS.data.artificial.white_noise">white_noise() (in module pyFTS.data.artificial)</a>
 </li>
       <li><a href="pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.common.FuzzySet.width">width (pyFTS.models.nonstationary.common.FuzzySet attribute)</a>
 </li>
-  </ul></td>
-  <td style="width: 33%; vertical-align: top;"><ul>
       <li><a href="pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.common.FuzzySet.width_params">width_params (pyFTS.models.nonstationary.common.FuzzySet attribute)</a>
 </li>
       <li><a href="pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.common.window_index">window_index() (in module pyFTS.models.nonstationary.common)</a>
+</li>
+      <li><a href="pyFTS.models.incremental.html#pyFTS.models.incremental.IncrementalEnsemble.IncrementalEnsembleFTS.window_length">window_length (pyFTS.models.incremental.IncrementalEnsemble.IncrementalEnsembleFTS attribute)</a>
+
+      <ul>
+        <li><a href="pyFTS.models.incremental.html#pyFTS.models.incremental.TimeVariant.Retrainer.window_length">(pyFTS.models.incremental.TimeVariant.Retrainer attribute)</a>
+</li>
+      </ul></li>
+      <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.Measures.winkler_mean">winkler_mean() (in module pyFTS.benchmarks.Measures)</a>
+</li>
+      <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.Measures.winkler_score">winkler_score() (in module pyFTS.benchmarks.Measures)</a>
 </li>
   </ul></td>
 </tr></table>
diff --git a/docs/build/html/modules.html b/docs/build/html/modules.html
index f02c694..9d65c19 100644
--- a/docs/build/html/modules.html
+++ b/docs/build/html/modules.html
@@ -70,17 +70,17 @@
 <li class="toctree-l3"><a class="reference internal" href="pyFTS.benchmarks.html">pyFTS.benchmarks package</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="pyFTS.benchmarks.html#module-pyFTS.benchmarks">Module contents</a></li>
 <li class="toctree-l4"><a class="reference internal" href="pyFTS.benchmarks.html#submodules">Submodules</a></li>
-<li class="toctree-l4"><a class="reference internal" href="pyFTS.benchmarks.html#pyfts-benchmarks-benchmarks-module">pyFTS.benchmarks.benchmarks module</a></li>
-<li class="toctree-l4"><a class="reference internal" href="pyFTS.benchmarks.html#pyfts-benchmarks-measures-module">pyFTS.benchmarks.Measures module</a></li>
-<li class="toctree-l4"><a class="reference internal" href="pyFTS.benchmarks.html#pyfts-benchmarks-residualanalysis-module">pyFTS.benchmarks.ResidualAnalysis module</a></li>
-<li class="toctree-l4"><a class="reference internal" href="pyFTS.benchmarks.html#pyfts-benchmarks-tests-module">pyFTS.benchmarks.Tests module</a></li>
-<li class="toctree-l4"><a class="reference internal" href="pyFTS.benchmarks.html#pyfts-benchmarks-util-module">pyFTS.benchmarks.Util module</a></li>
-<li class="toctree-l4"><a class="reference internal" href="pyFTS.benchmarks.html#pyfts-benchmarks-arima-module">pyFTS.benchmarks.arima module</a></li>
-<li class="toctree-l4"><a class="reference internal" href="pyFTS.benchmarks.html#pyfts-benchmarks-knn-module">pyFTS.benchmarks.knn module</a></li>
-<li class="toctree-l4"><a class="reference internal" href="pyFTS.benchmarks.html#pyfts-benchmarks-naive-module">pyFTS.benchmarks.naive module</a></li>
-<li class="toctree-l4"><a class="reference internal" href="pyFTS.benchmarks.html#pyfts-benchmarks-quantreg-module">pyFTS.benchmarks.quantreg module</a></li>
+<li class="toctree-l4"><a class="reference internal" href="pyFTS.benchmarks.html#module-pyFTS.benchmarks.benchmarks">pyFTS.benchmarks.benchmarks module</a></li>
+<li class="toctree-l4"><a class="reference internal" href="pyFTS.benchmarks.html#module-pyFTS.benchmarks.Measures">pyFTS.benchmarks.Measures module</a></li>
+<li class="toctree-l4"><a class="reference internal" href="pyFTS.benchmarks.html#module-pyFTS.benchmarks.ResidualAnalysis">pyFTS.benchmarks.ResidualAnalysis module</a></li>
+<li class="toctree-l4"><a class="reference internal" href="pyFTS.benchmarks.html#module-pyFTS.benchmarks.Tests">pyFTS.benchmarks.Tests module</a></li>
+<li class="toctree-l4"><a class="reference internal" href="pyFTS.benchmarks.html#module-pyFTS.benchmarks.Util">pyFTS.benchmarks.Util module</a></li>
+<li class="toctree-l4"><a class="reference internal" href="pyFTS.benchmarks.html#module-pyFTS.benchmarks.arima">pyFTS.benchmarks.arima module</a></li>
+<li class="toctree-l4"><a class="reference internal" href="pyFTS.benchmarks.html#module-pyFTS.benchmarks.knn">pyFTS.benchmarks.knn module</a></li>
+<li class="toctree-l4"><a class="reference internal" href="pyFTS.benchmarks.html#module-pyFTS.benchmarks.naive">pyFTS.benchmarks.naive module</a></li>
+<li class="toctree-l4"><a class="reference internal" href="pyFTS.benchmarks.html#module-pyFTS.benchmarks.quantreg">pyFTS.benchmarks.quantreg module</a></li>
 <li class="toctree-l4"><a class="reference internal" href="pyFTS.benchmarks.html#pyfts-benchmarks-gaussianproc-module">pyFTS.benchmarks.gaussianproc module</a></li>
-<li class="toctree-l4"><a class="reference internal" href="pyFTS.benchmarks.html#pyfts-benchmarks-bsts-module">pyFTS.benchmarks.BSTS module</a></li>
+<li class="toctree-l4"><a class="reference internal" href="pyFTS.benchmarks.html#module-pyFTS.benchmarks.BSTS">pyFTS.benchmarks.BSTS module</a></li>
 </ul>
 </li>
 <li class="toctree-l3"><a class="reference internal" href="pyFTS.common.html">pyFTS.common package</a><ul>
@@ -91,10 +91,9 @@
 <li class="toctree-l4"><a class="reference internal" href="pyFTS.common.html#module-pyFTS.common.FuzzySet">pyFTS.common.FuzzySet module</a></li>
 <li class="toctree-l4"><a class="reference internal" href="pyFTS.common.html#module-pyFTS.common.Membership">pyFTS.common.Membership module</a></li>
 <li class="toctree-l4"><a class="reference internal" href="pyFTS.common.html#module-pyFTS.common.SortedCollection">pyFTS.common.SortedCollection module</a></li>
-<li class="toctree-l4"><a class="reference internal" href="pyFTS.common.html#pyfts-common-transformations-module">pyFTS.common.transformations module</a></li>
-<li class="toctree-l4"><a class="reference internal" href="pyFTS.common.html#pyfts-common-util-module">pyFTS.common.Util module</a></li>
+<li class="toctree-l4"><a class="reference internal" href="pyFTS.common.html#module-pyFTS.common.Util">pyFTS.common.Util module</a></li>
 <li class="toctree-l4"><a class="reference internal" href="pyFTS.common.html#module-pyFTS.common.flrg">pyFTS.common.flrg module</a></li>
-<li class="toctree-l4"><a class="reference internal" href="pyFTS.common.html#pyfts-common-fts-module">pyFTS.common.fts module</a></li>
+<li class="toctree-l4"><a class="reference internal" href="pyFTS.common.html#module-pyFTS.common.fts">pyFTS.common.fts module</a></li>
 <li class="toctree-l4"><a class="reference internal" href="pyFTS.common.html#module-pyFTS.common.tree">pyFTS.common.tree module</a></li>
 </ul>
 </li>
@@ -107,6 +106,7 @@
 <li class="toctree-l4"><a class="reference internal" href="pyFTS.common.transformations.html#pyfts-common-transformations-normalization-module">pyFTS.common.transformations.normalization module</a></li>
 <li class="toctree-l4"><a class="reference internal" href="pyFTS.common.transformations.html#pyfts-common-transformations-roi-module">pyFTS.common.transformations.roi module</a></li>
 <li class="toctree-l4"><a class="reference internal" href="pyFTS.common.transformations.html#pyfts-common-transformations-scale-module">pyFTS.common.transformations.scale module</a></li>
+<li class="toctree-l4"><a class="reference internal" href="pyFTS.common.transformations.html#pyfts-common-transformations-smoothing-module">pyFTS.common.transformations.smoothing module</a></li>
 <li class="toctree-l4"><a class="reference internal" href="pyFTS.common.transformations.html#pyfts-common-transformations-som-module">pyFTS.common.transformations.som module</a></li>
 <li class="toctree-l4"><a class="reference internal" href="pyFTS.common.transformations.html#pyfts-common-transformations-transformation-module">pyFTS.common.transformations.transformation module</a></li>
 <li class="toctree-l4"><a class="reference internal" href="pyFTS.common.transformations.html#pyfts-common-transformations-trend-module">pyFTS.common.transformations.trend module</a></li>
@@ -143,7 +143,7 @@
 <li class="toctree-l3"><a class="reference internal" href="pyFTS.distributed.html">pyFTS.distributed package</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="pyFTS.distributed.html#module-pyFTS.distributed">Module contents</a></li>
 <li class="toctree-l4"><a class="reference internal" href="pyFTS.distributed.html#submodules">Submodules</a></li>
-<li class="toctree-l4"><a class="reference internal" href="pyFTS.distributed.html#pyfts-distributed-dispy-module">pyFTS.distributed.dispy module</a></li>
+<li class="toctree-l4"><a class="reference internal" href="pyFTS.distributed.html#module-pyFTS.distributed.dispy">pyFTS.distributed.dispy module</a></li>
 <li class="toctree-l4"><a class="reference internal" href="pyFTS.distributed.html#pyfts-distributed-spark-module">pyFTS.distributed.spark module</a></li>
 </ul>
 </li>
@@ -151,8 +151,8 @@
 <li class="toctree-l4"><a class="reference internal" href="pyFTS.hyperparam.html#module-pyFTS.hyperparam">Module contents</a></li>
 <li class="toctree-l4"><a class="reference internal" href="pyFTS.hyperparam.html#submodules">Submodules</a></li>
 <li class="toctree-l4"><a class="reference internal" href="pyFTS.hyperparam.html#module-pyFTS.hyperparam.Util">pyFTS.hyperparam.Util module</a></li>
-<li class="toctree-l4"><a class="reference internal" href="pyFTS.hyperparam.html#pyfts-hyperparam-gridsearch-module">pyFTS.hyperparam.GridSearch module</a></li>
-<li class="toctree-l4"><a class="reference internal" href="pyFTS.hyperparam.html#pyfts-hyperparam-evolutionary-module">pyFTS.hyperparam.Evolutionary module</a></li>
+<li class="toctree-l4"><a class="reference internal" href="pyFTS.hyperparam.html#module-pyFTS.hyperparam.GridSearch">pyFTS.hyperparam.GridSearch module</a></li>
+<li class="toctree-l4"><a class="reference internal" href="pyFTS.hyperparam.html#module-pyFTS.hyperparam.Evolutionary">pyFTS.hyperparam.Evolutionary module</a></li>
 </ul>
 </li>
 <li class="toctree-l3"><a class="reference internal" href="pyFTS.models.html">pyFTS.models package</a><ul>
@@ -166,16 +166,16 @@
 </ul>
 </li>
 <li class="toctree-l4"><a class="reference internal" href="pyFTS.models.html#submodules">Submodules</a></li>
-<li class="toctree-l4"><a class="reference internal" href="pyFTS.models.html#pyfts-models-song-module">pyFTS.models.song module</a></li>
-<li class="toctree-l4"><a class="reference internal" href="pyFTS.models.html#pyfts-models-chen-module">pyFTS.models.chen module</a></li>
-<li class="toctree-l4"><a class="reference internal" href="pyFTS.models.html#pyfts-models-yu-module">pyFTS.models.yu module</a></li>
-<li class="toctree-l4"><a class="reference internal" href="pyFTS.models.html#pyfts-models-cheng-module">pyFTS.models.cheng module</a></li>
-<li class="toctree-l4"><a class="reference internal" href="pyFTS.models.html#pyfts-models-hofts-module">pyFTS.models.hofts module</a></li>
-<li class="toctree-l4"><a class="reference internal" href="pyFTS.models.html#pyfts-models-hwang-module">pyFTS.models.hwang module</a></li>
-<li class="toctree-l4"><a class="reference internal" href="pyFTS.models.html#pyfts-models-ifts-module">pyFTS.models.ifts module</a></li>
-<li class="toctree-l4"><a class="reference internal" href="pyFTS.models.html#pyfts-models-ismailefendi-module">pyFTS.models.ismailefendi module</a></li>
-<li class="toctree-l4"><a class="reference internal" href="pyFTS.models.html#pyfts-models-pwfts-module">pyFTS.models.pwfts module</a></li>
-<li class="toctree-l4"><a class="reference internal" href="pyFTS.models.html#pyfts-models-sadaei-module">pyFTS.models.sadaei module</a></li>
+<li class="toctree-l4"><a class="reference internal" href="pyFTS.models.html#module-pyFTS.models.song">pyFTS.models.song module</a></li>
+<li class="toctree-l4"><a class="reference internal" href="pyFTS.models.html#module-pyFTS.models.chen">pyFTS.models.chen module</a></li>
+<li class="toctree-l4"><a class="reference internal" href="pyFTS.models.html#module-pyFTS.models.yu">pyFTS.models.yu module</a></li>
+<li class="toctree-l4"><a class="reference internal" href="pyFTS.models.html#module-pyFTS.models.cheng">pyFTS.models.cheng module</a></li>
+<li class="toctree-l4"><a class="reference internal" href="pyFTS.models.html#module-pyFTS.models.hofts">pyFTS.models.hofts module</a></li>
+<li class="toctree-l4"><a class="reference internal" href="pyFTS.models.html#module-pyFTS.models.hwang">pyFTS.models.hwang module</a></li>
+<li class="toctree-l4"><a class="reference internal" href="pyFTS.models.html#module-pyFTS.models.ifts">pyFTS.models.ifts module</a></li>
+<li class="toctree-l4"><a class="reference internal" href="pyFTS.models.html#module-pyFTS.models.ismailefendi">pyFTS.models.ismailefendi module</a></li>
+<li class="toctree-l4"><a class="reference internal" href="pyFTS.models.html#module-pyFTS.models.pwfts">pyFTS.models.pwfts module</a></li>
+<li class="toctree-l4"><a class="reference internal" href="pyFTS.models.html#module-pyFTS.models.sadaei">pyFTS.models.sadaei module</a></li>
 </ul>
 </li>
 <li class="toctree-l3"><a class="reference internal" href="pyFTS.partitioners.html">pyFTS.partitioners package</a><ul>
@@ -186,19 +186,19 @@
 <li class="toctree-l4"><a class="reference internal" href="pyFTS.partitioners.html#module-pyFTS.partitioners.Entropy">pyFTS.partitioners.Entropy module</a></li>
 <li class="toctree-l4"><a class="reference internal" href="pyFTS.partitioners.html#module-pyFTS.partitioners.FCM">pyFTS.partitioners.FCM module</a></li>
 <li class="toctree-l4"><a class="reference internal" href="pyFTS.partitioners.html#module-pyFTS.partitioners.Grid">pyFTS.partitioners.Grid module</a></li>
-<li class="toctree-l4"><a class="reference internal" href="pyFTS.partitioners.html#pyfts-partitioners-huarng-module">pyFTS.partitioners.Huarng module</a></li>
+<li class="toctree-l4"><a class="reference internal" href="pyFTS.partitioners.html#module-pyFTS.partitioners.Huarng">pyFTS.partitioners.Huarng module</a></li>
 <li class="toctree-l4"><a class="reference internal" href="pyFTS.partitioners.html#module-pyFTS.partitioners.Singleton">pyFTS.partitioners.Singleton module</a></li>
 <li class="toctree-l4"><a class="reference internal" href="pyFTS.partitioners.html#module-pyFTS.partitioners.Simple">pyFTS.partitioners.Simple module</a></li>
 <li class="toctree-l4"><a class="reference internal" href="pyFTS.partitioners.html#module-pyFTS.partitioners.SubClust">pyFTS.partitioners.SubClust module</a></li>
-<li class="toctree-l4"><a class="reference internal" href="pyFTS.partitioners.html#pyfts-partitioners-util-module">pyFTS.partitioners.Util module</a></li>
-<li class="toctree-l4"><a class="reference internal" href="pyFTS.partitioners.html#pyfts-partitioners-parallel-util-module">pyFTS.partitioners.parallel_util module</a></li>
+<li class="toctree-l4"><a class="reference internal" href="pyFTS.partitioners.html#module-pyFTS.partitioners.Util">pyFTS.partitioners.Util module</a></li>
+<li class="toctree-l4"><a class="reference internal" href="pyFTS.partitioners.html#module-pyFTS.partitioners.parallel_util">pyFTS.partitioners.parallel_util module</a></li>
 </ul>
 </li>
 <li class="toctree-l3"><a class="reference internal" href="pyFTS.probabilistic.html">pyFTS.probabilistic package</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="pyFTS.probabilistic.html#module-pyFTS.probabilistic">Module contents</a></li>
 <li class="toctree-l4"><a class="reference internal" href="pyFTS.probabilistic.html#submodules">Submodules</a></li>
-<li class="toctree-l4"><a class="reference internal" href="pyFTS.probabilistic.html#pyfts-probabilistic-probabilitydistribution-module">pyFTS.probabilistic.ProbabilityDistribution module</a></li>
-<li class="toctree-l4"><a class="reference internal" href="pyFTS.probabilistic.html#pyfts-probabilistic-kde-module">pyFTS.probabilistic.kde module</a></li>
+<li class="toctree-l4"><a class="reference internal" href="pyFTS.probabilistic.html#module-pyFTS.probabilistic.ProbabilityDistribution">pyFTS.probabilistic.ProbabilityDistribution module</a></li>
+<li class="toctree-l4"><a class="reference internal" href="pyFTS.probabilistic.html#module-pyFTS.probabilistic.kde">pyFTS.probabilistic.kde module</a></li>
 </ul>
 </li>
 </ul>
diff --git a/docs/build/html/objects.inv b/docs/build/html/objects.inv
index 66ad7f1..46a6754 100644
Binary files a/docs/build/html/objects.inv and b/docs/build/html/objects.inv differ
diff --git a/docs/build/html/py-modindex.html b/docs/build/html/py-modindex.html
index 496e274..611a9a7 100644
--- a/docs/build/html/py-modindex.html
+++ b/docs/build/html/py-modindex.html
@@ -78,6 +78,56 @@
        <td>&#160;&#160;&#160;
        <a href="pyFTS.benchmarks.html#module-pyFTS.benchmarks"><code class="xref">pyFTS.benchmarks</code></a></td><td>
        <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="pyFTS.benchmarks.html#module-pyFTS.benchmarks.arima"><code class="xref">pyFTS.benchmarks.arima</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="pyFTS.benchmarks.html#module-pyFTS.benchmarks.benchmarks"><code class="xref">pyFTS.benchmarks.benchmarks</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="pyFTS.benchmarks.html#module-pyFTS.benchmarks.BSTS"><code class="xref">pyFTS.benchmarks.BSTS</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="pyFTS.benchmarks.html#module-pyFTS.benchmarks.knn"><code class="xref">pyFTS.benchmarks.knn</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="pyFTS.benchmarks.html#module-pyFTS.benchmarks.Measures"><code class="xref">pyFTS.benchmarks.Measures</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="pyFTS.benchmarks.html#module-pyFTS.benchmarks.naive"><code class="xref">pyFTS.benchmarks.naive</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="pyFTS.benchmarks.html#module-pyFTS.benchmarks.quantreg"><code class="xref">pyFTS.benchmarks.quantreg</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="pyFTS.benchmarks.html#module-pyFTS.benchmarks.ResidualAnalysis"><code class="xref">pyFTS.benchmarks.ResidualAnalysis</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="pyFTS.benchmarks.html#module-pyFTS.benchmarks.Tests"><code class="xref">pyFTS.benchmarks.Tests</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="pyFTS.benchmarks.html#module-pyFTS.benchmarks.Util"><code class="xref">pyFTS.benchmarks.Util</code></a></td><td>
+       <em></em></td></tr>
      <tr class="cg-1">
        <td></td>
        <td>&#160;&#160;&#160;
@@ -98,6 +148,11 @@
        <td>&#160;&#160;&#160;
        <a href="pyFTS.common.html#module-pyFTS.common.flrg"><code class="xref">pyFTS.common.flrg</code></a></td><td>
        <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="pyFTS.common.html#module-pyFTS.common.fts"><code class="xref">pyFTS.common.fts</code></a></td><td>
+       <em></em></td></tr>
      <tr class="cg-1">
        <td></td>
        <td>&#160;&#160;&#160;
@@ -118,6 +173,11 @@
        <td>&#160;&#160;&#160;
        <a href="pyFTS.common.html#module-pyFTS.common.tree"><code class="xref">pyFTS.common.tree</code></a></td><td>
        <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="pyFTS.common.html#module-pyFTS.common.Util"><code class="xref">pyFTS.common.Util</code></a></td><td>
+       <em></em></td></tr>
      <tr class="cg-1">
        <td></td>
        <td>&#160;&#160;&#160;
@@ -243,11 +303,26 @@
        <td>&#160;&#160;&#160;
        <a href="pyFTS.distributed.html#module-pyFTS.distributed"><code class="xref">pyFTS.distributed</code></a></td><td>
        <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="pyFTS.distributed.html#module-pyFTS.distributed.dispy"><code class="xref">pyFTS.distributed.dispy</code></a></td><td>
+       <em></em></td></tr>
      <tr class="cg-1">
        <td></td>
        <td>&#160;&#160;&#160;
        <a href="pyFTS.hyperparam.html#module-pyFTS.hyperparam"><code class="xref">pyFTS.hyperparam</code></a></td><td>
        <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="pyFTS.hyperparam.html#module-pyFTS.hyperparam.Evolutionary"><code class="xref">pyFTS.hyperparam.Evolutionary</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="pyFTS.hyperparam.html#module-pyFTS.hyperparam.GridSearch"><code class="xref">pyFTS.hyperparam.GridSearch</code></a></td><td>
+       <em></em></td></tr>
      <tr class="cg-1">
        <td></td>
        <td>&#160;&#160;&#160;
@@ -258,21 +333,76 @@
        <td>&#160;&#160;&#160;
        <a href="pyFTS.models.html#module-pyFTS.models"><code class="xref">pyFTS.models</code></a></td><td>
        <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="pyFTS.models.html#module-pyFTS.models.chen"><code class="xref">pyFTS.models.chen</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="pyFTS.models.html#module-pyFTS.models.cheng"><code class="xref">pyFTS.models.cheng</code></a></td><td>
+       <em></em></td></tr>
      <tr class="cg-1">
        <td></td>
        <td>&#160;&#160;&#160;
        <a href="pyFTS.models.ensemble.html#module-pyFTS.models.ensemble"><code class="xref">pyFTS.models.ensemble</code></a></td><td>
        <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="pyFTS.models.ensemble.html#module-pyFTS.models.ensemble.ensemble"><code class="xref">pyFTS.models.ensemble.ensemble</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="pyFTS.models.ensemble.html#module-pyFTS.models.ensemble.multiseasonal"><code class="xref">pyFTS.models.ensemble.multiseasonal</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="pyFTS.models.html#module-pyFTS.models.hofts"><code class="xref">pyFTS.models.hofts</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="pyFTS.models.html#module-pyFTS.models.hwang"><code class="xref">pyFTS.models.hwang</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="pyFTS.models.html#module-pyFTS.models.ifts"><code class="xref">pyFTS.models.ifts</code></a></td><td>
+       <em></em></td></tr>
      <tr class="cg-1">
        <td></td>
        <td>&#160;&#160;&#160;
        <a href="pyFTS.models.incremental.html#module-pyFTS.models.incremental"><code class="xref">pyFTS.models.incremental</code></a></td><td>
        <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="pyFTS.models.incremental.html#module-pyFTS.models.incremental.IncrementalEnsemble"><code class="xref">pyFTS.models.incremental.IncrementalEnsemble</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="pyFTS.models.incremental.html#module-pyFTS.models.incremental.TimeVariant"><code class="xref">pyFTS.models.incremental.TimeVariant</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="pyFTS.models.html#module-pyFTS.models.ismailefendi"><code class="xref">pyFTS.models.ismailefendi</code></a></td><td>
+       <em></em></td></tr>
      <tr class="cg-1">
        <td></td>
        <td>&#160;&#160;&#160;
        <a href="pyFTS.models.multivariate.html#module-pyFTS.models.multivariate"><code class="xref">pyFTS.models.multivariate</code></a></td><td>
        <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="pyFTS.models.multivariate.html#module-pyFTS.models.multivariate.cmvfts"><code class="xref">pyFTS.models.multivariate.cmvfts</code></a></td><td>
+       <em></em></td></tr>
      <tr class="cg-1">
        <td></td>
        <td>&#160;&#160;&#160;
@@ -288,16 +418,36 @@
        <td>&#160;&#160;&#160;
        <a href="pyFTS.models.multivariate.html#module-pyFTS.models.multivariate.flrg"><code class="xref">pyFTS.models.multivariate.flrg</code></a></td><td>
        <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="pyFTS.models.multivariate.html#module-pyFTS.models.multivariate.granular"><code class="xref">pyFTS.models.multivariate.granular</code></a></td><td>
+       <em></em></td></tr>
      <tr class="cg-1">
        <td></td>
        <td>&#160;&#160;&#160;
        <a href="pyFTS.models.multivariate.html#module-pyFTS.models.multivariate.grid"><code class="xref">pyFTS.models.multivariate.grid</code></a></td><td>
        <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="pyFTS.models.multivariate.html#module-pyFTS.models.multivariate.mvfts"><code class="xref">pyFTS.models.multivariate.mvfts</code></a></td><td>
+       <em></em></td></tr>
      <tr class="cg-1">
        <td></td>
        <td>&#160;&#160;&#160;
        <a href="pyFTS.models.multivariate.html#module-pyFTS.models.multivariate.partitioner"><code class="xref">pyFTS.models.multivariate.partitioner</code></a></td><td>
        <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="pyFTS.models.multivariate.html#module-pyFTS.models.multivariate.variable"><code class="xref">pyFTS.models.multivariate.variable</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="pyFTS.models.multivariate.html#module-pyFTS.models.multivariate.wmvfts"><code class="xref">pyFTS.models.multivariate.wmvfts</code></a></td><td>
+       <em></em></td></tr>
      <tr class="cg-1">
        <td></td>
        <td>&#160;&#160;&#160;
@@ -308,11 +458,26 @@
        <td>&#160;&#160;&#160;
        <a href="pyFTS.models.nonstationary.html#module-pyFTS.models.nonstationary.common"><code class="xref">pyFTS.models.nonstationary.common</code></a></td><td>
        <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="pyFTS.models.nonstationary.html#module-pyFTS.models.nonstationary.cvfts"><code class="xref">pyFTS.models.nonstationary.cvfts</code></a></td><td>
+       <em></em></td></tr>
      <tr class="cg-1">
        <td></td>
        <td>&#160;&#160;&#160;
        <a href="pyFTS.models.nonstationary.html#module-pyFTS.models.nonstationary.flrg"><code class="xref">pyFTS.models.nonstationary.flrg</code></a></td><td>
        <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="pyFTS.models.nonstationary.html#module-pyFTS.models.nonstationary.honsfts"><code class="xref">pyFTS.models.nonstationary.honsfts</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="pyFTS.models.nonstationary.html#module-pyFTS.models.nonstationary.nsfts"><code class="xref">pyFTS.models.nonstationary.nsfts</code></a></td><td>
+       <em></em></td></tr>
      <tr class="cg-1">
        <td></td>
        <td>&#160;&#160;&#160;
@@ -323,16 +488,41 @@
        <td>&#160;&#160;&#160;
        <a href="pyFTS.models.nonstationary.html#module-pyFTS.models.nonstationary.perturbation"><code class="xref">pyFTS.models.nonstationary.perturbation</code></a></td><td>
        <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="pyFTS.models.nonstationary.html#module-pyFTS.models.nonstationary.util"><code class="xref">pyFTS.models.nonstationary.util</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="pyFTS.models.html#module-pyFTS.models.pwfts"><code class="xref">pyFTS.models.pwfts</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="pyFTS.models.html#module-pyFTS.models.sadaei"><code class="xref">pyFTS.models.sadaei</code></a></td><td>
+       <em></em></td></tr>
      <tr class="cg-1">
        <td></td>
        <td>&#160;&#160;&#160;
        <a href="pyFTS.models.seasonal.html#module-pyFTS.models.seasonal"><code class="xref">pyFTS.models.seasonal</code></a></td><td>
        <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="pyFTS.models.seasonal.html#module-pyFTS.models.seasonal.cmsfts"><code class="xref">pyFTS.models.seasonal.cmsfts</code></a></td><td>
+       <em></em></td></tr>
      <tr class="cg-1">
        <td></td>
        <td>&#160;&#160;&#160;
        <a href="pyFTS.models.seasonal.html#module-pyFTS.models.seasonal.common"><code class="xref">pyFTS.models.seasonal.common</code></a></td><td>
        <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="pyFTS.models.seasonal.html#module-pyFTS.models.seasonal.msfts"><code class="xref">pyFTS.models.seasonal.msfts</code></a></td><td>
+       <em></em></td></tr>
      <tr class="cg-1">
        <td></td>
        <td>&#160;&#160;&#160;
@@ -343,6 +533,21 @@
        <td>&#160;&#160;&#160;
        <a href="pyFTS.models.seasonal.html#module-pyFTS.models.seasonal.SeasonalIndexer"><code class="xref">pyFTS.models.seasonal.SeasonalIndexer</code></a></td><td>
        <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="pyFTS.models.seasonal.html#module-pyFTS.models.seasonal.sfts"><code class="xref">pyFTS.models.seasonal.sfts</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="pyFTS.models.html#module-pyFTS.models.song"><code class="xref">pyFTS.models.song</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="pyFTS.models.html#module-pyFTS.models.yu"><code class="xref">pyFTS.models.yu</code></a></td><td>
+       <em></em></td></tr>
      <tr class="cg-1">
        <td></td>
        <td>&#160;&#160;&#160;
@@ -368,6 +573,16 @@
        <td>&#160;&#160;&#160;
        <a href="pyFTS.partitioners.html#module-pyFTS.partitioners.Grid"><code class="xref">pyFTS.partitioners.Grid</code></a></td><td>
        <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="pyFTS.partitioners.html#module-pyFTS.partitioners.Huarng"><code class="xref">pyFTS.partitioners.Huarng</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="pyFTS.partitioners.html#module-pyFTS.partitioners.parallel_util"><code class="xref">pyFTS.partitioners.parallel_util</code></a></td><td>
+       <em></em></td></tr>
      <tr class="cg-1">
        <td></td>
        <td>&#160;&#160;&#160;
@@ -388,11 +603,26 @@
        <td>&#160;&#160;&#160;
        <a href="pyFTS.partitioners.html#module-pyFTS.partitioners.SubClust"><code class="xref">pyFTS.partitioners.SubClust</code></a></td><td>
        <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="pyFTS.partitioners.html#module-pyFTS.partitioners.Util"><code class="xref">pyFTS.partitioners.Util</code></a></td><td>
+       <em></em></td></tr>
      <tr class="cg-1">
        <td></td>
        <td>&#160;&#160;&#160;
        <a href="pyFTS.probabilistic.html#module-pyFTS.probabilistic"><code class="xref">pyFTS.probabilistic</code></a></td><td>
        <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="pyFTS.probabilistic.html#module-pyFTS.probabilistic.kde"><code class="xref">pyFTS.probabilistic.kde</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="pyFTS.probabilistic.html#module-pyFTS.probabilistic.ProbabilityDistribution"><code class="xref">pyFTS.probabilistic.ProbabilityDistribution</code></a></td><td>
+       <em></em></td></tr>
    </table>
 
 
diff --git a/docs/build/html/pyFTS.benchmarks.html b/docs/build/html/pyFTS.benchmarks.html
index 472bbfc..2454ef6 100644
--- a/docs/build/html/pyFTS.benchmarks.html
+++ b/docs/build/html/pyFTS.benchmarks.html
@@ -72,38 +72,1828 @@
 <div class="section" id="submodules">
 <h2>Submodules<a class="headerlink" href="#submodules" title="Permalink to this headline">¶</a></h2>
 </div>
-<div class="section" id="pyfts-benchmarks-benchmarks-module">
-<h2>pyFTS.benchmarks.benchmarks module<a class="headerlink" href="#pyfts-benchmarks-benchmarks-module" title="Permalink to this headline">¶</a></h2>
+<div class="section" id="module-pyFTS.benchmarks.benchmarks">
+<span id="pyfts-benchmarks-benchmarks-module"></span><h2>pyFTS.benchmarks.benchmarks module<a class="headerlink" href="#module-pyFTS.benchmarks.benchmarks" title="Permalink to this headline">¶</a></h2>
+<p>Benchmarks methods for FTS methods</p>
+<dl class="py function">
+<dt id="pyFTS.benchmarks.benchmarks.SelecaoSimples_MenorRMSE">
+<code class="sig-prename descclassname">pyFTS.benchmarks.benchmarks.</code><code class="sig-name descname">SelecaoSimples_MenorRMSE</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">original</span></em>, <em class="sig-param"><span class="n">parameters</span></em>, <em class="sig-param"><span class="n">modelo</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/benchmarks.html#SelecaoSimples_MenorRMSE"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.SelecaoSimples_MenorRMSE" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.benchmarks.benchmarks.common_process_interval_jobs">
+<code class="sig-prename descclassname">pyFTS.benchmarks.benchmarks.</code><code class="sig-name descname">common_process_interval_jobs</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">conn</span></em>, <em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="n">job</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/benchmarks.html#common_process_interval_jobs"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.common_process_interval_jobs" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.benchmarks.benchmarks.common_process_point_jobs">
+<code class="sig-prename descclassname">pyFTS.benchmarks.benchmarks.</code><code class="sig-name descname">common_process_point_jobs</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">conn</span></em>, <em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="n">job</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/benchmarks.html#common_process_point_jobs"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.common_process_point_jobs" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.benchmarks.benchmarks.common_process_probabilistic_jobs">
+<code class="sig-prename descclassname">pyFTS.benchmarks.benchmarks.</code><code class="sig-name descname">common_process_probabilistic_jobs</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">conn</span></em>, <em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="n">job</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/benchmarks.html#common_process_probabilistic_jobs"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.common_process_probabilistic_jobs" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.benchmarks.benchmarks.common_process_time_jobs">
+<code class="sig-prename descclassname">pyFTS.benchmarks.benchmarks.</code><code class="sig-name descname">common_process_time_jobs</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">conn</span></em>, <em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="n">job</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/benchmarks.html#common_process_time_jobs"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.common_process_time_jobs" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.benchmarks.benchmarks.compareModelsPlot">
+<code class="sig-prename descclassname">pyFTS.benchmarks.benchmarks.</code><code class="sig-name descname">compareModelsPlot</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">original</span></em>, <em class="sig-param"><span class="n">models_fo</span></em>, <em class="sig-param"><span class="n">models_ho</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/benchmarks.html#compareModelsPlot"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.compareModelsPlot" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.benchmarks.benchmarks.compareModelsTable">
+<code class="sig-prename descclassname">pyFTS.benchmarks.benchmarks.</code><code class="sig-name descname">compareModelsTable</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">original</span></em>, <em class="sig-param"><span class="n">models_fo</span></em>, <em class="sig-param"><span class="n">models_ho</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/benchmarks.html#compareModelsTable"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.compareModelsTable" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.benchmarks.benchmarks.distributed_model_train_test_time">
+<code class="sig-prename descclassname">pyFTS.benchmarks.benchmarks.</code><code class="sig-name descname">distributed_model_train_test_time</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">models</span></em>, <em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="n">windowsize</span></em>, <em class="sig-param"><span class="n">train</span><span class="o">=</span><span class="default_value">0.8</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/benchmarks.html#distributed_model_train_test_time"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.distributed_model_train_test_time" title="Permalink to this definition">¶</a></dt>
+<dd><p>Assess the train and test times for a given list of configured models and save the results on a database.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>models</strong> – A list of FTS models already configured, but not yet trained,</p></li>
+<li><p><strong>data</strong> – time series data, including train and test data</p></li>
+<li><p><strong>windowsize</strong> – Train/test data windows</p></li>
+<li><p><strong>train</strong> – Percent of data window that will be used to train the models</p></li>
+<li><p><strong>kwargs</strong> – </p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.benchmarks.benchmarks.get_benchmark_interval_methods">
+<code class="sig-prename descclassname">pyFTS.benchmarks.benchmarks.</code><code class="sig-name descname">get_benchmark_interval_methods</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/benchmarks.html#get_benchmark_interval_methods"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.get_benchmark_interval_methods" title="Permalink to this definition">¶</a></dt>
+<dd><p>Return all non FTS methods for point_to_interval forecasting</p>
+</dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.benchmarks.benchmarks.get_benchmark_point_methods">
+<code class="sig-prename descclassname">pyFTS.benchmarks.benchmarks.</code><code class="sig-name descname">get_benchmark_point_methods</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/benchmarks.html#get_benchmark_point_methods"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.get_benchmark_point_methods" title="Permalink to this definition">¶</a></dt>
+<dd><p>Return all non FTS methods for point forecasting</p>
+</dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.benchmarks.benchmarks.get_benchmark_probabilistic_methods">
+<code class="sig-prename descclassname">pyFTS.benchmarks.benchmarks.</code><code class="sig-name descname">get_benchmark_probabilistic_methods</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/benchmarks.html#get_benchmark_probabilistic_methods"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.get_benchmark_probabilistic_methods" title="Permalink to this definition">¶</a></dt>
+<dd><p>Return all FTS methods for probabilistic forecasting</p>
+</dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.benchmarks.benchmarks.get_interval_methods">
+<code class="sig-prename descclassname">pyFTS.benchmarks.benchmarks.</code><code class="sig-name descname">get_interval_methods</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/benchmarks.html#get_interval_methods"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.get_interval_methods" title="Permalink to this definition">¶</a></dt>
+<dd><p>Return all FTS methods for point_to_interval forecasting</p>
+</dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.benchmarks.benchmarks.get_point_methods">
+<code class="sig-prename descclassname">pyFTS.benchmarks.benchmarks.</code><code class="sig-name descname">get_point_methods</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/benchmarks.html#get_point_methods"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.get_point_methods" title="Permalink to this definition">¶</a></dt>
+<dd><p>Return all FTS methods for point forecasting</p>
+</dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.benchmarks.benchmarks.get_point_multivariate_methods">
+<code class="sig-prename descclassname">pyFTS.benchmarks.benchmarks.</code><code class="sig-name descname">get_point_multivariate_methods</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/benchmarks.html#get_point_multivariate_methods"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.get_point_multivariate_methods" title="Permalink to this definition">¶</a></dt>
+<dd><p>Return all multivariate FTS methods por point forecasting</p>
+</dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.benchmarks.benchmarks.get_probabilistic_methods">
+<code class="sig-prename descclassname">pyFTS.benchmarks.benchmarks.</code><code class="sig-name descname">get_probabilistic_methods</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/benchmarks.html#get_probabilistic_methods"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.get_probabilistic_methods" title="Permalink to this definition">¶</a></dt>
+<dd><p>Return all FTS methods for probabilistic forecasting</p>
+</dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.benchmarks.benchmarks.multivariate_sliding_window_benchmarks2">
+<code class="sig-prename descclassname">pyFTS.benchmarks.benchmarks.</code><code class="sig-name descname">multivariate_sliding_window_benchmarks2</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="n">windowsize</span></em>, <em class="sig-param"><span class="n">train</span><span class="o">=</span><span class="default_value">0.8</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/benchmarks.html#multivariate_sliding_window_benchmarks2"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.multivariate_sliding_window_benchmarks2" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.benchmarks.benchmarks.mv_run_interval2">
+<code class="sig-prename descclassname">pyFTS.benchmarks.benchmarks.</code><code class="sig-name descname">mv_run_interval2</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">mfts</span></em>, <em class="sig-param"><span class="n">train_data</span></em>, <em class="sig-param"><span class="n">test_data</span></em>, <em class="sig-param"><span class="n">window_key</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/benchmarks.html#mv_run_interval2"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.mv_run_interval2" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.benchmarks.benchmarks.mv_run_point2">
+<code class="sig-prename descclassname">pyFTS.benchmarks.benchmarks.</code><code class="sig-name descname">mv_run_point2</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">mfts</span></em>, <em class="sig-param"><span class="n">train_data</span></em>, <em class="sig-param"><span class="n">test_data</span></em>, <em class="sig-param"><span class="n">window_key</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/benchmarks.html#mv_run_point2"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.mv_run_point2" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.benchmarks.benchmarks.mv_run_probabilistic2">
+<code class="sig-prename descclassname">pyFTS.benchmarks.benchmarks.</code><code class="sig-name descname">mv_run_probabilistic2</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">mfts</span></em>, <em class="sig-param"><span class="n">train_data</span></em>, <em class="sig-param"><span class="n">test_data</span></em>, <em class="sig-param"><span class="n">window_key</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/benchmarks.html#mv_run_probabilistic2"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.mv_run_probabilistic2" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.benchmarks.benchmarks.pftsExploreOrderAndPartitions">
+<code class="sig-prename descclassname">pyFTS.benchmarks.benchmarks.</code><code class="sig-name descname">pftsExploreOrderAndPartitions</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="n">save</span><span class="o">=</span><span class="default_value">False</span></em>, <em class="sig-param"><span class="n">file</span><span class="o">=</span><span class="default_value">None</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/benchmarks.html#pftsExploreOrderAndPartitions"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.pftsExploreOrderAndPartitions" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.benchmarks.benchmarks.plotCompared">
+<code class="sig-prename descclassname">pyFTS.benchmarks.benchmarks.</code><code class="sig-name descname">plotCompared</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">original</span></em>, <em class="sig-param"><span class="n">forecasts</span></em>, <em class="sig-param"><span class="n">labels</span></em>, <em class="sig-param"><span class="n">title</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/benchmarks.html#plotCompared"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.plotCompared" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.benchmarks.benchmarks.plot_compared_series">
+<code class="sig-prename descclassname">pyFTS.benchmarks.benchmarks.</code><code class="sig-name descname">plot_compared_series</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">original</span></em>, <em class="sig-param"><span class="n">models</span></em>, <em class="sig-param"><span class="n">colors</span></em>, <em class="sig-param"><span class="n">typeonlegend</span><span class="o">=</span><span class="default_value">False</span></em>, <em class="sig-param"><span class="n">save</span><span class="o">=</span><span class="default_value">False</span></em>, <em class="sig-param"><span class="n">file</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">tam</span><span class="o">=</span><span class="default_value">[20, 5]</span></em>, <em class="sig-param"><span class="n">points</span><span class="o">=</span><span class="default_value">True</span></em>, <em class="sig-param"><span class="n">intervals</span><span class="o">=</span><span class="default_value">True</span></em>, <em class="sig-param"><span class="n">linewidth</span><span class="o">=</span><span class="default_value">1.5</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/benchmarks.html#plot_compared_series"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.plot_compared_series" title="Permalink to this definition">¶</a></dt>
+<dd><p>Plot the forecasts of several one step ahead models, by point or by interval</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>original</strong> – Original time series data (list)</p></li>
+<li><p><strong>models</strong> – List of models to compare</p></li>
+<li><p><strong>colors</strong> – List of models colors</p></li>
+<li><p><strong>typeonlegend</strong> – Add the type of forecast (point / interval) on legend</p></li>
+<li><p><strong>save</strong> – Save the picture on file</p></li>
+<li><p><strong>file</strong> – Filename to save the picture</p></li>
+<li><p><strong>tam</strong> – Size of the picture</p></li>
+<li><p><strong>points</strong> – True to plot the point forecasts, False otherwise</p></li>
+<li><p><strong>intervals</strong> – True to plot the interval forecasts, False otherwise</p></li>
+<li><p><strong>linewidth</strong> – </p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.benchmarks.benchmarks.plot_point">
+<code class="sig-prename descclassname">pyFTS.benchmarks.benchmarks.</code><code class="sig-name descname">plot_point</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">axis</span></em>, <em class="sig-param"><span class="n">points</span></em>, <em class="sig-param"><span class="n">order</span></em>, <em class="sig-param"><span class="n">label</span></em>, <em class="sig-param"><span class="n">color</span><span class="o">=</span><span class="default_value">'red'</span></em>, <em class="sig-param"><span class="n">ls</span><span class="o">=</span><span class="default_value">'-'</span></em>, <em class="sig-param"><span class="n">linewidth</span><span class="o">=</span><span class="default_value">1</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/benchmarks.html#plot_point"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.plot_point" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.benchmarks.benchmarks.print_distribution_statistics">
+<code class="sig-prename descclassname">pyFTS.benchmarks.benchmarks.</code><code class="sig-name descname">print_distribution_statistics</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">original</span></em>, <em class="sig-param"><span class="n">models</span></em>, <em class="sig-param"><span class="n">steps</span></em>, <em class="sig-param"><span class="n">resolution</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/benchmarks.html#print_distribution_statistics"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.print_distribution_statistics" title="Permalink to this definition">¶</a></dt>
+<dd><p>Run probabilistic benchmarks on given models and data and print the results</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> – test data</p></li>
+<li><p><strong>models</strong> – a list of FTS models to benchmark</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.benchmarks.benchmarks.print_interval_statistics">
+<code class="sig-prename descclassname">pyFTS.benchmarks.benchmarks.</code><code class="sig-name descname">print_interval_statistics</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">original</span></em>, <em class="sig-param"><span class="n">models</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/benchmarks.html#print_interval_statistics"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.print_interval_statistics" title="Permalink to this definition">¶</a></dt>
+<dd><p>Run interval benchmarks on given models and data and print the results</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> – test data</p></li>
+<li><p><strong>models</strong> – a list of FTS models to benchmark</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.benchmarks.benchmarks.print_point_statistics">
+<code class="sig-prename descclassname">pyFTS.benchmarks.benchmarks.</code><code class="sig-name descname">print_point_statistics</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="n">models</span></em>, <em class="sig-param"><span class="n">externalmodels</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">externalforecasts</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">indexers</span><span class="o">=</span><span class="default_value">None</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/benchmarks.html#print_point_statistics"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.print_point_statistics" title="Permalink to this definition">¶</a></dt>
+<dd><p>Run point benchmarks on given models and data and print the results</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> – test data</p></li>
+<li><p><strong>models</strong> – a list of FTS models to benchmark</p></li>
+<li><p><strong>externalmodels</strong> – a list with benchmark models (façades for other methods)</p></li>
+<li><p><strong>externalforecasts</strong> – </p></li>
+<li><p><strong>indexers</strong> – </p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.benchmarks.benchmarks.process_interval_jobs">
+<code class="sig-prename descclassname">pyFTS.benchmarks.benchmarks.</code><code class="sig-name descname">process_interval_jobs</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">dataset</span></em>, <em class="sig-param"><span class="n">tag</span></em>, <em class="sig-param"><span class="n">job</span></em>, <em class="sig-param"><span class="n">conn</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/benchmarks.html#process_interval_jobs"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.process_interval_jobs" title="Permalink to this definition">¶</a></dt>
+<dd><p>Extract information from an dictionary with interval benchmark results and save it on a database</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>dataset</strong> – the benchmark dataset name</p></li>
+<li><p><strong>tag</strong> – alias for the benchmark group being executed</p></li>
+<li><p><strong>job</strong> – a dictionary with the benchmark results</p></li>
+<li><p><strong>conn</strong> – a connection to a Sqlite database</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.benchmarks.benchmarks.process_interval_jobs2">
+<code class="sig-prename descclassname">pyFTS.benchmarks.benchmarks.</code><code class="sig-name descname">process_interval_jobs2</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">dataset</span></em>, <em class="sig-param"><span class="n">tag</span></em>, <em class="sig-param"><span class="n">job</span></em>, <em class="sig-param"><span class="n">conn</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/benchmarks.html#process_interval_jobs2"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.process_interval_jobs2" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.benchmarks.benchmarks.process_point_jobs">
+<code class="sig-prename descclassname">pyFTS.benchmarks.benchmarks.</code><code class="sig-name descname">process_point_jobs</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">dataset</span></em>, <em class="sig-param"><span class="n">tag</span></em>, <em class="sig-param"><span class="n">job</span></em>, <em class="sig-param"><span class="n">conn</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/benchmarks.html#process_point_jobs"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.process_point_jobs" title="Permalink to this definition">¶</a></dt>
+<dd><p>Extract information from a dictionary with point benchmark results and save it on a database</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>dataset</strong> – the benchmark dataset name</p></li>
+<li><p><strong>tag</strong> – alias for the benchmark group being executed</p></li>
+<li><p><strong>job</strong> – a dictionary with the benchmark results</p></li>
+<li><p><strong>conn</strong> – a connection to a Sqlite database</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.benchmarks.benchmarks.process_point_jobs2">
+<code class="sig-prename descclassname">pyFTS.benchmarks.benchmarks.</code><code class="sig-name descname">process_point_jobs2</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">dataset</span></em>, <em class="sig-param"><span class="n">tag</span></em>, <em class="sig-param"><span class="n">job</span></em>, <em class="sig-param"><span class="n">conn</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/benchmarks.html#process_point_jobs2"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.process_point_jobs2" title="Permalink to this definition">¶</a></dt>
+<dd><p>Extract information from a dictionary with point benchmark results and save it on a database</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>dataset</strong> – the benchmark dataset name</p></li>
+<li><p><strong>tag</strong> – alias for the benchmark group being executed</p></li>
+<li><p><strong>job</strong> – a dictionary with the benchmark results</p></li>
+<li><p><strong>conn</strong> – a connection to a Sqlite database</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.benchmarks.benchmarks.process_probabilistic_jobs">
+<code class="sig-prename descclassname">pyFTS.benchmarks.benchmarks.</code><code class="sig-name descname">process_probabilistic_jobs</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">dataset</span></em>, <em class="sig-param"><span class="n">tag</span></em>, <em class="sig-param"><span class="n">job</span></em>, <em class="sig-param"><span class="n">conn</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/benchmarks.html#process_probabilistic_jobs"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.process_probabilistic_jobs" title="Permalink to this definition">¶</a></dt>
+<dd><p>Extract information from an dictionary with probabilistic benchmark results and save it on a database</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>dataset</strong> – the benchmark dataset name</p></li>
+<li><p><strong>tag</strong> – alias for the benchmark group being executed</p></li>
+<li><p><strong>job</strong> – a dictionary with the benchmark results</p></li>
+<li><p><strong>conn</strong> – a connection to a Sqlite database</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.benchmarks.benchmarks.process_probabilistic_jobs2">
+<code class="sig-prename descclassname">pyFTS.benchmarks.benchmarks.</code><code class="sig-name descname">process_probabilistic_jobs2</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">dataset</span></em>, <em class="sig-param"><span class="n">tag</span></em>, <em class="sig-param"><span class="n">job</span></em>, <em class="sig-param"><span class="n">conn</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/benchmarks.html#process_probabilistic_jobs2"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.process_probabilistic_jobs2" title="Permalink to this definition">¶</a></dt>
+<dd><p>Extract information from an dictionary with probabilistic benchmark results and save it on a database</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>dataset</strong> – the benchmark dataset name</p></li>
+<li><p><strong>tag</strong> – alias for the benchmark group being executed</p></li>
+<li><p><strong>job</strong> – a dictionary with the benchmark results</p></li>
+<li><p><strong>conn</strong> – a connection to a Sqlite database</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.benchmarks.benchmarks.run_interval">
+<code class="sig-prename descclassname">pyFTS.benchmarks.benchmarks.</code><code class="sig-name descname">run_interval</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">mfts</span></em>, <em class="sig-param"><span class="n">partitioner</span></em>, <em class="sig-param"><span class="n">train_data</span></em>, <em class="sig-param"><span class="n">test_data</span></em>, <em class="sig-param"><span class="n">window_key</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/benchmarks.html#run_interval"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.run_interval" title="Permalink to this definition">¶</a></dt>
+<dd><p>Run the interval forecasting benchmarks</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>mfts</strong> – FTS model</p></li>
+<li><p><strong>partitioner</strong> – Universe of Discourse partitioner</p></li>
+<li><p><strong>train_data</strong> – data used to train the model</p></li>
+<li><p><strong>test_data</strong> – ata used to test the model</p></li>
+<li><p><strong>window_key</strong> – id of the sliding window</p></li>
+<li><p><strong>transformation</strong> – data transformation</p></li>
+<li><p><strong>indexer</strong> – seasonal indexer</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>a dictionary with the benchmark results</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.benchmarks.benchmarks.run_interval2">
+<code class="sig-prename descclassname">pyFTS.benchmarks.benchmarks.</code><code class="sig-name descname">run_interval2</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">fts_method</span></em>, <em class="sig-param"><span class="n">order</span></em>, <em class="sig-param"><span class="n">partitioner_method</span></em>, <em class="sig-param"><span class="n">partitions</span></em>, <em class="sig-param"><span class="n">transformation</span></em>, <em class="sig-param"><span class="n">train_data</span></em>, <em class="sig-param"><span class="n">test_data</span></em>, <em class="sig-param"><span class="n">window_key</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/benchmarks.html#run_interval2"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.run_interval2" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.benchmarks.benchmarks.run_point">
+<code class="sig-prename descclassname">pyFTS.benchmarks.benchmarks.</code><code class="sig-name descname">run_point</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">mfts</span></em>, <em class="sig-param"><span class="n">partitioner</span></em>, <em class="sig-param"><span class="n">train_data</span></em>, <em class="sig-param"><span class="n">test_data</span></em>, <em class="sig-param"><span class="n">window_key</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/benchmarks.html#run_point"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.run_point" title="Permalink to this definition">¶</a></dt>
+<dd><p>Run the point forecasting benchmarks</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>mfts</strong> – FTS model</p></li>
+<li><p><strong>partitioner</strong> – Universe of Discourse partitioner</p></li>
+<li><p><strong>train_data</strong> – data used to train the model</p></li>
+<li><p><strong>test_data</strong> – ata used to test the model</p></li>
+<li><p><strong>window_key</strong> – id of the sliding window</p></li>
+<li><p><strong>transformation</strong> – data transformation</p></li>
+<li><p><strong>indexer</strong> – seasonal indexer</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>a dictionary with the benchmark results</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.benchmarks.benchmarks.run_point2">
+<code class="sig-prename descclassname">pyFTS.benchmarks.benchmarks.</code><code class="sig-name descname">run_point2</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">fts_method</span></em>, <em class="sig-param"><span class="n">order</span></em>, <em class="sig-param"><span class="n">partitioner_method</span></em>, <em class="sig-param"><span class="n">partitions</span></em>, <em class="sig-param"><span class="n">transformation</span></em>, <em class="sig-param"><span class="n">train_data</span></em>, <em class="sig-param"><span class="n">test_data</span></em>, <em class="sig-param"><span class="n">window_key</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/benchmarks.html#run_point2"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.run_point2" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.benchmarks.benchmarks.run_probabilistic">
+<code class="sig-prename descclassname">pyFTS.benchmarks.benchmarks.</code><code class="sig-name descname">run_probabilistic</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">mfts</span></em>, <em class="sig-param"><span class="n">partitioner</span></em>, <em class="sig-param"><span class="n">train_data</span></em>, <em class="sig-param"><span class="n">test_data</span></em>, <em class="sig-param"><span class="n">window_key</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/benchmarks.html#run_probabilistic"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.run_probabilistic" title="Permalink to this definition">¶</a></dt>
+<dd><p>Run the probabilistic forecasting benchmarks</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>mfts</strong> – FTS model</p></li>
+<li><p><strong>partitioner</strong> – Universe of Discourse partitioner</p></li>
+<li><p><strong>train_data</strong> – data used to train the model</p></li>
+<li><p><strong>test_data</strong> – ata used to test the model</p></li>
+<li><p><strong>steps</strong> – </p></li>
+<li><p><strong>resolution</strong> – </p></li>
+<li><p><strong>window_key</strong> – id of the sliding window</p></li>
+<li><p><strong>transformation</strong> – data transformation</p></li>
+<li><p><strong>indexer</strong> – seasonal indexer</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>a dictionary with the benchmark results</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.benchmarks.benchmarks.run_probabilistic2">
+<code class="sig-prename descclassname">pyFTS.benchmarks.benchmarks.</code><code class="sig-name descname">run_probabilistic2</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">fts_method</span></em>, <em class="sig-param"><span class="n">order</span></em>, <em class="sig-param"><span class="n">partitioner_method</span></em>, <em class="sig-param"><span class="n">partitions</span></em>, <em class="sig-param"><span class="n">transformation</span></em>, <em class="sig-param"><span class="n">train_data</span></em>, <em class="sig-param"><span class="n">test_data</span></em>, <em class="sig-param"><span class="n">window_key</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/benchmarks.html#run_probabilistic2"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.run_probabilistic2" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.benchmarks.benchmarks.simpleSearch_RMSE">
+<code class="sig-prename descclassname">pyFTS.benchmarks.benchmarks.</code><code class="sig-name descname">simpleSearch_RMSE</code><span class="sig-paren">(</span><em class="sig-param">train, test, model, partitions, orders, save=False, file=None, tam=[10, 15], plotforecasts=False, elev=30, azim=144, intervals=False, parameters=None, partitioner=&lt;class 'pyFTS.partitioners.Grid.GridPartitioner'&gt;, transformation=None, indexer=None</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/benchmarks.html#simpleSearch_RMSE"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.simpleSearch_RMSE" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.benchmarks.benchmarks.sliding_window_benchmarks">
+<code class="sig-prename descclassname">pyFTS.benchmarks.benchmarks.</code><code class="sig-name descname">sliding_window_benchmarks</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="n">windowsize</span></em>, <em class="sig-param"><span class="n">train</span><span class="o">=</span><span class="default_value">0.8</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/benchmarks.html#sliding_window_benchmarks"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.sliding_window_benchmarks" title="Permalink to this definition">¶</a></dt>
+<dd><p>Sliding window benchmarks for FTS forecasters.</p>
+<p>For each data window, a train and test datasets will be splitted. For each train split, number of
+partitions and partitioning method will be created a partitioner model. And for each partitioner, order,
+steps ahead and FTS method a foreasting model will be trained.</p>
+<p>Then all trained models are benchmarked on the test data and the metrics are stored on a sqlite3 database
+(identified by the ‘file’ parameter) for posterior analysis.</p>
+<p>All these process can be distributed on a dispy cluster, setting the atributed ‘distributed’ to true and
+informing the list of dispy nodes on ‘nodes’ parameter.</p>
+<p>The number of experiments is determined by ‘windowsize’ and ‘inc’ parameters.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> – test data</p></li>
+<li><p><strong>windowsize</strong> – size of sliding window</p></li>
+<li><p><strong>train</strong> – percentual of sliding window data used to train the models</p></li>
+<li><p><strong>kwargs</strong> – dict, optional arguments</p></li>
+<li><p><strong>benchmark_methods</strong> – a list with Non FTS models to benchmark. The default is None.</p></li>
+<li><p><strong>benchmark_methods_parameters</strong> – a list with Non FTS models parameters. The default is None.</p></li>
+<li><p><strong>benchmark_models</strong> – A boolean value indicating if external FTS methods will be used on benchmark. The default is False.</p></li>
+<li><p><strong>build_methods</strong> – A boolean value indicating if the default FTS methods will be used on benchmark. The default is True.</p></li>
+<li><p><strong>dataset</strong> – the dataset name to identify the current set of benchmarks results on database.</p></li>
+<li><p><strong>distributed</strong> – A boolean value indicating if the forecasting procedure will be distributed in a dispy cluster. . The default is False</p></li>
+<li><p><strong>file</strong> – file path to save the results. The default is benchmarks.db.</p></li>
+<li><p><strong>inc</strong> – a float on interval [0,1] indicating the percentage of the windowsize to move the window</p></li>
+<li><p><strong>methods</strong> – a list with FTS class names. The default depends on the forecasting type and contains the list of all FTS methods.</p></li>
+<li><p><strong>models</strong> – a list with prebuilt FTS objects. The default is None.</p></li>
+<li><p><strong>nodes</strong> – a list with the dispy cluster nodes addresses. The default is [127.0.0.1].</p></li>
+<li><p><strong>orders</strong> – a list with orders of the models (for high order models). The default is [1,2,3].</p></li>
+<li><p><strong>partitions</strong> – a list with the numbers of partitions on the Universe of Discourse. The default is [10].</p></li>
+<li><p><strong>partitioners_models</strong> – a list with prebuilt Universe of Discourse partitioners objects. The default is None.</p></li>
+<li><p><strong>partitioners_methods</strong> – a list with Universe of Discourse partitioners class names. The default is [partitioners.Grid.GridPartitioner].</p></li>
+<li><p><strong>progress</strong> – If true a progress bar will be displayed during the benchmarks. The default is False.</p></li>
+<li><p><strong>start</strong> – in the multi step forecasting, the index of the data where to start forecasting. The default is 0.</p></li>
+<li><p><strong>steps_ahead</strong> – a list with  the forecasting horizons, i. e., the number of steps ahead to forecast. The default is 1.</p></li>
+<li><p><strong>tag</strong> – a name to identify the current set of benchmarks results on database.</p></li>
+<li><p><strong>type</strong> – the forecasting type, one of these values: point(default), interval or distribution. The default is point.</p></li>
+<li><p><strong>transformations</strong> – a list with data transformations do apply . The default is [None].</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.benchmarks.benchmarks.sliding_window_benchmarks2">
+<code class="sig-prename descclassname">pyFTS.benchmarks.benchmarks.</code><code class="sig-name descname">sliding_window_benchmarks2</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="n">windowsize</span></em>, <em class="sig-param"><span class="n">train</span><span class="o">=</span><span class="default_value">0.8</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/benchmarks.html#sliding_window_benchmarks2"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.sliding_window_benchmarks2" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.benchmarks.benchmarks.train_test_time">
+<code class="sig-prename descclassname">pyFTS.benchmarks.benchmarks.</code><code class="sig-name descname">train_test_time</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="n">windowsize</span></em>, <em class="sig-param"><span class="n">train</span><span class="o">=</span><span class="default_value">0.8</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/benchmarks.html#train_test_time"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.train_test_time" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
 </div>
-<div class="section" id="pyfts-benchmarks-measures-module">
-<h2>pyFTS.benchmarks.Measures module<a class="headerlink" href="#pyfts-benchmarks-measures-module" title="Permalink to this headline">¶</a></h2>
+<div class="section" id="module-pyFTS.benchmarks.Measures">
+<span id="pyfts-benchmarks-measures-module"></span><h2>pyFTS.benchmarks.Measures module<a class="headerlink" href="#module-pyFTS.benchmarks.Measures" title="Permalink to this headline">¶</a></h2>
+<p>pyFTS module for common benchmark metrics</p>
+<dl class="py function">
+<dt id="pyFTS.benchmarks.Measures.TheilsInequality">
+<code class="sig-prename descclassname">pyFTS.benchmarks.Measures.</code><code class="sig-name descname">TheilsInequality</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">targets</span></em>, <em class="sig-param"><span class="n">forecasts</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Measures.html#TheilsInequality"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.Measures.TheilsInequality" title="Permalink to this definition">¶</a></dt>
+<dd><p>Theil’s Inequality Coefficient</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>targets</strong> – </p></li>
+<li><p><strong>forecasts</strong> – </p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.benchmarks.Measures.UStatistic">
+<code class="sig-prename descclassname">pyFTS.benchmarks.Measures.</code><code class="sig-name descname">UStatistic</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">targets</span></em>, <em class="sig-param"><span class="n">forecasts</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Measures.html#UStatistic"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.Measures.UStatistic" title="Permalink to this definition">¶</a></dt>
+<dd><p>Theil’s U Statistic</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>targets</strong> – </p></li>
+<li><p><strong>forecasts</strong> – </p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.benchmarks.Measures.acf">
+<code class="sig-prename descclassname">pyFTS.benchmarks.Measures.</code><code class="sig-name descname">acf</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="n">k</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Measures.html#acf"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.Measures.acf" title="Permalink to this definition">¶</a></dt>
+<dd><p>Autocorrelation function estimative</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> – </p></li>
+<li><p><strong>k</strong> – </p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.benchmarks.Measures.brier_score">
+<code class="sig-prename descclassname">pyFTS.benchmarks.Measures.</code><code class="sig-name descname">brier_score</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">targets</span></em>, <em class="sig-param"><span class="n">densities</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Measures.html#brier_score"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.Measures.brier_score" title="Permalink to this definition">¶</a></dt>
+<dd><p>Brier Score for probabilistic forecasts.
+Brier (1950). “Verification of Forecasts Expressed in Terms of Probability”. Monthly Weather Review. 78: 1–3.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>targets</strong> – a list with the target values</p></li>
+<li><p><strong>densities</strong> – a list with pyFTS.probabil objectsistic.ProbabilityDistribution</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>float</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.benchmarks.Measures.coverage">
+<code class="sig-prename descclassname">pyFTS.benchmarks.Measures.</code><code class="sig-name descname">coverage</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">targets</span></em>, <em class="sig-param"><span class="n">forecasts</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Measures.html#coverage"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.Measures.coverage" title="Permalink to this definition">¶</a></dt>
+<dd><p>Percent of target values that fall inside forecasted interval</p>
+</dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.benchmarks.Measures.crps">
+<code class="sig-prename descclassname">pyFTS.benchmarks.Measures.</code><code class="sig-name descname">crps</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">targets</span></em>, <em class="sig-param"><span class="n">densities</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Measures.html#crps"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.Measures.crps" title="Permalink to this definition">¶</a></dt>
+<dd><p>Continuous Ranked Probability Score</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>targets</strong> – a list with the target values</p></li>
+<li><p><strong>densities</strong> – a list with pyFTS.probabil objectsistic.ProbabilityDistribution</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>float</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.benchmarks.Measures.get_distribution_ahead_statistics">
+<code class="sig-prename descclassname">pyFTS.benchmarks.Measures.</code><code class="sig-name descname">get_distribution_ahead_statistics</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="n">distributions</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Measures.html#get_distribution_ahead_statistics"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.Measures.get_distribution_ahead_statistics" title="Permalink to this definition">¶</a></dt>
+<dd><p>Get CRPS statistic and time for a forecasting model</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> – test data</p></li>
+<li><p><strong>model</strong> – FTS model with probabilistic forecasting capability</p></li>
+<li><p><strong>kwargs</strong> – </p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>a list with the CRPS and execution time</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.benchmarks.Measures.get_distribution_statistics">
+<code class="sig-prename descclassname">pyFTS.benchmarks.Measures.</code><code class="sig-name descname">get_distribution_statistics</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="n">model</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Measures.html#get_distribution_statistics"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.Measures.get_distribution_statistics" title="Permalink to this definition">¶</a></dt>
+<dd><p>Get CRPS statistic and time for a forecasting model</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> – test data</p></li>
+<li><p><strong>model</strong> – FTS model with probabilistic forecasting capability</p></li>
+<li><p><strong>kwargs</strong> – </p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>a list with the CRPS and execution time</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.benchmarks.Measures.get_interval_ahead_statistics">
+<code class="sig-prename descclassname">pyFTS.benchmarks.Measures.</code><code class="sig-name descname">get_interval_ahead_statistics</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="n">intervals</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Measures.html#get_interval_ahead_statistics"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.Measures.get_interval_ahead_statistics" title="Permalink to this definition">¶</a></dt>
+<dd><p>Condensate all measures for point interval forecasters</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> – test data</p></li>
+<li><p><strong>intervals</strong> – predicted intervals for each datapoint</p></li>
+<li><p><strong>kwargs</strong> – </p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>a list with the sharpness, resolution, coverage, .05 pinball mean,
+.25 pinball mean, .75 pinball mean and .95 pinball mean.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.benchmarks.Measures.get_interval_statistics">
+<code class="sig-prename descclassname">pyFTS.benchmarks.Measures.</code><code class="sig-name descname">get_interval_statistics</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="n">model</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Measures.html#get_interval_statistics"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.Measures.get_interval_statistics" title="Permalink to this definition">¶</a></dt>
+<dd><p>Condensate all measures for point interval forecasters</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> – test data</p></li>
+<li><p><strong>model</strong> – FTS model with interval forecasting capability</p></li>
+<li><p><strong>kwargs</strong> – </p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>a list with the sharpness, resolution, coverage, .05 pinball mean,
+.25 pinball mean, .75 pinball mean and .95 pinball mean.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.benchmarks.Measures.get_point_ahead_statistics">
+<code class="sig-prename descclassname">pyFTS.benchmarks.Measures.</code><code class="sig-name descname">get_point_ahead_statistics</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="n">forecasts</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Measures.html#get_point_ahead_statistics"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.Measures.get_point_ahead_statistics" title="Permalink to this definition">¶</a></dt>
+<dd><p>Condensate all measures for point forecasters</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> – test data</p></li>
+<li><p><strong>model</strong> – FTS model with point forecasting capability</p></li>
+<li><p><strong>kwargs</strong> – </p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>a list with the RMSE, SMAPE and U Statistic</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.benchmarks.Measures.get_point_statistics">
+<code class="sig-prename descclassname">pyFTS.benchmarks.Measures.</code><code class="sig-name descname">get_point_statistics</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="n">model</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Measures.html#get_point_statistics"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.Measures.get_point_statistics" title="Permalink to this definition">¶</a></dt>
+<dd><p>Condensate all measures for point forecasters</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> – test data</p></li>
+<li><p><strong>model</strong> – FTS model with point forecasting capability</p></li>
+<li><p><strong>kwargs</strong> – </p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>a list with the RMSE, SMAPE and U Statistic</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.benchmarks.Measures.logarithm_score">
+<code class="sig-prename descclassname">pyFTS.benchmarks.Measures.</code><code class="sig-name descname">logarithm_score</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">targets</span></em>, <em class="sig-param"><span class="n">densities</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Measures.html#logarithm_score"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.Measures.logarithm_score" title="Permalink to this definition">¶</a></dt>
+<dd><p>Logarithm Score for probabilistic forecasts.
+Good IJ (1952).  “Rational Decisions.”Journal of the Royal Statistical Society B,14(1),107–114. URLhttps://www.jstor.org/stable/2984087.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>targets</strong> – a list with the target values</p></li>
+<li><p><strong>densities</strong> – a list with pyFTS.probabil objectsistic.ProbabilityDistribution</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>float</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.benchmarks.Measures.mape">
+<code class="sig-prename descclassname">pyFTS.benchmarks.Measures.</code><code class="sig-name descname">mape</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">targets</span></em>, <em class="sig-param"><span class="n">forecasts</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Measures.html#mape"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.Measures.mape" title="Permalink to this definition">¶</a></dt>
+<dd><p>Mean Average Percentual Error</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>targets</strong> – </p></li>
+<li><p><strong>forecasts</strong> – </p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.benchmarks.Measures.mape_interval">
+<code class="sig-prename descclassname">pyFTS.benchmarks.Measures.</code><code class="sig-name descname">mape_interval</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">targets</span></em>, <em class="sig-param"><span class="n">forecasts</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Measures.html#mape_interval"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.Measures.mape_interval" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.benchmarks.Measures.pinball">
+<code class="sig-prename descclassname">pyFTS.benchmarks.Measures.</code><code class="sig-name descname">pinball</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">tau</span></em>, <em class="sig-param"><span class="n">target</span></em>, <em class="sig-param"><span class="n">forecast</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Measures.html#pinball"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.Measures.pinball" title="Permalink to this definition">¶</a></dt>
+<dd><p>Pinball loss function. Measure the distance of forecast to the tau-quantile of the target</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>tau</strong> – quantile value in the range (0,1)</p></li>
+<li><p><strong>target</strong> – </p></li>
+<li><p><strong>forecast</strong> – </p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>float, distance of forecast to the tau-quantile of the target</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.benchmarks.Measures.pinball_mean">
+<code class="sig-prename descclassname">pyFTS.benchmarks.Measures.</code><code class="sig-name descname">pinball_mean</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">tau</span></em>, <em class="sig-param"><span class="n">targets</span></em>, <em class="sig-param"><span class="n">forecasts</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Measures.html#pinball_mean"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.Measures.pinball_mean" title="Permalink to this definition">¶</a></dt>
+<dd><p>Mean pinball loss value of the forecast for a given tau-quantile of the targets</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>tau</strong> – quantile value in the range (0,1)</p></li>
+<li><p><strong>targets</strong> – list of target values</p></li>
+<li><p><strong>forecasts</strong> – list of prediction intervals</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>float, the pinball loss mean for tau quantile</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.benchmarks.Measures.resolution">
+<code class="sig-prename descclassname">pyFTS.benchmarks.Measures.</code><code class="sig-name descname">resolution</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">forecasts</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Measures.html#resolution"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.Measures.resolution" title="Permalink to this definition">¶</a></dt>
+<dd><p>Resolution - Standard deviation of the intervals</p>
+</dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.benchmarks.Measures.rmse">
+<code class="sig-prename descclassname">pyFTS.benchmarks.Measures.</code><code class="sig-name descname">rmse</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">targets</span></em>, <em class="sig-param"><span class="n">forecasts</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Measures.html#rmse"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.Measures.rmse" title="Permalink to this definition">¶</a></dt>
+<dd><p>Root Mean Squared Error</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>targets</strong> – </p></li>
+<li><p><strong>forecasts</strong> – </p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.benchmarks.Measures.rmse_interval">
+<code class="sig-prename descclassname">pyFTS.benchmarks.Measures.</code><code class="sig-name descname">rmse_interval</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">targets</span></em>, <em class="sig-param"><span class="n">forecasts</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Measures.html#rmse_interval"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.Measures.rmse_interval" title="Permalink to this definition">¶</a></dt>
+<dd><p>Root Mean Squared Error</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>targets</strong> – </p></li>
+<li><p><strong>forecasts</strong> – </p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.benchmarks.Measures.sharpness">
+<code class="sig-prename descclassname">pyFTS.benchmarks.Measures.</code><code class="sig-name descname">sharpness</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">forecasts</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Measures.html#sharpness"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.Measures.sharpness" title="Permalink to this definition">¶</a></dt>
+<dd><p>Sharpness - Mean size of the intervals</p>
+</dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.benchmarks.Measures.smape">
+<code class="sig-prename descclassname">pyFTS.benchmarks.Measures.</code><code class="sig-name descname">smape</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">targets</span></em>, <em class="sig-param"><span class="n">forecasts</span></em>, <em class="sig-param"><span class="n">type</span><span class="o">=</span><span class="default_value">2</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Measures.html#smape"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.Measures.smape" title="Permalink to this definition">¶</a></dt>
+<dd><p>Symmetric Mean Average Percentual Error</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>targets</strong> – </p></li>
+<li><p><strong>forecasts</strong> – </p></li>
+<li><p><strong>type</strong> – </p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.benchmarks.Measures.winkler_mean">
+<code class="sig-prename descclassname">pyFTS.benchmarks.Measures.</code><code class="sig-name descname">winkler_mean</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">tau</span></em>, <em class="sig-param"><span class="n">targets</span></em>, <em class="sig-param"><span class="n">forecasts</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Measures.html#winkler_mean"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.Measures.winkler_mean" title="Permalink to this definition">¶</a></dt>
+<dd><p>Mean Winkler score value of the forecast for a given tau-quantile of the targets</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>tau</strong> – quantile value in the range (0,1)</p></li>
+<li><p><strong>targets</strong> – list of target values</p></li>
+<li><p><strong>forecasts</strong> – list of prediction intervals</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>float, the Winkler score mean for tau quantile</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.benchmarks.Measures.winkler_score">
+<code class="sig-prename descclassname">pyFTS.benchmarks.Measures.</code><code class="sig-name descname">winkler_score</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">tau</span></em>, <em class="sig-param"><span class="n">target</span></em>, <em class="sig-param"><span class="n">forecast</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Measures.html#winkler_score"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.Measures.winkler_score" title="Permalink to this definition">¶</a></dt>
+<dd><ol class="upperalpha simple" start="18">
+<li><ol class="upperalpha simple" start="12">
+<li><p>Winkler, A Decision-Theoretic Approach to Interval Estimation, J. Am. Stat. Assoc. 67 (337) (1972) 187–191. doi:10.2307/2284720.</p></li>
+</ol>
+</li>
+</ol>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>tau</strong> – </p></li>
+<li><p><strong>target</strong> – </p></li>
+<li><p><strong>forecast</strong> – </p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p></p>
+</dd>
+</dl>
+</dd></dl>
+
 </div>
-<div class="section" id="pyfts-benchmarks-residualanalysis-module">
-<h2>pyFTS.benchmarks.ResidualAnalysis module<a class="headerlink" href="#pyfts-benchmarks-residualanalysis-module" title="Permalink to this headline">¶</a></h2>
+<div class="section" id="module-pyFTS.benchmarks.ResidualAnalysis">
+<span id="pyfts-benchmarks-residualanalysis-module"></span><h2>pyFTS.benchmarks.ResidualAnalysis module<a class="headerlink" href="#module-pyFTS.benchmarks.ResidualAnalysis" title="Permalink to this headline">¶</a></h2>
+<p>Residual Analysis methods</p>
+<dl class="py function">
+<dt id="pyFTS.benchmarks.ResidualAnalysis.compare_residuals">
+<code class="sig-prename descclassname">pyFTS.benchmarks.ResidualAnalysis.</code><code class="sig-name descname">compare_residuals</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="n">models</span></em>, <em class="sig-param"><span class="n">alpha</span><span class="o">=</span><span class="default_value">0.05</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/ResidualAnalysis.html#compare_residuals"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.ResidualAnalysis.compare_residuals" title="Permalink to this definition">¶</a></dt>
+<dd><p>Compare residual’s statistics of several models</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> – test data</p></li>
+<li><p><strong>models</strong> – </p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>a Pandas dataframe with the Box-Ljung statistic for each model</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.benchmarks.ResidualAnalysis.ljung_box_test">
+<code class="sig-prename descclassname">pyFTS.benchmarks.ResidualAnalysis.</code><code class="sig-name descname">ljung_box_test</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">residuals</span></em>, <em class="sig-param"><span class="n">lags</span><span class="o">=</span><span class="default_value">[1, 2, 3]</span></em>, <em class="sig-param"><span class="n">alpha</span><span class="o">=</span><span class="default_value">0.5</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/ResidualAnalysis.html#ljung_box_test"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.ResidualAnalysis.ljung_box_test" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.benchmarks.ResidualAnalysis.plot_residuals_by_model">
+<code class="sig-prename descclassname">pyFTS.benchmarks.ResidualAnalysis.</code><code class="sig-name descname">plot_residuals_by_model</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">targets</span></em>, <em class="sig-param"><span class="n">models</span></em>, <em class="sig-param"><span class="n">tam</span><span class="o">=</span><span class="default_value">[8, 8]</span></em>, <em class="sig-param"><span class="n">save</span><span class="o">=</span><span class="default_value">False</span></em>, <em class="sig-param"><span class="n">file</span><span class="o">=</span><span class="default_value">None</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/ResidualAnalysis.html#plot_residuals_by_model"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.ResidualAnalysis.plot_residuals_by_model" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.benchmarks.ResidualAnalysis.residuals">
+<code class="sig-prename descclassname">pyFTS.benchmarks.ResidualAnalysis.</code><code class="sig-name descname">residuals</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">targets</span></em>, <em class="sig-param"><span class="n">forecasts</span></em>, <em class="sig-param"><span class="n">order</span><span class="o">=</span><span class="default_value">1</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/ResidualAnalysis.html#residuals"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.ResidualAnalysis.residuals" title="Permalink to this definition">¶</a></dt>
+<dd><p>First order residuals</p>
+</dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.benchmarks.ResidualAnalysis.single_plot_residuals">
+<code class="sig-prename descclassname">pyFTS.benchmarks.ResidualAnalysis.</code><code class="sig-name descname">single_plot_residuals</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">res</span></em>, <em class="sig-param"><span class="n">order</span></em>, <em class="sig-param"><span class="n">tam</span><span class="o">=</span><span class="default_value">[10, 7]</span></em>, <em class="sig-param"><span class="n">save</span><span class="o">=</span><span class="default_value">False</span></em>, <em class="sig-param"><span class="n">file</span><span class="o">=</span><span class="default_value">None</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/ResidualAnalysis.html#single_plot_residuals"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.ResidualAnalysis.single_plot_residuals" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
 </div>
-<div class="section" id="pyfts-benchmarks-tests-module">
-<h2>pyFTS.benchmarks.Tests module<a class="headerlink" href="#pyfts-benchmarks-tests-module" title="Permalink to this headline">¶</a></h2>
+<div class="section" id="module-pyFTS.benchmarks.Tests">
+<span id="pyfts-benchmarks-tests-module"></span><h2>pyFTS.benchmarks.Tests module<a class="headerlink" href="#module-pyFTS.benchmarks.Tests" title="Permalink to this headline">¶</a></h2>
+<dl class="py function">
+<dt id="pyFTS.benchmarks.Tests.BoxLjungStatistic">
+<code class="sig-prename descclassname">pyFTS.benchmarks.Tests.</code><code class="sig-name descname">BoxLjungStatistic</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="n">h</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Tests.html#BoxLjungStatistic"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.Tests.BoxLjungStatistic" title="Permalink to this definition">¶</a></dt>
+<dd><p>Q Statistic for Ljung–Box test</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> – </p></li>
+<li><p><strong>h</strong> – </p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.benchmarks.Tests.BoxPierceStatistic">
+<code class="sig-prename descclassname">pyFTS.benchmarks.Tests.</code><code class="sig-name descname">BoxPierceStatistic</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="n">h</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Tests.html#BoxPierceStatistic"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.Tests.BoxPierceStatistic" title="Permalink to this definition">¶</a></dt>
+<dd><p>Q Statistic for Box-Pierce test</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> – </p></li>
+<li><p><strong>h</strong> – </p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.benchmarks.Tests.format_experiment_table">
+<code class="sig-prename descclassname">pyFTS.benchmarks.Tests.</code><code class="sig-name descname">format_experiment_table</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">df</span></em>, <em class="sig-param"><span class="n">exclude</span><span class="o">=</span><span class="default_value">[]</span></em>, <em class="sig-param"><span class="n">replace</span><span class="o">=</span><span class="default_value">{}</span></em>, <em class="sig-param"><span class="n">csv</span><span class="o">=</span><span class="default_value">True</span></em>, <em class="sig-param"><span class="n">std</span><span class="o">=</span><span class="default_value">False</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Tests.html#format_experiment_table"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.Tests.format_experiment_table" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.benchmarks.Tests.post_hoc_tests">
+<code class="sig-prename descclassname">pyFTS.benchmarks.Tests.</code><code class="sig-name descname">post_hoc_tests</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">post_hoc</span></em>, <em class="sig-param"><span class="n">control_method</span></em>, <em class="sig-param"><span class="n">alpha</span><span class="o">=</span><span class="default_value">0.05</span></em>, <em class="sig-param"><span class="n">method</span><span class="o">=</span><span class="default_value">'finner'</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Tests.html#post_hoc_tests"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.Tests.post_hoc_tests" title="Permalink to this definition">¶</a></dt>
+<dd><p>Finner paired post-hoc test with NSFTS as control method.</p>
+<p>$H_0$: There is no significant difference between the means</p>
+<p>$H_1$: There is a significant difference between the means</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>post_hoc</strong> – </p></li>
+<li><p><strong>control_method</strong> – </p></li>
+<li><p><strong>alpha</strong> – </p></li>
+<li><p><strong>method</strong> – </p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.benchmarks.Tests.test_mean_equality">
+<code class="sig-prename descclassname">pyFTS.benchmarks.Tests.</code><code class="sig-name descname">test_mean_equality</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">tests</span></em>, <em class="sig-param"><span class="n">alpha</span><span class="o">=</span><span class="default_value">0.05</span></em>, <em class="sig-param"><span class="n">method</span><span class="o">=</span><span class="default_value">'friedman'</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Tests.html#test_mean_equality"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.Tests.test_mean_equality" title="Permalink to this definition">¶</a></dt>
+<dd><p>Test for the equality of the means, with alpha confidence level.</p>
+<p>H_0: There’s no significant difference between the means
+H_1: There is at least one significant difference between the means</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>tests</strong> – </p></li>
+<li><p><strong>alpha</strong> – </p></li>
+<li><p><strong>method</strong> – </p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p></p>
+</dd>
+</dl>
+</dd></dl>
+
 </div>
-<div class="section" id="pyfts-benchmarks-util-module">
-<h2>pyFTS.benchmarks.Util module<a class="headerlink" href="#pyfts-benchmarks-util-module" title="Permalink to this headline">¶</a></h2>
+<div class="section" id="module-pyFTS.benchmarks.Util">
+<span id="pyfts-benchmarks-util-module"></span><h2>pyFTS.benchmarks.Util module<a class="headerlink" href="#module-pyFTS.benchmarks.Util" title="Permalink to this headline">¶</a></h2>
+<p>Facilities for pyFTS Benchmark module</p>
+<dl class="py function">
+<dt id="pyFTS.benchmarks.Util.analytic_tabular_dataframe">
+<code class="sig-prename descclassname">pyFTS.benchmarks.Util.</code><code class="sig-name descname">analytic_tabular_dataframe</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">dataframe</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Util.html#analytic_tabular_dataframe"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.Util.analytic_tabular_dataframe" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.benchmarks.Util.analytical_data_columns">
+<code class="sig-prename descclassname">pyFTS.benchmarks.Util.</code><code class="sig-name descname">analytical_data_columns</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">experiments</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Util.html#analytical_data_columns"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.Util.analytical_data_columns" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.benchmarks.Util.base_dataframe_columns">
+<code class="sig-prename descclassname">pyFTS.benchmarks.Util.</code><code class="sig-name descname">base_dataframe_columns</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Util.html#base_dataframe_columns"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.Util.base_dataframe_columns" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.benchmarks.Util.cast_dataframe_to_synthetic">
+<code class="sig-prename descclassname">pyFTS.benchmarks.Util.</code><code class="sig-name descname">cast_dataframe_to_synthetic</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">infile</span></em>, <em class="sig-param"><span class="n">outfile</span></em>, <em class="sig-param"><span class="n">experiments</span></em>, <em class="sig-param"><span class="n">type</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Util.html#cast_dataframe_to_synthetic"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.Util.cast_dataframe_to_synthetic" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.benchmarks.Util.cast_dataframe_to_synthetic_interval">
+<code class="sig-prename descclassname">pyFTS.benchmarks.Util.</code><code class="sig-name descname">cast_dataframe_to_synthetic_interval</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">df</span></em>, <em class="sig-param"><span class="n">data_columns</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Util.html#cast_dataframe_to_synthetic_interval"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.Util.cast_dataframe_to_synthetic_interval" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.benchmarks.Util.cast_dataframe_to_synthetic_point">
+<code class="sig-prename descclassname">pyFTS.benchmarks.Util.</code><code class="sig-name descname">cast_dataframe_to_synthetic_point</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">df</span></em>, <em class="sig-param"><span class="n">data_columns</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Util.html#cast_dataframe_to_synthetic_point"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.Util.cast_dataframe_to_synthetic_point" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.benchmarks.Util.cast_dataframe_to_synthetic_probabilistic">
+<code class="sig-prename descclassname">pyFTS.benchmarks.Util.</code><code class="sig-name descname">cast_dataframe_to_synthetic_probabilistic</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">df</span></em>, <em class="sig-param"><span class="n">data_columns</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Util.html#cast_dataframe_to_synthetic_probabilistic"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.Util.cast_dataframe_to_synthetic_probabilistic" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.benchmarks.Util.check_ignore_list">
+<code class="sig-prename descclassname">pyFTS.benchmarks.Util.</code><code class="sig-name descname">check_ignore_list</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">b</span></em>, <em class="sig-param"><span class="n">ignore</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Util.html#check_ignore_list"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.Util.check_ignore_list" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.benchmarks.Util.check_replace_list">
+<code class="sig-prename descclassname">pyFTS.benchmarks.Util.</code><code class="sig-name descname">check_replace_list</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">m</span></em>, <em class="sig-param"><span class="n">replace</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Util.html#check_replace_list"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.Util.check_replace_list" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.benchmarks.Util.create_benchmark_tables">
+<code class="sig-prename descclassname">pyFTS.benchmarks.Util.</code><code class="sig-name descname">create_benchmark_tables</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">conn</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Util.html#create_benchmark_tables"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.Util.create_benchmark_tables" title="Permalink to this definition">¶</a></dt>
+<dd><p>Create a sqlite3 table designed to store benchmark results.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>conn</strong> – a sqlite3 database connection</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.benchmarks.Util.extract_measure">
+<code class="sig-prename descclassname">pyFTS.benchmarks.Util.</code><code class="sig-name descname">extract_measure</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">dataframe</span></em>, <em class="sig-param"><span class="n">measure</span></em>, <em class="sig-param"><span class="n">data_columns</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Util.html#extract_measure"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.Util.extract_measure" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.benchmarks.Util.find_best">
+<code class="sig-prename descclassname">pyFTS.benchmarks.Util.</code><code class="sig-name descname">find_best</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">dataframe</span></em>, <em class="sig-param"><span class="n">criteria</span></em>, <em class="sig-param"><span class="n">ascending</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Util.html#find_best"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.Util.find_best" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.benchmarks.Util.get_dataframe_from_bd">
+<code class="sig-prename descclassname">pyFTS.benchmarks.Util.</code><code class="sig-name descname">get_dataframe_from_bd</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">file</span></em>, <em class="sig-param"><span class="n">filter</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Util.html#get_dataframe_from_bd"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.Util.get_dataframe_from_bd" title="Permalink to this definition">¶</a></dt>
+<dd><p>Query the sqlite benchmark database and return a pandas dataframe with the results</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>file</strong> – the url of the benchmark database</p></li>
+<li><p><strong>filter</strong> – sql conditions to filter</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>pandas dataframe with the query results</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.benchmarks.Util.insert_benchmark">
+<code class="sig-prename descclassname">pyFTS.benchmarks.Util.</code><code class="sig-name descname">insert_benchmark</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="n">conn</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Util.html#insert_benchmark"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.Util.insert_benchmark" title="Permalink to this definition">¶</a></dt>
+<dd><p>Insert benchmark data on database</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>data</strong> – a tuple with the benchmark data with format:</p>
+</dd>
+</dl>
+<p>ID: integer incremental primary key
+Date: Date/hour of benchmark execution
+Dataset: Identify on which dataset the dataset was performed
+Tag: a user defined word that indentify a benchmark set
+Type: forecasting type (point, interval, distribution)
+Model: FTS model
+Transformation: The name of data transformation, if one was used
+Order: the order of the FTS method
+Scheme: UoD partitioning scheme
+Partitions: Number of partitions
+Size: Number of rules of the FTS model
+Steps: prediction horizon, i. e., the number of steps ahead
+Measure: accuracy measure
+Value: the measure value</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>conn</strong> – a sqlite3 database connection</p>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.benchmarks.Util.interval_dataframe_analytic_columns">
+<code class="sig-prename descclassname">pyFTS.benchmarks.Util.</code><code class="sig-name descname">interval_dataframe_analytic_columns</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">experiments</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Util.html#interval_dataframe_analytic_columns"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.Util.interval_dataframe_analytic_columns" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.benchmarks.Util.interval_dataframe_synthetic_columns">
+<code class="sig-prename descclassname">pyFTS.benchmarks.Util.</code><code class="sig-name descname">interval_dataframe_synthetic_columns</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Util.html#interval_dataframe_synthetic_columns"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.Util.interval_dataframe_synthetic_columns" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.benchmarks.Util.open_benchmark_db">
+<code class="sig-prename descclassname">pyFTS.benchmarks.Util.</code><code class="sig-name descname">open_benchmark_db</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">name</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Util.html#open_benchmark_db"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.Util.open_benchmark_db" title="Permalink to this definition">¶</a></dt>
+<dd><p>Open a connection with a Sqlite database designed to store benchmark results.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>name</strong> – database filenem</p>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>a sqlite3 database connection</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.benchmarks.Util.plot_dataframe_interval">
+<code class="sig-prename descclassname">pyFTS.benchmarks.Util.</code><code class="sig-name descname">plot_dataframe_interval</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">file_synthetic</span></em>, <em class="sig-param"><span class="n">file_analytic</span></em>, <em class="sig-param"><span class="n">experiments</span></em>, <em class="sig-param"><span class="n">tam</span></em>, <em class="sig-param"><span class="n">save</span><span class="o">=</span><span class="default_value">False</span></em>, <em class="sig-param"><span class="n">file</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">sort_columns</span><span class="o">=</span><span class="default_value">['COVAVG', 'SHARPAVG', 'COVSTD', 'SHARPSTD']</span></em>, <em class="sig-param"><span class="n">sort_ascend</span><span class="o">=</span><span class="default_value">[True, False, True, True]</span></em>, <em class="sig-param"><span class="n">save_best</span><span class="o">=</span><span class="default_value">False</span></em>, <em class="sig-param"><span class="n">ignore</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">replace</span><span class="o">=</span><span class="default_value">None</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Util.html#plot_dataframe_interval"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.Util.plot_dataframe_interval" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.benchmarks.Util.plot_dataframe_interval_pinball">
+<code class="sig-prename descclassname">pyFTS.benchmarks.Util.</code><code class="sig-name descname">plot_dataframe_interval_pinball</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">file_synthetic</span></em>, <em class="sig-param"><span class="n">file_analytic</span></em>, <em class="sig-param"><span class="n">experiments</span></em>, <em class="sig-param"><span class="n">tam</span></em>, <em class="sig-param"><span class="n">save</span><span class="o">=</span><span class="default_value">False</span></em>, <em class="sig-param"><span class="n">file</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">sort_columns</span><span class="o">=</span><span class="default_value">['COVAVG', 'SHARPAVG', 'COVSTD', 'SHARPSTD']</span></em>, <em class="sig-param"><span class="n">sort_ascend</span><span class="o">=</span><span class="default_value">[True, False, True, True]</span></em>, <em class="sig-param"><span class="n">save_best</span><span class="o">=</span><span class="default_value">False</span></em>, <em class="sig-param"><span class="n">ignore</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">replace</span><span class="o">=</span><span class="default_value">None</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Util.html#plot_dataframe_interval_pinball"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.Util.plot_dataframe_interval_pinball" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.benchmarks.Util.plot_dataframe_point">
+<code class="sig-prename descclassname">pyFTS.benchmarks.Util.</code><code class="sig-name descname">plot_dataframe_point</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">file_synthetic</span></em>, <em class="sig-param"><span class="n">file_analytic</span></em>, <em class="sig-param"><span class="n">experiments</span></em>, <em class="sig-param"><span class="n">tam</span></em>, <em class="sig-param"><span class="n">save</span><span class="o">=</span><span class="default_value">False</span></em>, <em class="sig-param"><span class="n">file</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">sort_columns</span><span class="o">=</span><span class="default_value">['UAVG', 'RMSEAVG', 'USTD', 'RMSESTD']</span></em>, <em class="sig-param"><span class="n">sort_ascend</span><span class="o">=</span><span class="default_value">[1, 1, 1, 1]</span></em>, <em class="sig-param"><span class="n">save_best</span><span class="o">=</span><span class="default_value">False</span></em>, <em class="sig-param"><span class="n">ignore</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">replace</span><span class="o">=</span><span class="default_value">None</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Util.html#plot_dataframe_point"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.Util.plot_dataframe_point" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.benchmarks.Util.plot_dataframe_probabilistic">
+<code class="sig-prename descclassname">pyFTS.benchmarks.Util.</code><code class="sig-name descname">plot_dataframe_probabilistic</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">file_synthetic</span></em>, <em class="sig-param"><span class="n">file_analytic</span></em>, <em class="sig-param"><span class="n">experiments</span></em>, <em class="sig-param"><span class="n">tam</span></em>, <em class="sig-param"><span class="n">save</span><span class="o">=</span><span class="default_value">False</span></em>, <em class="sig-param"><span class="n">file</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">sort_columns</span><span class="o">=</span><span class="default_value">['CRPS1AVG', 'CRPS2AVG', 'CRPS1STD', 'CRPS2STD']</span></em>, <em class="sig-param"><span class="n">sort_ascend</span><span class="o">=</span><span class="default_value">[True, True, True, True]</span></em>, <em class="sig-param"><span class="n">save_best</span><span class="o">=</span><span class="default_value">False</span></em>, <em class="sig-param"><span class="n">ignore</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">replace</span><span class="o">=</span><span class="default_value">None</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Util.html#plot_dataframe_probabilistic"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.Util.plot_dataframe_probabilistic" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.benchmarks.Util.point_dataframe_analytic_columns">
+<code class="sig-prename descclassname">pyFTS.benchmarks.Util.</code><code class="sig-name descname">point_dataframe_analytic_columns</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">experiments</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Util.html#point_dataframe_analytic_columns"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.Util.point_dataframe_analytic_columns" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.benchmarks.Util.point_dataframe_synthetic_columns">
+<code class="sig-prename descclassname">pyFTS.benchmarks.Util.</code><code class="sig-name descname">point_dataframe_synthetic_columns</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Util.html#point_dataframe_synthetic_columns"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.Util.point_dataframe_synthetic_columns" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.benchmarks.Util.probabilistic_dataframe_analytic_columns">
+<code class="sig-prename descclassname">pyFTS.benchmarks.Util.</code><code class="sig-name descname">probabilistic_dataframe_analytic_columns</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">experiments</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Util.html#probabilistic_dataframe_analytic_columns"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.Util.probabilistic_dataframe_analytic_columns" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.benchmarks.Util.probabilistic_dataframe_synthetic_columns">
+<code class="sig-prename descclassname">pyFTS.benchmarks.Util.</code><code class="sig-name descname">probabilistic_dataframe_synthetic_columns</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Util.html#probabilistic_dataframe_synthetic_columns"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.Util.probabilistic_dataframe_synthetic_columns" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.benchmarks.Util.process_common_data">
+<code class="sig-prename descclassname">pyFTS.benchmarks.Util.</code><code class="sig-name descname">process_common_data</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">dataset</span></em>, <em class="sig-param"><span class="n">tag</span></em>, <em class="sig-param"><span class="n">type</span></em>, <em class="sig-param"><span class="n">job</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Util.html#process_common_data"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.Util.process_common_data" title="Permalink to this definition">¶</a></dt>
+<dd><p>Wraps benchmark information on a tuple for sqlite database</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>dataset</strong> – benchmark dataset</p></li>
+<li><p><strong>tag</strong> – benchmark set alias</p></li>
+<li><p><strong>type</strong> – forecasting type</p></li>
+<li><p><strong>job</strong> – a dictionary with benchmark data</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>tuple for sqlite database</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.benchmarks.Util.process_common_data2">
+<code class="sig-prename descclassname">pyFTS.benchmarks.Util.</code><code class="sig-name descname">process_common_data2</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">dataset</span></em>, <em class="sig-param"><span class="n">tag</span></em>, <em class="sig-param"><span class="n">type</span></em>, <em class="sig-param"><span class="n">job</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Util.html#process_common_data2"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.Util.process_common_data2" title="Permalink to this definition">¶</a></dt>
+<dd><p>Wraps benchmark information on a tuple for sqlite database</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>dataset</strong> – benchmark dataset</p></li>
+<li><p><strong>tag</strong> – benchmark set alias</p></li>
+<li><p><strong>type</strong> – forecasting type</p></li>
+<li><p><strong>job</strong> – a dictionary with benchmark data</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>tuple for sqlite database</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.benchmarks.Util.save_dataframe_interval">
+<code class="sig-prename descclassname">pyFTS.benchmarks.Util.</code><code class="sig-name descname">save_dataframe_interval</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">coverage</span></em>, <em class="sig-param"><span class="n">experiments</span></em>, <em class="sig-param"><span class="n">file</span></em>, <em class="sig-param"><span class="n">objs</span></em>, <em class="sig-param"><span class="n">resolution</span></em>, <em class="sig-param"><span class="n">save</span></em>, <em class="sig-param"><span class="n">sharpness</span></em>, <em class="sig-param"><span class="n">synthetic</span></em>, <em class="sig-param"><span class="n">times</span></em>, <em class="sig-param"><span class="n">q05</span></em>, <em class="sig-param"><span class="n">q25</span></em>, <em class="sig-param"><span class="n">q75</span></em>, <em class="sig-param"><span class="n">q95</span></em>, <em class="sig-param"><span class="n">steps</span></em>, <em class="sig-param"><span class="n">method</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Util.html#save_dataframe_interval"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.Util.save_dataframe_interval" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.benchmarks.Util.save_dataframe_point">
+<code class="sig-prename descclassname">pyFTS.benchmarks.Util.</code><code class="sig-name descname">save_dataframe_point</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">experiments</span></em>, <em class="sig-param"><span class="n">file</span></em>, <em class="sig-param"><span class="n">objs</span></em>, <em class="sig-param"><span class="n">rmse</span></em>, <em class="sig-param"><span class="n">save</span></em>, <em class="sig-param"><span class="n">synthetic</span></em>, <em class="sig-param"><span class="n">smape</span></em>, <em class="sig-param"><span class="n">times</span></em>, <em class="sig-param"><span class="n">u</span></em>, <em class="sig-param"><span class="n">steps</span></em>, <em class="sig-param"><span class="n">method</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Util.html#save_dataframe_point"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.Util.save_dataframe_point" title="Permalink to this definition">¶</a></dt>
+<dd><p>Create a dataframe to store the benchmark results</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>experiments</strong> – dictionary with the execution results</p></li>
+<li><p><strong>file</strong> – </p></li>
+<li><p><strong>objs</strong> – </p></li>
+<li><p><strong>rmse</strong> – </p></li>
+<li><p><strong>save</strong> – </p></li>
+<li><p><strong>synthetic</strong> – </p></li>
+<li><p><strong>smape</strong> – </p></li>
+<li><p><strong>times</strong> – </p></li>
+<li><p><strong>u</strong> – </p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.benchmarks.Util.save_dataframe_probabilistic">
+<code class="sig-prename descclassname">pyFTS.benchmarks.Util.</code><code class="sig-name descname">save_dataframe_probabilistic</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">experiments</span></em>, <em class="sig-param"><span class="n">file</span></em>, <em class="sig-param"><span class="n">objs</span></em>, <em class="sig-param"><span class="n">crps</span></em>, <em class="sig-param"><span class="n">times</span></em>, <em class="sig-param"><span class="n">save</span></em>, <em class="sig-param"><span class="n">synthetic</span></em>, <em class="sig-param"><span class="n">steps</span></em>, <em class="sig-param"><span class="n">method</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Util.html#save_dataframe_probabilistic"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.Util.save_dataframe_probabilistic" title="Permalink to this definition">¶</a></dt>
+<dd><p>Save benchmark results for m-step ahead probabilistic forecasters 
+:param experiments: 
+:param file: 
+:param objs: 
+:param crps_interval: 
+:param crps_distr: 
+:param times: 
+:param times2: 
+:param save: 
+:param synthetic: 
+:return:</p>
+</dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.benchmarks.Util.scale">
+<code class="sig-prename descclassname">pyFTS.benchmarks.Util.</code><code class="sig-name descname">scale</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="n">params</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Util.html#scale"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.Util.scale" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.benchmarks.Util.scale_params">
+<code class="sig-prename descclassname">pyFTS.benchmarks.Util.</code><code class="sig-name descname">scale_params</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Util.html#scale_params"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.Util.scale_params" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.benchmarks.Util.simple_synthetic_dataframe">
+<code class="sig-prename descclassname">pyFTS.benchmarks.Util.</code><code class="sig-name descname">simple_synthetic_dataframe</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">file</span></em>, <em class="sig-param"><span class="n">tag</span></em>, <em class="sig-param"><span class="n">measure</span></em>, <em class="sig-param"><span class="n">sql</span><span class="o">=</span><span class="default_value">None</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Util.html#simple_synthetic_dataframe"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.Util.simple_synthetic_dataframe" title="Permalink to this definition">¶</a></dt>
+<dd><p>Read experiments results from sqlite3 database in ‘file’, make a synthesis of the results
+of the metric ‘measure’ with the same ‘tag’, returning a Pandas DataFrame with the mean results.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>file</strong> – sqlite3 database file name</p></li>
+<li><p><strong>tag</strong> – common tag of the experiments</p></li>
+<li><p><strong>measure</strong> – metric to synthetize</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>Pandas DataFrame with the mean results</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.benchmarks.Util.stats">
+<code class="sig-prename descclassname">pyFTS.benchmarks.Util.</code><code class="sig-name descname">stats</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">measure</span></em>, <em class="sig-param"><span class="n">data</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Util.html#stats"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.Util.stats" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.benchmarks.Util.tabular_dataframe_columns">
+<code class="sig-prename descclassname">pyFTS.benchmarks.Util.</code><code class="sig-name descname">tabular_dataframe_columns</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Util.html#tabular_dataframe_columns"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.Util.tabular_dataframe_columns" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.benchmarks.Util.unified_scaled_interval">
+<code class="sig-prename descclassname">pyFTS.benchmarks.Util.</code><code class="sig-name descname">unified_scaled_interval</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">experiments</span></em>, <em class="sig-param"><span class="n">tam</span></em>, <em class="sig-param"><span class="n">save</span><span class="o">=</span><span class="default_value">False</span></em>, <em class="sig-param"><span class="n">file</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">sort_columns</span><span class="o">=</span><span class="default_value">['COVAVG', 'SHARPAVG', 'COVSTD', 'SHARPSTD']</span></em>, <em class="sig-param"><span class="n">sort_ascend</span><span class="o">=</span><span class="default_value">[True, False, True, True]</span></em>, <em class="sig-param"><span class="n">save_best</span><span class="o">=</span><span class="default_value">False</span></em>, <em class="sig-param"><span class="n">ignore</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">replace</span><span class="o">=</span><span class="default_value">None</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Util.html#unified_scaled_interval"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.Util.unified_scaled_interval" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.benchmarks.Util.unified_scaled_interval_pinball">
+<code class="sig-prename descclassname">pyFTS.benchmarks.Util.</code><code class="sig-name descname">unified_scaled_interval_pinball</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">experiments</span></em>, <em class="sig-param"><span class="n">tam</span></em>, <em class="sig-param"><span class="n">save</span><span class="o">=</span><span class="default_value">False</span></em>, <em class="sig-param"><span class="n">file</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">sort_columns</span><span class="o">=</span><span class="default_value">['COVAVG', 'SHARPAVG', 'COVSTD', 'SHARPSTD']</span></em>, <em class="sig-param"><span class="n">sort_ascend</span><span class="o">=</span><span class="default_value">[True, False, True, True]</span></em>, <em class="sig-param"><span class="n">save_best</span><span class="o">=</span><span class="default_value">False</span></em>, <em class="sig-param"><span class="n">ignore</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">replace</span><span class="o">=</span><span class="default_value">None</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Util.html#unified_scaled_interval_pinball"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.Util.unified_scaled_interval_pinball" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.benchmarks.Util.unified_scaled_point">
+<code class="sig-prename descclassname">pyFTS.benchmarks.Util.</code><code class="sig-name descname">unified_scaled_point</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">experiments</span></em>, <em class="sig-param"><span class="n">tam</span></em>, <em class="sig-param"><span class="n">save</span><span class="o">=</span><span class="default_value">False</span></em>, <em class="sig-param"><span class="n">file</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">sort_columns</span><span class="o">=</span><span class="default_value">['UAVG', 'RMSEAVG', 'USTD', 'RMSESTD']</span></em>, <em class="sig-param"><span class="n">sort_ascend</span><span class="o">=</span><span class="default_value">[1, 1, 1, 1]</span></em>, <em class="sig-param"><span class="n">save_best</span><span class="o">=</span><span class="default_value">False</span></em>, <em class="sig-param"><span class="n">ignore</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">replace</span><span class="o">=</span><span class="default_value">None</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Util.html#unified_scaled_point"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.Util.unified_scaled_point" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.benchmarks.Util.unified_scaled_probabilistic">
+<code class="sig-prename descclassname">pyFTS.benchmarks.Util.</code><code class="sig-name descname">unified_scaled_probabilistic</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">experiments</span></em>, <em class="sig-param"><span class="n">tam</span></em>, <em class="sig-param"><span class="n">save</span><span class="o">=</span><span class="default_value">False</span></em>, <em class="sig-param"><span class="n">file</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">sort_columns</span><span class="o">=</span><span class="default_value">['CRPSAVG', 'CRPSSTD']</span></em>, <em class="sig-param"><span class="n">sort_ascend</span><span class="o">=</span><span class="default_value">[True, True]</span></em>, <em class="sig-param"><span class="n">save_best</span><span class="o">=</span><span class="default_value">False</span></em>, <em class="sig-param"><span class="n">ignore</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">replace</span><span class="o">=</span><span class="default_value">None</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Util.html#unified_scaled_probabilistic"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.Util.unified_scaled_probabilistic" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
 </div>
-<div class="section" id="pyfts-benchmarks-arima-module">
-<h2>pyFTS.benchmarks.arima module<a class="headerlink" href="#pyfts-benchmarks-arima-module" title="Permalink to this headline">¶</a></h2>
+<div class="section" id="module-pyFTS.benchmarks.arima">
+<span id="pyfts-benchmarks-arima-module"></span><h2>pyFTS.benchmarks.arima module<a class="headerlink" href="#module-pyFTS.benchmarks.arima" title="Permalink to this headline">¶</a></h2>
+<dl class="py class">
+<dt id="pyFTS.benchmarks.arima.ARIMA">
+<em class="property">class </em><code class="sig-prename descclassname">pyFTS.benchmarks.arima.</code><code class="sig-name descname">ARIMA</code><span class="sig-paren">(</span><em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/arima.html#ARIMA"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.arima.ARIMA" title="Permalink to this definition">¶</a></dt>
+<dd><p>Bases: <a class="reference internal" href="pyFTS.common.html#pyFTS.common.fts.FTS" title="pyFTS.common.fts.FTS"><code class="xref py py-class docutils literal notranslate"><span class="pre">pyFTS.common.fts.FTS</span></code></a></p>
+<p>Façade for statsmodels.tsa.arima_model</p>
+<dl class="py method">
+<dt id="pyFTS.benchmarks.arima.ARIMA.ar">
+<code class="sig-name descname">ar</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/arima.html#ARIMA.ar"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.arima.ARIMA.ar" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.benchmarks.arima.ARIMA.forecast">
+<code class="sig-name descname">forecast</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">ndata</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/arima.html#ARIMA.forecast"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.arima.ARIMA.forecast" title="Permalink to this definition">¶</a></dt>
+<dd><p>Point forecast one step ahead</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> – time series data with the minimal length equal to the max_lag of the model</p></li>
+<li><p><strong>kwargs</strong> – model specific parameters</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>a list with the forecasted values</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.benchmarks.arima.ARIMA.forecast_ahead_distribution">
+<code class="sig-name descname">forecast_ahead_distribution</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="n">steps</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/arima.html#ARIMA.forecast_ahead_distribution"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.arima.ARIMA.forecast_ahead_distribution" title="Permalink to this definition">¶</a></dt>
+<dd><p>Probabilistic forecast from 1 to H steps ahead, where H is given by the steps parameter</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> – time series data with the minimal length equal to the max_lag of the model</p></li>
+<li><p><strong>steps</strong> – the number of steps ahead to forecast</p></li>
+<li><p><strong>start_at</strong> – in the multi step forecasting, the index of the data where to start forecasting (default: 0)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>a list with the forecasted Probability Distributions</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.benchmarks.arima.ARIMA.forecast_ahead_interval">
+<code class="sig-name descname">forecast_ahead_interval</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">ndata</span></em>, <em class="sig-param"><span class="n">steps</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/arima.html#ARIMA.forecast_ahead_interval"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.arima.ARIMA.forecast_ahead_interval" title="Permalink to this definition">¶</a></dt>
+<dd><p>Interval forecast from 1 to H steps ahead, where H is given by the steps parameter</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> – time series data with the minimal length equal to the max_lag of the model</p></li>
+<li><p><strong>steps</strong> – the number of steps ahead to forecast</p></li>
+<li><p><strong>start_at</strong> – in the multi step forecasting, the index of the data where to start forecasting (default: 0)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>a list with the forecasted intervals</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.benchmarks.arima.ARIMA.forecast_distribution">
+<code class="sig-name descname">forecast_distribution</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/arima.html#ARIMA.forecast_distribution"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.arima.ARIMA.forecast_distribution" title="Permalink to this definition">¶</a></dt>
+<dd><p>Probabilistic forecast one step ahead</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> – time series data with the minimal length equal to the max_lag of the model</p></li>
+<li><p><strong>kwargs</strong> – model specific parameters</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>a list with probabilistic.ProbabilityDistribution objects representing the forecasted Probability Distributions</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.benchmarks.arima.ARIMA.forecast_interval">
+<code class="sig-name descname">forecast_interval</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/arima.html#ARIMA.forecast_interval"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.arima.ARIMA.forecast_interval" title="Permalink to this definition">¶</a></dt>
+<dd><p>Interval forecast one step ahead</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> – time series data with the minimal length equal to the max_lag of the model</p></li>
+<li><p><strong>kwargs</strong> – model specific parameters</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>a list with the prediction intervals</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.benchmarks.arima.ARIMA.ma">
+<code class="sig-name descname">ma</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/arima.html#ARIMA.ma"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.arima.ARIMA.ma" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.benchmarks.arima.ARIMA.train">
+<code class="sig-name descname">train</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/arima.html#ARIMA.train"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.arima.ARIMA.train" title="Permalink to this definition">¶</a></dt>
+<dd><p>Method specific parameter fitting</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> – training time series data</p></li>
+<li><p><strong>kwargs</strong> – Method specific parameters</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
 </div>
-<div class="section" id="pyfts-benchmarks-knn-module">
-<h2>pyFTS.benchmarks.knn module<a class="headerlink" href="#pyfts-benchmarks-knn-module" title="Permalink to this headline">¶</a></h2>
+<div class="section" id="module-pyFTS.benchmarks.knn">
+<span id="pyfts-benchmarks-knn-module"></span><h2>pyFTS.benchmarks.knn module<a class="headerlink" href="#module-pyFTS.benchmarks.knn" title="Permalink to this headline">¶</a></h2>
+<dl class="py class">
+<dt id="pyFTS.benchmarks.knn.KNearestNeighbors">
+<em class="property">class </em><code class="sig-prename descclassname">pyFTS.benchmarks.knn.</code><code class="sig-name descname">KNearestNeighbors</code><span class="sig-paren">(</span><em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/knn.html#KNearestNeighbors"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.knn.KNearestNeighbors" title="Permalink to this definition">¶</a></dt>
+<dd><p>Bases: <a class="reference internal" href="pyFTS.common.html#pyFTS.common.fts.FTS" title="pyFTS.common.fts.FTS"><code class="xref py py-class docutils literal notranslate"><span class="pre">pyFTS.common.fts.FTS</span></code></a></p>
+<p>A façade for sklearn.neighbors</p>
+<dl class="py method">
+<dt id="pyFTS.benchmarks.knn.KNearestNeighbors.forecast">
+<code class="sig-name descname">forecast</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/knn.html#KNearestNeighbors.forecast"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.knn.KNearestNeighbors.forecast" title="Permalink to this definition">¶</a></dt>
+<dd><p>Point forecast one step ahead</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> – time series data with the minimal length equal to the max_lag of the model</p></li>
+<li><p><strong>kwargs</strong> – model specific parameters</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>a list with the forecasted values</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.benchmarks.knn.KNearestNeighbors.forecast_ahead_distribution">
+<code class="sig-name descname">forecast_ahead_distribution</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="n">steps</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/knn.html#KNearestNeighbors.forecast_ahead_distribution"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.knn.KNearestNeighbors.forecast_ahead_distribution" title="Permalink to this definition">¶</a></dt>
+<dd><p>Probabilistic forecast from 1 to H steps ahead, where H is given by the steps parameter</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> – time series data with the minimal length equal to the max_lag of the model</p></li>
+<li><p><strong>steps</strong> – the number of steps ahead to forecast</p></li>
+<li><p><strong>start_at</strong> – in the multi step forecasting, the index of the data where to start forecasting (default: 0)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>a list with the forecasted Probability Distributions</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.benchmarks.knn.KNearestNeighbors.forecast_ahead_interval">
+<code class="sig-name descname">forecast_ahead_interval</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="n">steps</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/knn.html#KNearestNeighbors.forecast_ahead_interval"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.knn.KNearestNeighbors.forecast_ahead_interval" title="Permalink to this definition">¶</a></dt>
+<dd><p>Interval forecast from 1 to H steps ahead, where H is given by the steps parameter</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> – time series data with the minimal length equal to the max_lag of the model</p></li>
+<li><p><strong>steps</strong> – the number of steps ahead to forecast</p></li>
+<li><p><strong>start_at</strong> – in the multi step forecasting, the index of the data where to start forecasting (default: 0)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>a list with the forecasted intervals</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.benchmarks.knn.KNearestNeighbors.forecast_distribution">
+<code class="sig-name descname">forecast_distribution</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/knn.html#KNearestNeighbors.forecast_distribution"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.knn.KNearestNeighbors.forecast_distribution" title="Permalink to this definition">¶</a></dt>
+<dd><p>Probabilistic forecast one step ahead</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> – time series data with the minimal length equal to the max_lag of the model</p></li>
+<li><p><strong>kwargs</strong> – model specific parameters</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>a list with probabilistic.ProbabilityDistribution objects representing the forecasted Probability Distributions</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.benchmarks.knn.KNearestNeighbors.forecast_interval">
+<code class="sig-name descname">forecast_interval</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/knn.html#KNearestNeighbors.forecast_interval"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.knn.KNearestNeighbors.forecast_interval" title="Permalink to this definition">¶</a></dt>
+<dd><p>Interval forecast one step ahead</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> – time series data with the minimal length equal to the max_lag of the model</p></li>
+<li><p><strong>kwargs</strong> – model specific parameters</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>a list with the prediction intervals</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.benchmarks.knn.KNearestNeighbors.knn">
+<code class="sig-name descname">knn</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">sample</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/knn.html#KNearestNeighbors.knn"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.knn.KNearestNeighbors.knn" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.benchmarks.knn.KNearestNeighbors.train">
+<code class="sig-name descname">train</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/knn.html#KNearestNeighbors.train"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.knn.KNearestNeighbors.train" title="Permalink to this definition">¶</a></dt>
+<dd><p>Method specific parameter fitting</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> – training time series data</p></li>
+<li><p><strong>kwargs</strong> – Method specific parameters</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
 </div>
-<div class="section" id="pyfts-benchmarks-naive-module">
-<h2>pyFTS.benchmarks.naive module<a class="headerlink" href="#pyfts-benchmarks-naive-module" title="Permalink to this headline">¶</a></h2>
+<div class="section" id="module-pyFTS.benchmarks.naive">
+<span id="pyfts-benchmarks-naive-module"></span><h2>pyFTS.benchmarks.naive module<a class="headerlink" href="#module-pyFTS.benchmarks.naive" title="Permalink to this headline">¶</a></h2>
+<dl class="py class">
+<dt id="pyFTS.benchmarks.naive.Naive">
+<em class="property">class </em><code class="sig-prename descclassname">pyFTS.benchmarks.naive.</code><code class="sig-name descname">Naive</code><span class="sig-paren">(</span><em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/naive.html#Naive"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.naive.Naive" title="Permalink to this definition">¶</a></dt>
+<dd><p>Bases: <a class="reference internal" href="pyFTS.common.html#pyFTS.common.fts.FTS" title="pyFTS.common.fts.FTS"><code class="xref py py-class docutils literal notranslate"><span class="pre">pyFTS.common.fts.FTS</span></code></a></p>
+<p>Naïve Forecasting method</p>
+<dl class="py method">
+<dt id="pyFTS.benchmarks.naive.Naive.forecast">
+<code class="sig-name descname">forecast</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/naive.html#Naive.forecast"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.naive.Naive.forecast" title="Permalink to this definition">¶</a></dt>
+<dd><p>Point forecast one step ahead</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> – time series data with the minimal length equal to the max_lag of the model</p></li>
+<li><p><strong>kwargs</strong> – model specific parameters</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>a list with the forecasted values</p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
 </div>
-<div class="section" id="pyfts-benchmarks-quantreg-module">
-<h2>pyFTS.benchmarks.quantreg module<a class="headerlink" href="#pyfts-benchmarks-quantreg-module" title="Permalink to this headline">¶</a></h2>
+<div class="section" id="module-pyFTS.benchmarks.quantreg">
+<span id="pyfts-benchmarks-quantreg-module"></span><h2>pyFTS.benchmarks.quantreg module<a class="headerlink" href="#module-pyFTS.benchmarks.quantreg" title="Permalink to this headline">¶</a></h2>
+<dl class="py class">
+<dt id="pyFTS.benchmarks.quantreg.QuantileRegression">
+<em class="property">class </em><code class="sig-prename descclassname">pyFTS.benchmarks.quantreg.</code><code class="sig-name descname">QuantileRegression</code><span class="sig-paren">(</span><em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/quantreg.html#QuantileRegression"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.quantreg.QuantileRegression" title="Permalink to this definition">¶</a></dt>
+<dd><p>Bases: <a class="reference internal" href="pyFTS.common.html#pyFTS.common.fts.FTS" title="pyFTS.common.fts.FTS"><code class="xref py py-class docutils literal notranslate"><span class="pre">pyFTS.common.fts.FTS</span></code></a></p>
+<p>Façade for statsmodels.regression.quantile_regression</p>
+<dl class="py method">
+<dt id="pyFTS.benchmarks.quantreg.QuantileRegression.forecast">
+<code class="sig-name descname">forecast</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">ndata</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/quantreg.html#QuantileRegression.forecast"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.quantreg.QuantileRegression.forecast" title="Permalink to this definition">¶</a></dt>
+<dd><p>Point forecast one step ahead</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> – time series data with the minimal length equal to the max_lag of the model</p></li>
+<li><p><strong>kwargs</strong> – model specific parameters</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>a list with the forecasted values</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.benchmarks.quantreg.QuantileRegression.forecast_ahead_distribution">
+<code class="sig-name descname">forecast_ahead_distribution</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">ndata</span></em>, <em class="sig-param"><span class="n">steps</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/quantreg.html#QuantileRegression.forecast_ahead_distribution"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.quantreg.QuantileRegression.forecast_ahead_distribution" title="Permalink to this definition">¶</a></dt>
+<dd><p>Probabilistic forecast from 1 to H steps ahead, where H is given by the steps parameter</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> – time series data with the minimal length equal to the max_lag of the model</p></li>
+<li><p><strong>steps</strong> – the number of steps ahead to forecast</p></li>
+<li><p><strong>start_at</strong> – in the multi step forecasting, the index of the data where to start forecasting (default: 0)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>a list with the forecasted Probability Distributions</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.benchmarks.quantreg.QuantileRegression.forecast_ahead_interval">
+<code class="sig-name descname">forecast_ahead_interval</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">ndata</span></em>, <em class="sig-param"><span class="n">steps</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/quantreg.html#QuantileRegression.forecast_ahead_interval"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.quantreg.QuantileRegression.forecast_ahead_interval" title="Permalink to this definition">¶</a></dt>
+<dd><p>Interval forecast from 1 to H steps ahead, where H is given by the steps parameter</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> – time series data with the minimal length equal to the max_lag of the model</p></li>
+<li><p><strong>steps</strong> – the number of steps ahead to forecast</p></li>
+<li><p><strong>start_at</strong> – in the multi step forecasting, the index of the data where to start forecasting (default: 0)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>a list with the forecasted intervals</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.benchmarks.quantreg.QuantileRegression.forecast_distribution">
+<code class="sig-name descname">forecast_distribution</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">ndata</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/quantreg.html#QuantileRegression.forecast_distribution"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.quantreg.QuantileRegression.forecast_distribution" title="Permalink to this definition">¶</a></dt>
+<dd><p>Probabilistic forecast one step ahead</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> – time series data with the minimal length equal to the max_lag of the model</p></li>
+<li><p><strong>kwargs</strong> – model specific parameters</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>a list with probabilistic.ProbabilityDistribution objects representing the forecasted Probability Distributions</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.benchmarks.quantreg.QuantileRegression.forecast_interval">
+<code class="sig-name descname">forecast_interval</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">ndata</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/quantreg.html#QuantileRegression.forecast_interval"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.quantreg.QuantileRegression.forecast_interval" title="Permalink to this definition">¶</a></dt>
+<dd><p>Interval forecast one step ahead</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> – time series data with the minimal length equal to the max_lag of the model</p></li>
+<li><p><strong>kwargs</strong> – model specific parameters</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>a list with the prediction intervals</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.benchmarks.quantreg.QuantileRegression.interval_to_interval">
+<code class="sig-name descname">interval_to_interval</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="n">lo_params</span></em>, <em class="sig-param"><span class="n">up_params</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/quantreg.html#QuantileRegression.interval_to_interval"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.quantreg.QuantileRegression.interval_to_interval" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.benchmarks.quantreg.QuantileRegression.linearmodel">
+<code class="sig-name descname">linearmodel</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="n">params</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/quantreg.html#QuantileRegression.linearmodel"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.quantreg.QuantileRegression.linearmodel" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.benchmarks.quantreg.QuantileRegression.point_to_interval">
+<code class="sig-name descname">point_to_interval</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="n">lo_params</span></em>, <em class="sig-param"><span class="n">up_params</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/quantreg.html#QuantileRegression.point_to_interval"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.quantreg.QuantileRegression.point_to_interval" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.benchmarks.quantreg.QuantileRegression.train">
+<code class="sig-name descname">train</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/quantreg.html#QuantileRegression.train"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.quantreg.QuantileRegression.train" title="Permalink to this definition">¶</a></dt>
+<dd><p>Method specific parameter fitting</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> – training time series data</p></li>
+<li><p><strong>kwargs</strong> – Method specific parameters</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
 </div>
 <div class="section" id="pyfts-benchmarks-gaussianproc-module">
 <h2>pyFTS.benchmarks.gaussianproc module<a class="headerlink" href="#pyfts-benchmarks-gaussianproc-module" title="Permalink to this headline">¶</a></h2>
 </div>
-<div class="section" id="pyfts-benchmarks-bsts-module">
-<h2>pyFTS.benchmarks.BSTS module<a class="headerlink" href="#pyfts-benchmarks-bsts-module" title="Permalink to this headline">¶</a></h2>
+<div class="section" id="module-pyFTS.benchmarks.BSTS">
+<span id="pyfts-benchmarks-bsts-module"></span><h2>pyFTS.benchmarks.BSTS module<a class="headerlink" href="#module-pyFTS.benchmarks.BSTS" title="Permalink to this headline">¶</a></h2>
+<dl class="py class">
+<dt id="pyFTS.benchmarks.BSTS.ARIMA">
+<em class="property">class </em><code class="sig-prename descclassname">pyFTS.benchmarks.BSTS.</code><code class="sig-name descname">ARIMA</code><span class="sig-paren">(</span><em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/BSTS.html#ARIMA"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.BSTS.ARIMA" title="Permalink to this definition">¶</a></dt>
+<dd><p>Bases: <a class="reference internal" href="pyFTS.common.html#pyFTS.common.fts.FTS" title="pyFTS.common.fts.FTS"><code class="xref py py-class docutils literal notranslate"><span class="pre">pyFTS.common.fts.FTS</span></code></a></p>
+<p>Façade for statsmodels.tsa.arima_model</p>
+<dl class="py method">
+<dt id="pyFTS.benchmarks.BSTS.ARIMA.forecast">
+<code class="sig-name descname">forecast</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">ndata</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/BSTS.html#ARIMA.forecast"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.BSTS.ARIMA.forecast" title="Permalink to this definition">¶</a></dt>
+<dd><p>Point forecast one step ahead</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> – time series data with the minimal length equal to the max_lag of the model</p></li>
+<li><p><strong>kwargs</strong> – model specific parameters</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>a list with the forecasted values</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.benchmarks.BSTS.ARIMA.forecast_ahead">
+<code class="sig-name descname">forecast_ahead</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="n">steps</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/BSTS.html#ARIMA.forecast_ahead"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.BSTS.ARIMA.forecast_ahead" title="Permalink to this definition">¶</a></dt>
+<dd><p>Point forecast from 1 to H steps ahead, where H is given by the steps parameter</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> – time series data with the minimal length equal to the max_lag of the model</p></li>
+<li><p><strong>steps</strong> – the number of steps ahead to forecast (default: 1)</p></li>
+<li><p><strong>start_at</strong> – in the multi step forecasting, the index of the data where to start forecasting (default: 0)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>a list with the forecasted values</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.benchmarks.BSTS.ARIMA.forecast_ahead_distribution">
+<code class="sig-name descname">forecast_ahead_distribution</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="n">steps</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/BSTS.html#ARIMA.forecast_ahead_distribution"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.BSTS.ARIMA.forecast_ahead_distribution" title="Permalink to this definition">¶</a></dt>
+<dd><p>Probabilistic forecast from 1 to H steps ahead, where H is given by the steps parameter</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> – time series data with the minimal length equal to the max_lag of the model</p></li>
+<li><p><strong>steps</strong> – the number of steps ahead to forecast</p></li>
+<li><p><strong>start_at</strong> – in the multi step forecasting, the index of the data where to start forecasting (default: 0)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>a list with the forecasted Probability Distributions</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.benchmarks.BSTS.ARIMA.forecast_ahead_interval">
+<code class="sig-name descname">forecast_ahead_interval</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">ndata</span></em>, <em class="sig-param"><span class="n">steps</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/BSTS.html#ARIMA.forecast_ahead_interval"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.BSTS.ARIMA.forecast_ahead_interval" title="Permalink to this definition">¶</a></dt>
+<dd><p>Interval forecast from 1 to H steps ahead, where H is given by the steps parameter</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> – time series data with the minimal length equal to the max_lag of the model</p></li>
+<li><p><strong>steps</strong> – the number of steps ahead to forecast</p></li>
+<li><p><strong>start_at</strong> – in the multi step forecasting, the index of the data where to start forecasting (default: 0)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>a list with the forecasted intervals</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.benchmarks.BSTS.ARIMA.forecast_distribution">
+<code class="sig-name descname">forecast_distribution</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/BSTS.html#ARIMA.forecast_distribution"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.BSTS.ARIMA.forecast_distribution" title="Permalink to this definition">¶</a></dt>
+<dd><p>Probabilistic forecast one step ahead</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> – time series data with the minimal length equal to the max_lag of the model</p></li>
+<li><p><strong>kwargs</strong> – model specific parameters</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>a list with probabilistic.ProbabilityDistribution objects representing the forecasted Probability Distributions</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.benchmarks.BSTS.ARIMA.forecast_interval">
+<code class="sig-name descname">forecast_interval</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/BSTS.html#ARIMA.forecast_interval"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.BSTS.ARIMA.forecast_interval" title="Permalink to this definition">¶</a></dt>
+<dd><p>Interval forecast one step ahead</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> – time series data with the minimal length equal to the max_lag of the model</p></li>
+<li><p><strong>kwargs</strong> – model specific parameters</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>a list with the prediction intervals</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.benchmarks.BSTS.ARIMA.inference">
+<code class="sig-name descname">inference</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">steps</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/BSTS.html#ARIMA.inference"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.BSTS.ARIMA.inference" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.benchmarks.BSTS.ARIMA.train">
+<code class="sig-name descname">train</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/BSTS.html#ARIMA.train"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.BSTS.ARIMA.train" title="Permalink to this definition">¶</a></dt>
+<dd><p>Method specific parameter fitting</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> – training time series data</p></li>
+<li><p><strong>kwargs</strong> – Method specific parameters</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
 </div>
 </div>
 
@@ -119,17 +1909,17 @@
 <li><a class="reference internal" href="#">pyFTS.benchmarks package</a><ul>
 <li><a class="reference internal" href="#module-pyFTS.benchmarks">Module contents</a></li>
 <li><a class="reference internal" href="#submodules">Submodules</a></li>
-<li><a class="reference internal" href="#pyfts-benchmarks-benchmarks-module">pyFTS.benchmarks.benchmarks module</a></li>
-<li><a class="reference internal" href="#pyfts-benchmarks-measures-module">pyFTS.benchmarks.Measures module</a></li>
-<li><a class="reference internal" href="#pyfts-benchmarks-residualanalysis-module">pyFTS.benchmarks.ResidualAnalysis module</a></li>
-<li><a class="reference internal" href="#pyfts-benchmarks-tests-module">pyFTS.benchmarks.Tests module</a></li>
-<li><a class="reference internal" href="#pyfts-benchmarks-util-module">pyFTS.benchmarks.Util module</a></li>
-<li><a class="reference internal" href="#pyfts-benchmarks-arima-module">pyFTS.benchmarks.arima module</a></li>
-<li><a class="reference internal" href="#pyfts-benchmarks-knn-module">pyFTS.benchmarks.knn module</a></li>
-<li><a class="reference internal" href="#pyfts-benchmarks-naive-module">pyFTS.benchmarks.naive module</a></li>
-<li><a class="reference internal" href="#pyfts-benchmarks-quantreg-module">pyFTS.benchmarks.quantreg module</a></li>
+<li><a class="reference internal" href="#module-pyFTS.benchmarks.benchmarks">pyFTS.benchmarks.benchmarks module</a></li>
+<li><a class="reference internal" href="#module-pyFTS.benchmarks.Measures">pyFTS.benchmarks.Measures module</a></li>
+<li><a class="reference internal" href="#module-pyFTS.benchmarks.ResidualAnalysis">pyFTS.benchmarks.ResidualAnalysis module</a></li>
+<li><a class="reference internal" href="#module-pyFTS.benchmarks.Tests">pyFTS.benchmarks.Tests module</a></li>
+<li><a class="reference internal" href="#module-pyFTS.benchmarks.Util">pyFTS.benchmarks.Util module</a></li>
+<li><a class="reference internal" href="#module-pyFTS.benchmarks.arima">pyFTS.benchmarks.arima module</a></li>
+<li><a class="reference internal" href="#module-pyFTS.benchmarks.knn">pyFTS.benchmarks.knn module</a></li>
+<li><a class="reference internal" href="#module-pyFTS.benchmarks.naive">pyFTS.benchmarks.naive module</a></li>
+<li><a class="reference internal" href="#module-pyFTS.benchmarks.quantreg">pyFTS.benchmarks.quantreg module</a></li>
 <li><a class="reference internal" href="#pyfts-benchmarks-gaussianproc-module">pyFTS.benchmarks.gaussianproc module</a></li>
-<li><a class="reference internal" href="#pyfts-benchmarks-bsts-module">pyFTS.benchmarks.BSTS module</a></li>
+<li><a class="reference internal" href="#module-pyFTS.benchmarks.BSTS">pyFTS.benchmarks.BSTS module</a></li>
 </ul>
 </li>
 </ul>
diff --git a/docs/build/html/pyFTS.common.html b/docs/build/html/pyFTS.common.html
index c4dabe1..c7b82d1 100644
--- a/docs/build/html/pyFTS.common.html
+++ b/docs/build/html/pyFTS.common.html
@@ -762,11 +762,272 @@ bisect but with a simpler API and support for key functions.</p>
 </dd></dl>
 
 </div>
-<div class="section" id="pyfts-common-transformations-module">
-<h2>pyFTS.common.transformations module<a class="headerlink" href="#pyfts-common-transformations-module" title="Permalink to this headline">¶</a></h2>
-</div>
-<div class="section" id="pyfts-common-util-module">
-<h2>pyFTS.common.Util module<a class="headerlink" href="#pyfts-common-util-module" title="Permalink to this headline">¶</a></h2>
+<div class="section" id="module-pyFTS.common.Util">
+<span id="pyfts-common-util-module"></span><h2>pyFTS.common.Util module<a class="headerlink" href="#module-pyFTS.common.Util" title="Permalink to this headline">¶</a></h2>
+<p>Common facilities for pyFTS</p>
+<dl class="py function">
+<dt id="pyFTS.common.Util.current_milli_time">
+<code class="sig-prename descclassname">pyFTS.common.Util.</code><code class="sig-name descname">current_milli_time</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.common.Util.current_milli_time" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.common.Util.draw_sets_on_axis">
+<code class="sig-prename descclassname">pyFTS.common.Util.</code><code class="sig-name descname">draw_sets_on_axis</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">axis</span></em>, <em class="sig-param"><span class="n">model</span></em>, <em class="sig-param"><span class="n">size</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/common/Util.html#draw_sets_on_axis"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.common.Util.draw_sets_on_axis" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.common.Util.enumerate2">
+<code class="sig-prename descclassname">pyFTS.common.Util.</code><code class="sig-name descname">enumerate2</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">xs</span></em>, <em class="sig-param"><span class="n">start</span><span class="o">=</span><span class="default_value">0</span></em>, <em class="sig-param"><span class="n">step</span><span class="o">=</span><span class="default_value">1</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/common/Util.html#enumerate2"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.common.Util.enumerate2" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.common.Util.load_env">
+<code class="sig-prename descclassname">pyFTS.common.Util.</code><code class="sig-name descname">load_env</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">file</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/common/Util.html#load_env"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.common.Util.load_env" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.common.Util.load_obj">
+<code class="sig-prename descclassname">pyFTS.common.Util.</code><code class="sig-name descname">load_obj</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">file</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/common/Util.html#load_obj"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.common.Util.load_obj" title="Permalink to this definition">¶</a></dt>
+<dd><p>Load to memory an object stored filesystem. This function depends on Dill package</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>file</strong> – file name where the object is stored</p>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>object</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.common.Util.persist_env">
+<code class="sig-prename descclassname">pyFTS.common.Util.</code><code class="sig-name descname">persist_env</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">file</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/common/Util.html#persist_env"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.common.Util.persist_env" title="Permalink to this definition">¶</a></dt>
+<dd><p>Persist an entire environment on file. This function depends on Dill package</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>file</strong> – file name to store the environment</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.common.Util.persist_obj">
+<code class="sig-prename descclassname">pyFTS.common.Util.</code><code class="sig-name descname">persist_obj</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">obj</span></em>, <em class="sig-param"><span class="n">file</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/common/Util.html#persist_obj"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.common.Util.persist_obj" title="Permalink to this definition">¶</a></dt>
+<dd><p>Persist an object on filesystem. This function depends on Dill package</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>obj</strong> – object on memory</p></li>
+<li><p><strong>file</strong> – file name to store the object</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.common.Util.plot_compared_intervals_ahead">
+<code class="sig-prename descclassname">pyFTS.common.Util.</code><code class="sig-name descname">plot_compared_intervals_ahead</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">original</span></em>, <em class="sig-param"><span class="n">models</span></em>, <em class="sig-param"><span class="n">colors</span></em>, <em class="sig-param"><span class="n">distributions</span></em>, <em class="sig-param"><span class="n">time_from</span></em>, <em class="sig-param"><span class="n">time_to</span></em>, <em class="sig-param"><span class="n">intervals</span><span class="o">=</span><span class="default_value">True</span></em>, <em class="sig-param"><span class="n">save</span><span class="o">=</span><span class="default_value">False</span></em>, <em class="sig-param"><span class="n">file</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">tam</span><span class="o">=</span><span class="default_value">[20, 5]</span></em>, <em class="sig-param"><span class="n">resolution</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">cmap</span><span class="o">=</span><span class="default_value">'Blues'</span></em>, <em class="sig-param"><span class="n">linewidth</span><span class="o">=</span><span class="default_value">1.5</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/common/Util.html#plot_compared_intervals_ahead"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.common.Util.plot_compared_intervals_ahead" title="Permalink to this definition">¶</a></dt>
+<dd><p>Plot the forecasts of several one step ahead models, by point or by interval</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>original</strong> – Original time series data (list)</p></li>
+<li><p><strong>models</strong> – List of models to compare</p></li>
+<li><p><strong>colors</strong> – List of models colors</p></li>
+<li><p><strong>distributions</strong> – True to plot a distribution</p></li>
+<li><p><strong>time_from</strong> – index of data poit to start the ahead forecasting</p></li>
+<li><p><strong>time_to</strong> – number of steps ahead to forecast</p></li>
+<li><p><strong>interpol</strong> – Fill space between distribution plots</p></li>
+<li><p><strong>save</strong> – Save the picture on file</p></li>
+<li><p><strong>file</strong> – Filename to save the picture</p></li>
+<li><p><strong>tam</strong> – Size of the picture</p></li>
+<li><p><strong>resolution</strong> – </p></li>
+<li><p><strong>cmap</strong> – Color map to be used on distribution plot</p></li>
+<li><p><strong>option</strong> – Distribution type to be passed for models</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.common.Util.plot_density_rectange">
+<code class="sig-prename descclassname">pyFTS.common.Util.</code><code class="sig-name descname">plot_density_rectange</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">ax</span></em>, <em class="sig-param"><span class="n">cmap</span></em>, <em class="sig-param"><span class="n">density</span></em>, <em class="sig-param"><span class="n">fig</span></em>, <em class="sig-param"><span class="n">resolution</span></em>, <em class="sig-param"><span class="n">time_from</span></em>, <em class="sig-param"><span class="n">time_to</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/common/Util.html#plot_density_rectange"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.common.Util.plot_density_rectange" title="Permalink to this definition">¶</a></dt>
+<dd><p>Auxiliar function to plot_compared_intervals_ahead</p>
+</dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.common.Util.plot_distribution">
+<code class="sig-prename descclassname">pyFTS.common.Util.</code><code class="sig-name descname">plot_distribution</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">ax</span></em>, <em class="sig-param"><span class="n">cmap</span></em>, <em class="sig-param"><span class="n">probabilitydist</span></em>, <em class="sig-param"><span class="n">fig</span></em>, <em class="sig-param"><span class="n">time_from</span></em>, <em class="sig-param"><span class="n">reference_data</span><span class="o">=</span><span class="default_value">None</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/common/Util.html#plot_distribution"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.common.Util.plot_distribution" title="Permalink to this definition">¶</a></dt>
+<dd><p>Plot forecasted ProbabilityDistribution objects on a matplotlib axis</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>ax</strong> – matplotlib axis</p></li>
+<li><p><strong>cmap</strong> – matplotlib colormap name</p></li>
+<li><p><strong>probabilitydist</strong> – list of ProbabilityDistribution objects</p></li>
+<li><p><strong>fig</strong> – matplotlib figure</p></li>
+<li><p><strong>time_from</strong> – starting time (on x axis) to begin the plots</p></li>
+<li><p><strong>reference_data</strong> – </p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.common.Util.plot_distribution2">
+<code class="sig-prename descclassname">pyFTS.common.Util.</code><code class="sig-name descname">plot_distribution2</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">probabilitydist</span></em>, <em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/common/Util.html#plot_distribution2"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.common.Util.plot_distribution2" title="Permalink to this definition">¶</a></dt>
+<dd><p>Plot distributions in y-axis over the time (x-axis)</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>probabilitydist</strong> – the forecasted probability distributions to plot</p></li>
+<li><p><strong>data</strong> – the original test sample</p></li>
+<li><p><strong>start_at</strong> – the time index (inside of data) to start to plot the probability distributions</p></li>
+<li><p><strong>ax</strong> – a matplotlib axis. If no value was provided a new axis is created.</p></li>
+<li><p><strong>cmap</strong> – a matplotlib colormap name, the default value is ‘Blues’</p></li>
+<li><p><strong>quantiles</strong> – the list of quantiles intervals to plot, e. g. [.05, .25, .75, .95]</p></li>
+<li><p><strong>median</strong> – a boolean value indicating if the median value will be plot.</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.common.Util.plot_distribution_tiled">
+<code class="sig-prename descclassname">pyFTS.common.Util.</code><code class="sig-name descname">plot_distribution_tiled</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">distributions</span></em>, <em class="sig-param"><span class="n">data</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">rows</span><span class="o">=</span><span class="default_value">5</span></em>, <em class="sig-param"><span class="n">cols</span><span class="o">=</span><span class="default_value">5</span></em>, <em class="sig-param"><span class="n">index</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">axis</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">size</span><span class="o">=</span><span class="default_value">[10, 20]</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/common/Util.html#plot_distribution_tiled"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.common.Util.plot_distribution_tiled" title="Permalink to this definition">¶</a></dt>
+<dd><p>Plot one distribution individually in each axis, with probability in y-axis and UoD on x-axis</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>distributions</strong> – </p></li>
+<li><p><strong>data</strong> – </p></li>
+<li><p><strong>rows</strong> – </p></li>
+<li><p><strong>cols</strong> – </p></li>
+<li><p><strong>index</strong> – </p></li>
+<li><p><strong>axis</strong> – </p></li>
+<li><p><strong>size</strong> – </p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.common.Util.plot_interval">
+<code class="sig-prename descclassname">pyFTS.common.Util.</code><code class="sig-name descname">plot_interval</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">axis</span></em>, <em class="sig-param"><span class="n">intervals</span></em>, <em class="sig-param"><span class="n">order</span></em>, <em class="sig-param"><span class="n">label</span></em>, <em class="sig-param"><span class="n">color</span><span class="o">=</span><span class="default_value">'red'</span></em>, <em class="sig-param"><span class="n">typeonlegend</span><span class="o">=</span><span class="default_value">False</span></em>, <em class="sig-param"><span class="n">ls</span><span class="o">=</span><span class="default_value">'-'</span></em>, <em class="sig-param"><span class="n">linewidth</span><span class="o">=</span><span class="default_value">1</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/common/Util.html#plot_interval"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.common.Util.plot_interval" title="Permalink to this definition">¶</a></dt>
+<dd><p>Plot forecasted intervals on matplotlib</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>axis</strong> – matplotlib axis</p></li>
+<li><p><strong>intervals</strong> – list of forecasted intervals</p></li>
+<li><p><strong>order</strong> – order of the model that create the forecasts</p></li>
+<li><p><strong>label</strong> – figure label</p></li>
+<li><p><strong>color</strong> – matplotlib color name</p></li>
+<li><p><strong>typeonlegend</strong> – </p></li>
+<li><p><strong>ls</strong> – matplotlib line style</p></li>
+<li><p><strong>linewidth</strong> – matplotlib width</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.common.Util.plot_interval2">
+<code class="sig-prename descclassname">pyFTS.common.Util.</code><code class="sig-name descname">plot_interval2</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">intervals</span></em>, <em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/common/Util.html#plot_interval2"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.common.Util.plot_interval2" title="Permalink to this definition">¶</a></dt>
+<dd><p>Plot forecasted intervals on matplotlib</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>intervals</strong> – list of forecasted intervals</p></li>
+<li><p><strong>data</strong> – the original test sample</p></li>
+<li><p><strong>start_at</strong> – the time index (inside of data) to start to plot the intervals</p></li>
+<li><p><strong>label</strong> – figure label</p></li>
+<li><p><strong>color</strong> – matplotlib color name</p></li>
+<li><p><strong>typeonlegend</strong> – </p></li>
+<li><p><strong>ls</strong> – matplotlib line style</p></li>
+<li><p><strong>linewidth</strong> – matplotlib width</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.common.Util.plot_probability_distributions">
+<code class="sig-prename descclassname">pyFTS.common.Util.</code><code class="sig-name descname">plot_probability_distributions</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">pmfs</span></em>, <em class="sig-param"><span class="n">lcolors</span></em>, <em class="sig-param"><span class="n">tam</span><span class="o">=</span><span class="default_value">[15, 7]</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/common/Util.html#plot_probability_distributions"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.common.Util.plot_probability_distributions" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.common.Util.plot_rules">
+<code class="sig-prename descclassname">pyFTS.common.Util.</code><code class="sig-name descname">plot_rules</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">model</span></em>, <em class="sig-param"><span class="n">size</span><span class="o">=</span><span class="default_value">[5, 5]</span></em>, <em class="sig-param"><span class="n">axis</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">rules_by_axis</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">columns</span><span class="o">=</span><span class="default_value">1</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/common/Util.html#plot_rules"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.common.Util.plot_rules" title="Permalink to this definition">¶</a></dt>
+<dd><p>Plot the FLRG rules of a FTS model on a matplotlib axis</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>model</strong> – FTS model</p></li>
+<li><p><strong>size</strong> – figure size</p></li>
+<li><p><strong>axis</strong> – matplotlib axis</p></li>
+<li><p><strong>rules_by_axis</strong> – number of rules plotted by column</p></li>
+<li><p><strong>columns</strong> – number of columns</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.common.Util.show_and_save_image">
+<code class="sig-prename descclassname">pyFTS.common.Util.</code><code class="sig-name descname">show_and_save_image</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">fig</span></em>, <em class="sig-param"><span class="n">file</span></em>, <em class="sig-param"><span class="n">flag</span></em>, <em class="sig-param"><span class="n">lgd</span><span class="o">=</span><span class="default_value">None</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/common/Util.html#show_and_save_image"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.common.Util.show_and_save_image" title="Permalink to this definition">¶</a></dt>
+<dd><p>Show and image and save on file</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>fig</strong> – Matplotlib Figure object</p></li>
+<li><p><strong>file</strong> – filename to save the picture</p></li>
+<li><p><strong>flag</strong> – if True the image will be saved</p></li>
+<li><p><strong>lgd</strong> – legend</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.common.Util.sliding_window">
+<code class="sig-prename descclassname">pyFTS.common.Util.</code><code class="sig-name descname">sliding_window</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="n">windowsize</span></em>, <em class="sig-param"><span class="n">train</span><span class="o">=</span><span class="default_value">0.8</span></em>, <em class="sig-param"><span class="n">inc</span><span class="o">=</span><span class="default_value">0.1</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/common/Util.html#sliding_window"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.common.Util.sliding_window" title="Permalink to this definition">¶</a></dt>
+<dd><p>Sliding window method of cross validation for time series</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> – the entire dataset</p></li>
+<li><p><strong>windowsize</strong> – window size</p></li>
+<li><p><strong>train</strong> – percentual of the window size will be used for training the models</p></li>
+<li><p><strong>inc</strong> – percentual of data used for slide the window</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>window count, training set, test set</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.common.Util.uniquefilename">
+<code class="sig-prename descclassname">pyFTS.common.Util.</code><code class="sig-name descname">uniquefilename</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">name</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/common/Util.html#uniquefilename"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.common.Util.uniquefilename" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
 </div>
 <div class="section" id="module-pyFTS.common.flrg">
 <span id="pyfts-common-flrg-module"></span><h2>pyFTS.common.flrg module<a class="headerlink" href="#module-pyFTS.common.flrg" title="Permalink to this headline">¶</a></h2>
@@ -878,8 +1139,562 @@ when the LHS pattern is identified on time t.</p>
 </dd></dl>
 
 </div>
-<div class="section" id="pyfts-common-fts-module">
-<h2>pyFTS.common.fts module<a class="headerlink" href="#pyfts-common-fts-module" title="Permalink to this headline">¶</a></h2>
+<div class="section" id="module-pyFTS.common.fts">
+<span id="pyfts-common-fts-module"></span><h2>pyFTS.common.fts module<a class="headerlink" href="#module-pyFTS.common.fts" title="Permalink to this headline">¶</a></h2>
+<dl class="py class">
+<dt id="pyFTS.common.fts.FTS">
+<em class="property">class </em><code class="sig-prename descclassname">pyFTS.common.fts.</code><code class="sig-name descname">FTS</code><span class="sig-paren">(</span><em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/common/fts.html#FTS"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.common.fts.FTS" title="Permalink to this definition">¶</a></dt>
+<dd><p>Bases: <a class="reference external" href="https://docs.python.org/3/library/functions.html#object" title="(in Python v3.9)"><code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></a></p>
+<p>Fuzzy Time Series object model</p>
+<dl class="py attribute">
+<dt id="pyFTS.common.fts.FTS.alpha_cut">
+<code class="sig-name descname">alpha_cut</code><a class="headerlink" href="#pyFTS.common.fts.FTS.alpha_cut" title="Permalink to this definition">¶</a></dt>
+<dd><p>A float with the minimal membership to be considered on fuzzyfication process</p>
+</dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.common.fts.FTS.append_log">
+<code class="sig-name descname">append_log</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">operation</span></em>, <em class="sig-param"><span class="n">value</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/common/fts.html#FTS.append_log"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.common.fts.FTS.append_log" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.common.fts.FTS.append_rule">
+<code class="sig-name descname">append_rule</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">flrg</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/common/fts.html#FTS.append_rule"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.common.fts.FTS.append_rule" title="Permalink to this definition">¶</a></dt>
+<dd><p>Append FLRG rule to the model</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>flrg</strong> – rule</p>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.common.fts.FTS.append_transformation">
+<code class="sig-name descname">append_transformation</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">transformation</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/common/fts.html#FTS.append_transformation"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.common.fts.FTS.append_transformation" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.common.fts.FTS.apply_inverse_transformations">
+<code class="sig-name descname">apply_inverse_transformations</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="n">params</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/common/fts.html#FTS.apply_inverse_transformations"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.common.fts.FTS.apply_inverse_transformations" title="Permalink to this definition">¶</a></dt>
+<dd><p>Apply the data transformations for data postprocessing</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> – input data</p></li>
+<li><p><strong>params</strong> – transformation parameters</p></li>
+<li><p><strong>updateUoD</strong> – </p></li>
+<li><p><strong>kwargs</strong> – </p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>postprocessed data</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.common.fts.FTS.apply_transformations">
+<code class="sig-name descname">apply_transformations</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="n">params</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">updateUoD</span><span class="o">=</span><span class="default_value">False</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/common/fts.html#FTS.apply_transformations"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.common.fts.FTS.apply_transformations" title="Permalink to this definition">¶</a></dt>
+<dd><p>Apply the data transformations for data preprocessing</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> – input data</p></li>
+<li><p><strong>params</strong> – transformation parameters</p></li>
+<li><p><strong>updateUoD</strong> – </p></li>
+<li><p><strong>kwargs</strong> – </p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>preprocessed data</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt id="pyFTS.common.fts.FTS.auto_update">
+<code class="sig-name descname">auto_update</code><a class="headerlink" href="#pyFTS.common.fts.FTS.auto_update" title="Permalink to this definition">¶</a></dt>
+<dd><p>A boolean value indicating that model is incremental</p>
+</dd></dl>
+
+<dl class="py attribute">
+<dt id="pyFTS.common.fts.FTS.benchmark_only">
+<code class="sig-name descname">benchmark_only</code><a class="headerlink" href="#pyFTS.common.fts.FTS.benchmark_only" title="Permalink to this definition">¶</a></dt>
+<dd><p>A boolean value indicating a façade for external (non-FTS) model used on benchmarks or ensembles.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.common.fts.FTS.clip_uod">
+<code class="sig-name descname">clip_uod</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">ndata</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/common/fts.html#FTS.clip_uod"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.common.fts.FTS.clip_uod" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.common.fts.FTS.clone_parameters">
+<code class="sig-name descname">clone_parameters</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">model</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/common/fts.html#FTS.clone_parameters"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.common.fts.FTS.clone_parameters" title="Permalink to this definition">¶</a></dt>
+<dd><p>Import the parameters values from other model</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>model</strong> – a model to clone the parameters</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt id="pyFTS.common.fts.FTS.detail">
+<code class="sig-name descname">detail</code><a class="headerlink" href="#pyFTS.common.fts.FTS.detail" title="Permalink to this definition">¶</a></dt>
+<dd><p>A string with the model detailed information</p>
+</dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.common.fts.FTS.fit">
+<code class="sig-name descname">fit</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">ndata</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/common/fts.html#FTS.fit"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.common.fts.FTS.fit" title="Permalink to this definition">¶</a></dt>
+<dd><p>Fit the model’s parameters based on the training data.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>ndata</strong> – training time series data</p></li>
+<li><p><strong>kwargs</strong> – </p></li>
+<li><p><strong>num_batches</strong> – split the training data in num_batches to save memory during the training process</p></li>
+<li><p><strong>save_model</strong> – save final model on disk</p></li>
+<li><p><strong>batch_save</strong> – save the model between each batch</p></li>
+<li><p><strong>file_path</strong> – path to save the model</p></li>
+<li><p><strong>distributed</strong> – boolean, indicate if the training procedure will be distributed in a dispy cluster</p></li>
+<li><p><strong>nodes</strong> – a list with the dispy cluster nodes addresses</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt id="pyFTS.common.fts.FTS.flrgs">
+<code class="sig-name descname">flrgs</code><a class="headerlink" href="#pyFTS.common.fts.FTS.flrgs" title="Permalink to this definition">¶</a></dt>
+<dd><p>The list of Fuzzy Logical Relationship Groups - FLRG</p>
+</dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.common.fts.FTS.forecast">
+<code class="sig-name descname">forecast</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/common/fts.html#FTS.forecast"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.common.fts.FTS.forecast" title="Permalink to this definition">¶</a></dt>
+<dd><p>Point forecast one step ahead</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> – time series data with the minimal length equal to the max_lag of the model</p></li>
+<li><p><strong>kwargs</strong> – model specific parameters</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>a list with the forecasted values</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.common.fts.FTS.forecast_ahead">
+<code class="sig-name descname">forecast_ahead</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="n">steps</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/common/fts.html#FTS.forecast_ahead"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.common.fts.FTS.forecast_ahead" title="Permalink to this definition">¶</a></dt>
+<dd><p>Point forecast from 1 to H steps ahead, where H is given by the steps parameter</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> – time series data with the minimal length equal to the max_lag of the model</p></li>
+<li><p><strong>steps</strong> – the number of steps ahead to forecast (default: 1)</p></li>
+<li><p><strong>start_at</strong> – in the multi step forecasting, the index of the data where to start forecasting (default: 0)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>a list with the forecasted values</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.common.fts.FTS.forecast_ahead_distribution">
+<code class="sig-name descname">forecast_ahead_distribution</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="n">steps</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/common/fts.html#FTS.forecast_ahead_distribution"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.common.fts.FTS.forecast_ahead_distribution" title="Permalink to this definition">¶</a></dt>
+<dd><p>Probabilistic forecast from 1 to H steps ahead, where H is given by the steps parameter</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> – time series data with the minimal length equal to the max_lag of the model</p></li>
+<li><p><strong>steps</strong> – the number of steps ahead to forecast</p></li>
+<li><p><strong>start_at</strong> – in the multi step forecasting, the index of the data where to start forecasting (default: 0)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>a list with the forecasted Probability Distributions</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.common.fts.FTS.forecast_ahead_interval">
+<code class="sig-name descname">forecast_ahead_interval</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="n">steps</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/common/fts.html#FTS.forecast_ahead_interval"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.common.fts.FTS.forecast_ahead_interval" title="Permalink to this definition">¶</a></dt>
+<dd><p>Interval forecast from 1 to H steps ahead, where H is given by the steps parameter</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> – time series data with the minimal length equal to the max_lag of the model</p></li>
+<li><p><strong>steps</strong> – the number of steps ahead to forecast</p></li>
+<li><p><strong>start_at</strong> – in the multi step forecasting, the index of the data where to start forecasting (default: 0)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>a list with the forecasted intervals</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.common.fts.FTS.forecast_ahead_multivariate">
+<code class="sig-name descname">forecast_ahead_multivariate</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="n">steps</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/common/fts.html#FTS.forecast_ahead_multivariate"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.common.fts.FTS.forecast_ahead_multivariate" title="Permalink to this definition">¶</a></dt>
+<dd><p>Multivariate forecast n step ahead</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> – Pandas dataframe with one column for each variable and with the minimal length equal to the max_lag of the model</p></li>
+<li><p><strong>steps</strong> – the number of steps ahead to forecast</p></li>
+<li><p><strong>start_at</strong> – in the multi step forecasting, the index of the data where to start forecasting (default: 0)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>a Pandas Dataframe object representing the forecasted values for each variable</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.common.fts.FTS.forecast_distribution">
+<code class="sig-name descname">forecast_distribution</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/common/fts.html#FTS.forecast_distribution"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.common.fts.FTS.forecast_distribution" title="Permalink to this definition">¶</a></dt>
+<dd><p>Probabilistic forecast one step ahead</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> – time series data with the minimal length equal to the max_lag of the model</p></li>
+<li><p><strong>kwargs</strong> – model specific parameters</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>a list with probabilistic.ProbabilityDistribution objects representing the forecasted Probability Distributions</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.common.fts.FTS.forecast_interval">
+<code class="sig-name descname">forecast_interval</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/common/fts.html#FTS.forecast_interval"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.common.fts.FTS.forecast_interval" title="Permalink to this definition">¶</a></dt>
+<dd><p>Interval forecast one step ahead</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> – time series data with the minimal length equal to the max_lag of the model</p></li>
+<li><p><strong>kwargs</strong> – model specific parameters</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>a list with the prediction intervals</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.common.fts.FTS.forecast_multivariate">
+<code class="sig-name descname">forecast_multivariate</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/common/fts.html#FTS.forecast_multivariate"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.common.fts.FTS.forecast_multivariate" title="Permalink to this definition">¶</a></dt>
+<dd><p>Multivariate forecast one step ahead</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> – Pandas dataframe with one column for each variable and with the minimal length equal to the max_lag of the model</p></li>
+<li><p><strong>kwargs</strong> – model specific parameters</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>a Pandas Dataframe object representing the forecasted values for each variable</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.common.fts.FTS.forecast_step">
+<code class="sig-name descname">forecast_step</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="n">step</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/common/fts.html#FTS.forecast_step"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.common.fts.FTS.forecast_step" title="Permalink to this definition">¶</a></dt>
+<dd><p>Point forecast for H steps ahead, where H is given by the step parameter</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> – time series data with the minimal length equal to the max_lag of the model</p></li>
+<li><p><strong>step</strong> – the forecasting horizon (default: 1)</p></li>
+<li><p><strong>start_at</strong> – in the multi step forecasting, the index of the data where to start forecasting (default: 0)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>a list with the forecasted values</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.common.fts.FTS.fuzzy">
+<code class="sig-name descname">fuzzy</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/common/fts.html#FTS.fuzzy"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.common.fts.FTS.fuzzy" title="Permalink to this definition">¶</a></dt>
+<dd><p>Fuzzify a data point</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>data</strong> – data point</p>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>maximum membership fuzzy set</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.common.fts.FTS.get_UoD">
+<code class="sig-name descname">get_UoD</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/common/fts.html#FTS.get_UoD"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.common.fts.FTS.get_UoD" title="Permalink to this definition">¶</a></dt>
+<dd><p>Returns the interval of the known bounds of the universe of discourse (UoD), i. e.,
+the known minimum and maximum values of the time series.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns</dt>
+<dd class="field-odd"><p>A set with the lower and the upper bounds of the UoD</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt id="pyFTS.common.fts.FTS.has_interval_forecasting">
+<code class="sig-name descname">has_interval_forecasting</code><a class="headerlink" href="#pyFTS.common.fts.FTS.has_interval_forecasting" title="Permalink to this definition">¶</a></dt>
+<dd><p>A boolean value indicating if the model supports interval forecasting, default: False</p>
+</dd></dl>
+
+<dl class="py attribute">
+<dt id="pyFTS.common.fts.FTS.has_point_forecasting">
+<code class="sig-name descname">has_point_forecasting</code><a class="headerlink" href="#pyFTS.common.fts.FTS.has_point_forecasting" title="Permalink to this definition">¶</a></dt>
+<dd><p>A boolean value indicating if the model supports point forecasting, default: True</p>
+</dd></dl>
+
+<dl class="py attribute">
+<dt id="pyFTS.common.fts.FTS.has_probability_forecasting">
+<code class="sig-name descname">has_probability_forecasting</code><a class="headerlink" href="#pyFTS.common.fts.FTS.has_probability_forecasting" title="Permalink to this definition">¶</a></dt>
+<dd><p>A boolean value indicating if the model support probabilistic forecasting, default: False</p>
+</dd></dl>
+
+<dl class="py attribute">
+<dt id="pyFTS.common.fts.FTS.has_seasonality">
+<code class="sig-name descname">has_seasonality</code><a class="headerlink" href="#pyFTS.common.fts.FTS.has_seasonality" title="Permalink to this definition">¶</a></dt>
+<dd><p>A boolean value indicating if the model supports seasonal indexers, default: False</p>
+</dd></dl>
+
+<dl class="py attribute">
+<dt id="pyFTS.common.fts.FTS.indexer">
+<code class="sig-name descname">indexer</code><a class="headerlink" href="#pyFTS.common.fts.FTS.indexer" title="Permalink to this definition">¶</a></dt>
+<dd><p>An pyFTS.models.seasonal.Indexer object for indexing the time series data</p>
+</dd></dl>
+
+<dl class="py attribute">
+<dt id="pyFTS.common.fts.FTS.is_clustered">
+<code class="sig-name descname">is_clustered</code><a class="headerlink" href="#pyFTS.common.fts.FTS.is_clustered" title="Permalink to this definition">¶</a></dt>
+<dd><p>A boolean value indicating if the model support multivariate time series (Pandas DataFrame), but works like 
+a monovariate method, default: False</p>
+</dd></dl>
+
+<dl class="py attribute">
+<dt id="pyFTS.common.fts.FTS.is_high_order">
+<code class="sig-name descname">is_high_order</code><a class="headerlink" href="#pyFTS.common.fts.FTS.is_high_order" title="Permalink to this definition">¶</a></dt>
+<dd><p>A boolean value indicating if the model support orders greater than 1, default: False</p>
+</dd></dl>
+
+<dl class="py attribute">
+<dt id="pyFTS.common.fts.FTS.is_multivariate">
+<code class="sig-name descname">is_multivariate</code><a class="headerlink" href="#pyFTS.common.fts.FTS.is_multivariate" title="Permalink to this definition">¶</a></dt>
+<dd><p>A boolean value indicating if the model support multivariate time series (Pandas DataFrame), default: False</p>
+</dd></dl>
+
+<dl class="py attribute">
+<dt id="pyFTS.common.fts.FTS.is_time_variant">
+<code class="sig-name descname">is_time_variant</code><a class="headerlink" href="#pyFTS.common.fts.FTS.is_time_variant" title="Permalink to this definition">¶</a></dt>
+<dd><p>A boolean value indicating if this model is time variant</p>
+</dd></dl>
+
+<dl class="py attribute">
+<dt id="pyFTS.common.fts.FTS.is_wrapper">
+<code class="sig-name descname">is_wrapper</code><a class="headerlink" href="#pyFTS.common.fts.FTS.is_wrapper" title="Permalink to this definition">¶</a></dt>
+<dd><p>Indicates that this model is a wrapper for other(s) method(s)</p>
+</dd></dl>
+
+<dl class="py attribute">
+<dt id="pyFTS.common.fts.FTS.lags">
+<code class="sig-name descname">lags</code><a class="headerlink" href="#pyFTS.common.fts.FTS.lags" title="Permalink to this definition">¶</a></dt>
+<dd><p>The list of lag indexes for high order models</p>
+</dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.common.fts.FTS.len_total">
+<code class="sig-name descname">len_total</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/common/fts.html#FTS.len_total"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.common.fts.FTS.len_total" title="Permalink to this definition">¶</a></dt>
+<dd><p>Total length of the model, adding the number of terms in all rules</p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns</dt>
+<dd class="field-odd"><p></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt id="pyFTS.common.fts.FTS.log">
+<code class="sig-name descname">log</code><a class="headerlink" href="#pyFTS.common.fts.FTS.log" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py attribute">
+<dt id="pyFTS.common.fts.FTS.max_lag">
+<code class="sig-name descname">max_lag</code><a class="headerlink" href="#pyFTS.common.fts.FTS.max_lag" title="Permalink to this definition">¶</a></dt>
+<dd><p>A integer indicating the largest lag used by the model. This value also indicates the minimum number of past lags 
+needed to forecast a single step ahead</p>
+</dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.common.fts.FTS.merge">
+<code class="sig-name descname">merge</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">model</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/common/fts.html#FTS.merge"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.common.fts.FTS.merge" title="Permalink to this definition">¶</a></dt>
+<dd><p>Merge the FLRG rules from other model</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>model</strong> – source model</p>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt id="pyFTS.common.fts.FTS.min_order">
+<code class="sig-name descname">min_order</code><a class="headerlink" href="#pyFTS.common.fts.FTS.min_order" title="Permalink to this definition">¶</a></dt>
+<dd><p>In high order models, this integer value indicates the minimal order supported for the model, default: 1</p>
+</dd></dl>
+
+<dl class="py attribute">
+<dt id="pyFTS.common.fts.FTS.name">
+<code class="sig-name descname">name</code><a class="headerlink" href="#pyFTS.common.fts.FTS.name" title="Permalink to this definition">¶</a></dt>
+<dd><p>A string with the model name</p>
+</dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.common.fts.FTS.offset">
+<code class="sig-name descname">offset</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/common/fts.html#FTS.offset"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.common.fts.FTS.offset" title="Permalink to this definition">¶</a></dt>
+<dd><p>Returns the number of lags to skip in the input test data in order to synchronize it with
+the forecasted values given by the predict function. This is necessary due to the order of the
+model, among other parameters.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns</dt>
+<dd class="field-odd"><p>An integer with the number of lags to skip</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt id="pyFTS.common.fts.FTS.order">
+<code class="sig-name descname">order</code><a class="headerlink" href="#pyFTS.common.fts.FTS.order" title="Permalink to this definition">¶</a></dt>
+<dd><p>A integer with the model order (number of past lags are used on forecasting)</p>
+</dd></dl>
+
+<dl class="py attribute">
+<dt id="pyFTS.common.fts.FTS.original_max">
+<code class="sig-name descname">original_max</code><a class="headerlink" href="#pyFTS.common.fts.FTS.original_max" title="Permalink to this definition">¶</a></dt>
+<dd><p>A float with the upper limit of the Universe of Discourse, the maximal value found on training data</p>
+</dd></dl>
+
+<dl class="py attribute">
+<dt id="pyFTS.common.fts.FTS.original_min">
+<code class="sig-name descname">original_min</code><a class="headerlink" href="#pyFTS.common.fts.FTS.original_min" title="Permalink to this definition">¶</a></dt>
+<dd><p>A float with the lower limit of the Universe of Discourse, the minimal value found on training data</p>
+</dd></dl>
+
+<dl class="py attribute">
+<dt id="pyFTS.common.fts.FTS.partitioner">
+<code class="sig-name descname">partitioner</code><a class="headerlink" href="#pyFTS.common.fts.FTS.partitioner" title="Permalink to this definition">¶</a></dt>
+<dd><p>A pyFTS.partitioners.Partitioner object with the Universe of Discourse partitioner used on the model. This is a mandatory dependecy.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.common.fts.FTS.predict">
+<code class="sig-name descname">predict</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/common/fts.html#FTS.predict"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.common.fts.FTS.predict" title="Permalink to this definition">¶</a></dt>
+<dd><p>Forecast using trained model</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> – time series with minimal length to the order of the model</p></li>
+<li><p><strong>type</strong> – the forecasting type, one of these values: point(default), interval, distribution or multivariate.</p></li>
+<li><p><strong>steps_ahead</strong> – The forecasting path H, i. e., tell the model to forecast from t+1 to t+H.</p></li>
+<li><p><strong>step_to</strong> – The forecasting step H, i. e., tell the model to forecast to t+H for each input sample</p></li>
+<li><p><strong>start_at</strong> – in the multi step forecasting, the index of the data where to start forecasting (default value: 0)</p></li>
+<li><p><strong>distributed</strong> – boolean, indicate if the forecasting procedure will be distributed in a dispy cluster (default value: False)</p></li>
+<li><p><strong>nodes</strong> – a list with the dispy cluster nodes addresses</p></li>
+<li><p><strong>explain</strong> – try to explain, step by step, the one-step-ahead point forecasting result given the input data. (default value: False)</p></li>
+<li><p><strong>generators</strong> – for multivariate methods on multi step ahead forecasting, generators is a dict where the keys
+are the dataframe columun names (except the target_variable) and the values are lambda functions that
+accept one value (the actual value of the variable) and return the next value or trained FTS
+models that accept the actual values and forecast new ones.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>a numpy array with the forecasted data</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.common.fts.FTS.reset_calculated_values">
+<code class="sig-name descname">reset_calculated_values</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/common/fts.html#FTS.reset_calculated_values"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.common.fts.FTS.reset_calculated_values" title="Permalink to this definition">¶</a></dt>
+<dd><p>Reset all pre-calculated values on the FLRG’s</p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns</dt>
+<dd class="field-odd"><p></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt id="pyFTS.common.fts.FTS.shortname">
+<code class="sig-name descname">shortname</code><a class="headerlink" href="#pyFTS.common.fts.FTS.shortname" title="Permalink to this definition">¶</a></dt>
+<dd><p>A string with a short name or alias for the model</p>
+</dd></dl>
+
+<dl class="py attribute">
+<dt id="pyFTS.common.fts.FTS.standard_horizon">
+<code class="sig-name descname">standard_horizon</code><a class="headerlink" href="#pyFTS.common.fts.FTS.standard_horizon" title="Permalink to this definition">¶</a></dt>
+<dd><p>Standard forecasting horizon (Default: 1)</p>
+</dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.common.fts.FTS.train">
+<code class="sig-name descname">train</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/common/fts.html#FTS.train"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.common.fts.FTS.train" title="Permalink to this definition">¶</a></dt>
+<dd><p>Method specific parameter fitting</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> – training time series data</p></li>
+<li><p><strong>kwargs</strong> – Method specific parameters</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt id="pyFTS.common.fts.FTS.transformations">
+<code class="sig-name descname">transformations</code><a class="headerlink" href="#pyFTS.common.fts.FTS.transformations" title="Permalink to this definition">¶</a></dt>
+<dd><p>A list with the data transformations (common.Transformations) applied on model pre and post processing, default: []</p>
+</dd></dl>
+
+<dl class="py attribute">
+<dt id="pyFTS.common.fts.FTS.transformations_param">
+<code class="sig-name descname">transformations_param</code><a class="headerlink" href="#pyFTS.common.fts.FTS.transformations_param" title="Permalink to this definition">¶</a></dt>
+<dd><p>A list with the specific parameters for each data transformation</p>
+</dd></dl>
+
+<dl class="py attribute">
+<dt id="pyFTS.common.fts.FTS.uod_clip">
+<code class="sig-name descname">uod_clip</code><a class="headerlink" href="#pyFTS.common.fts.FTS.uod_clip" title="Permalink to this definition">¶</a></dt>
+<dd><p>Flag indicating if the test data will be clipped inside the training Universe of Discourse</p>
+</dd></dl>
+
+</dd></dl>
+
 </div>
 <div class="section" id="module-pyFTS.common.tree">
 <span id="pyfts-common-tree-module"></span><h2>pyFTS.common.tree module<a class="headerlink" href="#module-pyFTS.common.tree" title="Permalink to this headline">¶</a></h2>
@@ -948,10 +1763,9 @@ when the LHS pattern is identified on time t.</p>
 <li><a class="reference internal" href="#module-pyFTS.common.FuzzySet">pyFTS.common.FuzzySet module</a></li>
 <li><a class="reference internal" href="#module-pyFTS.common.Membership">pyFTS.common.Membership module</a></li>
 <li><a class="reference internal" href="#module-pyFTS.common.SortedCollection">pyFTS.common.SortedCollection module</a></li>
-<li><a class="reference internal" href="#pyfts-common-transformations-module">pyFTS.common.transformations module</a></li>
-<li><a class="reference internal" href="#pyfts-common-util-module">pyFTS.common.Util module</a></li>
+<li><a class="reference internal" href="#module-pyFTS.common.Util">pyFTS.common.Util module</a></li>
 <li><a class="reference internal" href="#module-pyFTS.common.flrg">pyFTS.common.flrg module</a></li>
-<li><a class="reference internal" href="#pyfts-common-fts-module">pyFTS.common.fts module</a></li>
+<li><a class="reference internal" href="#module-pyFTS.common.fts">pyFTS.common.fts module</a></li>
 <li><a class="reference internal" href="#module-pyFTS.common.tree">pyFTS.common.tree module</a></li>
 </ul>
 </li>
diff --git a/docs/build/html/pyFTS.common.transformations.html b/docs/build/html/pyFTS.common.transformations.html
index 0d8c65e..ef76567 100644
--- a/docs/build/html/pyFTS.common.transformations.html
+++ b/docs/build/html/pyFTS.common.transformations.html
@@ -89,6 +89,9 @@
 <div class="section" id="pyfts-common-transformations-scale-module">
 <h2>pyFTS.common.transformations.scale module<a class="headerlink" href="#pyfts-common-transformations-scale-module" title="Permalink to this headline">¶</a></h2>
 </div>
+<div class="section" id="pyfts-common-transformations-smoothing-module">
+<h2>pyFTS.common.transformations.smoothing module<a class="headerlink" href="#pyfts-common-transformations-smoothing-module" title="Permalink to this headline">¶</a></h2>
+</div>
 <div class="section" id="pyfts-common-transformations-som-module">
 <h2>pyFTS.common.transformations.som module<a class="headerlink" href="#pyfts-common-transformations-som-module" title="Permalink to this headline">¶</a></h2>
 </div>
@@ -118,6 +121,7 @@
 <li><a class="reference internal" href="#pyfts-common-transformations-normalization-module">pyFTS.common.transformations.normalization module</a></li>
 <li><a class="reference internal" href="#pyfts-common-transformations-roi-module">pyFTS.common.transformations.roi module</a></li>
 <li><a class="reference internal" href="#pyfts-common-transformations-scale-module">pyFTS.common.transformations.scale module</a></li>
+<li><a class="reference internal" href="#pyfts-common-transformations-smoothing-module">pyFTS.common.transformations.smoothing module</a></li>
 <li><a class="reference internal" href="#pyfts-common-transformations-som-module">pyFTS.common.transformations.som module</a></li>
 <li><a class="reference internal" href="#pyfts-common-transformations-transformation-module">pyFTS.common.transformations.transformation module</a></li>
 <li><a class="reference internal" href="#pyfts-common-transformations-trend-module">pyFTS.common.transformations.trend module</a></li>
diff --git a/docs/build/html/pyFTS.distributed.html b/docs/build/html/pyFTS.distributed.html
index 77a1d17..b846d38 100644
--- a/docs/build/html/pyFTS.distributed.html
+++ b/docs/build/html/pyFTS.distributed.html
@@ -71,8 +71,67 @@
 <div class="section" id="submodules">
 <h2>Submodules<a class="headerlink" href="#submodules" title="Permalink to this headline">¶</a></h2>
 </div>
-<div class="section" id="pyfts-distributed-dispy-module">
-<h2>pyFTS.distributed.dispy module<a class="headerlink" href="#pyfts-distributed-dispy-module" title="Permalink to this headline">¶</a></h2>
+<div class="section" id="module-pyFTS.distributed.dispy">
+<span id="pyfts-distributed-dispy-module"></span><h2>pyFTS.distributed.dispy module<a class="headerlink" href="#module-pyFTS.distributed.dispy" title="Permalink to this headline">¶</a></h2>
+<dl class="py function">
+<dt id="pyFTS.distributed.dispy.distributed_predict">
+<code class="sig-prename descclassname">pyFTS.distributed.dispy.</code><code class="sig-name descname">distributed_predict</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">model</span></em>, <em class="sig-param"><span class="n">parameters</span></em>, <em class="sig-param"><span class="n">nodes</span></em>, <em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="n">num_batches</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/distributed/dispy.html#distributed_predict"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.distributed.dispy.distributed_predict" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.distributed.dispy.distributed_train">
+<code class="sig-prename descclassname">pyFTS.distributed.dispy.</code><code class="sig-name descname">distributed_train</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">model</span></em>, <em class="sig-param"><span class="n">train_method</span></em>, <em class="sig-param"><span class="n">nodes</span></em>, <em class="sig-param"><span class="n">fts_method</span></em>, <em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="n">num_batches</span><span class="o">=</span><span class="default_value">10</span></em>, <em class="sig-param"><span class="n">train_parameters</span><span class="o">=</span><span class="default_value">{}</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/distributed/dispy.html#distributed_train"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.distributed.dispy.distributed_train" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.distributed.dispy.get_number_of_cpus">
+<code class="sig-prename descclassname">pyFTS.distributed.dispy.</code><code class="sig-name descname">get_number_of_cpus</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">cluster</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/distributed/dispy.html#get_number_of_cpus"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.distributed.dispy.get_number_of_cpus" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.distributed.dispy.simple_model_predict">
+<code class="sig-prename descclassname">pyFTS.distributed.dispy.</code><code class="sig-name descname">simple_model_predict</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">model</span></em>, <em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="n">parameters</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/distributed/dispy.html#simple_model_predict"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.distributed.dispy.simple_model_predict" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.distributed.dispy.simple_model_train">
+<code class="sig-prename descclassname">pyFTS.distributed.dispy.</code><code class="sig-name descname">simple_model_train</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">model</span></em>, <em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="n">parameters</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/distributed/dispy.html#simple_model_train"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.distributed.dispy.simple_model_train" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.distributed.dispy.start_dispy_cluster">
+<code class="sig-prename descclassname">pyFTS.distributed.dispy.</code><code class="sig-name descname">start_dispy_cluster</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">method</span></em>, <em class="sig-param"><span class="n">nodes</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/distributed/dispy.html#start_dispy_cluster"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.distributed.dispy.start_dispy_cluster" title="Permalink to this definition">¶</a></dt>
+<dd><p>Start a new Dispy cluster on ‘nodes’ to execute the method ‘method’</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>method</strong> – function to be executed on each cluster node</p></li>
+<li><p><strong>nodes</strong> – list of node names or IP’s.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>the dispy cluster instance and the http_server for monitoring</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.distributed.dispy.stop_dispy_cluster">
+<code class="sig-prename descclassname">pyFTS.distributed.dispy.</code><code class="sig-name descname">stop_dispy_cluster</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">cluster</span></em>, <em class="sig-param"><span class="n">http_server</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/distributed/dispy.html#stop_dispy_cluster"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.distributed.dispy.stop_dispy_cluster" title="Permalink to this definition">¶</a></dt>
+<dd><p>Stop a dispy cluster and http_server</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>cluster</strong> – </p></li>
+<li><p><strong>http_server</strong> – </p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p></p>
+</dd>
+</dl>
+</dd></dl>
+
 </div>
 <div class="section" id="pyfts-distributed-spark-module">
 <h2>pyFTS.distributed.spark module<a class="headerlink" href="#pyfts-distributed-spark-module" title="Permalink to this headline">¶</a></h2>
@@ -91,7 +150,7 @@
 <li><a class="reference internal" href="#">pyFTS.distributed package</a><ul>
 <li><a class="reference internal" href="#module-pyFTS.distributed">Module contents</a></li>
 <li><a class="reference internal" href="#submodules">Submodules</a></li>
-<li><a class="reference internal" href="#pyfts-distributed-dispy-module">pyFTS.distributed.dispy module</a></li>
+<li><a class="reference internal" href="#module-pyFTS.distributed.dispy">pyFTS.distributed.dispy module</a></li>
 <li><a class="reference internal" href="#pyfts-distributed-spark-module">pyFTS.distributed.spark module</a></li>
 </ul>
 </li>
diff --git a/docs/build/html/pyFTS.html b/docs/build/html/pyFTS.html
index 472a6fb..7fb0ad2 100644
--- a/docs/build/html/pyFTS.html
+++ b/docs/build/html/pyFTS.html
@@ -71,17 +71,17 @@
 <li class="toctree-l1"><a class="reference internal" href="pyFTS.benchmarks.html">pyFTS.benchmarks package</a><ul>
 <li class="toctree-l2"><a class="reference internal" href="pyFTS.benchmarks.html#module-pyFTS.benchmarks">Module contents</a></li>
 <li class="toctree-l2"><a class="reference internal" href="pyFTS.benchmarks.html#submodules">Submodules</a></li>
-<li class="toctree-l2"><a class="reference internal" href="pyFTS.benchmarks.html#pyfts-benchmarks-benchmarks-module">pyFTS.benchmarks.benchmarks module</a></li>
-<li class="toctree-l2"><a class="reference internal" href="pyFTS.benchmarks.html#pyfts-benchmarks-measures-module">pyFTS.benchmarks.Measures module</a></li>
-<li class="toctree-l2"><a class="reference internal" href="pyFTS.benchmarks.html#pyfts-benchmarks-residualanalysis-module">pyFTS.benchmarks.ResidualAnalysis module</a></li>
-<li class="toctree-l2"><a class="reference internal" href="pyFTS.benchmarks.html#pyfts-benchmarks-tests-module">pyFTS.benchmarks.Tests module</a></li>
-<li class="toctree-l2"><a class="reference internal" href="pyFTS.benchmarks.html#pyfts-benchmarks-util-module">pyFTS.benchmarks.Util module</a></li>
-<li class="toctree-l2"><a class="reference internal" href="pyFTS.benchmarks.html#pyfts-benchmarks-arima-module">pyFTS.benchmarks.arima module</a></li>
-<li class="toctree-l2"><a class="reference internal" href="pyFTS.benchmarks.html#pyfts-benchmarks-knn-module">pyFTS.benchmarks.knn module</a></li>
-<li class="toctree-l2"><a class="reference internal" href="pyFTS.benchmarks.html#pyfts-benchmarks-naive-module">pyFTS.benchmarks.naive module</a></li>
-<li class="toctree-l2"><a class="reference internal" href="pyFTS.benchmarks.html#pyfts-benchmarks-quantreg-module">pyFTS.benchmarks.quantreg module</a></li>
+<li class="toctree-l2"><a class="reference internal" href="pyFTS.benchmarks.html#module-pyFTS.benchmarks.benchmarks">pyFTS.benchmarks.benchmarks module</a></li>
+<li class="toctree-l2"><a class="reference internal" href="pyFTS.benchmarks.html#module-pyFTS.benchmarks.Measures">pyFTS.benchmarks.Measures module</a></li>
+<li class="toctree-l2"><a class="reference internal" href="pyFTS.benchmarks.html#module-pyFTS.benchmarks.ResidualAnalysis">pyFTS.benchmarks.ResidualAnalysis module</a></li>
+<li class="toctree-l2"><a class="reference internal" href="pyFTS.benchmarks.html#module-pyFTS.benchmarks.Tests">pyFTS.benchmarks.Tests module</a></li>
+<li class="toctree-l2"><a class="reference internal" href="pyFTS.benchmarks.html#module-pyFTS.benchmarks.Util">pyFTS.benchmarks.Util module</a></li>
+<li class="toctree-l2"><a class="reference internal" href="pyFTS.benchmarks.html#module-pyFTS.benchmarks.arima">pyFTS.benchmarks.arima module</a></li>
+<li class="toctree-l2"><a class="reference internal" href="pyFTS.benchmarks.html#module-pyFTS.benchmarks.knn">pyFTS.benchmarks.knn module</a></li>
+<li class="toctree-l2"><a class="reference internal" href="pyFTS.benchmarks.html#module-pyFTS.benchmarks.naive">pyFTS.benchmarks.naive module</a></li>
+<li class="toctree-l2"><a class="reference internal" href="pyFTS.benchmarks.html#module-pyFTS.benchmarks.quantreg">pyFTS.benchmarks.quantreg module</a></li>
 <li class="toctree-l2"><a class="reference internal" href="pyFTS.benchmarks.html#pyfts-benchmarks-gaussianproc-module">pyFTS.benchmarks.gaussianproc module</a></li>
-<li class="toctree-l2"><a class="reference internal" href="pyFTS.benchmarks.html#pyfts-benchmarks-bsts-module">pyFTS.benchmarks.BSTS module</a></li>
+<li class="toctree-l2"><a class="reference internal" href="pyFTS.benchmarks.html#module-pyFTS.benchmarks.BSTS">pyFTS.benchmarks.BSTS module</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="pyFTS.common.html">pyFTS.common package</a><ul>
@@ -92,10 +92,9 @@
 <li class="toctree-l2"><a class="reference internal" href="pyFTS.common.html#module-pyFTS.common.FuzzySet">pyFTS.common.FuzzySet module</a></li>
 <li class="toctree-l2"><a class="reference internal" href="pyFTS.common.html#module-pyFTS.common.Membership">pyFTS.common.Membership module</a></li>
 <li class="toctree-l2"><a class="reference internal" href="pyFTS.common.html#module-pyFTS.common.SortedCollection">pyFTS.common.SortedCollection module</a></li>
-<li class="toctree-l2"><a class="reference internal" href="pyFTS.common.html#pyfts-common-transformations-module">pyFTS.common.transformations module</a></li>
-<li class="toctree-l2"><a class="reference internal" href="pyFTS.common.html#pyfts-common-util-module">pyFTS.common.Util module</a></li>
+<li class="toctree-l2"><a class="reference internal" href="pyFTS.common.html#module-pyFTS.common.Util">pyFTS.common.Util module</a></li>
 <li class="toctree-l2"><a class="reference internal" href="pyFTS.common.html#module-pyFTS.common.flrg">pyFTS.common.flrg module</a></li>
-<li class="toctree-l2"><a class="reference internal" href="pyFTS.common.html#pyfts-common-fts-module">pyFTS.common.fts module</a></li>
+<li class="toctree-l2"><a class="reference internal" href="pyFTS.common.html#module-pyFTS.common.fts">pyFTS.common.fts module</a></li>
 <li class="toctree-l2"><a class="reference internal" href="pyFTS.common.html#module-pyFTS.common.tree">pyFTS.common.tree module</a></li>
 </ul>
 </li>
@@ -108,6 +107,7 @@
 <li class="toctree-l2"><a class="reference internal" href="pyFTS.common.transformations.html#pyfts-common-transformations-normalization-module">pyFTS.common.transformations.normalization module</a></li>
 <li class="toctree-l2"><a class="reference internal" href="pyFTS.common.transformations.html#pyfts-common-transformations-roi-module">pyFTS.common.transformations.roi module</a></li>
 <li class="toctree-l2"><a class="reference internal" href="pyFTS.common.transformations.html#pyfts-common-transformations-scale-module">pyFTS.common.transformations.scale module</a></li>
+<li class="toctree-l2"><a class="reference internal" href="pyFTS.common.transformations.html#pyfts-common-transformations-smoothing-module">pyFTS.common.transformations.smoothing module</a></li>
 <li class="toctree-l2"><a class="reference internal" href="pyFTS.common.transformations.html#pyfts-common-transformations-som-module">pyFTS.common.transformations.som module</a></li>
 <li class="toctree-l2"><a class="reference internal" href="pyFTS.common.transformations.html#pyfts-common-transformations-transformation-module">pyFTS.common.transformations.transformation module</a></li>
 <li class="toctree-l2"><a class="reference internal" href="pyFTS.common.transformations.html#pyfts-common-transformations-trend-module">pyFTS.common.transformations.trend module</a></li>
@@ -144,7 +144,7 @@
 <li class="toctree-l1"><a class="reference internal" href="pyFTS.distributed.html">pyFTS.distributed package</a><ul>
 <li class="toctree-l2"><a class="reference internal" href="pyFTS.distributed.html#module-pyFTS.distributed">Module contents</a></li>
 <li class="toctree-l2"><a class="reference internal" href="pyFTS.distributed.html#submodules">Submodules</a></li>
-<li class="toctree-l2"><a class="reference internal" href="pyFTS.distributed.html#pyfts-distributed-dispy-module">pyFTS.distributed.dispy module</a></li>
+<li class="toctree-l2"><a class="reference internal" href="pyFTS.distributed.html#module-pyFTS.distributed.dispy">pyFTS.distributed.dispy module</a></li>
 <li class="toctree-l2"><a class="reference internal" href="pyFTS.distributed.html#pyfts-distributed-spark-module">pyFTS.distributed.spark module</a></li>
 </ul>
 </li>
@@ -152,8 +152,8 @@
 <li class="toctree-l2"><a class="reference internal" href="pyFTS.hyperparam.html#module-pyFTS.hyperparam">Module contents</a></li>
 <li class="toctree-l2"><a class="reference internal" href="pyFTS.hyperparam.html#submodules">Submodules</a></li>
 <li class="toctree-l2"><a class="reference internal" href="pyFTS.hyperparam.html#module-pyFTS.hyperparam.Util">pyFTS.hyperparam.Util module</a></li>
-<li class="toctree-l2"><a class="reference internal" href="pyFTS.hyperparam.html#pyfts-hyperparam-gridsearch-module">pyFTS.hyperparam.GridSearch module</a></li>
-<li class="toctree-l2"><a class="reference internal" href="pyFTS.hyperparam.html#pyfts-hyperparam-evolutionary-module">pyFTS.hyperparam.Evolutionary module</a></li>
+<li class="toctree-l2"><a class="reference internal" href="pyFTS.hyperparam.html#module-pyFTS.hyperparam.GridSearch">pyFTS.hyperparam.GridSearch module</a></li>
+<li class="toctree-l2"><a class="reference internal" href="pyFTS.hyperparam.html#module-pyFTS.hyperparam.Evolutionary">pyFTS.hyperparam.Evolutionary module</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="pyFTS.models.html">pyFTS.models package</a><ul>
@@ -161,16 +161,16 @@
 <li class="toctree-l2"><a class="reference internal" href="pyFTS.models.html#subpackages">Subpackages</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="pyFTS.models.ensemble.html">pyFTS.models.ensemble package</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="pyFTS.models.ensemble.html#submodules">Submodules</a></li>
-<li class="toctree-l4"><a class="reference internal" href="pyFTS.models.ensemble.html#pyfts-models-ensemble-ensemble-module">pyFTS.models.ensemble.ensemble module</a></li>
-<li class="toctree-l4"><a class="reference internal" href="pyFTS.models.ensemble.html#pyfts-models-ensemble-multiseasonal-module">pyFTS.models.ensemble.multiseasonal module</a></li>
+<li class="toctree-l4"><a class="reference internal" href="pyFTS.models.ensemble.html#module-pyFTS.models.ensemble.ensemble">pyFTS.models.ensemble.ensemble module</a></li>
+<li class="toctree-l4"><a class="reference internal" href="pyFTS.models.ensemble.html#module-pyFTS.models.ensemble.multiseasonal">pyFTS.models.ensemble.multiseasonal module</a></li>
 <li class="toctree-l4"><a class="reference internal" href="pyFTS.models.ensemble.html#module-pyFTS.models.ensemble">Module contents</a></li>
 </ul>
 </li>
 <li class="toctree-l3"><a class="reference internal" href="pyFTS.models.incremental.html">pyFTS.models.incremental package</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="pyFTS.models.incremental.html#module-pyFTS.models.incremental">Module contents</a></li>
 <li class="toctree-l4"><a class="reference internal" href="pyFTS.models.incremental.html#submodules">Submodules</a></li>
-<li class="toctree-l4"><a class="reference internal" href="pyFTS.models.incremental.html#pyfts-models-incremental-timevariant-module">pyFTS.models.incremental.TimeVariant module</a></li>
-<li class="toctree-l4"><a class="reference internal" href="pyFTS.models.incremental.html#pyfts-models-incremental-incrementalensemble-module">pyFTS.models.incremental.IncrementalEnsemble module</a></li>
+<li class="toctree-l4"><a class="reference internal" href="pyFTS.models.incremental.html#module-pyFTS.models.incremental.TimeVariant">pyFTS.models.incremental.TimeVariant module</a></li>
+<li class="toctree-l4"><a class="reference internal" href="pyFTS.models.incremental.html#module-pyFTS.models.incremental.IncrementalEnsemble">pyFTS.models.incremental.IncrementalEnsemble module</a></li>
 </ul>
 </li>
 <li class="toctree-l3"><a class="reference internal" href="pyFTS.models.multivariate.html">pyFTS.models.multivariate package</a><ul>
@@ -178,53 +178,53 @@
 <li class="toctree-l4"><a class="reference internal" href="pyFTS.models.multivariate.html#submodules">Submodules</a></li>
 <li class="toctree-l4"><a class="reference internal" href="pyFTS.models.multivariate.html#module-pyFTS.models.multivariate.FLR">pyFTS.models.multivariate.FLR module</a></li>
 <li class="toctree-l4"><a class="reference internal" href="pyFTS.models.multivariate.html#module-pyFTS.models.multivariate.common">pyFTS.models.multivariate.common module</a></li>
-<li class="toctree-l4"><a class="reference internal" href="pyFTS.models.multivariate.html#pyfts-models-multivariate-variable-module">pyFTS.models.multivariate.variable module</a></li>
+<li class="toctree-l4"><a class="reference internal" href="pyFTS.models.multivariate.html#module-pyFTS.models.multivariate.variable">pyFTS.models.multivariate.variable module</a></li>
 <li class="toctree-l4"><a class="reference internal" href="pyFTS.models.multivariate.html#module-pyFTS.models.multivariate.flrg">pyFTS.models.multivariate.flrg module</a></li>
 <li class="toctree-l4"><a class="reference internal" href="pyFTS.models.multivariate.html#module-pyFTS.models.multivariate.partitioner">pyFTS.models.multivariate.partitioner module</a></li>
 <li class="toctree-l4"><a class="reference internal" href="pyFTS.models.multivariate.html#module-pyFTS.models.multivariate.grid">pyFTS.models.multivariate.grid module</a></li>
-<li class="toctree-l4"><a class="reference internal" href="pyFTS.models.multivariate.html#pyfts-models-multivariate-mvfts-module">pyFTS.models.multivariate.mvfts module</a></li>
-<li class="toctree-l4"><a class="reference internal" href="pyFTS.models.multivariate.html#pyfts-models-multivariate-wmvfts-module">pyFTS.models.multivariate.wmvfts module</a></li>
-<li class="toctree-l4"><a class="reference internal" href="pyFTS.models.multivariate.html#pyfts-models-multivariate-cmvfts-module">pyFTS.models.multivariate.cmvfts module</a></li>
-<li class="toctree-l4"><a class="reference internal" href="pyFTS.models.multivariate.html#pyfts-models-multivariate-granular-module">pyFTS.models.multivariate.granular module</a></li>
+<li class="toctree-l4"><a class="reference internal" href="pyFTS.models.multivariate.html#module-pyFTS.models.multivariate.mvfts">pyFTS.models.multivariate.mvfts module</a></li>
+<li class="toctree-l4"><a class="reference internal" href="pyFTS.models.multivariate.html#module-pyFTS.models.multivariate.wmvfts">pyFTS.models.multivariate.wmvfts module</a></li>
+<li class="toctree-l4"><a class="reference internal" href="pyFTS.models.multivariate.html#module-pyFTS.models.multivariate.cmvfts">pyFTS.models.multivariate.cmvfts module</a></li>
+<li class="toctree-l4"><a class="reference internal" href="pyFTS.models.multivariate.html#module-pyFTS.models.multivariate.granular">pyFTS.models.multivariate.granular module</a></li>
 </ul>
 </li>
 <li class="toctree-l3"><a class="reference internal" href="pyFTS.models.nonstationary.html">pyFTS.models.nonstationary package</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="pyFTS.models.nonstationary.html#submodules">Submodules</a></li>
 <li class="toctree-l4"><a class="reference internal" href="pyFTS.models.nonstationary.html#module-pyFTS.models.nonstationary.common">pyFTS.models.nonstationary.common module</a></li>
-<li class="toctree-l4"><a class="reference internal" href="pyFTS.models.nonstationary.html#pyfts-models-nonstationary-cvfts-module">pyFTS.models.nonstationary.cvfts module</a></li>
+<li class="toctree-l4"><a class="reference internal" href="pyFTS.models.nonstationary.html#module-pyFTS.models.nonstationary.cvfts">pyFTS.models.nonstationary.cvfts module</a></li>
 <li class="toctree-l4"><a class="reference internal" href="pyFTS.models.nonstationary.html#module-pyFTS.models.nonstationary.flrg">pyFTS.models.nonstationary.flrg module</a></li>
-<li class="toctree-l4"><a class="reference internal" href="pyFTS.models.nonstationary.html#pyfts-models-nonstationary-honsfts-module">pyFTS.models.nonstationary.honsfts module</a></li>
-<li class="toctree-l4"><a class="reference internal" href="pyFTS.models.nonstationary.html#pyfts-models-nonstationary-nsfts-module">pyFTS.models.nonstationary.nsfts module</a></li>
+<li class="toctree-l4"><a class="reference internal" href="pyFTS.models.nonstationary.html#module-pyFTS.models.nonstationary.honsfts">pyFTS.models.nonstationary.honsfts module</a></li>
+<li class="toctree-l4"><a class="reference internal" href="pyFTS.models.nonstationary.html#module-pyFTS.models.nonstationary.nsfts">pyFTS.models.nonstationary.nsfts module</a></li>
 <li class="toctree-l4"><a class="reference internal" href="pyFTS.models.nonstationary.html#module-pyFTS.models.nonstationary.partitioners">pyFTS.models.nonstationary.partitioners module</a></li>
 <li class="toctree-l4"><a class="reference internal" href="pyFTS.models.nonstationary.html#module-pyFTS.models.nonstationary.perturbation">pyFTS.models.nonstationary.perturbation module</a></li>
-<li class="toctree-l4"><a class="reference internal" href="pyFTS.models.nonstationary.html#pyfts-models-nonstationary-util-module">pyFTS.models.nonstationary.util module</a></li>
+<li class="toctree-l4"><a class="reference internal" href="pyFTS.models.nonstationary.html#module-pyFTS.models.nonstationary.util">pyFTS.models.nonstationary.util module</a></li>
 <li class="toctree-l4"><a class="reference internal" href="pyFTS.models.nonstationary.html#module-pyFTS.models.nonstationary">Module contents</a></li>
 </ul>
 </li>
 <li class="toctree-l3"><a class="reference internal" href="pyFTS.models.seasonal.html">pyFTS.models.seasonal package</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="pyFTS.models.seasonal.html#submodules">Submodules</a></li>
 <li class="toctree-l4"><a class="reference internal" href="pyFTS.models.seasonal.html#module-pyFTS.models.seasonal.SeasonalIndexer">pyFTS.models.seasonal.SeasonalIndexer module</a></li>
-<li class="toctree-l4"><a class="reference internal" href="pyFTS.models.seasonal.html#pyfts-models-seasonal-cmsfts-module">pyFTS.models.seasonal.cmsfts module</a></li>
+<li class="toctree-l4"><a class="reference internal" href="pyFTS.models.seasonal.html#module-pyFTS.models.seasonal.cmsfts">pyFTS.models.seasonal.cmsfts module</a></li>
 <li class="toctree-l4"><a class="reference internal" href="pyFTS.models.seasonal.html#module-pyFTS.models.seasonal.common">pyFTS.models.seasonal.common module</a></li>
-<li class="toctree-l4"><a class="reference internal" href="pyFTS.models.seasonal.html#pyfts-models-seasonal-msfts-module">pyFTS.models.seasonal.msfts module</a></li>
+<li class="toctree-l4"><a class="reference internal" href="pyFTS.models.seasonal.html#module-pyFTS.models.seasonal.msfts">pyFTS.models.seasonal.msfts module</a></li>
 <li class="toctree-l4"><a class="reference internal" href="pyFTS.models.seasonal.html#module-pyFTS.models.seasonal.partitioner">pyFTS.models.seasonal.partitioner module</a></li>
-<li class="toctree-l4"><a class="reference internal" href="pyFTS.models.seasonal.html#pyfts-models-seasonal-sfts-module">pyFTS.models.seasonal.sfts module</a></li>
+<li class="toctree-l4"><a class="reference internal" href="pyFTS.models.seasonal.html#module-pyFTS.models.seasonal.sfts">pyFTS.models.seasonal.sfts module</a></li>
 <li class="toctree-l4"><a class="reference internal" href="pyFTS.models.seasonal.html#module-pyFTS.models.seasonal">Module contents</a></li>
 </ul>
 </li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="pyFTS.models.html#submodules">Submodules</a></li>
-<li class="toctree-l2"><a class="reference internal" href="pyFTS.models.html#pyfts-models-song-module">pyFTS.models.song module</a></li>
-<li class="toctree-l2"><a class="reference internal" href="pyFTS.models.html#pyfts-models-chen-module">pyFTS.models.chen module</a></li>
-<li class="toctree-l2"><a class="reference internal" href="pyFTS.models.html#pyfts-models-yu-module">pyFTS.models.yu module</a></li>
-<li class="toctree-l2"><a class="reference internal" href="pyFTS.models.html#pyfts-models-cheng-module">pyFTS.models.cheng module</a></li>
-<li class="toctree-l2"><a class="reference internal" href="pyFTS.models.html#pyfts-models-hofts-module">pyFTS.models.hofts module</a></li>
-<li class="toctree-l2"><a class="reference internal" href="pyFTS.models.html#pyfts-models-hwang-module">pyFTS.models.hwang module</a></li>
-<li class="toctree-l2"><a class="reference internal" href="pyFTS.models.html#pyfts-models-ifts-module">pyFTS.models.ifts module</a></li>
-<li class="toctree-l2"><a class="reference internal" href="pyFTS.models.html#pyfts-models-ismailefendi-module">pyFTS.models.ismailefendi module</a></li>
-<li class="toctree-l2"><a class="reference internal" href="pyFTS.models.html#pyfts-models-pwfts-module">pyFTS.models.pwfts module</a></li>
-<li class="toctree-l2"><a class="reference internal" href="pyFTS.models.html#pyfts-models-sadaei-module">pyFTS.models.sadaei module</a></li>
+<li class="toctree-l2"><a class="reference internal" href="pyFTS.models.html#module-pyFTS.models.song">pyFTS.models.song module</a></li>
+<li class="toctree-l2"><a class="reference internal" href="pyFTS.models.html#module-pyFTS.models.chen">pyFTS.models.chen module</a></li>
+<li class="toctree-l2"><a class="reference internal" href="pyFTS.models.html#module-pyFTS.models.yu">pyFTS.models.yu module</a></li>
+<li class="toctree-l2"><a class="reference internal" href="pyFTS.models.html#module-pyFTS.models.cheng">pyFTS.models.cheng module</a></li>
+<li class="toctree-l2"><a class="reference internal" href="pyFTS.models.html#module-pyFTS.models.hofts">pyFTS.models.hofts module</a></li>
+<li class="toctree-l2"><a class="reference internal" href="pyFTS.models.html#module-pyFTS.models.hwang">pyFTS.models.hwang module</a></li>
+<li class="toctree-l2"><a class="reference internal" href="pyFTS.models.html#module-pyFTS.models.ifts">pyFTS.models.ifts module</a></li>
+<li class="toctree-l2"><a class="reference internal" href="pyFTS.models.html#module-pyFTS.models.ismailefendi">pyFTS.models.ismailefendi module</a></li>
+<li class="toctree-l2"><a class="reference internal" href="pyFTS.models.html#module-pyFTS.models.pwfts">pyFTS.models.pwfts module</a></li>
+<li class="toctree-l2"><a class="reference internal" href="pyFTS.models.html#module-pyFTS.models.sadaei">pyFTS.models.sadaei module</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="pyFTS.partitioners.html">pyFTS.partitioners package</a><ul>
@@ -235,19 +235,19 @@
 <li class="toctree-l2"><a class="reference internal" href="pyFTS.partitioners.html#module-pyFTS.partitioners.Entropy">pyFTS.partitioners.Entropy module</a></li>
 <li class="toctree-l2"><a class="reference internal" href="pyFTS.partitioners.html#module-pyFTS.partitioners.FCM">pyFTS.partitioners.FCM module</a></li>
 <li class="toctree-l2"><a class="reference internal" href="pyFTS.partitioners.html#module-pyFTS.partitioners.Grid">pyFTS.partitioners.Grid module</a></li>
-<li class="toctree-l2"><a class="reference internal" href="pyFTS.partitioners.html#pyfts-partitioners-huarng-module">pyFTS.partitioners.Huarng module</a></li>
+<li class="toctree-l2"><a class="reference internal" href="pyFTS.partitioners.html#module-pyFTS.partitioners.Huarng">pyFTS.partitioners.Huarng module</a></li>
 <li class="toctree-l2"><a class="reference internal" href="pyFTS.partitioners.html#module-pyFTS.partitioners.Singleton">pyFTS.partitioners.Singleton module</a></li>
 <li class="toctree-l2"><a class="reference internal" href="pyFTS.partitioners.html#module-pyFTS.partitioners.Simple">pyFTS.partitioners.Simple module</a></li>
 <li class="toctree-l2"><a class="reference internal" href="pyFTS.partitioners.html#module-pyFTS.partitioners.SubClust">pyFTS.partitioners.SubClust module</a></li>
-<li class="toctree-l2"><a class="reference internal" href="pyFTS.partitioners.html#pyfts-partitioners-util-module">pyFTS.partitioners.Util module</a></li>
-<li class="toctree-l2"><a class="reference internal" href="pyFTS.partitioners.html#pyfts-partitioners-parallel-util-module">pyFTS.partitioners.parallel_util module</a></li>
+<li class="toctree-l2"><a class="reference internal" href="pyFTS.partitioners.html#module-pyFTS.partitioners.Util">pyFTS.partitioners.Util module</a></li>
+<li class="toctree-l2"><a class="reference internal" href="pyFTS.partitioners.html#module-pyFTS.partitioners.parallel_util">pyFTS.partitioners.parallel_util module</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="pyFTS.probabilistic.html">pyFTS.probabilistic package</a><ul>
 <li class="toctree-l2"><a class="reference internal" href="pyFTS.probabilistic.html#module-pyFTS.probabilistic">Module contents</a></li>
 <li class="toctree-l2"><a class="reference internal" href="pyFTS.probabilistic.html#submodules">Submodules</a></li>
-<li class="toctree-l2"><a class="reference internal" href="pyFTS.probabilistic.html#pyfts-probabilistic-probabilitydistribution-module">pyFTS.probabilistic.ProbabilityDistribution module</a></li>
-<li class="toctree-l2"><a class="reference internal" href="pyFTS.probabilistic.html#pyfts-probabilistic-kde-module">pyFTS.probabilistic.kde module</a></li>
+<li class="toctree-l2"><a class="reference internal" href="pyFTS.probabilistic.html#module-pyFTS.probabilistic.ProbabilityDistribution">pyFTS.probabilistic.ProbabilityDistribution module</a></li>
+<li class="toctree-l2"><a class="reference internal" href="pyFTS.probabilistic.html#module-pyFTS.probabilistic.kde">pyFTS.probabilistic.kde module</a></li>
 </ul>
 </li>
 </ul>
diff --git a/docs/build/html/pyFTS.hyperparam.html b/docs/build/html/pyFTS.hyperparam.html
index 6178e52..7e39589 100644
--- a/docs/build/html/pyFTS.hyperparam.html
+++ b/docs/build/html/pyFTS.hyperparam.html
@@ -131,11 +131,343 @@ Value: the measure value</p>
 </dd></dl>
 
 </div>
-<div class="section" id="pyfts-hyperparam-gridsearch-module">
-<h2>pyFTS.hyperparam.GridSearch module<a class="headerlink" href="#pyfts-hyperparam-gridsearch-module" title="Permalink to this headline">¶</a></h2>
+<div class="section" id="module-pyFTS.hyperparam.GridSearch">
+<span id="pyfts-hyperparam-gridsearch-module"></span><h2>pyFTS.hyperparam.GridSearch module<a class="headerlink" href="#module-pyFTS.hyperparam.GridSearch" title="Permalink to this headline">¶</a></h2>
+<dl class="py function">
+<dt id="pyFTS.hyperparam.GridSearch.cluster_method">
+<code class="sig-prename descclassname">pyFTS.hyperparam.GridSearch.</code><code class="sig-name descname">cluster_method</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">individual</span></em>, <em class="sig-param"><span class="n">dataset</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/hyperparam/GridSearch.html#cluster_method"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.hyperparam.GridSearch.cluster_method" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.hyperparam.GridSearch.dict_individual">
+<code class="sig-prename descclassname">pyFTS.hyperparam.GridSearch.</code><code class="sig-name descname">dict_individual</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">mf</span></em>, <em class="sig-param"><span class="n">partitioner</span></em>, <em class="sig-param"><span class="n">partitions</span></em>, <em class="sig-param"><span class="n">order</span></em>, <em class="sig-param"><span class="n">lags</span></em>, <em class="sig-param"><span class="n">alpha_cut</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/hyperparam/GridSearch.html#dict_individual"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.hyperparam.GridSearch.dict_individual" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.hyperparam.GridSearch.execute">
+<code class="sig-prename descclassname">pyFTS.hyperparam.GridSearch.</code><code class="sig-name descname">execute</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">hyperparams</span></em>, <em class="sig-param"><span class="n">datasetname</span></em>, <em class="sig-param"><span class="n">dataset</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/hyperparam/GridSearch.html#execute"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.hyperparam.GridSearch.execute" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.hyperparam.GridSearch.process_jobs">
+<code class="sig-prename descclassname">pyFTS.hyperparam.GridSearch.</code><code class="sig-name descname">process_jobs</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">jobs</span></em>, <em class="sig-param"><span class="n">datasetname</span></em>, <em class="sig-param"><span class="n">conn</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/hyperparam/GridSearch.html#process_jobs"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.hyperparam.GridSearch.process_jobs" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
 </div>
-<div class="section" id="pyfts-hyperparam-evolutionary-module">
-<h2>pyFTS.hyperparam.Evolutionary module<a class="headerlink" href="#pyfts-hyperparam-evolutionary-module" title="Permalink to this headline">¶</a></h2>
+<div class="section" id="module-pyFTS.hyperparam.Evolutionary">
+<span id="pyfts-hyperparam-evolutionary-module"></span><h2>pyFTS.hyperparam.Evolutionary module<a class="headerlink" href="#module-pyFTS.hyperparam.Evolutionary" title="Permalink to this headline">¶</a></h2>
+<p>Distributed Evolutionary Hyperparameter Optimization (DEHO) for MVFTS</p>
+<dl class="py function">
+<dt id="pyFTS.hyperparam.Evolutionary.GeneticAlgorithm">
+<code class="sig-prename descclassname">pyFTS.hyperparam.Evolutionary.</code><code class="sig-name descname">GeneticAlgorithm</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">dataset</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/hyperparam/Evolutionary.html#GeneticAlgorithm"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.hyperparam.Evolutionary.GeneticAlgorithm" title="Permalink to this definition">¶</a></dt>
+<dd><p>Genetic algoritm for Distributed Evolutionary Hyperparameter Optimization (DEHO)</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>dataset</strong> – The time series to optimize the FTS</p></li>
+<li><p><strong>ngen</strong> – An integer value with the maximum number of generations, default value: 30</p></li>
+<li><p><strong>mgen</strong> – An integer value with the maximum number of generations without improvement to stop, default value 7</p></li>
+<li><p><strong>npop</strong> – An integer value with the population size, default value: 20</p></li>
+<li><p><strong>pcross</strong> – A float value between 0 and 1 with the probability of crossover, default: .5</p></li>
+<li><p><strong>psel</strong> – A float value between 0 and 1 with the probability of selection, default: .5</p></li>
+<li><p><strong>pmut</strong> – A float value between 0 and 1 with the probability of mutation, default: .3</p></li>
+<li><p><strong>fts_method</strong> – The FTS method to optimize</p></li>
+<li><p><strong>parameters</strong> – dict with model specific arguments for fts_method</p></li>
+<li><p><strong>elitism</strong> – A boolean value indicating if the best individual must always survive to next population</p></li>
+<li><p><strong>initial_operator</strong> – a function that receives npop and return a random population with size npop</p></li>
+<li><p><strong>evalutation_operator</strong> – a function that receives a dataset and an individual and return its fitness</p></li>
+<li><p><strong>selection_operator</strong> – a function that receives the whole population and return a selected individual</p></li>
+<li><p><strong>crossover_operator</strong> – a function that receives the whole population and return a descendent individual</p></li>
+<li><p><strong>mutation_operator</strong> – a function that receives one individual and return a changed individual</p></li>
+<li><p><strong>window_size</strong> – An integer value with the the length of scrolling window for train/test on dataset</p></li>
+<li><p><strong>train_rate</strong> – A float value between 0 and 1 with the train/test split ([0,1])</p></li>
+<li><p><strong>increment_rate</strong> – A float value between 0 and 1 with the the increment of the scrolling window,
+relative to the window_size ([0,1])</p></li>
+<li><p><strong>collect_statistics</strong> – A boolean value indicating to collect statistics for each generation</p></li>
+<li><p><strong>distributed</strong> – A value indicating it the execution will be local and sequential (distributed=False),
+or parallel and distributed (distributed=’dispy’ or distributed=’spark’)</p></li>
+<li><p><strong>cluster</strong> – If distributed=’dispy’ the list of cluster nodes, else if distributed=’spark’ it is the master node</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>the best genotype</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.hyperparam.Evolutionary.crossover">
+<code class="sig-prename descclassname">pyFTS.hyperparam.Evolutionary.</code><code class="sig-name descname">crossover</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">population</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/hyperparam/Evolutionary.html#crossover"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.hyperparam.Evolutionary.crossover" title="Permalink to this definition">¶</a></dt>
+<dd><p>Crossover operation between two parents</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>population</strong> – the original population</p>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>a genotype</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.hyperparam.Evolutionary.double_tournament">
+<code class="sig-prename descclassname">pyFTS.hyperparam.Evolutionary.</code><code class="sig-name descname">double_tournament</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">population</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/hyperparam/Evolutionary.html#double_tournament"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.hyperparam.Evolutionary.double_tournament" title="Permalink to this definition">¶</a></dt>
+<dd><p>Double tournament selection strategy.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>population</strong> – </p>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.hyperparam.Evolutionary.elitism">
+<code class="sig-prename descclassname">pyFTS.hyperparam.Evolutionary.</code><code class="sig-name descname">elitism</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">population</span></em>, <em class="sig-param"><span class="n">new_population</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/hyperparam/Evolutionary.html#elitism"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.hyperparam.Evolutionary.elitism" title="Permalink to this definition">¶</a></dt>
+<dd><p>Elitism operation, always select the best individual of the population and discard the worst</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>population</strong> – </p></li>
+<li><p><strong>new_population</strong> – </p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.hyperparam.Evolutionary.evaluate">
+<code class="sig-prename descclassname">pyFTS.hyperparam.Evolutionary.</code><code class="sig-name descname">evaluate</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">dataset</span></em>, <em class="sig-param"><span class="n">individual</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/hyperparam/Evolutionary.html#evaluate"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.hyperparam.Evolutionary.evaluate" title="Permalink to this definition">¶</a></dt>
+<dd><p>Evaluate an individual using a sliding window cross validation over the dataset.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>dataset</strong> – Evaluation dataset</p></li>
+<li><p><strong>individual</strong> – genotype to be tested</p></li>
+<li><p><strong>window_size</strong> – The length of scrolling window for train/test on dataset</p></li>
+<li><p><strong>train_rate</strong> – The train/test split ([0,1])</p></li>
+<li><p><strong>increment_rate</strong> – The increment of the scrolling window, relative to the window_size ([0,1])</p></li>
+<li><p><strong>parameters</strong> – dict with model specific arguments for fit method.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>a tuple (len_lags, rmse) with the parsimony fitness value and the accuracy fitness value</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.hyperparam.Evolutionary.execute">
+<code class="sig-prename descclassname">pyFTS.hyperparam.Evolutionary.</code><code class="sig-name descname">execute</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">datasetname</span></em>, <em class="sig-param"><span class="n">dataset</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/hyperparam/Evolutionary.html#execute"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.hyperparam.Evolutionary.execute" title="Permalink to this definition">¶</a></dt>
+<dd><p>Batch execution of Distributed Evolutionary Hyperparameter Optimization (DEHO) for monovariate methods</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>datasetname</strong> – </p></li>
+<li><p><strong>dataset</strong> – The time series to optimize the FTS</p></li>
+<li><p><strong>file</strong> – </p></li>
+<li><p><strong>experiments</strong> – </p></li>
+<li><p><strong>distributed</strong> – </p></li>
+<li><p><strong>ngen</strong> – An integer value with the maximum number of generations, default value: 30</p></li>
+<li><p><strong>mgen</strong> – An integer value with the maximum number of generations without improvement to stop, default value 7</p></li>
+<li><p><strong>npop</strong> – An integer value with the population size, default value: 20</p></li>
+<li><p><strong>pcross</strong> – A float value between 0 and 1 with the probability of crossover, default: .5</p></li>
+<li><p><strong>psel</strong> – A float value between 0 and 1 with the probability of selection, default: .5</p></li>
+<li><p><strong>pmut</strong> – A float value between 0 and 1 with the probability of mutation, default: .3</p></li>
+<li><p><strong>fts_method</strong> – The FTS method to optimize</p></li>
+<li><p><strong>parameters</strong> – dict with model specific arguments for fts_method</p></li>
+<li><p><strong>elitism</strong> – A boolean value indicating if the best individual must always survive to next population</p></li>
+<li><p><strong>initial_operator</strong> – a function that receives npop and return a random population with size npop</p></li>
+<li><p><strong>random_individual</strong> – create an random genotype</p></li>
+<li><p><strong>evalutation_operator</strong> – a function that receives a dataset and an individual and return its fitness</p></li>
+<li><p><strong>selection_operator</strong> – a function that receives the whole population and return a selected individual</p></li>
+<li><p><strong>crossover_operator</strong> – a function that receives the whole population and return a descendent individual</p></li>
+<li><p><strong>mutation_operator</strong> – a function that receives one individual and return a changed individual</p></li>
+<li><p><strong>window_size</strong> – An integer value with the the length of scrolling window for train/test on dataset</p></li>
+<li><p><strong>train_rate</strong> – A float value between 0 and 1 with the train/test split ([0,1])</p></li>
+<li><p><strong>increment_rate</strong> – A float value between 0 and 1 with the the increment of the scrolling window,
+relative to the window_size ([0,1])</p></li>
+<li><p><strong>collect_statistics</strong> – A boolean value indicating to collect statistics for each generation</p></li>
+<li><p><strong>distributed</strong> – A value indicating it the execution will be local and sequential (distributed=False),
+or parallel and distributed (distributed=’dispy’ or distributed=’spark’)</p></li>
+<li><p><strong>cluster</strong> – If distributed=’dispy’ the list of cluster nodes, else if distributed=’spark’ it is the master node</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>the best genotype</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.hyperparam.Evolutionary.genotype">
+<code class="sig-prename descclassname">pyFTS.hyperparam.Evolutionary.</code><code class="sig-name descname">genotype</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">mf</span></em>, <em class="sig-param"><span class="n">npart</span></em>, <em class="sig-param"><span class="n">partitioner</span></em>, <em class="sig-param"><span class="n">order</span></em>, <em class="sig-param"><span class="n">alpha</span></em>, <em class="sig-param"><span class="n">lags</span></em>, <em class="sig-param"><span class="n">f1</span></em>, <em class="sig-param"><span class="n">f2</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/hyperparam/Evolutionary.html#genotype"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.hyperparam.Evolutionary.genotype" title="Permalink to this definition">¶</a></dt>
+<dd><p>Create the individual genotype</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>mf</strong> – membership function</p></li>
+<li><p><strong>npart</strong> – number of partitions</p></li>
+<li><p><strong>partitioner</strong> – partitioner method</p></li>
+<li><p><strong>order</strong> – model order</p></li>
+<li><p><strong>alpha</strong> – alpha-cut</p></li>
+<li><p><strong>lags</strong> – array with lag indexes</p></li>
+<li><p><strong>f1</strong> – accuracy fitness value</p></li>
+<li><p><strong>f2</strong> – parsimony fitness value</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>the genotype, a dictionary with all hyperparameters</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.hyperparam.Evolutionary.initial_population">
+<code class="sig-prename descclassname">pyFTS.hyperparam.Evolutionary.</code><code class="sig-name descname">initial_population</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">n</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/hyperparam/Evolutionary.html#initial_population"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.hyperparam.Evolutionary.initial_population" title="Permalink to this definition">¶</a></dt>
+<dd><p>Create a random population of size n</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>n</strong> – the size of the population</p>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>a list with n random individuals</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.hyperparam.Evolutionary.lag_crossover2">
+<code class="sig-prename descclassname">pyFTS.hyperparam.Evolutionary.</code><code class="sig-name descname">lag_crossover2</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">best</span></em>, <em class="sig-param"><span class="n">worst</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/hyperparam/Evolutionary.html#lag_crossover2"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.hyperparam.Evolutionary.lag_crossover2" title="Permalink to this definition">¶</a></dt>
+<dd><p>Cross over two lag genes</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>best</strong> – best genotype</p></li>
+<li><p><strong>worst</strong> – worst genotype</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>a tuple (order, lags)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.hyperparam.Evolutionary.log_result">
+<code class="sig-prename descclassname">pyFTS.hyperparam.Evolutionary.</code><code class="sig-name descname">log_result</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">conn</span></em>, <em class="sig-param"><span class="n">datasetname</span></em>, <em class="sig-param"><span class="n">fts_method</span></em>, <em class="sig-param"><span class="n">result</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/hyperparam/Evolutionary.html#log_result"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.hyperparam.Evolutionary.log_result" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.hyperparam.Evolutionary.mutation">
+<code class="sig-prename descclassname">pyFTS.hyperparam.Evolutionary.</code><code class="sig-name descname">mutation</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">individual</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/hyperparam/Evolutionary.html#mutation"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.hyperparam.Evolutionary.mutation" title="Permalink to this definition">¶</a></dt>
+<dd><p>Mutation operator</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>individual</strong> – an individual genotype</p></li>
+<li><p><strong>pmut</strong> – individual probability o</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.hyperparam.Evolutionary.mutation_lags">
+<code class="sig-prename descclassname">pyFTS.hyperparam.Evolutionary.</code><code class="sig-name descname">mutation_lags</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">lags</span></em>, <em class="sig-param"><span class="n">order</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/hyperparam/Evolutionary.html#mutation_lags"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.hyperparam.Evolutionary.mutation_lags" title="Permalink to this definition">¶</a></dt>
+<dd><p>Mutation operation for lags gene</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>lags</strong> – </p></li>
+<li><p><strong>order</strong> – </p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.hyperparam.Evolutionary.persist_statistics">
+<code class="sig-prename descclassname">pyFTS.hyperparam.Evolutionary.</code><code class="sig-name descname">persist_statistics</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">datasetname</span></em>, <em class="sig-param"><span class="n">statistics</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/hyperparam/Evolutionary.html#persist_statistics"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.hyperparam.Evolutionary.persist_statistics" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.hyperparam.Evolutionary.phenotype">
+<code class="sig-prename descclassname">pyFTS.hyperparam.Evolutionary.</code><code class="sig-name descname">phenotype</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">individual</span></em>, <em class="sig-param"><span class="n">train</span></em>, <em class="sig-param"><span class="n">fts_method</span></em>, <em class="sig-param"><span class="n">parameters</span><span class="o">=</span><span class="default_value">{}</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/hyperparam/Evolutionary.html#phenotype"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.hyperparam.Evolutionary.phenotype" title="Permalink to this definition">¶</a></dt>
+<dd><p>Instantiate the genotype, creating a fitted model with the genotype hyperparameters</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>individual</strong> – a genotype</p></li>
+<li><p><strong>train</strong> – the training dataset</p></li>
+<li><p><strong>fts_method</strong> – the FTS method</p></li>
+<li><p><strong>parameters</strong> – dict with model specific arguments for fit method.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>a fitted FTS model</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.hyperparam.Evolutionary.process_experiment">
+<code class="sig-prename descclassname">pyFTS.hyperparam.Evolutionary.</code><code class="sig-name descname">process_experiment</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">fts_method</span></em>, <em class="sig-param"><span class="n">result</span></em>, <em class="sig-param"><span class="n">datasetname</span></em>, <em class="sig-param"><span class="n">conn</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/hyperparam/Evolutionary.html#process_experiment"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.hyperparam.Evolutionary.process_experiment" title="Permalink to this definition">¶</a></dt>
+<dd><p>Persist the results of an DEHO execution in sqlite database (best hyperparameters) and json file (generation statistics)</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>fts_method</strong> – </p></li>
+<li><p><strong>result</strong> – </p></li>
+<li><p><strong>datasetname</strong> – </p></li>
+<li><p><strong>conn</strong> – </p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.hyperparam.Evolutionary.random_genotype">
+<code class="sig-prename descclassname">pyFTS.hyperparam.Evolutionary.</code><code class="sig-name descname">random_genotype</code><span class="sig-paren">(</span><em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/hyperparam/Evolutionary.html#random_genotype"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.hyperparam.Evolutionary.random_genotype" title="Permalink to this definition">¶</a></dt>
+<dd><p>Create random genotype</p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns</dt>
+<dd class="field-odd"><p>the genotype, a dictionary with all hyperparameters</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.hyperparam.Evolutionary.tournament">
+<code class="sig-prename descclassname">pyFTS.hyperparam.Evolutionary.</code><code class="sig-name descname">tournament</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">population</span></em>, <em class="sig-param"><span class="n">objective</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/hyperparam/Evolutionary.html#tournament"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.hyperparam.Evolutionary.tournament" title="Permalink to this definition">¶</a></dt>
+<dd><p>Simple tournament selection strategy.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>population</strong> – the population</p></li>
+<li><p><strong>objective</strong> – the objective to be considered on tournament</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p></p>
+</dd>
+</dl>
+</dd></dl>
+
 </div>
 </div>
 
@@ -152,8 +484,8 @@ Value: the measure value</p>
 <li><a class="reference internal" href="#module-pyFTS.hyperparam">Module contents</a></li>
 <li><a class="reference internal" href="#submodules">Submodules</a></li>
 <li><a class="reference internal" href="#module-pyFTS.hyperparam.Util">pyFTS.hyperparam.Util module</a></li>
-<li><a class="reference internal" href="#pyfts-hyperparam-gridsearch-module">pyFTS.hyperparam.GridSearch module</a></li>
-<li><a class="reference internal" href="#pyfts-hyperparam-evolutionary-module">pyFTS.hyperparam.Evolutionary module</a></li>
+<li><a class="reference internal" href="#module-pyFTS.hyperparam.GridSearch">pyFTS.hyperparam.GridSearch module</a></li>
+<li><a class="reference internal" href="#module-pyFTS.hyperparam.Evolutionary">pyFTS.hyperparam.Evolutionary module</a></li>
 </ul>
 </li>
 </ul>
diff --git a/docs/build/html/pyFTS.models.ensemble.html b/docs/build/html/pyFTS.models.ensemble.html
index 6a4754b..23a1266 100644
--- a/docs/build/html/pyFTS.models.ensemble.html
+++ b/docs/build/html/pyFTS.models.ensemble.html
@@ -69,11 +69,321 @@
 <div class="section" id="submodules">
 <h2>Submodules<a class="headerlink" href="#submodules" title="Permalink to this headline">¶</a></h2>
 </div>
-<div class="section" id="pyfts-models-ensemble-ensemble-module">
-<h2>pyFTS.models.ensemble.ensemble module<a class="headerlink" href="#pyfts-models-ensemble-ensemble-module" title="Permalink to this headline">¶</a></h2>
+<div class="section" id="module-pyFTS.models.ensemble.ensemble">
+<span id="pyfts-models-ensemble-ensemble-module"></span><h2>pyFTS.models.ensemble.ensemble module<a class="headerlink" href="#module-pyFTS.models.ensemble.ensemble" title="Permalink to this headline">¶</a></h2>
+<p>EnsembleFTS wraps several FTS methods to ensemble their forecasts, providing point,
+interval and probabilistic forecasting.</p>
+<p>Silva, P. C. L et al. Probabilistic Forecasting with Seasonal Ensemble Fuzzy Time-Series
+XIII Brazilian Congress on Computational Intelligence, 2017. Rio de Janeiro, Brazil.</p>
+<dl class="py class">
+<dt id="pyFTS.models.ensemble.ensemble.AllMethodEnsembleFTS">
+<em class="property">class </em><code class="sig-prename descclassname">pyFTS.models.ensemble.ensemble.</code><code class="sig-name descname">AllMethodEnsembleFTS</code><span class="sig-paren">(</span><em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/ensemble/ensemble.html#AllMethodEnsembleFTS"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.ensemble.ensemble.AllMethodEnsembleFTS" title="Permalink to this definition">¶</a></dt>
+<dd><p>Bases: <a class="reference internal" href="#pyFTS.models.ensemble.ensemble.EnsembleFTS" title="pyFTS.models.ensemble.ensemble.EnsembleFTS"><code class="xref py py-class docutils literal notranslate"><span class="pre">pyFTS.models.ensemble.ensemble.EnsembleFTS</span></code></a></p>
+<p>Creates an EnsembleFTS with all point forecast methods, sharing the same partitioner</p>
+<dl class="py method">
+<dt id="pyFTS.models.ensemble.ensemble.AllMethodEnsembleFTS.set_transformations">
+<code class="sig-name descname">set_transformations</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">model</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/ensemble/ensemble.html#AllMethodEnsembleFTS.set_transformations"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.ensemble.ensemble.AllMethodEnsembleFTS.set_transformations" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.ensemble.ensemble.AllMethodEnsembleFTS.train">
+<code class="sig-name descname">train</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/ensemble/ensemble.html#AllMethodEnsembleFTS.train"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.ensemble.ensemble.AllMethodEnsembleFTS.train" title="Permalink to this definition">¶</a></dt>
+<dd><p>Method specific parameter fitting</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> – training time series data</p></li>
+<li><p><strong>kwargs</strong> – Method specific parameters</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt id="pyFTS.models.ensemble.ensemble.EnsembleFTS">
+<em class="property">class </em><code class="sig-prename descclassname">pyFTS.models.ensemble.ensemble.</code><code class="sig-name descname">EnsembleFTS</code><span class="sig-paren">(</span><em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/ensemble/ensemble.html#EnsembleFTS"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.ensemble.ensemble.EnsembleFTS" title="Permalink to this definition">¶</a></dt>
+<dd><p>Bases: <a class="reference internal" href="pyFTS.common.html#pyFTS.common.fts.FTS" title="pyFTS.common.fts.FTS"><code class="xref py py-class docutils literal notranslate"><span class="pre">pyFTS.common.fts.FTS</span></code></a></p>
+<p>Ensemble FTS</p>
+<dl class="py attribute">
+<dt id="pyFTS.models.ensemble.ensemble.EnsembleFTS.alpha">
+<code class="sig-name descname">alpha</code><a class="headerlink" href="#pyFTS.models.ensemble.ensemble.EnsembleFTS.alpha" title="Permalink to this definition">¶</a></dt>
+<dd><p>The quantiles</p>
+</dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.ensemble.ensemble.EnsembleFTS.append_model">
+<code class="sig-name descname">append_model</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">model</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/ensemble/ensemble.html#EnsembleFTS.append_model"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.ensemble.ensemble.EnsembleFTS.append_model" title="Permalink to this definition">¶</a></dt>
+<dd><p>Append a new trained model to the ensemble</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>model</strong> – FTS model</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.ensemble.ensemble.EnsembleFTS.forecast">
+<code class="sig-name descname">forecast</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/ensemble/ensemble.html#EnsembleFTS.forecast"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.ensemble.ensemble.EnsembleFTS.forecast" title="Permalink to this definition">¶</a></dt>
+<dd><p>Point forecast one step ahead</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> – time series data with the minimal length equal to the max_lag of the model</p></li>
+<li><p><strong>kwargs</strong> – model specific parameters</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>a list with the forecasted values</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.ensemble.ensemble.EnsembleFTS.forecast_ahead_distribution">
+<code class="sig-name descname">forecast_ahead_distribution</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="n">steps</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/ensemble/ensemble.html#EnsembleFTS.forecast_ahead_distribution"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.ensemble.ensemble.EnsembleFTS.forecast_ahead_distribution" title="Permalink to this definition">¶</a></dt>
+<dd><p>Probabilistic forecast from 1 to H steps ahead, where H is given by the steps parameter</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> – time series data with the minimal length equal to the max_lag of the model</p></li>
+<li><p><strong>steps</strong> – the number of steps ahead to forecast</p></li>
+<li><p><strong>start_at</strong> – in the multi step forecasting, the index of the data where to start forecasting (default: 0)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>a list with the forecasted Probability Distributions</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.ensemble.ensemble.EnsembleFTS.forecast_ahead_interval">
+<code class="sig-name descname">forecast_ahead_interval</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="n">steps</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/ensemble/ensemble.html#EnsembleFTS.forecast_ahead_interval"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.ensemble.ensemble.EnsembleFTS.forecast_ahead_interval" title="Permalink to this definition">¶</a></dt>
+<dd><p>Interval forecast from 1 to H steps ahead, where H is given by the steps parameter</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> – time series data with the minimal length equal to the max_lag of the model</p></li>
+<li><p><strong>steps</strong> – the number of steps ahead to forecast</p></li>
+<li><p><strong>start_at</strong> – in the multi step forecasting, the index of the data where to start forecasting (default: 0)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>a list with the forecasted intervals</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.ensemble.ensemble.EnsembleFTS.forecast_distribution">
+<code class="sig-name descname">forecast_distribution</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/ensemble/ensemble.html#EnsembleFTS.forecast_distribution"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.ensemble.ensemble.EnsembleFTS.forecast_distribution" title="Permalink to this definition">¶</a></dt>
+<dd><p>Probabilistic forecast one step ahead</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> – time series data with the minimal length equal to the max_lag of the model</p></li>
+<li><p><strong>kwargs</strong> – model specific parameters</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>a list with probabilistic.ProbabilityDistribution objects representing the forecasted Probability Distributions</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.ensemble.ensemble.EnsembleFTS.forecast_interval">
+<code class="sig-name descname">forecast_interval</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/ensemble/ensemble.html#EnsembleFTS.forecast_interval"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.ensemble.ensemble.EnsembleFTS.forecast_interval" title="Permalink to this definition">¶</a></dt>
+<dd><p>Interval forecast one step ahead</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> – time series data with the minimal length equal to the max_lag of the model</p></li>
+<li><p><strong>kwargs</strong> – model specific parameters</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>a list with the prediction intervals</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.ensemble.ensemble.EnsembleFTS.get_UoD">
+<code class="sig-name descname">get_UoD</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/ensemble/ensemble.html#EnsembleFTS.get_UoD"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.ensemble.ensemble.EnsembleFTS.get_UoD" title="Permalink to this definition">¶</a></dt>
+<dd><p>Returns the interval of the known bounds of the universe of discourse (UoD), i. e.,
+the known minimum and maximum values of the time series.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns</dt>
+<dd class="field-odd"><p>A set with the lower and the upper bounds of the UoD</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.ensemble.ensemble.EnsembleFTS.get_distribution_interquantile">
+<code class="sig-name descname">get_distribution_interquantile</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">forecasts</span></em>, <em class="sig-param"><span class="n">alpha</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/ensemble/ensemble.html#EnsembleFTS.get_distribution_interquantile"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.ensemble.ensemble.EnsembleFTS.get_distribution_interquantile" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.ensemble.ensemble.EnsembleFTS.get_interval">
+<code class="sig-name descname">get_interval</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">forecasts</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/ensemble/ensemble.html#EnsembleFTS.get_interval"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.ensemble.ensemble.EnsembleFTS.get_interval" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.ensemble.ensemble.EnsembleFTS.get_models_forecasts">
+<code class="sig-name descname">get_models_forecasts</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/ensemble/ensemble.html#EnsembleFTS.get_models_forecasts"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.ensemble.ensemble.EnsembleFTS.get_models_forecasts" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.ensemble.ensemble.EnsembleFTS.get_point">
+<code class="sig-name descname">get_point</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">forecasts</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/ensemble/ensemble.html#EnsembleFTS.get_point"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.ensemble.ensemble.EnsembleFTS.get_point" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py attribute">
+<dt id="pyFTS.models.ensemble.ensemble.EnsembleFTS.interval_method">
+<code class="sig-name descname">interval_method</code><a class="headerlink" href="#pyFTS.models.ensemble.ensemble.EnsembleFTS.interval_method" title="Permalink to this definition">¶</a></dt>
+<dd><p>The method used to mix the several model’s forecasts into a interval forecast. Options: quantile, extremum, normal</p>
+</dd></dl>
+
+<dl class="py attribute">
+<dt id="pyFTS.models.ensemble.ensemble.EnsembleFTS.models">
+<code class="sig-name descname">models</code><a class="headerlink" href="#pyFTS.models.ensemble.ensemble.EnsembleFTS.models" title="Permalink to this definition">¶</a></dt>
+<dd><p>A list of FTS models, the ensemble components</p>
+</dd></dl>
+
+<dl class="py attribute">
+<dt id="pyFTS.models.ensemble.ensemble.EnsembleFTS.parameters">
+<code class="sig-name descname">parameters</code><a class="headerlink" href="#pyFTS.models.ensemble.ensemble.EnsembleFTS.parameters" title="Permalink to this definition">¶</a></dt>
+<dd><p>A list with the parameters for each component model</p>
+</dd></dl>
+
+<dl class="py attribute">
+<dt id="pyFTS.models.ensemble.ensemble.EnsembleFTS.point_method">
+<code class="sig-name descname">point_method</code><a class="headerlink" href="#pyFTS.models.ensemble.ensemble.EnsembleFTS.point_method" title="Permalink to this definition">¶</a></dt>
+<dd><p>The method used to mix the several model’s forecasts into a unique point forecast. Options: mean, median, quantile, exponential</p>
+</dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.ensemble.ensemble.EnsembleFTS.train">
+<code class="sig-name descname">train</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/ensemble/ensemble.html#EnsembleFTS.train"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.ensemble.ensemble.EnsembleFTS.train" title="Permalink to this definition">¶</a></dt>
+<dd><p>Method specific parameter fitting</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> – training time series data</p></li>
+<li><p><strong>kwargs</strong> – Method specific parameters</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt id="pyFTS.models.ensemble.ensemble.SimpleEnsembleFTS">
+<em class="property">class </em><code class="sig-prename descclassname">pyFTS.models.ensemble.ensemble.</code><code class="sig-name descname">SimpleEnsembleFTS</code><span class="sig-paren">(</span><em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/ensemble/ensemble.html#SimpleEnsembleFTS"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.ensemble.ensemble.SimpleEnsembleFTS" title="Permalink to this definition">¶</a></dt>
+<dd><p>Bases: <a class="reference internal" href="#pyFTS.models.ensemble.ensemble.EnsembleFTS" title="pyFTS.models.ensemble.ensemble.EnsembleFTS"><code class="xref py py-class docutils literal notranslate"><span class="pre">pyFTS.models.ensemble.ensemble.EnsembleFTS</span></code></a></p>
+<p>An homogeneous FTS method ensemble with variations on partitionings and orders.</p>
+<dl class="py attribute">
+<dt id="pyFTS.models.ensemble.ensemble.SimpleEnsembleFTS.method">
+<code class="sig-name descname">method</code><a class="headerlink" href="#pyFTS.models.ensemble.ensemble.SimpleEnsembleFTS.method" title="Permalink to this definition">¶</a></dt>
+<dd><p>FTS method class that will be used on internal models</p>
+</dd></dl>
+
+<dl class="py attribute">
+<dt id="pyFTS.models.ensemble.ensemble.SimpleEnsembleFTS.orders">
+<code class="sig-name descname">orders</code><a class="headerlink" href="#pyFTS.models.ensemble.ensemble.SimpleEnsembleFTS.orders" title="Permalink to this definition">¶</a></dt>
+<dd><p>Possible variations of order on internal models</p>
+</dd></dl>
+
+<dl class="py attribute">
+<dt id="pyFTS.models.ensemble.ensemble.SimpleEnsembleFTS.partitioner_method">
+<code class="sig-name descname">partitioner_method</code><a class="headerlink" href="#pyFTS.models.ensemble.ensemble.SimpleEnsembleFTS.partitioner_method" title="Permalink to this definition">¶</a></dt>
+<dd><p>UoD partitioner class that will be used on internal methods</p>
+</dd></dl>
+
+<dl class="py attribute">
+<dt id="pyFTS.models.ensemble.ensemble.SimpleEnsembleFTS.partitions">
+<code class="sig-name descname">partitions</code><a class="headerlink" href="#pyFTS.models.ensemble.ensemble.SimpleEnsembleFTS.partitions" title="Permalink to this definition">¶</a></dt>
+<dd><p>Possible variations of number of partitions on internal models</p>
+</dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.ensemble.ensemble.SimpleEnsembleFTS.train">
+<code class="sig-name descname">train</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/ensemble/ensemble.html#SimpleEnsembleFTS.train"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.ensemble.ensemble.SimpleEnsembleFTS.train" title="Permalink to this definition">¶</a></dt>
+<dd><p>Method specific parameter fitting</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> – training time series data</p></li>
+<li><p><strong>kwargs</strong> – Method specific parameters</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.models.ensemble.ensemble.sampler">
+<code class="sig-prename descclassname">pyFTS.models.ensemble.ensemble.</code><code class="sig-name descname">sampler</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="n">quantiles</span></em>, <em class="sig-param"><span class="n">bounds</span><span class="o">=</span><span class="default_value">False</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/ensemble/ensemble.html#sampler"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.ensemble.ensemble.sampler" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
 </div>
-<div class="section" id="pyfts-models-ensemble-multiseasonal-module">
-<h2>pyFTS.models.ensemble.multiseasonal module<a class="headerlink" href="#pyfts-models-ensemble-multiseasonal-module" title="Permalink to this headline">¶</a></h2>
+<div class="section" id="module-pyFTS.models.ensemble.multiseasonal">
+<span id="pyfts-models-ensemble-multiseasonal-module"></span><h2>pyFTS.models.ensemble.multiseasonal module<a class="headerlink" href="#module-pyFTS.models.ensemble.multiseasonal" title="Permalink to this headline">¶</a></h2>
+<p>Silva, P. C. L et al. Probabilistic Forecasting with Seasonal Ensemble Fuzzy Time-Series
+XIII Brazilian Congress on Computational Intelligence, 2017. Rio de Janeiro, Brazil.</p>
+<dl class="py class">
+<dt id="pyFTS.models.ensemble.multiseasonal.SeasonalEnsembleFTS">
+<em class="property">class </em><code class="sig-prename descclassname">pyFTS.models.ensemble.multiseasonal.</code><code class="sig-name descname">SeasonalEnsembleFTS</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">name</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/ensemble/multiseasonal.html#SeasonalEnsembleFTS"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.ensemble.multiseasonal.SeasonalEnsembleFTS" title="Permalink to this definition">¶</a></dt>
+<dd><p>Bases: <a class="reference internal" href="#pyFTS.models.ensemble.ensemble.EnsembleFTS" title="pyFTS.models.ensemble.ensemble.EnsembleFTS"><code class="xref py py-class docutils literal notranslate"><span class="pre">pyFTS.models.ensemble.ensemble.EnsembleFTS</span></code></a></p>
+<dl class="py method">
+<dt id="pyFTS.models.ensemble.multiseasonal.SeasonalEnsembleFTS.forecast_distribution">
+<code class="sig-name descname">forecast_distribution</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/ensemble/multiseasonal.html#SeasonalEnsembleFTS.forecast_distribution"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.ensemble.multiseasonal.SeasonalEnsembleFTS.forecast_distribution" title="Permalink to this definition">¶</a></dt>
+<dd><p>Probabilistic forecast one step ahead</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> – time series data with the minimal length equal to the max_lag of the model</p></li>
+<li><p><strong>kwargs</strong> – model specific parameters</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>a list with probabilistic.ProbabilityDistribution objects representing the forecasted Probability Distributions</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.ensemble.multiseasonal.SeasonalEnsembleFTS.train">
+<code class="sig-name descname">train</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/ensemble/multiseasonal.html#SeasonalEnsembleFTS.train"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.ensemble.multiseasonal.SeasonalEnsembleFTS.train" title="Permalink to this definition">¶</a></dt>
+<dd><p>Method specific parameter fitting</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> – training time series data</p></li>
+<li><p><strong>kwargs</strong> – Method specific parameters</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.ensemble.multiseasonal.SeasonalEnsembleFTS.update_uod">
+<code class="sig-name descname">update_uod</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/ensemble/multiseasonal.html#SeasonalEnsembleFTS.update_uod"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.ensemble.multiseasonal.SeasonalEnsembleFTS.update_uod" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.models.ensemble.multiseasonal.train_individual_model">
+<code class="sig-prename descclassname">pyFTS.models.ensemble.multiseasonal.</code><code class="sig-name descname">train_individual_model</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">partitioner</span></em>, <em class="sig-param"><span class="n">train_data</span></em>, <em class="sig-param"><span class="n">indexer</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/ensemble/multiseasonal.html#train_individual_model"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.ensemble.multiseasonal.train_individual_model" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
 </div>
 <div class="section" id="module-pyFTS.models.ensemble">
 <span id="module-contents"></span><h2>Module contents<a class="headerlink" href="#module-pyFTS.models.ensemble" title="Permalink to this headline">¶</a></h2>
@@ -92,8 +402,8 @@
   <ul>
 <li><a class="reference internal" href="#">pyFTS.models.ensemble package</a><ul>
 <li><a class="reference internal" href="#submodules">Submodules</a></li>
-<li><a class="reference internal" href="#pyfts-models-ensemble-ensemble-module">pyFTS.models.ensemble.ensemble module</a></li>
-<li><a class="reference internal" href="#pyfts-models-ensemble-multiseasonal-module">pyFTS.models.ensemble.multiseasonal module</a></li>
+<li><a class="reference internal" href="#module-pyFTS.models.ensemble.ensemble">pyFTS.models.ensemble.ensemble module</a></li>
+<li><a class="reference internal" href="#module-pyFTS.models.ensemble.multiseasonal">pyFTS.models.ensemble.multiseasonal module</a></li>
 <li><a class="reference internal" href="#module-pyFTS.models.ensemble">Module contents</a></li>
 </ul>
 </li>
diff --git a/docs/build/html/pyFTS.models.html b/docs/build/html/pyFTS.models.html
index 8888a65..0fb2b44 100644
--- a/docs/build/html/pyFTS.models.html
+++ b/docs/build/html/pyFTS.models.html
@@ -75,16 +75,16 @@
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="pyFTS.models.ensemble.html">pyFTS.models.ensemble package</a><ul>
 <li class="toctree-l2"><a class="reference internal" href="pyFTS.models.ensemble.html#submodules">Submodules</a></li>
-<li class="toctree-l2"><a class="reference internal" href="pyFTS.models.ensemble.html#pyfts-models-ensemble-ensemble-module">pyFTS.models.ensemble.ensemble module</a></li>
-<li class="toctree-l2"><a class="reference internal" href="pyFTS.models.ensemble.html#pyfts-models-ensemble-multiseasonal-module">pyFTS.models.ensemble.multiseasonal module</a></li>
+<li class="toctree-l2"><a class="reference internal" href="pyFTS.models.ensemble.html#module-pyFTS.models.ensemble.ensemble">pyFTS.models.ensemble.ensemble module</a></li>
+<li class="toctree-l2"><a class="reference internal" href="pyFTS.models.ensemble.html#module-pyFTS.models.ensemble.multiseasonal">pyFTS.models.ensemble.multiseasonal module</a></li>
 <li class="toctree-l2"><a class="reference internal" href="pyFTS.models.ensemble.html#module-pyFTS.models.ensemble">Module contents</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="pyFTS.models.incremental.html">pyFTS.models.incremental package</a><ul>
 <li class="toctree-l2"><a class="reference internal" href="pyFTS.models.incremental.html#module-pyFTS.models.incremental">Module contents</a></li>
 <li class="toctree-l2"><a class="reference internal" href="pyFTS.models.incremental.html#submodules">Submodules</a></li>
-<li class="toctree-l2"><a class="reference internal" href="pyFTS.models.incremental.html#pyfts-models-incremental-timevariant-module">pyFTS.models.incremental.TimeVariant module</a></li>
-<li class="toctree-l2"><a class="reference internal" href="pyFTS.models.incremental.html#pyfts-models-incremental-incrementalensemble-module">pyFTS.models.incremental.IncrementalEnsemble module</a></li>
+<li class="toctree-l2"><a class="reference internal" href="pyFTS.models.incremental.html#module-pyFTS.models.incremental.TimeVariant">pyFTS.models.incremental.TimeVariant module</a></li>
+<li class="toctree-l2"><a class="reference internal" href="pyFTS.models.incremental.html#module-pyFTS.models.incremental.IncrementalEnsemble">pyFTS.models.incremental.IncrementalEnsemble module</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="pyFTS.models.multivariate.html">pyFTS.models.multivariate package</a><ul>
@@ -92,37 +92,37 @@
 <li class="toctree-l2"><a class="reference internal" href="pyFTS.models.multivariate.html#submodules">Submodules</a></li>
 <li class="toctree-l2"><a class="reference internal" href="pyFTS.models.multivariate.html#module-pyFTS.models.multivariate.FLR">pyFTS.models.multivariate.FLR module</a></li>
 <li class="toctree-l2"><a class="reference internal" href="pyFTS.models.multivariate.html#module-pyFTS.models.multivariate.common">pyFTS.models.multivariate.common module</a></li>
-<li class="toctree-l2"><a class="reference internal" href="pyFTS.models.multivariate.html#pyfts-models-multivariate-variable-module">pyFTS.models.multivariate.variable module</a></li>
+<li class="toctree-l2"><a class="reference internal" href="pyFTS.models.multivariate.html#module-pyFTS.models.multivariate.variable">pyFTS.models.multivariate.variable module</a></li>
 <li class="toctree-l2"><a class="reference internal" href="pyFTS.models.multivariate.html#module-pyFTS.models.multivariate.flrg">pyFTS.models.multivariate.flrg module</a></li>
 <li class="toctree-l2"><a class="reference internal" href="pyFTS.models.multivariate.html#module-pyFTS.models.multivariate.partitioner">pyFTS.models.multivariate.partitioner module</a></li>
 <li class="toctree-l2"><a class="reference internal" href="pyFTS.models.multivariate.html#module-pyFTS.models.multivariate.grid">pyFTS.models.multivariate.grid module</a></li>
-<li class="toctree-l2"><a class="reference internal" href="pyFTS.models.multivariate.html#pyfts-models-multivariate-mvfts-module">pyFTS.models.multivariate.mvfts module</a></li>
-<li class="toctree-l2"><a class="reference internal" href="pyFTS.models.multivariate.html#pyfts-models-multivariate-wmvfts-module">pyFTS.models.multivariate.wmvfts module</a></li>
-<li class="toctree-l2"><a class="reference internal" href="pyFTS.models.multivariate.html#pyfts-models-multivariate-cmvfts-module">pyFTS.models.multivariate.cmvfts module</a></li>
-<li class="toctree-l2"><a class="reference internal" href="pyFTS.models.multivariate.html#pyfts-models-multivariate-granular-module">pyFTS.models.multivariate.granular module</a></li>
+<li class="toctree-l2"><a class="reference internal" href="pyFTS.models.multivariate.html#module-pyFTS.models.multivariate.mvfts">pyFTS.models.multivariate.mvfts module</a></li>
+<li class="toctree-l2"><a class="reference internal" href="pyFTS.models.multivariate.html#module-pyFTS.models.multivariate.wmvfts">pyFTS.models.multivariate.wmvfts module</a></li>
+<li class="toctree-l2"><a class="reference internal" href="pyFTS.models.multivariate.html#module-pyFTS.models.multivariate.cmvfts">pyFTS.models.multivariate.cmvfts module</a></li>
+<li class="toctree-l2"><a class="reference internal" href="pyFTS.models.multivariate.html#module-pyFTS.models.multivariate.granular">pyFTS.models.multivariate.granular module</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="pyFTS.models.nonstationary.html">pyFTS.models.nonstationary package</a><ul>
 <li class="toctree-l2"><a class="reference internal" href="pyFTS.models.nonstationary.html#submodules">Submodules</a></li>
 <li class="toctree-l2"><a class="reference internal" href="pyFTS.models.nonstationary.html#module-pyFTS.models.nonstationary.common">pyFTS.models.nonstationary.common module</a></li>
-<li class="toctree-l2"><a class="reference internal" href="pyFTS.models.nonstationary.html#pyfts-models-nonstationary-cvfts-module">pyFTS.models.nonstationary.cvfts module</a></li>
+<li class="toctree-l2"><a class="reference internal" href="pyFTS.models.nonstationary.html#module-pyFTS.models.nonstationary.cvfts">pyFTS.models.nonstationary.cvfts module</a></li>
 <li class="toctree-l2"><a class="reference internal" href="pyFTS.models.nonstationary.html#module-pyFTS.models.nonstationary.flrg">pyFTS.models.nonstationary.flrg module</a></li>
-<li class="toctree-l2"><a class="reference internal" href="pyFTS.models.nonstationary.html#pyfts-models-nonstationary-honsfts-module">pyFTS.models.nonstationary.honsfts module</a></li>
-<li class="toctree-l2"><a class="reference internal" href="pyFTS.models.nonstationary.html#pyfts-models-nonstationary-nsfts-module">pyFTS.models.nonstationary.nsfts module</a></li>
+<li class="toctree-l2"><a class="reference internal" href="pyFTS.models.nonstationary.html#module-pyFTS.models.nonstationary.honsfts">pyFTS.models.nonstationary.honsfts module</a></li>
+<li class="toctree-l2"><a class="reference internal" href="pyFTS.models.nonstationary.html#module-pyFTS.models.nonstationary.nsfts">pyFTS.models.nonstationary.nsfts module</a></li>
 <li class="toctree-l2"><a class="reference internal" href="pyFTS.models.nonstationary.html#module-pyFTS.models.nonstationary.partitioners">pyFTS.models.nonstationary.partitioners module</a></li>
 <li class="toctree-l2"><a class="reference internal" href="pyFTS.models.nonstationary.html#module-pyFTS.models.nonstationary.perturbation">pyFTS.models.nonstationary.perturbation module</a></li>
-<li class="toctree-l2"><a class="reference internal" href="pyFTS.models.nonstationary.html#pyfts-models-nonstationary-util-module">pyFTS.models.nonstationary.util module</a></li>
+<li class="toctree-l2"><a class="reference internal" href="pyFTS.models.nonstationary.html#module-pyFTS.models.nonstationary.util">pyFTS.models.nonstationary.util module</a></li>
 <li class="toctree-l2"><a class="reference internal" href="pyFTS.models.nonstationary.html#module-pyFTS.models.nonstationary">Module contents</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="pyFTS.models.seasonal.html">pyFTS.models.seasonal package</a><ul>
 <li class="toctree-l2"><a class="reference internal" href="pyFTS.models.seasonal.html#submodules">Submodules</a></li>
 <li class="toctree-l2"><a class="reference internal" href="pyFTS.models.seasonal.html#module-pyFTS.models.seasonal.SeasonalIndexer">pyFTS.models.seasonal.SeasonalIndexer module</a></li>
-<li class="toctree-l2"><a class="reference internal" href="pyFTS.models.seasonal.html#pyfts-models-seasonal-cmsfts-module">pyFTS.models.seasonal.cmsfts module</a></li>
+<li class="toctree-l2"><a class="reference internal" href="pyFTS.models.seasonal.html#module-pyFTS.models.seasonal.cmsfts">pyFTS.models.seasonal.cmsfts module</a></li>
 <li class="toctree-l2"><a class="reference internal" href="pyFTS.models.seasonal.html#module-pyFTS.models.seasonal.common">pyFTS.models.seasonal.common module</a></li>
-<li class="toctree-l2"><a class="reference internal" href="pyFTS.models.seasonal.html#pyfts-models-seasonal-msfts-module">pyFTS.models.seasonal.msfts module</a></li>
+<li class="toctree-l2"><a class="reference internal" href="pyFTS.models.seasonal.html#module-pyFTS.models.seasonal.msfts">pyFTS.models.seasonal.msfts module</a></li>
 <li class="toctree-l2"><a class="reference internal" href="pyFTS.models.seasonal.html#module-pyFTS.models.seasonal.partitioner">pyFTS.models.seasonal.partitioner module</a></li>
-<li class="toctree-l2"><a class="reference internal" href="pyFTS.models.seasonal.html#pyfts-models-seasonal-sfts-module">pyFTS.models.seasonal.sfts module</a></li>
+<li class="toctree-l2"><a class="reference internal" href="pyFTS.models.seasonal.html#module-pyFTS.models.seasonal.sfts">pyFTS.models.seasonal.sfts module</a></li>
 <li class="toctree-l2"><a class="reference internal" href="pyFTS.models.seasonal.html#module-pyFTS.models.seasonal">Module contents</a></li>
 </ul>
 </li>
@@ -132,35 +132,1011 @@
 <div class="section" id="submodules">
 <h2>Submodules<a class="headerlink" href="#submodules" title="Permalink to this headline">¶</a></h2>
 </div>
-<div class="section" id="pyfts-models-song-module">
-<h2>pyFTS.models.song module<a class="headerlink" href="#pyfts-models-song-module" title="Permalink to this headline">¶</a></h2>
+<div class="section" id="module-pyFTS.models.song">
+<span id="pyfts-models-song-module"></span><h2>pyFTS.models.song module<a class="headerlink" href="#module-pyFTS.models.song" title="Permalink to this headline">¶</a></h2>
+<p>First Order Traditional Fuzzy Time Series method by Song &amp; Chissom (1993)</p>
+<ol class="upperalpha simple" start="17">
+<li><p>Song and B. S. Chissom, “Fuzzy time series and its models,” Fuzzy Sets Syst., vol. 54, no. 3, pp. 269–277, 1993.</p></li>
+</ol>
+<dl class="py class">
+<dt id="pyFTS.models.song.ConventionalFTS">
+<em class="property">class </em><code class="sig-prename descclassname">pyFTS.models.song.</code><code class="sig-name descname">ConventionalFTS</code><span class="sig-paren">(</span><em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/song.html#ConventionalFTS"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.song.ConventionalFTS" title="Permalink to this definition">¶</a></dt>
+<dd><p>Bases: <a class="reference internal" href="pyFTS.common.html#pyFTS.common.fts.FTS" title="pyFTS.common.fts.FTS"><code class="xref py py-class docutils literal notranslate"><span class="pre">pyFTS.common.fts.FTS</span></code></a></p>
+<p>Traditional Fuzzy Time Series</p>
+<dl class="py method">
+<dt id="pyFTS.models.song.ConventionalFTS.flr_membership_matrix">
+<code class="sig-name descname">flr_membership_matrix</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">flr</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/song.html#ConventionalFTS.flr_membership_matrix"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.song.ConventionalFTS.flr_membership_matrix" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.song.ConventionalFTS.forecast">
+<code class="sig-name descname">forecast</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">ndata</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/song.html#ConventionalFTS.forecast"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.song.ConventionalFTS.forecast" title="Permalink to this definition">¶</a></dt>
+<dd><p>Point forecast one step ahead</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> – time series data with the minimal length equal to the max_lag of the model</p></li>
+<li><p><strong>kwargs</strong> – model specific parameters</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>a list with the forecasted values</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.song.ConventionalFTS.operation_matrix">
+<code class="sig-name descname">operation_matrix</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">flrs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/song.html#ConventionalFTS.operation_matrix"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.song.ConventionalFTS.operation_matrix" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.song.ConventionalFTS.train">
+<code class="sig-name descname">train</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/song.html#ConventionalFTS.train"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.song.ConventionalFTS.train" title="Permalink to this definition">¶</a></dt>
+<dd><p>Method specific parameter fitting</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> – training time series data</p></li>
+<li><p><strong>kwargs</strong> – Method specific parameters</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
 </div>
-<div class="section" id="pyfts-models-chen-module">
-<h2>pyFTS.models.chen module<a class="headerlink" href="#pyfts-models-chen-module" title="Permalink to this headline">¶</a></h2>
+<div class="section" id="module-pyFTS.models.chen">
+<span id="pyfts-models-chen-module"></span><h2>pyFTS.models.chen module<a class="headerlink" href="#module-pyFTS.models.chen" title="Permalink to this headline">¶</a></h2>
+<p>First Order Conventional Fuzzy Time Series by Chen (1996)</p>
+<p>S.-M. Chen, “Forecasting enrollments based on fuzzy time series,” Fuzzy Sets Syst., vol. 81, no. 3, pp. 311–319, 1996.</p>
+<dl class="py class">
+<dt id="pyFTS.models.chen.ConventionalFLRG">
+<em class="property">class </em><code class="sig-prename descclassname">pyFTS.models.chen.</code><code class="sig-name descname">ConventionalFLRG</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">LHS</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/chen.html#ConventionalFLRG"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.chen.ConventionalFLRG" title="Permalink to this definition">¶</a></dt>
+<dd><p>Bases: <a class="reference internal" href="pyFTS.common.html#pyFTS.common.flrg.FLRG" title="pyFTS.common.flrg.FLRG"><code class="xref py py-class docutils literal notranslate"><span class="pre">pyFTS.common.flrg.FLRG</span></code></a></p>
+<p>First Order Conventional Fuzzy Logical Relationship Group</p>
+<dl class="py method">
+<dt id="pyFTS.models.chen.ConventionalFLRG.append_rhs">
+<code class="sig-name descname">append_rhs</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">c</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/chen.html#ConventionalFLRG.append_rhs"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.chen.ConventionalFLRG.append_rhs" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.chen.ConventionalFLRG.get_key">
+<code class="sig-name descname">get_key</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">sets</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/chen.html#ConventionalFLRG.get_key"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.chen.ConventionalFLRG.get_key" title="Permalink to this definition">¶</a></dt>
+<dd><p>Returns a unique identifier for this FLRG</p>
+</dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt id="pyFTS.models.chen.ConventionalFTS">
+<em class="property">class </em><code class="sig-prename descclassname">pyFTS.models.chen.</code><code class="sig-name descname">ConventionalFTS</code><span class="sig-paren">(</span><em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/chen.html#ConventionalFTS"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.chen.ConventionalFTS" title="Permalink to this definition">¶</a></dt>
+<dd><p>Bases: <a class="reference internal" href="pyFTS.common.html#pyFTS.common.fts.FTS" title="pyFTS.common.fts.FTS"><code class="xref py py-class docutils literal notranslate"><span class="pre">pyFTS.common.fts.FTS</span></code></a></p>
+<p>Conventional Fuzzy Time Series</p>
+<dl class="py method">
+<dt id="pyFTS.models.chen.ConventionalFTS.forecast">
+<code class="sig-name descname">forecast</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">ndata</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/chen.html#ConventionalFTS.forecast"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.chen.ConventionalFTS.forecast" title="Permalink to this definition">¶</a></dt>
+<dd><p>Point forecast one step ahead</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> – time series data with the minimal length equal to the max_lag of the model</p></li>
+<li><p><strong>kwargs</strong> – model specific parameters</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>a list with the forecasted values</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.chen.ConventionalFTS.generate_flrg">
+<code class="sig-name descname">generate_flrg</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">flrs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/chen.html#ConventionalFTS.generate_flrg"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.chen.ConventionalFTS.generate_flrg" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.chen.ConventionalFTS.train">
+<code class="sig-name descname">train</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/chen.html#ConventionalFTS.train"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.chen.ConventionalFTS.train" title="Permalink to this definition">¶</a></dt>
+<dd><p>Method specific parameter fitting</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> – training time series data</p></li>
+<li><p><strong>kwargs</strong> – Method specific parameters</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
 </div>
-<div class="section" id="pyfts-models-yu-module">
-<h2>pyFTS.models.yu module<a class="headerlink" href="#pyfts-models-yu-module" title="Permalink to this headline">¶</a></h2>
+<div class="section" id="module-pyFTS.models.yu">
+<span id="pyfts-models-yu-module"></span><h2>pyFTS.models.yu module<a class="headerlink" href="#module-pyFTS.models.yu" title="Permalink to this headline">¶</a></h2>
+<p>First Order Weighted Fuzzy Time Series by Yu(2005)</p>
+<p>H.-K. Yu, “Weighted fuzzy time series models for TAIEX forecasting,” 
+Phys. A Stat. Mech. its Appl., vol. 349, no. 3, pp. 609–624, 2005.</p>
+<dl class="py class">
+<dt id="pyFTS.models.yu.WeightedFLRG">
+<em class="property">class </em><code class="sig-prename descclassname">pyFTS.models.yu.</code><code class="sig-name descname">WeightedFLRG</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">LHS</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/yu.html#WeightedFLRG"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.yu.WeightedFLRG" title="Permalink to this definition">¶</a></dt>
+<dd><p>Bases: <a class="reference internal" href="pyFTS.common.html#pyFTS.common.flrg.FLRG" title="pyFTS.common.flrg.FLRG"><code class="xref py py-class docutils literal notranslate"><span class="pre">pyFTS.common.flrg.FLRG</span></code></a></p>
+<p>First Order Weighted Fuzzy Logical Relationship Group</p>
+<dl class="py method">
+<dt id="pyFTS.models.yu.WeightedFLRG.append_rhs">
+<code class="sig-name descname">append_rhs</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">c</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/yu.html#WeightedFLRG.append_rhs"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.yu.WeightedFLRG.append_rhs" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.yu.WeightedFLRG.weights">
+<code class="sig-name descname">weights</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">sets</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/yu.html#WeightedFLRG.weights"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.yu.WeightedFLRG.weights" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt id="pyFTS.models.yu.WeightedFTS">
+<em class="property">class </em><code class="sig-prename descclassname">pyFTS.models.yu.</code><code class="sig-name descname">WeightedFTS</code><span class="sig-paren">(</span><em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/yu.html#WeightedFTS"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.yu.WeightedFTS" title="Permalink to this definition">¶</a></dt>
+<dd><p>Bases: <a class="reference internal" href="pyFTS.common.html#pyFTS.common.fts.FTS" title="pyFTS.common.fts.FTS"><code class="xref py py-class docutils literal notranslate"><span class="pre">pyFTS.common.fts.FTS</span></code></a></p>
+<p>First Order Weighted Fuzzy Time Series</p>
+<dl class="py method">
+<dt id="pyFTS.models.yu.WeightedFTS.forecast">
+<code class="sig-name descname">forecast</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">ndata</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/yu.html#WeightedFTS.forecast"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.yu.WeightedFTS.forecast" title="Permalink to this definition">¶</a></dt>
+<dd><p>Point forecast one step ahead</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> – time series data with the minimal length equal to the max_lag of the model</p></li>
+<li><p><strong>kwargs</strong> – model specific parameters</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>a list with the forecasted values</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.yu.WeightedFTS.generate_FLRG">
+<code class="sig-name descname">generate_FLRG</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">flrs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/yu.html#WeightedFTS.generate_FLRG"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.yu.WeightedFTS.generate_FLRG" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.yu.WeightedFTS.train">
+<code class="sig-name descname">train</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">ndata</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/yu.html#WeightedFTS.train"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.yu.WeightedFTS.train" title="Permalink to this definition">¶</a></dt>
+<dd><p>Method specific parameter fitting</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> – training time series data</p></li>
+<li><p><strong>kwargs</strong> – Method specific parameters</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
 </div>
-<div class="section" id="pyfts-models-cheng-module">
-<h2>pyFTS.models.cheng module<a class="headerlink" href="#pyfts-models-cheng-module" title="Permalink to this headline">¶</a></h2>
+<div class="section" id="module-pyFTS.models.cheng">
+<span id="pyfts-models-cheng-module"></span><h2>pyFTS.models.cheng module<a class="headerlink" href="#module-pyFTS.models.cheng" title="Permalink to this headline">¶</a></h2>
+<p>Trend Weighted Fuzzy Time Series by Cheng, Chen and Wu (2009)</p>
+<p>C.-H. Cheng, Y.-S. Chen, and Y.-L. Wu, “Forecasting innovation diffusion of products using trend-weighted fuzzy time-series model,” 
+Expert Syst. Appl., vol. 36, no. 2, pp. 1826–1832, 2009.</p>
+<dl class="py class">
+<dt id="pyFTS.models.cheng.TrendWeightedFLRG">
+<em class="property">class </em><code class="sig-prename descclassname">pyFTS.models.cheng.</code><code class="sig-name descname">TrendWeightedFLRG</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">LHS</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/cheng.html#TrendWeightedFLRG"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.cheng.TrendWeightedFLRG" title="Permalink to this definition">¶</a></dt>
+<dd><p>Bases: <a class="reference internal" href="#pyFTS.models.yu.WeightedFLRG" title="pyFTS.models.yu.WeightedFLRG"><code class="xref py py-class docutils literal notranslate"><span class="pre">pyFTS.models.yu.WeightedFLRG</span></code></a></p>
+<p>First Order Trend Weighted Fuzzy Logical Relationship Group</p>
+<dl class="py method">
+<dt id="pyFTS.models.cheng.TrendWeightedFLRG.weights">
+<code class="sig-name descname">weights</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">sets</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/cheng.html#TrendWeightedFLRG.weights"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.cheng.TrendWeightedFLRG.weights" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt id="pyFTS.models.cheng.TrendWeightedFTS">
+<em class="property">class </em><code class="sig-prename descclassname">pyFTS.models.cheng.</code><code class="sig-name descname">TrendWeightedFTS</code><span class="sig-paren">(</span><em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/cheng.html#TrendWeightedFTS"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.cheng.TrendWeightedFTS" title="Permalink to this definition">¶</a></dt>
+<dd><p>Bases: <a class="reference internal" href="#pyFTS.models.yu.WeightedFTS" title="pyFTS.models.yu.WeightedFTS"><code class="xref py py-class docutils literal notranslate"><span class="pre">pyFTS.models.yu.WeightedFTS</span></code></a></p>
+<p>First Order Trend Weighted Fuzzy Time Series</p>
+<dl class="py method">
+<dt id="pyFTS.models.cheng.TrendWeightedFTS.generate_FLRG">
+<code class="sig-name descname">generate_FLRG</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">flrs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/cheng.html#TrendWeightedFTS.generate_FLRG"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.cheng.TrendWeightedFTS.generate_FLRG" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
 </div>
-<div class="section" id="pyfts-models-hofts-module">
-<h2>pyFTS.models.hofts module<a class="headerlink" href="#pyfts-models-hofts-module" title="Permalink to this headline">¶</a></h2>
+<div class="section" id="module-pyFTS.models.hofts">
+<span id="pyfts-models-hofts-module"></span><h2>pyFTS.models.hofts module<a class="headerlink" href="#module-pyFTS.models.hofts" title="Permalink to this headline">¶</a></h2>
+<p>High Order FTS</p>
+<p>Severiano, S. A. Jr; Silva, P. C. L.; Sadaei, H. J.; Guimarães, F. G. Very Short-term Solar Forecasting
+using Fuzzy Time Series. 2017 IEEE International Conference on Fuzzy Systems. DOI10.1109/FUZZ-IEEE.2017.8015732</p>
+<dl class="py class">
+<dt id="pyFTS.models.hofts.HighOrderFLRG">
+<em class="property">class </em><code class="sig-prename descclassname">pyFTS.models.hofts.</code><code class="sig-name descname">HighOrderFLRG</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">order</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/hofts.html#HighOrderFLRG"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.hofts.HighOrderFLRG" title="Permalink to this definition">¶</a></dt>
+<dd><p>Bases: <a class="reference internal" href="pyFTS.common.html#pyFTS.common.flrg.FLRG" title="pyFTS.common.flrg.FLRG"><code class="xref py py-class docutils literal notranslate"><span class="pre">pyFTS.common.flrg.FLRG</span></code></a></p>
+<p>Conventional High Order Fuzzy Logical Relationship Group</p>
+<dl class="py method">
+<dt id="pyFTS.models.hofts.HighOrderFLRG.append_lhs">
+<code class="sig-name descname">append_lhs</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">c</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/hofts.html#HighOrderFLRG.append_lhs"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.hofts.HighOrderFLRG.append_lhs" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.hofts.HighOrderFLRG.append_rhs">
+<code class="sig-name descname">append_rhs</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">c</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/hofts.html#HighOrderFLRG.append_rhs"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.hofts.HighOrderFLRG.append_rhs" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt id="pyFTS.models.hofts.HighOrderFTS">
+<em class="property">class </em><code class="sig-prename descclassname">pyFTS.models.hofts.</code><code class="sig-name descname">HighOrderFTS</code><span class="sig-paren">(</span><em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/hofts.html#HighOrderFTS"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.hofts.HighOrderFTS" title="Permalink to this definition">¶</a></dt>
+<dd><p>Bases: <a class="reference internal" href="pyFTS.common.html#pyFTS.common.fts.FTS" title="pyFTS.common.fts.FTS"><code class="xref py py-class docutils literal notranslate"><span class="pre">pyFTS.common.fts.FTS</span></code></a></p>
+<p>Conventional High Order Fuzzy Time Series</p>
+<dl class="py method">
+<dt id="pyFTS.models.hofts.HighOrderFTS.configure_lags">
+<code class="sig-name descname">configure_lags</code><span class="sig-paren">(</span><em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/hofts.html#HighOrderFTS.configure_lags"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.hofts.HighOrderFTS.configure_lags" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.hofts.HighOrderFTS.forecast">
+<code class="sig-name descname">forecast</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">ndata</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/hofts.html#HighOrderFTS.forecast"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.hofts.HighOrderFTS.forecast" title="Permalink to this definition">¶</a></dt>
+<dd><p>Point forecast one step ahead</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> – time series data with the minimal length equal to the max_lag of the model</p></li>
+<li><p><strong>kwargs</strong> – model specific parameters</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>a list with the forecasted values</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.hofts.HighOrderFTS.generate_flrg">
+<code class="sig-name descname">generate_flrg</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/hofts.html#HighOrderFTS.generate_flrg"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.hofts.HighOrderFTS.generate_flrg" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.hofts.HighOrderFTS.generate_flrg_fuzzyfied">
+<code class="sig-name descname">generate_flrg_fuzzyfied</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/hofts.html#HighOrderFTS.generate_flrg_fuzzyfied"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.hofts.HighOrderFTS.generate_flrg_fuzzyfied" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.hofts.HighOrderFTS.generate_lhs_flrg">
+<code class="sig-name descname">generate_lhs_flrg</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">sample</span></em>, <em class="sig-param"><span class="n">explain</span><span class="o">=</span><span class="default_value">False</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/hofts.html#HighOrderFTS.generate_lhs_flrg"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.hofts.HighOrderFTS.generate_lhs_flrg" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.hofts.HighOrderFTS.generate_lhs_flrg_fuzzyfied">
+<code class="sig-name descname">generate_lhs_flrg_fuzzyfied</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">sample</span></em>, <em class="sig-param"><span class="n">explain</span><span class="o">=</span><span class="default_value">False</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/hofts.html#HighOrderFTS.generate_lhs_flrg_fuzzyfied"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.hofts.HighOrderFTS.generate_lhs_flrg_fuzzyfied" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.hofts.HighOrderFTS.train">
+<code class="sig-name descname">train</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/hofts.html#HighOrderFTS.train"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.hofts.HighOrderFTS.train" title="Permalink to this definition">¶</a></dt>
+<dd><p>Method specific parameter fitting</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> – training time series data</p></li>
+<li><p><strong>kwargs</strong> – Method specific parameters</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt id="pyFTS.models.hofts.WeightedHighOrderFLRG">
+<em class="property">class </em><code class="sig-prename descclassname">pyFTS.models.hofts.</code><code class="sig-name descname">WeightedHighOrderFLRG</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">order</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/hofts.html#WeightedHighOrderFLRG"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.hofts.WeightedHighOrderFLRG" title="Permalink to this definition">¶</a></dt>
+<dd><p>Bases: <a class="reference internal" href="pyFTS.common.html#pyFTS.common.flrg.FLRG" title="pyFTS.common.flrg.FLRG"><code class="xref py py-class docutils literal notranslate"><span class="pre">pyFTS.common.flrg.FLRG</span></code></a></p>
+<p>Weighted High Order Fuzzy Logical Relationship Group</p>
+<dl class="py method">
+<dt id="pyFTS.models.hofts.WeightedHighOrderFLRG.append_lhs">
+<code class="sig-name descname">append_lhs</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">c</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/hofts.html#WeightedHighOrderFLRG.append_lhs"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.hofts.WeightedHighOrderFLRG.append_lhs" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.hofts.WeightedHighOrderFLRG.append_rhs">
+<code class="sig-name descname">append_rhs</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">fset</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/hofts.html#WeightedHighOrderFLRG.append_rhs"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.hofts.WeightedHighOrderFLRG.append_rhs" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.hofts.WeightedHighOrderFLRG.get_lower">
+<code class="sig-name descname">get_lower</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">sets</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/hofts.html#WeightedHighOrderFLRG.get_lower"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.hofts.WeightedHighOrderFLRG.get_lower" title="Permalink to this definition">¶</a></dt>
+<dd><p>Returns the lower bound value for the RHS fuzzy sets</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>sets</strong> – fuzzy sets</p>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>lower bound value</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.hofts.WeightedHighOrderFLRG.get_midpoint">
+<code class="sig-name descname">get_midpoint</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">sets</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/hofts.html#WeightedHighOrderFLRG.get_midpoint"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.hofts.WeightedHighOrderFLRG.get_midpoint" title="Permalink to this definition">¶</a></dt>
+<dd><p>Returns the midpoint value for the RHS fuzzy sets</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>sets</strong> – fuzzy sets</p>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>the midpoint value</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.hofts.WeightedHighOrderFLRG.get_upper">
+<code class="sig-name descname">get_upper</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">sets</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/hofts.html#WeightedHighOrderFLRG.get_upper"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.hofts.WeightedHighOrderFLRG.get_upper" title="Permalink to this definition">¶</a></dt>
+<dd><p>Returns the upper bound value for the RHS fuzzy sets</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>sets</strong> – fuzzy sets</p>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>upper bound value</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.hofts.WeightedHighOrderFLRG.weights">
+<code class="sig-name descname">weights</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/hofts.html#WeightedHighOrderFLRG.weights"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.hofts.WeightedHighOrderFLRG.weights" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt id="pyFTS.models.hofts.WeightedHighOrderFTS">
+<em class="property">class </em><code class="sig-prename descclassname">pyFTS.models.hofts.</code><code class="sig-name descname">WeightedHighOrderFTS</code><span class="sig-paren">(</span><em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/hofts.html#WeightedHighOrderFTS"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.hofts.WeightedHighOrderFTS" title="Permalink to this definition">¶</a></dt>
+<dd><p>Bases: <a class="reference internal" href="#pyFTS.models.hofts.HighOrderFTS" title="pyFTS.models.hofts.HighOrderFTS"><code class="xref py py-class docutils literal notranslate"><span class="pre">pyFTS.models.hofts.HighOrderFTS</span></code></a></p>
+<p>Weighted High Order Fuzzy Time Series</p>
+<dl class="py method">
+<dt id="pyFTS.models.hofts.WeightedHighOrderFTS.generate_lhs_flrg_fuzzyfied">
+<code class="sig-name descname">generate_lhs_flrg_fuzzyfied</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">sample</span></em>, <em class="sig-param"><span class="n">explain</span><span class="o">=</span><span class="default_value">False</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/hofts.html#WeightedHighOrderFTS.generate_lhs_flrg_fuzzyfied"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.hofts.WeightedHighOrderFTS.generate_lhs_flrg_fuzzyfied" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
 </div>
-<div class="section" id="pyfts-models-hwang-module">
-<h2>pyFTS.models.hwang module<a class="headerlink" href="#pyfts-models-hwang-module" title="Permalink to this headline">¶</a></h2>
+<div class="section" id="module-pyFTS.models.hwang">
+<span id="pyfts-models-hwang-module"></span><h2>pyFTS.models.hwang module<a class="headerlink" href="#module-pyFTS.models.hwang" title="Permalink to this headline">¶</a></h2>
+<p>High Order Fuzzy Time Series by Hwang, Chen and Lee (1998)</p>
+<p>Jeng-Ren Hwang, Shyi-Ming Chen, and Chia-Hoang Lee, “Handling forecasting problems using fuzzy time series,” 
+Fuzzy Sets Syst., no. 100, pp. 217–228, 1998.</p>
+<dl class="py class">
+<dt id="pyFTS.models.hwang.HighOrderFTS">
+<em class="property">class </em><code class="sig-prename descclassname">pyFTS.models.hwang.</code><code class="sig-name descname">HighOrderFTS</code><span class="sig-paren">(</span><em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/hwang.html#HighOrderFTS"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.hwang.HighOrderFTS" title="Permalink to this definition">¶</a></dt>
+<dd><p>Bases: <a class="reference internal" href="pyFTS.common.html#pyFTS.common.fts.FTS" title="pyFTS.common.fts.FTS"><code class="xref py py-class docutils literal notranslate"><span class="pre">pyFTS.common.fts.FTS</span></code></a></p>
+<dl class="py method">
+<dt id="pyFTS.models.hwang.HighOrderFTS.configure_lags">
+<code class="sig-name descname">configure_lags</code><span class="sig-paren">(</span><em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/hwang.html#HighOrderFTS.configure_lags"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.hwang.HighOrderFTS.configure_lags" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.hwang.HighOrderFTS.forecast">
+<code class="sig-name descname">forecast</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">ndata</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/hwang.html#HighOrderFTS.forecast"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.hwang.HighOrderFTS.forecast" title="Permalink to this definition">¶</a></dt>
+<dd><p>Point forecast one step ahead</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> – time series data with the minimal length equal to the max_lag of the model</p></li>
+<li><p><strong>kwargs</strong> – model specific parameters</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>a list with the forecasted values</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.hwang.HighOrderFTS.train">
+<code class="sig-name descname">train</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/hwang.html#HighOrderFTS.train"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.hwang.HighOrderFTS.train" title="Permalink to this definition">¶</a></dt>
+<dd><p>Method specific parameter fitting</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> – training time series data</p></li>
+<li><p><strong>kwargs</strong> – Method specific parameters</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
 </div>
-<div class="section" id="pyfts-models-ifts-module">
-<h2>pyFTS.models.ifts module<a class="headerlink" href="#pyfts-models-ifts-module" title="Permalink to this headline">¶</a></h2>
+<div class="section" id="module-pyFTS.models.ifts">
+<span id="pyfts-models-ifts-module"></span><h2>pyFTS.models.ifts module<a class="headerlink" href="#module-pyFTS.models.ifts" title="Permalink to this headline">¶</a></h2>
+<p>High Order Interval Fuzzy Time Series</p>
+<p>SILVA, Petrônio CL; SADAEI, Hossein Javedani; GUIMARÃES, Frederico Gadelha. Interval Forecasting with Fuzzy Time Series.
+In: Computational Intelligence (SSCI), 2016 IEEE Symposium Series on. IEEE, 2016. p. 1-8.</p>
+<dl class="py class">
+<dt id="pyFTS.models.ifts.IntervalFTS">
+<em class="property">class </em><code class="sig-prename descclassname">pyFTS.models.ifts.</code><code class="sig-name descname">IntervalFTS</code><span class="sig-paren">(</span><em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/ifts.html#IntervalFTS"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.ifts.IntervalFTS" title="Permalink to this definition">¶</a></dt>
+<dd><p>Bases: <a class="reference internal" href="#pyFTS.models.hofts.HighOrderFTS" title="pyFTS.models.hofts.HighOrderFTS"><code class="xref py py-class docutils literal notranslate"><span class="pre">pyFTS.models.hofts.HighOrderFTS</span></code></a></p>
+<p>High Order Interval Fuzzy Time Series</p>
+<dl class="py method">
+<dt id="pyFTS.models.ifts.IntervalFTS.forecast_ahead_interval">
+<code class="sig-name descname">forecast_ahead_interval</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="n">steps</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/ifts.html#IntervalFTS.forecast_ahead_interval"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.ifts.IntervalFTS.forecast_ahead_interval" title="Permalink to this definition">¶</a></dt>
+<dd><p>Interval forecast from 1 to H steps ahead, where H is given by the steps parameter</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> – time series data with the minimal length equal to the max_lag of the model</p></li>
+<li><p><strong>steps</strong> – the number of steps ahead to forecast</p></li>
+<li><p><strong>start_at</strong> – in the multi step forecasting, the index of the data where to start forecasting (default: 0)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>a list with the forecasted intervals</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.ifts.IntervalFTS.forecast_interval">
+<code class="sig-name descname">forecast_interval</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">ndata</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/ifts.html#IntervalFTS.forecast_interval"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.ifts.IntervalFTS.forecast_interval" title="Permalink to this definition">¶</a></dt>
+<dd><p>Interval forecast one step ahead</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> – time series data with the minimal length equal to the max_lag of the model</p></li>
+<li><p><strong>kwargs</strong> – model specific parameters</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>a list with the prediction intervals</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.ifts.IntervalFTS.get_lower">
+<code class="sig-name descname">get_lower</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">flrg</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/ifts.html#IntervalFTS.get_lower"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.ifts.IntervalFTS.get_lower" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.ifts.IntervalFTS.get_sequence_membership">
+<code class="sig-name descname">get_sequence_membership</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="n">fuzzySets</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/ifts.html#IntervalFTS.get_sequence_membership"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.ifts.IntervalFTS.get_sequence_membership" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.ifts.IntervalFTS.get_upper">
+<code class="sig-name descname">get_upper</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">flrg</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/ifts.html#IntervalFTS.get_upper"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.ifts.IntervalFTS.get_upper" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt id="pyFTS.models.ifts.WeightedIntervalFTS">
+<em class="property">class </em><code class="sig-prename descclassname">pyFTS.models.ifts.</code><code class="sig-name descname">WeightedIntervalFTS</code><span class="sig-paren">(</span><em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/ifts.html#WeightedIntervalFTS"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.ifts.WeightedIntervalFTS" title="Permalink to this definition">¶</a></dt>
+<dd><p>Bases: <a class="reference internal" href="#pyFTS.models.hofts.WeightedHighOrderFTS" title="pyFTS.models.hofts.WeightedHighOrderFTS"><code class="xref py py-class docutils literal notranslate"><span class="pre">pyFTS.models.hofts.WeightedHighOrderFTS</span></code></a></p>
+<p>Weighted High Order Interval Fuzzy Time Series</p>
+<dl class="py method">
+<dt id="pyFTS.models.ifts.WeightedIntervalFTS.forecast_ahead_interval">
+<code class="sig-name descname">forecast_ahead_interval</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="n">steps</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/ifts.html#WeightedIntervalFTS.forecast_ahead_interval"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.ifts.WeightedIntervalFTS.forecast_ahead_interval" title="Permalink to this definition">¶</a></dt>
+<dd><p>Interval forecast from 1 to H steps ahead, where H is given by the steps parameter</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> – time series data with the minimal length equal to the max_lag of the model</p></li>
+<li><p><strong>steps</strong> – the number of steps ahead to forecast</p></li>
+<li><p><strong>start_at</strong> – in the multi step forecasting, the index of the data where to start forecasting (default: 0)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>a list with the forecasted intervals</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.ifts.WeightedIntervalFTS.forecast_interval">
+<code class="sig-name descname">forecast_interval</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">ndata</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/ifts.html#WeightedIntervalFTS.forecast_interval"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.ifts.WeightedIntervalFTS.forecast_interval" title="Permalink to this definition">¶</a></dt>
+<dd><p>Interval forecast one step ahead</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> – time series data with the minimal length equal to the max_lag of the model</p></li>
+<li><p><strong>kwargs</strong> – model specific parameters</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>a list with the prediction intervals</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.ifts.WeightedIntervalFTS.get_lower">
+<code class="sig-name descname">get_lower</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">flrg</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/ifts.html#WeightedIntervalFTS.get_lower"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.ifts.WeightedIntervalFTS.get_lower" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.ifts.WeightedIntervalFTS.get_sequence_membership">
+<code class="sig-name descname">get_sequence_membership</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="n">fuzzySets</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/ifts.html#WeightedIntervalFTS.get_sequence_membership"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.ifts.WeightedIntervalFTS.get_sequence_membership" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.ifts.WeightedIntervalFTS.get_upper">
+<code class="sig-name descname">get_upper</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">flrg</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/ifts.html#WeightedIntervalFTS.get_upper"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.ifts.WeightedIntervalFTS.get_upper" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
 </div>
-<div class="section" id="pyfts-models-ismailefendi-module">
-<h2>pyFTS.models.ismailefendi module<a class="headerlink" href="#pyfts-models-ismailefendi-module" title="Permalink to this headline">¶</a></h2>
+<div class="section" id="module-pyFTS.models.ismailefendi">
+<span id="pyfts-models-ismailefendi-module"></span><h2>pyFTS.models.ismailefendi module<a class="headerlink" href="#module-pyFTS.models.ismailefendi" title="Permalink to this headline">¶</a></h2>
+<p>First Order Improved Weighted Fuzzy Time Series by Efendi, Ismail and Deris (2013)</p>
+<p>R. Efendi, Z. Ismail, and M. M. Deris, “Improved weight Fuzzy Time Series as used in the exchange rates forecasting of 
+US Dollar to Ringgit Malaysia,” Int. J. Comput. Intell. Appl., vol. 12, no. 1, p. 1350005, 2013.</p>
+<dl class="py class">
+<dt id="pyFTS.models.ismailefendi.ImprovedWeightedFLRG">
+<em class="property">class </em><code class="sig-prename descclassname">pyFTS.models.ismailefendi.</code><code class="sig-name descname">ImprovedWeightedFLRG</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">LHS</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/ismailefendi.html#ImprovedWeightedFLRG"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.ismailefendi.ImprovedWeightedFLRG" title="Permalink to this definition">¶</a></dt>
+<dd><p>Bases: <a class="reference internal" href="pyFTS.common.html#pyFTS.common.flrg.FLRG" title="pyFTS.common.flrg.FLRG"><code class="xref py py-class docutils literal notranslate"><span class="pre">pyFTS.common.flrg.FLRG</span></code></a></p>
+<p>First Order Improved Weighted Fuzzy Logical Relationship Group</p>
+<dl class="py method">
+<dt id="pyFTS.models.ismailefendi.ImprovedWeightedFLRG.append_rhs">
+<code class="sig-name descname">append_rhs</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">c</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/ismailefendi.html#ImprovedWeightedFLRG.append_rhs"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.ismailefendi.ImprovedWeightedFLRG.append_rhs" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.ismailefendi.ImprovedWeightedFLRG.weights">
+<code class="sig-name descname">weights</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/ismailefendi.html#ImprovedWeightedFLRG.weights"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.ismailefendi.ImprovedWeightedFLRG.weights" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt id="pyFTS.models.ismailefendi.ImprovedWeightedFTS">
+<em class="property">class </em><code class="sig-prename descclassname">pyFTS.models.ismailefendi.</code><code class="sig-name descname">ImprovedWeightedFTS</code><span class="sig-paren">(</span><em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/ismailefendi.html#ImprovedWeightedFTS"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.ismailefendi.ImprovedWeightedFTS" title="Permalink to this definition">¶</a></dt>
+<dd><p>Bases: <a class="reference internal" href="pyFTS.common.html#pyFTS.common.fts.FTS" title="pyFTS.common.fts.FTS"><code class="xref py py-class docutils literal notranslate"><span class="pre">pyFTS.common.fts.FTS</span></code></a></p>
+<p>First Order Improved Weighted Fuzzy Time Series</p>
+<dl class="py method">
+<dt id="pyFTS.models.ismailefendi.ImprovedWeightedFTS.forecast">
+<code class="sig-name descname">forecast</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">ndata</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/ismailefendi.html#ImprovedWeightedFTS.forecast"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.ismailefendi.ImprovedWeightedFTS.forecast" title="Permalink to this definition">¶</a></dt>
+<dd><p>Point forecast one step ahead</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> – time series data with the minimal length equal to the max_lag of the model</p></li>
+<li><p><strong>kwargs</strong> – model specific parameters</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>a list with the forecasted values</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.ismailefendi.ImprovedWeightedFTS.generate_flrg">
+<code class="sig-name descname">generate_flrg</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">flrs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/ismailefendi.html#ImprovedWeightedFTS.generate_flrg"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.ismailefendi.ImprovedWeightedFTS.generate_flrg" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.ismailefendi.ImprovedWeightedFTS.train">
+<code class="sig-name descname">train</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">ndata</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/ismailefendi.html#ImprovedWeightedFTS.train"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.ismailefendi.ImprovedWeightedFTS.train" title="Permalink to this definition">¶</a></dt>
+<dd><p>Method specific parameter fitting</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> – training time series data</p></li>
+<li><p><strong>kwargs</strong> – Method specific parameters</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
 </div>
-<div class="section" id="pyfts-models-pwfts-module">
-<h2>pyFTS.models.pwfts module<a class="headerlink" href="#pyfts-models-pwfts-module" title="Permalink to this headline">¶</a></h2>
+<div class="section" id="module-pyFTS.models.pwfts">
+<span id="pyfts-models-pwfts-module"></span><h2>pyFTS.models.pwfts module<a class="headerlink" href="#module-pyFTS.models.pwfts" title="Permalink to this headline">¶</a></h2>
+<dl class="py class">
+<dt id="pyFTS.models.pwfts.ProbabilisticWeightedFLRG">
+<em class="property">class </em><code class="sig-prename descclassname">pyFTS.models.pwfts.</code><code class="sig-name descname">ProbabilisticWeightedFLRG</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">order</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/pwfts.html#ProbabilisticWeightedFLRG"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.pwfts.ProbabilisticWeightedFLRG" title="Permalink to this definition">¶</a></dt>
+<dd><p>Bases: <a class="reference internal" href="#pyFTS.models.hofts.HighOrderFLRG" title="pyFTS.models.hofts.HighOrderFLRG"><code class="xref py py-class docutils literal notranslate"><span class="pre">pyFTS.models.hofts.HighOrderFLRG</span></code></a></p>
+<p>High Order Probabilistic Weighted Fuzzy Logical Relationship Group</p>
+<dl class="py method">
+<dt id="pyFTS.models.pwfts.ProbabilisticWeightedFLRG.append_rhs">
+<code class="sig-name descname">append_rhs</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">c</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/pwfts.html#ProbabilisticWeightedFLRG.append_rhs"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.pwfts.ProbabilisticWeightedFLRG.append_rhs" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.pwfts.ProbabilisticWeightedFLRG.get_lower">
+<code class="sig-name descname">get_lower</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">sets</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/pwfts.html#ProbabilisticWeightedFLRG.get_lower"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.pwfts.ProbabilisticWeightedFLRG.get_lower" title="Permalink to this definition">¶</a></dt>
+<dd><p>Returns the lower bound value for the RHS fuzzy sets</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>sets</strong> – fuzzy sets</p>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>lower bound value</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.pwfts.ProbabilisticWeightedFLRG.get_membership">
+<code class="sig-name descname">get_membership</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="n">sets</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/pwfts.html#ProbabilisticWeightedFLRG.get_membership"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.pwfts.ProbabilisticWeightedFLRG.get_membership" title="Permalink to this definition">¶</a></dt>
+<dd><p>Returns the membership value of the FLRG for the input data</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> – input data</p></li>
+<li><p><strong>sets</strong> – fuzzy sets</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>the membership value</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.pwfts.ProbabilisticWeightedFLRG.get_midpoint">
+<code class="sig-name descname">get_midpoint</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">sets</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/pwfts.html#ProbabilisticWeightedFLRG.get_midpoint"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.pwfts.ProbabilisticWeightedFLRG.get_midpoint" title="Permalink to this definition">¶</a></dt>
+<dd><p>Return the expectation of the PWFLRG, the weighted sum</p>
+</dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.pwfts.ProbabilisticWeightedFLRG.get_upper">
+<code class="sig-name descname">get_upper</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">sets</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/pwfts.html#ProbabilisticWeightedFLRG.get_upper"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.pwfts.ProbabilisticWeightedFLRG.get_upper" title="Permalink to this definition">¶</a></dt>
+<dd><p>Returns the upper bound value for the RHS fuzzy sets</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>sets</strong> – fuzzy sets</p>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>upper bound value</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.pwfts.ProbabilisticWeightedFLRG.lhs_conditional_probability">
+<code class="sig-name descname">lhs_conditional_probability</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">x</span></em>, <em class="sig-param"><span class="n">sets</span></em>, <em class="sig-param"><span class="n">norm</span></em>, <em class="sig-param"><span class="n">uod</span></em>, <em class="sig-param"><span class="n">nbins</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/pwfts.html#ProbabilisticWeightedFLRG.lhs_conditional_probability"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.pwfts.ProbabilisticWeightedFLRG.lhs_conditional_probability" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.pwfts.ProbabilisticWeightedFLRG.lhs_conditional_probability_fuzzyfied">
+<code class="sig-name descname">lhs_conditional_probability_fuzzyfied</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">lhs_mv</span></em>, <em class="sig-param"><span class="n">sets</span></em>, <em class="sig-param"><span class="n">norm</span></em>, <em class="sig-param"><span class="n">uod</span></em>, <em class="sig-param"><span class="n">nbins</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/pwfts.html#ProbabilisticWeightedFLRG.lhs_conditional_probability_fuzzyfied"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.pwfts.ProbabilisticWeightedFLRG.lhs_conditional_probability_fuzzyfied" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.pwfts.ProbabilisticWeightedFLRG.partition_function">
+<code class="sig-name descname">partition_function</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">sets</span></em>, <em class="sig-param"><span class="n">uod</span></em>, <em class="sig-param"><span class="n">nbins</span><span class="o">=</span><span class="default_value">100</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/pwfts.html#ProbabilisticWeightedFLRG.partition_function"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.pwfts.ProbabilisticWeightedFLRG.partition_function" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.pwfts.ProbabilisticWeightedFLRG.rhs_conditional_probability">
+<code class="sig-name descname">rhs_conditional_probability</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">x</span></em>, <em class="sig-param"><span class="n">sets</span></em>, <em class="sig-param"><span class="n">uod</span></em>, <em class="sig-param"><span class="n">nbins</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/pwfts.html#ProbabilisticWeightedFLRG.rhs_conditional_probability"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.pwfts.ProbabilisticWeightedFLRG.rhs_conditional_probability" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.pwfts.ProbabilisticWeightedFLRG.rhs_unconditional_probability">
+<code class="sig-name descname">rhs_unconditional_probability</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">c</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/pwfts.html#ProbabilisticWeightedFLRG.rhs_unconditional_probability"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.pwfts.ProbabilisticWeightedFLRG.rhs_unconditional_probability" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt id="pyFTS.models.pwfts.ProbabilisticWeightedFTS">
+<em class="property">class </em><code class="sig-prename descclassname">pyFTS.models.pwfts.</code><code class="sig-name descname">ProbabilisticWeightedFTS</code><span class="sig-paren">(</span><em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/pwfts.html#ProbabilisticWeightedFTS"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.pwfts.ProbabilisticWeightedFTS" title="Permalink to this definition">¶</a></dt>
+<dd><p>Bases: <a class="reference internal" href="#pyFTS.models.ifts.IntervalFTS" title="pyFTS.models.ifts.IntervalFTS"><code class="xref py py-class docutils literal notranslate"><span class="pre">pyFTS.models.ifts.IntervalFTS</span></code></a></p>
+<p>High Order Probabilistic Weighted Fuzzy Time Series</p>
+<dl class="py method">
+<dt id="pyFTS.models.pwfts.ProbabilisticWeightedFTS.add_new_PWFLGR">
+<code class="sig-name descname">add_new_PWFLGR</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">flrg</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/pwfts.html#ProbabilisticWeightedFTS.add_new_PWFLGR"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.pwfts.ProbabilisticWeightedFTS.add_new_PWFLGR" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.pwfts.ProbabilisticWeightedFTS.flrg_lhs_conditional_probability">
+<code class="sig-name descname">flrg_lhs_conditional_probability</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">x</span></em>, <em class="sig-param"><span class="n">flrg</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/pwfts.html#ProbabilisticWeightedFTS.flrg_lhs_conditional_probability"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.pwfts.ProbabilisticWeightedFTS.flrg_lhs_conditional_probability" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.pwfts.ProbabilisticWeightedFTS.flrg_lhs_conditional_probability_fuzzyfied">
+<code class="sig-name descname">flrg_lhs_conditional_probability_fuzzyfied</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">x</span></em>, <em class="sig-param"><span class="n">flrg</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/pwfts.html#ProbabilisticWeightedFTS.flrg_lhs_conditional_probability_fuzzyfied"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.pwfts.ProbabilisticWeightedFTS.flrg_lhs_conditional_probability_fuzzyfied" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.pwfts.ProbabilisticWeightedFTS.flrg_lhs_unconditional_probability">
+<code class="sig-name descname">flrg_lhs_unconditional_probability</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">flrg</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/pwfts.html#ProbabilisticWeightedFTS.flrg_lhs_unconditional_probability"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.pwfts.ProbabilisticWeightedFTS.flrg_lhs_unconditional_probability" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.pwfts.ProbabilisticWeightedFTS.flrg_rhs_conditional_probability">
+<code class="sig-name descname">flrg_rhs_conditional_probability</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">x</span></em>, <em class="sig-param"><span class="n">flrg</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/pwfts.html#ProbabilisticWeightedFTS.flrg_rhs_conditional_probability"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.pwfts.ProbabilisticWeightedFTS.flrg_rhs_conditional_probability" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.pwfts.ProbabilisticWeightedFTS.forecast">
+<code class="sig-name descname">forecast</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/pwfts.html#ProbabilisticWeightedFTS.forecast"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.pwfts.ProbabilisticWeightedFTS.forecast" title="Permalink to this definition">¶</a></dt>
+<dd><p>Point forecast one step ahead</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> – time series data with the minimal length equal to the max_lag of the model</p></li>
+<li><p><strong>kwargs</strong> – model specific parameters</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>a list with the forecasted values</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.pwfts.ProbabilisticWeightedFTS.forecast_ahead">
+<code class="sig-name descname">forecast_ahead</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="n">steps</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/pwfts.html#ProbabilisticWeightedFTS.forecast_ahead"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.pwfts.ProbabilisticWeightedFTS.forecast_ahead" title="Permalink to this definition">¶</a></dt>
+<dd><p>Point forecast from 1 to H steps ahead, where H is given by the steps parameter</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> – time series data with the minimal length equal to the max_lag of the model</p></li>
+<li><p><strong>steps</strong> – the number of steps ahead to forecast (default: 1)</p></li>
+<li><p><strong>start_at</strong> – in the multi step forecasting, the index of the data where to start forecasting (default: 0)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>a list with the forecasted values</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.pwfts.ProbabilisticWeightedFTS.forecast_ahead_distribution">
+<code class="sig-name descname">forecast_ahead_distribution</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">ndata</span></em>, <em class="sig-param"><span class="n">steps</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/pwfts.html#ProbabilisticWeightedFTS.forecast_ahead_distribution"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.pwfts.ProbabilisticWeightedFTS.forecast_ahead_distribution" title="Permalink to this definition">¶</a></dt>
+<dd><p>Probabilistic forecast from 1 to H steps ahead, where H is given by the steps parameter</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> – time series data with the minimal length equal to the max_lag of the model</p></li>
+<li><p><strong>steps</strong> – the number of steps ahead to forecast</p></li>
+<li><p><strong>start_at</strong> – in the multi step forecasting, the index of the data where to start forecasting (default: 0)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>a list with the forecasted Probability Distributions</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.pwfts.ProbabilisticWeightedFTS.forecast_ahead_interval">
+<code class="sig-name descname">forecast_ahead_interval</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="n">steps</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/pwfts.html#ProbabilisticWeightedFTS.forecast_ahead_interval"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.pwfts.ProbabilisticWeightedFTS.forecast_ahead_interval" title="Permalink to this definition">¶</a></dt>
+<dd><p>Interval forecast from 1 to H steps ahead, where H is given by the steps parameter</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> – time series data with the minimal length equal to the max_lag of the model</p></li>
+<li><p><strong>steps</strong> – the number of steps ahead to forecast</p></li>
+<li><p><strong>start_at</strong> – in the multi step forecasting, the index of the data where to start forecasting (default: 0)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>a list with the forecasted intervals</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.pwfts.ProbabilisticWeightedFTS.forecast_distribution">
+<code class="sig-name descname">forecast_distribution</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">ndata</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/pwfts.html#ProbabilisticWeightedFTS.forecast_distribution"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.pwfts.ProbabilisticWeightedFTS.forecast_distribution" title="Permalink to this definition">¶</a></dt>
+<dd><p>Probabilistic forecast one step ahead</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> – time series data with the minimal length equal to the max_lag of the model</p></li>
+<li><p><strong>kwargs</strong> – model specific parameters</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>a list with probabilistic.ProbabilityDistribution objects representing the forecasted Probability Distributions</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.pwfts.ProbabilisticWeightedFTS.forecast_distribution_from_distribution">
+<code class="sig-name descname">forecast_distribution_from_distribution</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">previous_dist</span></em>, <em class="sig-param"><span class="n">smooth</span></em>, <em class="sig-param"><span class="n">uod</span></em>, <em class="sig-param"><span class="n">bins</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/pwfts.html#ProbabilisticWeightedFTS.forecast_distribution_from_distribution"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.pwfts.ProbabilisticWeightedFTS.forecast_distribution_from_distribution" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.pwfts.ProbabilisticWeightedFTS.forecast_interval">
+<code class="sig-name descname">forecast_interval</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">ndata</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/pwfts.html#ProbabilisticWeightedFTS.forecast_interval"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.pwfts.ProbabilisticWeightedFTS.forecast_interval" title="Permalink to this definition">¶</a></dt>
+<dd><p>Interval forecast one step ahead</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> – time series data with the minimal length equal to the max_lag of the model</p></li>
+<li><p><strong>kwargs</strong> – model specific parameters</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>a list with the prediction intervals</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.pwfts.ProbabilisticWeightedFTS.generate_flrg">
+<code class="sig-name descname">generate_flrg</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/pwfts.html#ProbabilisticWeightedFTS.generate_flrg"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.pwfts.ProbabilisticWeightedFTS.generate_flrg" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.pwfts.ProbabilisticWeightedFTS.generate_flrg2">
+<code class="sig-name descname">generate_flrg2</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/pwfts.html#ProbabilisticWeightedFTS.generate_flrg2"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.pwfts.ProbabilisticWeightedFTS.generate_flrg2" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.pwfts.ProbabilisticWeightedFTS.generate_flrg_fuzzyfied">
+<code class="sig-name descname">generate_flrg_fuzzyfied</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/pwfts.html#ProbabilisticWeightedFTS.generate_flrg_fuzzyfied"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.pwfts.ProbabilisticWeightedFTS.generate_flrg_fuzzyfied" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.pwfts.ProbabilisticWeightedFTS.generate_lhs_flrg">
+<code class="sig-name descname">generate_lhs_flrg</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">sample</span></em>, <em class="sig-param"><span class="n">explain</span><span class="o">=</span><span class="default_value">False</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/pwfts.html#ProbabilisticWeightedFTS.generate_lhs_flrg"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.pwfts.ProbabilisticWeightedFTS.generate_lhs_flrg" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.pwfts.ProbabilisticWeightedFTS.generate_lhs_flrg_fuzzyfied">
+<code class="sig-name descname">generate_lhs_flrg_fuzzyfied</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">sample</span></em>, <em class="sig-param"><span class="n">explain</span><span class="o">=</span><span class="default_value">False</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/pwfts.html#ProbabilisticWeightedFTS.generate_lhs_flrg_fuzzyfied"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.pwfts.ProbabilisticWeightedFTS.generate_lhs_flrg_fuzzyfied" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.pwfts.ProbabilisticWeightedFTS.get_lower">
+<code class="sig-name descname">get_lower</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">flrg</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/pwfts.html#ProbabilisticWeightedFTS.get_lower"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.pwfts.ProbabilisticWeightedFTS.get_lower" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.pwfts.ProbabilisticWeightedFTS.get_midpoint">
+<code class="sig-name descname">get_midpoint</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">flrg</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/pwfts.html#ProbabilisticWeightedFTS.get_midpoint"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.pwfts.ProbabilisticWeightedFTS.get_midpoint" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.pwfts.ProbabilisticWeightedFTS.get_sets_from_both_fuzzyfication">
+<code class="sig-name descname">get_sets_from_both_fuzzyfication</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">sample</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/pwfts.html#ProbabilisticWeightedFTS.get_sets_from_both_fuzzyfication"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.pwfts.ProbabilisticWeightedFTS.get_sets_from_both_fuzzyfication" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.pwfts.ProbabilisticWeightedFTS.get_upper">
+<code class="sig-name descname">get_upper</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">flrg</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/pwfts.html#ProbabilisticWeightedFTS.get_upper"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.pwfts.ProbabilisticWeightedFTS.get_upper" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.pwfts.ProbabilisticWeightedFTS.interval_heuristic">
+<code class="sig-name descname">interval_heuristic</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">sample</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/pwfts.html#ProbabilisticWeightedFTS.interval_heuristic"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.pwfts.ProbabilisticWeightedFTS.interval_heuristic" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.pwfts.ProbabilisticWeightedFTS.interval_quantile">
+<code class="sig-name descname">interval_quantile</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">ndata</span></em>, <em class="sig-param"><span class="n">alpha</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/pwfts.html#ProbabilisticWeightedFTS.interval_quantile"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.pwfts.ProbabilisticWeightedFTS.interval_quantile" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.pwfts.ProbabilisticWeightedFTS.point_expected_value">
+<code class="sig-name descname">point_expected_value</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">sample</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/pwfts.html#ProbabilisticWeightedFTS.point_expected_value"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.pwfts.ProbabilisticWeightedFTS.point_expected_value" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.pwfts.ProbabilisticWeightedFTS.point_heuristic">
+<code class="sig-name descname">point_heuristic</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">sample</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/pwfts.html#ProbabilisticWeightedFTS.point_heuristic"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.pwfts.ProbabilisticWeightedFTS.point_heuristic" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.pwfts.ProbabilisticWeightedFTS.pwflrg_lhs_memberhip_fuzzyfied">
+<code class="sig-name descname">pwflrg_lhs_memberhip_fuzzyfied</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">flrg</span></em>, <em class="sig-param"><span class="n">sample</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/pwfts.html#ProbabilisticWeightedFTS.pwflrg_lhs_memberhip_fuzzyfied"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.pwfts.ProbabilisticWeightedFTS.pwflrg_lhs_memberhip_fuzzyfied" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.pwfts.ProbabilisticWeightedFTS.train">
+<code class="sig-name descname">train</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/pwfts.html#ProbabilisticWeightedFTS.train"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.pwfts.ProbabilisticWeightedFTS.train" title="Permalink to this definition">¶</a></dt>
+<dd><p>Method specific parameter fitting</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> – training time series data</p></li>
+<li><p><strong>kwargs</strong> – Method specific parameters</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.pwfts.ProbabilisticWeightedFTS.update_model">
+<code class="sig-name descname">update_model</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/pwfts.html#ProbabilisticWeightedFTS.update_model"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.pwfts.ProbabilisticWeightedFTS.update_model" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.models.pwfts.visualize_distributions">
+<code class="sig-prename descclassname">pyFTS.models.pwfts.</code><code class="sig-name descname">visualize_distributions</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">model</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/pwfts.html#visualize_distributions"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.pwfts.visualize_distributions" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
 </div>
-<div class="section" id="pyfts-models-sadaei-module">
-<h2>pyFTS.models.sadaei module<a class="headerlink" href="#pyfts-models-sadaei-module" title="Permalink to this headline">¶</a></h2>
+<div class="section" id="module-pyFTS.models.sadaei">
+<span id="pyfts-models-sadaei-module"></span><h2>pyFTS.models.sadaei module<a class="headerlink" href="#module-pyFTS.models.sadaei" title="Permalink to this headline">¶</a></h2>
+<p>First Order Exponentialy Weighted Fuzzy Time Series by Sadaei et al. (2013)</p>
+<p>H. J. Sadaei, R. Enayatifar, A. H. Abdullah, and A. Gani, “Short-term load forecasting using a hybrid model with a 
+refined exponentially weighted fuzzy time series and an improved harmony search,” Int. J. Electr. Power Energy Syst., vol. 62, no. from 2005, pp. 118–129, 2014.</p>
+<dl class="py class">
+<dt id="pyFTS.models.sadaei.ExponentialyWeightedFLRG">
+<em class="property">class </em><code class="sig-prename descclassname">pyFTS.models.sadaei.</code><code class="sig-name descname">ExponentialyWeightedFLRG</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">LHS</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/sadaei.html#ExponentialyWeightedFLRG"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.sadaei.ExponentialyWeightedFLRG" title="Permalink to this definition">¶</a></dt>
+<dd><p>Bases: <a class="reference internal" href="pyFTS.common.html#pyFTS.common.flrg.FLRG" title="pyFTS.common.flrg.FLRG"><code class="xref py py-class docutils literal notranslate"><span class="pre">pyFTS.common.flrg.FLRG</span></code></a></p>
+<p>First Order Exponentialy Weighted Fuzzy Logical Relationship Group</p>
+<dl class="py method">
+<dt id="pyFTS.models.sadaei.ExponentialyWeightedFLRG.append_rhs">
+<code class="sig-name descname">append_rhs</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">c</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/sadaei.html#ExponentialyWeightedFLRG.append_rhs"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.sadaei.ExponentialyWeightedFLRG.append_rhs" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.sadaei.ExponentialyWeightedFLRG.weights">
+<code class="sig-name descname">weights</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/sadaei.html#ExponentialyWeightedFLRG.weights"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.sadaei.ExponentialyWeightedFLRG.weights" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt id="pyFTS.models.sadaei.ExponentialyWeightedFTS">
+<em class="property">class </em><code class="sig-prename descclassname">pyFTS.models.sadaei.</code><code class="sig-name descname">ExponentialyWeightedFTS</code><span class="sig-paren">(</span><em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/sadaei.html#ExponentialyWeightedFTS"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.sadaei.ExponentialyWeightedFTS" title="Permalink to this definition">¶</a></dt>
+<dd><p>Bases: <a class="reference internal" href="pyFTS.common.html#pyFTS.common.fts.FTS" title="pyFTS.common.fts.FTS"><code class="xref py py-class docutils literal notranslate"><span class="pre">pyFTS.common.fts.FTS</span></code></a></p>
+<p>First Order Exponentialy Weighted Fuzzy Time Series</p>
+<dl class="py method">
+<dt id="pyFTS.models.sadaei.ExponentialyWeightedFTS.forecast">
+<code class="sig-name descname">forecast</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">ndata</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/sadaei.html#ExponentialyWeightedFTS.forecast"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.sadaei.ExponentialyWeightedFTS.forecast" title="Permalink to this definition">¶</a></dt>
+<dd><p>Point forecast one step ahead</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> – time series data with the minimal length equal to the max_lag of the model</p></li>
+<li><p><strong>kwargs</strong> – model specific parameters</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>a list with the forecasted values</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.sadaei.ExponentialyWeightedFTS.generate_flrg">
+<code class="sig-name descname">generate_flrg</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">flrs</span></em>, <em class="sig-param"><span class="n">c</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/sadaei.html#ExponentialyWeightedFTS.generate_flrg"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.sadaei.ExponentialyWeightedFTS.generate_flrg" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.sadaei.ExponentialyWeightedFTS.train">
+<code class="sig-name descname">train</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/sadaei.html#ExponentialyWeightedFTS.train"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.sadaei.ExponentialyWeightedFTS.train" title="Permalink to this definition">¶</a></dt>
+<dd><p>Method specific parameter fitting</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> – training time series data</p></li>
+<li><p><strong>kwargs</strong> – Method specific parameters</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
 </div>
 </div>
 
@@ -177,16 +1153,16 @@
 <li><a class="reference internal" href="#module-pyFTS.models">Module contents</a></li>
 <li><a class="reference internal" href="#subpackages">Subpackages</a></li>
 <li><a class="reference internal" href="#submodules">Submodules</a></li>
-<li><a class="reference internal" href="#pyfts-models-song-module">pyFTS.models.song module</a></li>
-<li><a class="reference internal" href="#pyfts-models-chen-module">pyFTS.models.chen module</a></li>
-<li><a class="reference internal" href="#pyfts-models-yu-module">pyFTS.models.yu module</a></li>
-<li><a class="reference internal" href="#pyfts-models-cheng-module">pyFTS.models.cheng module</a></li>
-<li><a class="reference internal" href="#pyfts-models-hofts-module">pyFTS.models.hofts module</a></li>
-<li><a class="reference internal" href="#pyfts-models-hwang-module">pyFTS.models.hwang module</a></li>
-<li><a class="reference internal" href="#pyfts-models-ifts-module">pyFTS.models.ifts module</a></li>
-<li><a class="reference internal" href="#pyfts-models-ismailefendi-module">pyFTS.models.ismailefendi module</a></li>
-<li><a class="reference internal" href="#pyfts-models-pwfts-module">pyFTS.models.pwfts module</a></li>
-<li><a class="reference internal" href="#pyfts-models-sadaei-module">pyFTS.models.sadaei module</a></li>
+<li><a class="reference internal" href="#module-pyFTS.models.song">pyFTS.models.song module</a></li>
+<li><a class="reference internal" href="#module-pyFTS.models.chen">pyFTS.models.chen module</a></li>
+<li><a class="reference internal" href="#module-pyFTS.models.yu">pyFTS.models.yu module</a></li>
+<li><a class="reference internal" href="#module-pyFTS.models.cheng">pyFTS.models.cheng module</a></li>
+<li><a class="reference internal" href="#module-pyFTS.models.hofts">pyFTS.models.hofts module</a></li>
+<li><a class="reference internal" href="#module-pyFTS.models.hwang">pyFTS.models.hwang module</a></li>
+<li><a class="reference internal" href="#module-pyFTS.models.ifts">pyFTS.models.ifts module</a></li>
+<li><a class="reference internal" href="#module-pyFTS.models.ismailefendi">pyFTS.models.ismailefendi module</a></li>
+<li><a class="reference internal" href="#module-pyFTS.models.pwfts">pyFTS.models.pwfts module</a></li>
+<li><a class="reference internal" href="#module-pyFTS.models.sadaei">pyFTS.models.sadaei module</a></li>
 </ul>
 </li>
 </ul>
diff --git a/docs/build/html/pyFTS.models.incremental.html b/docs/build/html/pyFTS.models.incremental.html
index 6d70773..283d487 100644
--- a/docs/build/html/pyFTS.models.incremental.html
+++ b/docs/build/html/pyFTS.models.incremental.html
@@ -73,11 +73,250 @@
 <div class="section" id="submodules">
 <h2>Submodules<a class="headerlink" href="#submodules" title="Permalink to this headline">¶</a></h2>
 </div>
-<div class="section" id="pyfts-models-incremental-timevariant-module">
-<h2>pyFTS.models.incremental.TimeVariant module<a class="headerlink" href="#pyfts-models-incremental-timevariant-module" title="Permalink to this headline">¶</a></h2>
+<div class="section" id="module-pyFTS.models.incremental.TimeVariant">
+<span id="pyfts-models-incremental-timevariant-module"></span><h2>pyFTS.models.incremental.TimeVariant module<a class="headerlink" href="#module-pyFTS.models.incremental.TimeVariant" title="Permalink to this headline">¶</a></h2>
+<p>Meta model that wraps another FTS method and continously retrain it using a data window with
+the most recent data</p>
+<dl class="py class">
+<dt id="pyFTS.models.incremental.TimeVariant.Retrainer">
+<em class="property">class </em><code class="sig-prename descclassname">pyFTS.models.incremental.TimeVariant.</code><code class="sig-name descname">Retrainer</code><span class="sig-paren">(</span><em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/incremental/TimeVariant.html#Retrainer"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.incremental.TimeVariant.Retrainer" title="Permalink to this definition">¶</a></dt>
+<dd><p>Bases: <a class="reference internal" href="pyFTS.common.html#pyFTS.common.fts.FTS" title="pyFTS.common.fts.FTS"><code class="xref py py-class docutils literal notranslate"><span class="pre">pyFTS.common.fts.FTS</span></code></a></p>
+<p>Meta model for incremental/online learning that retrain its internal model after
+data windows controlled by the parameter ‘batch_size’, using as the training data a
+window of recent lags, whose size is controlled by the parameter ‘window_length’.</p>
+<dl class="py attribute">
+<dt id="pyFTS.models.incremental.TimeVariant.Retrainer.auto_update">
+<code class="sig-name descname">auto_update</code><a class="headerlink" href="#pyFTS.models.incremental.TimeVariant.Retrainer.auto_update" title="Permalink to this definition">¶</a></dt>
+<dd><p>If true the model is updated at each time and not recreated</p>
+</dd></dl>
+
+<dl class="py attribute">
+<dt id="pyFTS.models.incremental.TimeVariant.Retrainer.batch_size">
+<code class="sig-name descname">batch_size</code><a class="headerlink" href="#pyFTS.models.incremental.TimeVariant.Retrainer.batch_size" title="Permalink to this definition">¶</a></dt>
+<dd><p>The batch interval between each retraining</p>
+</dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.incremental.TimeVariant.Retrainer.forecast">
+<code class="sig-name descname">forecast</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/incremental/TimeVariant.html#Retrainer.forecast"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.incremental.TimeVariant.Retrainer.forecast" title="Permalink to this definition">¶</a></dt>
+<dd><p>Point forecast one step ahead</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> – time series data with the minimal length equal to the max_lag of the model</p></li>
+<li><p><strong>kwargs</strong> – model specific parameters</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>a list with the forecasted values</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.incremental.TimeVariant.Retrainer.forecast_ahead">
+<code class="sig-name descname">forecast_ahead</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="n">steps</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/incremental/TimeVariant.html#Retrainer.forecast_ahead"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.incremental.TimeVariant.Retrainer.forecast_ahead" title="Permalink to this definition">¶</a></dt>
+<dd><p>Point forecast from 1 to H steps ahead, where H is given by the steps parameter</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> – time series data with the minimal length equal to the max_lag of the model</p></li>
+<li><p><strong>steps</strong> – the number of steps ahead to forecast (default: 1)</p></li>
+<li><p><strong>start_at</strong> – in the multi step forecasting, the index of the data where to start forecasting (default: 0)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>a list with the forecasted values</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt id="pyFTS.models.incremental.TimeVariant.Retrainer.fts_method">
+<code class="sig-name descname">fts_method</code><a class="headerlink" href="#pyFTS.models.incremental.TimeVariant.Retrainer.fts_method" title="Permalink to this definition">¶</a></dt>
+<dd><p>The FTS method to be called when a new model is build</p>
+</dd></dl>
+
+<dl class="py attribute">
+<dt id="pyFTS.models.incremental.TimeVariant.Retrainer.fts_params">
+<code class="sig-name descname">fts_params</code><a class="headerlink" href="#pyFTS.models.incremental.TimeVariant.Retrainer.fts_params" title="Permalink to this definition">¶</a></dt>
+<dd><p>The FTS method specific parameters</p>
+</dd></dl>
+
+<dl class="py attribute">
+<dt id="pyFTS.models.incremental.TimeVariant.Retrainer.model">
+<code class="sig-name descname">model</code><a class="headerlink" href="#pyFTS.models.incremental.TimeVariant.Retrainer.model" title="Permalink to this definition">¶</a></dt>
+<dd><p>The most recent trained model</p>
+</dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.incremental.TimeVariant.Retrainer.offset">
+<code class="sig-name descname">offset</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/incremental/TimeVariant.html#Retrainer.offset"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.incremental.TimeVariant.Retrainer.offset" title="Permalink to this definition">¶</a></dt>
+<dd><p>Returns the number of lags to skip in the input test data in order to synchronize it with
+the forecasted values given by the predict function. This is necessary due to the order of the
+model, among other parameters.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns</dt>
+<dd class="field-odd"><p>An integer with the number of lags to skip</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt id="pyFTS.models.incremental.TimeVariant.Retrainer.partitioner">
+<code class="sig-name descname">partitioner</code><a class="headerlink" href="#pyFTS.models.incremental.TimeVariant.Retrainer.partitioner" title="Permalink to this definition">¶</a></dt>
+<dd><p>The most recent trained partitioner</p>
+</dd></dl>
+
+<dl class="py attribute">
+<dt id="pyFTS.models.incremental.TimeVariant.Retrainer.partitioner_method">
+<code class="sig-name descname">partitioner_method</code><a class="headerlink" href="#pyFTS.models.incremental.TimeVariant.Retrainer.partitioner_method" title="Permalink to this definition">¶</a></dt>
+<dd><p>The partitioner method to be called when a new model is build</p>
+</dd></dl>
+
+<dl class="py attribute">
+<dt id="pyFTS.models.incremental.TimeVariant.Retrainer.partitioner_params">
+<code class="sig-name descname">partitioner_params</code><a class="headerlink" href="#pyFTS.models.incremental.TimeVariant.Retrainer.partitioner_params" title="Permalink to this definition">¶</a></dt>
+<dd><p>The partitioner method parameters</p>
+</dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.incremental.TimeVariant.Retrainer.train">
+<code class="sig-name descname">train</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/incremental/TimeVariant.html#Retrainer.train"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.incremental.TimeVariant.Retrainer.train" title="Permalink to this definition">¶</a></dt>
+<dd><p>Method specific parameter fitting</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> – training time series data</p></li>
+<li><p><strong>kwargs</strong> – Method specific parameters</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt id="pyFTS.models.incremental.TimeVariant.Retrainer.window_length">
+<code class="sig-name descname">window_length</code><a class="headerlink" href="#pyFTS.models.incremental.TimeVariant.Retrainer.window_length" title="Permalink to this definition">¶</a></dt>
+<dd><p>The memory window length</p>
+</dd></dl>
+
+</dd></dl>
+
 </div>
-<div class="section" id="pyfts-models-incremental-incrementalensemble-module">
-<h2>pyFTS.models.incremental.IncrementalEnsemble module<a class="headerlink" href="#pyfts-models-incremental-incrementalensemble-module" title="Permalink to this headline">¶</a></h2>
+<div class="section" id="module-pyFTS.models.incremental.IncrementalEnsemble">
+<span id="pyfts-models-incremental-incrementalensemble-module"></span><h2>pyFTS.models.incremental.IncrementalEnsemble module<a class="headerlink" href="#module-pyFTS.models.incremental.IncrementalEnsemble" title="Permalink to this headline">¶</a></h2>
+<p>Time Variant/Incremental Ensemble of FTS methods</p>
+<dl class="py class">
+<dt id="pyFTS.models.incremental.IncrementalEnsemble.IncrementalEnsembleFTS">
+<em class="property">class </em><code class="sig-prename descclassname">pyFTS.models.incremental.IncrementalEnsemble.</code><code class="sig-name descname">IncrementalEnsembleFTS</code><span class="sig-paren">(</span><em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/incremental/IncrementalEnsemble.html#IncrementalEnsembleFTS"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.incremental.IncrementalEnsemble.IncrementalEnsembleFTS" title="Permalink to this definition">¶</a></dt>
+<dd><p>Bases: <a class="reference internal" href="pyFTS.models.ensemble.html#pyFTS.models.ensemble.ensemble.EnsembleFTS" title="pyFTS.models.ensemble.ensemble.EnsembleFTS"><code class="xref py py-class docutils literal notranslate"><span class="pre">pyFTS.models.ensemble.ensemble.EnsembleFTS</span></code></a></p>
+<p>Time Variant/Incremental Ensemble of FTS methods</p>
+<dl class="py attribute">
+<dt id="pyFTS.models.incremental.IncrementalEnsemble.IncrementalEnsembleFTS.batch_size">
+<code class="sig-name descname">batch_size</code><a class="headerlink" href="#pyFTS.models.incremental.IncrementalEnsemble.IncrementalEnsembleFTS.batch_size" title="Permalink to this definition">¶</a></dt>
+<dd><p>The batch interval between each retraining</p>
+</dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.incremental.IncrementalEnsemble.IncrementalEnsembleFTS.forecast">
+<code class="sig-name descname">forecast</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/incremental/IncrementalEnsemble.html#IncrementalEnsembleFTS.forecast"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.incremental.IncrementalEnsemble.IncrementalEnsembleFTS.forecast" title="Permalink to this definition">¶</a></dt>
+<dd><p>Point forecast one step ahead</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> – time series data with the minimal length equal to the max_lag of the model</p></li>
+<li><p><strong>kwargs</strong> – model specific parameters</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>a list with the forecasted values</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.incremental.IncrementalEnsemble.IncrementalEnsembleFTS.forecast_ahead">
+<code class="sig-name descname">forecast_ahead</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="n">steps</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/incremental/IncrementalEnsemble.html#IncrementalEnsembleFTS.forecast_ahead"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.incremental.IncrementalEnsemble.IncrementalEnsembleFTS.forecast_ahead" title="Permalink to this definition">¶</a></dt>
+<dd><p>Point forecast from 1 to H steps ahead, where H is given by the steps parameter</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> – time series data with the minimal length equal to the max_lag of the model</p></li>
+<li><p><strong>steps</strong> – the number of steps ahead to forecast (default: 1)</p></li>
+<li><p><strong>start_at</strong> – in the multi step forecasting, the index of the data where to start forecasting (default: 0)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>a list with the forecasted values</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt id="pyFTS.models.incremental.IncrementalEnsemble.IncrementalEnsembleFTS.fts_method">
+<code class="sig-name descname">fts_method</code><a class="headerlink" href="#pyFTS.models.incremental.IncrementalEnsemble.IncrementalEnsembleFTS.fts_method" title="Permalink to this definition">¶</a></dt>
+<dd><p>The FTS method to be called when a new model is build</p>
+</dd></dl>
+
+<dl class="py attribute">
+<dt id="pyFTS.models.incremental.IncrementalEnsemble.IncrementalEnsembleFTS.fts_params">
+<code class="sig-name descname">fts_params</code><a class="headerlink" href="#pyFTS.models.incremental.IncrementalEnsemble.IncrementalEnsembleFTS.fts_params" title="Permalink to this definition">¶</a></dt>
+<dd><p>The FTS method specific parameters</p>
+</dd></dl>
+
+<dl class="py attribute">
+<dt id="pyFTS.models.incremental.IncrementalEnsemble.IncrementalEnsembleFTS.num_models">
+<code class="sig-name descname">num_models</code><a class="headerlink" href="#pyFTS.models.incremental.IncrementalEnsemble.IncrementalEnsembleFTS.num_models" title="Permalink to this definition">¶</a></dt>
+<dd><p>The number of models to hold in the ensemble</p>
+</dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.incremental.IncrementalEnsemble.IncrementalEnsembleFTS.offset">
+<code class="sig-name descname">offset</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/incremental/IncrementalEnsemble.html#IncrementalEnsembleFTS.offset"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.incremental.IncrementalEnsemble.IncrementalEnsembleFTS.offset" title="Permalink to this definition">¶</a></dt>
+<dd><p>Returns the number of lags to skip in the input test data in order to synchronize it with
+the forecasted values given by the predict function. This is necessary due to the order of the
+model, among other parameters.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns</dt>
+<dd class="field-odd"><p>An integer with the number of lags to skip</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt id="pyFTS.models.incremental.IncrementalEnsemble.IncrementalEnsembleFTS.partitioner_method">
+<code class="sig-name descname">partitioner_method</code><a class="headerlink" href="#pyFTS.models.incremental.IncrementalEnsemble.IncrementalEnsembleFTS.partitioner_method" title="Permalink to this definition">¶</a></dt>
+<dd><p>The partitioner method to be called when a new model is build</p>
+</dd></dl>
+
+<dl class="py attribute">
+<dt id="pyFTS.models.incremental.IncrementalEnsemble.IncrementalEnsembleFTS.partitioner_params">
+<code class="sig-name descname">partitioner_params</code><a class="headerlink" href="#pyFTS.models.incremental.IncrementalEnsemble.IncrementalEnsembleFTS.partitioner_params" title="Permalink to this definition">¶</a></dt>
+<dd><p>The partitioner method parameters</p>
+</dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.incremental.IncrementalEnsemble.IncrementalEnsembleFTS.train">
+<code class="sig-name descname">train</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/incremental/IncrementalEnsemble.html#IncrementalEnsembleFTS.train"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.incremental.IncrementalEnsemble.IncrementalEnsembleFTS.train" title="Permalink to this definition">¶</a></dt>
+<dd><p>Method specific parameter fitting</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> – training time series data</p></li>
+<li><p><strong>kwargs</strong> – Method specific parameters</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt id="pyFTS.models.incremental.IncrementalEnsemble.IncrementalEnsembleFTS.window_length">
+<code class="sig-name descname">window_length</code><a class="headerlink" href="#pyFTS.models.incremental.IncrementalEnsemble.IncrementalEnsembleFTS.window_length" title="Permalink to this definition">¶</a></dt>
+<dd><p>The memory window length</p>
+</dd></dl>
+
+</dd></dl>
+
 </div>
 </div>
 
@@ -93,8 +332,8 @@
 <li><a class="reference internal" href="#">pyFTS.models.incremental package</a><ul>
 <li><a class="reference internal" href="#module-pyFTS.models.incremental">Module contents</a></li>
 <li><a class="reference internal" href="#submodules">Submodules</a></li>
-<li><a class="reference internal" href="#pyfts-models-incremental-timevariant-module">pyFTS.models.incremental.TimeVariant module</a></li>
-<li><a class="reference internal" href="#pyfts-models-incremental-incrementalensemble-module">pyFTS.models.incremental.IncrementalEnsemble module</a></li>
+<li><a class="reference internal" href="#module-pyFTS.models.incremental.TimeVariant">pyFTS.models.incremental.TimeVariant module</a></li>
+<li><a class="reference internal" href="#module-pyFTS.models.incremental.IncrementalEnsemble">pyFTS.models.incremental.IncrementalEnsemble module</a></li>
 </ul>
 </li>
 </ul>
diff --git a/docs/build/html/pyFTS.models.multivariate.html b/docs/build/html/pyFTS.models.multivariate.html
index 0e97cd3..dfb59c8 100644
--- a/docs/build/html/pyFTS.models.multivariate.html
+++ b/docs/build/html/pyFTS.models.multivariate.html
@@ -146,8 +146,87 @@
 <dd></dd></dl>
 
 </div>
-<div class="section" id="pyfts-models-multivariate-variable-module">
-<h2>pyFTS.models.multivariate.variable module<a class="headerlink" href="#pyfts-models-multivariate-variable-module" title="Permalink to this headline">¶</a></h2>
+<div class="section" id="module-pyFTS.models.multivariate.variable">
+<span id="pyfts-models-multivariate-variable-module"></span><h2>pyFTS.models.multivariate.variable module<a class="headerlink" href="#module-pyFTS.models.multivariate.variable" title="Permalink to this headline">¶</a></h2>
+<dl class="py class">
+<dt id="pyFTS.models.multivariate.variable.Variable">
+<em class="property">class </em><code class="sig-prename descclassname">pyFTS.models.multivariate.variable.</code><code class="sig-name descname">Variable</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">name</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/multivariate/variable.html#Variable"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.multivariate.variable.Variable" title="Permalink to this definition">¶</a></dt>
+<dd><p>Bases: <a class="reference external" href="https://docs.python.org/3/library/functions.html#object" title="(in Python v3.9)"><code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></a></p>
+<p>A variable of a fuzzy time series multivariate model. Each variable contains its own
+transformations and partitioners.</p>
+<dl class="py attribute">
+<dt id="pyFTS.models.multivariate.variable.Variable.alias">
+<code class="sig-name descname">alias</code><a class="headerlink" href="#pyFTS.models.multivariate.variable.Variable.alias" title="Permalink to this definition">¶</a></dt>
+<dd><p>A string with the alias of the variable</p>
+</dd></dl>
+
+<dl class="py attribute">
+<dt id="pyFTS.models.multivariate.variable.Variable.alpha_cut">
+<code class="sig-name descname">alpha_cut</code><a class="headerlink" href="#pyFTS.models.multivariate.variable.Variable.alpha_cut" title="Permalink to this definition">¶</a></dt>
+<dd><p>Minimal membership value to be considered on fuzzyfication process</p>
+</dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.multivariate.variable.Variable.apply_inverse_transformations">
+<code class="sig-name descname">apply_inverse_transformations</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/multivariate/variable.html#Variable.apply_inverse_transformations"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.multivariate.variable.Variable.apply_inverse_transformations" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.multivariate.variable.Variable.apply_transformations">
+<code class="sig-name descname">apply_transformations</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/multivariate/variable.html#Variable.apply_transformations"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.multivariate.variable.Variable.apply_transformations" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.multivariate.variable.Variable.build">
+<code class="sig-name descname">build</code><span class="sig-paren">(</span><em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/multivariate/variable.html#Variable.build"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.multivariate.variable.Variable.build" title="Permalink to this definition">¶</a></dt>
+<dd><dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>kwargs</strong> – </p>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt id="pyFTS.models.multivariate.variable.Variable.data_label">
+<code class="sig-name descname">data_label</code><a class="headerlink" href="#pyFTS.models.multivariate.variable.Variable.data_label" title="Permalink to this definition">¶</a></dt>
+<dd><p>A string with the column name on DataFrame</p>
+</dd></dl>
+
+<dl class="py attribute">
+<dt id="pyFTS.models.multivariate.variable.Variable.data_type">
+<code class="sig-name descname">data_type</code><a class="headerlink" href="#pyFTS.models.multivariate.variable.Variable.data_type" title="Permalink to this definition">¶</a></dt>
+<dd><p>The type of the data column on Pandas Dataframe</p>
+</dd></dl>
+
+<dl class="py attribute">
+<dt id="pyFTS.models.multivariate.variable.Variable.mask">
+<code class="sig-name descname">mask</code><a class="headerlink" href="#pyFTS.models.multivariate.variable.Variable.mask" title="Permalink to this definition">¶</a></dt>
+<dd><p>The mask for format the data column on Pandas Dataframe</p>
+</dd></dl>
+
+<dl class="py attribute">
+<dt id="pyFTS.models.multivariate.variable.Variable.name">
+<code class="sig-name descname">name</code><a class="headerlink" href="#pyFTS.models.multivariate.variable.Variable.name" title="Permalink to this definition">¶</a></dt>
+<dd><p>A string with the name of the variable</p>
+</dd></dl>
+
+<dl class="py attribute">
+<dt id="pyFTS.models.multivariate.variable.Variable.partitioner">
+<code class="sig-name descname">partitioner</code><a class="headerlink" href="#pyFTS.models.multivariate.variable.Variable.partitioner" title="Permalink to this definition">¶</a></dt>
+<dd><p>UoD partitioner for the variable data</p>
+</dd></dl>
+
+<dl class="py attribute">
+<dt id="pyFTS.models.multivariate.variable.Variable.transformation">
+<code class="sig-name descname">transformation</code><a class="headerlink" href="#pyFTS.models.multivariate.variable.Variable.transformation" title="Permalink to this definition">¶</a></dt>
+<dd><p>Pre processing transformation for the variable</p>
+</dd></dl>
+
+</dd></dl>
+
 </div>
 <div class="section" id="module-pyFTS.models.multivariate.flrg">
 <span id="pyfts-models-multivariate-flrg-module"></span><h2>pyFTS.models.multivariate.flrg module<a class="headerlink" href="#module-pyFTS.models.multivariate.flrg" title="Permalink to this headline">¶</a></h2>
@@ -364,17 +443,436 @@ multivariate fuzzy set base.</p>
 </dd></dl>
 
 </div>
-<div class="section" id="pyfts-models-multivariate-mvfts-module">
-<h2>pyFTS.models.multivariate.mvfts module<a class="headerlink" href="#pyfts-models-multivariate-mvfts-module" title="Permalink to this headline">¶</a></h2>
+<div class="section" id="module-pyFTS.models.multivariate.mvfts">
+<span id="pyfts-models-multivariate-mvfts-module"></span><h2>pyFTS.models.multivariate.mvfts module<a class="headerlink" href="#module-pyFTS.models.multivariate.mvfts" title="Permalink to this headline">¶</a></h2>
+<dl class="py class">
+<dt id="pyFTS.models.multivariate.mvfts.MVFTS">
+<em class="property">class </em><code class="sig-prename descclassname">pyFTS.models.multivariate.mvfts.</code><code class="sig-name descname">MVFTS</code><span class="sig-paren">(</span><em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/multivariate/mvfts.html#MVFTS"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.multivariate.mvfts.MVFTS" title="Permalink to this definition">¶</a></dt>
+<dd><p>Bases: <a class="reference internal" href="pyFTS.common.html#pyFTS.common.fts.FTS" title="pyFTS.common.fts.FTS"><code class="xref py py-class docutils literal notranslate"><span class="pre">pyFTS.common.fts.FTS</span></code></a></p>
+<p>Multivariate extension of Chen’s ConventionalFTS method</p>
+<dl class="py method">
+<dt id="pyFTS.models.multivariate.mvfts.MVFTS.append_transformation">
+<code class="sig-name descname">append_transformation</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">transformation</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/multivariate/mvfts.html#MVFTS.append_transformation"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.multivariate.mvfts.MVFTS.append_transformation" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.multivariate.mvfts.MVFTS.append_variable">
+<code class="sig-name descname">append_variable</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">var</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/multivariate/mvfts.html#MVFTS.append_variable"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.multivariate.mvfts.MVFTS.append_variable" title="Permalink to this definition">¶</a></dt>
+<dd><p>Append a new endogenous variable to the model</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>var</strong> – variable object</p>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.multivariate.mvfts.MVFTS.apply_transformations">
+<code class="sig-name descname">apply_transformations</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="n">params</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">updateUoD</span><span class="o">=</span><span class="default_value">False</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/multivariate/mvfts.html#MVFTS.apply_transformations"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.multivariate.mvfts.MVFTS.apply_transformations" title="Permalink to this definition">¶</a></dt>
+<dd><p>Apply the data transformations for data preprocessing</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> – input data</p></li>
+<li><p><strong>params</strong> – transformation parameters</p></li>
+<li><p><strong>updateUoD</strong> – </p></li>
+<li><p><strong>kwargs</strong> – </p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>preprocessed data</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.multivariate.mvfts.MVFTS.clone_parameters">
+<code class="sig-name descname">clone_parameters</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">model</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/multivariate/mvfts.html#MVFTS.clone_parameters"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.multivariate.mvfts.MVFTS.clone_parameters" title="Permalink to this definition">¶</a></dt>
+<dd><p>Import the parameters values from other model</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>model</strong> – a model to clone the parameters</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.multivariate.mvfts.MVFTS.forecast">
+<code class="sig-name descname">forecast</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/multivariate/mvfts.html#MVFTS.forecast"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.multivariate.mvfts.MVFTS.forecast" title="Permalink to this definition">¶</a></dt>
+<dd><p>Point forecast one step ahead</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> – time series data with the minimal length equal to the max_lag of the model</p></li>
+<li><p><strong>kwargs</strong> – model specific parameters</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>a list with the forecasted values</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.multivariate.mvfts.MVFTS.forecast_ahead">
+<code class="sig-name descname">forecast_ahead</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="n">steps</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/multivariate/mvfts.html#MVFTS.forecast_ahead"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.multivariate.mvfts.MVFTS.forecast_ahead" title="Permalink to this definition">¶</a></dt>
+<dd><p>Point forecast from 1 to H steps ahead, where H is given by the steps parameter</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> – time series data with the minimal length equal to the max_lag of the model</p></li>
+<li><p><strong>steps</strong> – the number of steps ahead to forecast (default: 1)</p></li>
+<li><p><strong>start_at</strong> – in the multi step forecasting, the index of the data where to start forecasting (default: 0)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>a list with the forecasted values</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.multivariate.mvfts.MVFTS.forecast_ahead_interval">
+<code class="sig-name descname">forecast_ahead_interval</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="n">steps</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/multivariate/mvfts.html#MVFTS.forecast_ahead_interval"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.multivariate.mvfts.MVFTS.forecast_ahead_interval" title="Permalink to this definition">¶</a></dt>
+<dd><p>Interval forecast from 1 to H steps ahead, where H is given by the steps parameter</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> – time series data with the minimal length equal to the max_lag of the model</p></li>
+<li><p><strong>steps</strong> – the number of steps ahead to forecast</p></li>
+<li><p><strong>start_at</strong> – in the multi step forecasting, the index of the data where to start forecasting (default: 0)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>a list with the forecasted intervals</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.multivariate.mvfts.MVFTS.forecast_interval">
+<code class="sig-name descname">forecast_interval</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/multivariate/mvfts.html#MVFTS.forecast_interval"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.multivariate.mvfts.MVFTS.forecast_interval" title="Permalink to this definition">¶</a></dt>
+<dd><p>Interval forecast one step ahead</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> – time series data with the minimal length equal to the max_lag of the model</p></li>
+<li><p><strong>kwargs</strong> – model specific parameters</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>a list with the prediction intervals</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.multivariate.mvfts.MVFTS.format_data">
+<code class="sig-name descname">format_data</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/multivariate/mvfts.html#MVFTS.format_data"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.multivariate.mvfts.MVFTS.format_data" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.multivariate.mvfts.MVFTS.generate_flrg">
+<code class="sig-name descname">generate_flrg</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">flrs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/multivariate/mvfts.html#MVFTS.generate_flrg"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.multivariate.mvfts.MVFTS.generate_flrg" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.multivariate.mvfts.MVFTS.generate_flrs">
+<code class="sig-name descname">generate_flrs</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/multivariate/mvfts.html#MVFTS.generate_flrs"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.multivariate.mvfts.MVFTS.generate_flrs" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.multivariate.mvfts.MVFTS.generate_lhs_flrs">
+<code class="sig-name descname">generate_lhs_flrs</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/multivariate/mvfts.html#MVFTS.generate_lhs_flrs"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.multivariate.mvfts.MVFTS.generate_lhs_flrs" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.multivariate.mvfts.MVFTS.train">
+<code class="sig-name descname">train</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/multivariate/mvfts.html#MVFTS.train"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.multivariate.mvfts.MVFTS.train" title="Permalink to this definition">¶</a></dt>
+<dd><p>Method specific parameter fitting</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> – training time series data</p></li>
+<li><p><strong>kwargs</strong> – Method specific parameters</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.models.multivariate.mvfts.product_dict">
+<code class="sig-prename descclassname">pyFTS.models.multivariate.mvfts.</code><code class="sig-name descname">product_dict</code><span class="sig-paren">(</span><em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/multivariate/mvfts.html#product_dict"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.multivariate.mvfts.product_dict" title="Permalink to this definition">¶</a></dt>
+<dd><p>Code by Seth Johnson
+:param kwargs:
+:return:</p>
+</dd></dl>
+
 </div>
-<div class="section" id="pyfts-models-multivariate-wmvfts-module">
-<h2>pyFTS.models.multivariate.wmvfts module<a class="headerlink" href="#pyfts-models-multivariate-wmvfts-module" title="Permalink to this headline">¶</a></h2>
+<div class="section" id="module-pyFTS.models.multivariate.wmvfts">
+<span id="pyfts-models-multivariate-wmvfts-module"></span><h2>pyFTS.models.multivariate.wmvfts module<a class="headerlink" href="#module-pyFTS.models.multivariate.wmvfts" title="Permalink to this headline">¶</a></h2>
+<dl class="py class">
+<dt id="pyFTS.models.multivariate.wmvfts.WeightedFLRG">
+<em class="property">class </em><code class="sig-prename descclassname">pyFTS.models.multivariate.wmvfts.</code><code class="sig-name descname">WeightedFLRG</code><span class="sig-paren">(</span><em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/multivariate/wmvfts.html#WeightedFLRG"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.multivariate.wmvfts.WeightedFLRG" title="Permalink to this definition">¶</a></dt>
+<dd><p>Bases: <a class="reference internal" href="#pyFTS.models.multivariate.flrg.FLRG" title="pyFTS.models.multivariate.flrg.FLRG"><code class="xref py py-class docutils literal notranslate"><span class="pre">pyFTS.models.multivariate.flrg.FLRG</span></code></a></p>
+<p>Weighted Multivariate Fuzzy Logical Rule Group</p>
+<dl class="py method">
+<dt id="pyFTS.models.multivariate.wmvfts.WeightedFLRG.append_rhs">
+<code class="sig-name descname">append_rhs</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">fset</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/multivariate/wmvfts.html#WeightedFLRG.append_rhs"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.multivariate.wmvfts.WeightedFLRG.append_rhs" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.multivariate.wmvfts.WeightedFLRG.get_lower">
+<code class="sig-name descname">get_lower</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">sets</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/multivariate/wmvfts.html#WeightedFLRG.get_lower"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.multivariate.wmvfts.WeightedFLRG.get_lower" title="Permalink to this definition">¶</a></dt>
+<dd><p>Returns the lower bound value for the RHS fuzzy sets</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>sets</strong> – fuzzy sets</p>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>lower bound value</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.multivariate.wmvfts.WeightedFLRG.get_midpoint">
+<code class="sig-name descname">get_midpoint</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">sets</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/multivariate/wmvfts.html#WeightedFLRG.get_midpoint"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.multivariate.wmvfts.WeightedFLRG.get_midpoint" title="Permalink to this definition">¶</a></dt>
+<dd><p>Returns the midpoint value for the RHS fuzzy sets</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>sets</strong> – fuzzy sets</p>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>the midpoint value</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.multivariate.wmvfts.WeightedFLRG.get_upper">
+<code class="sig-name descname">get_upper</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">sets</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/multivariate/wmvfts.html#WeightedFLRG.get_upper"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.multivariate.wmvfts.WeightedFLRG.get_upper" title="Permalink to this definition">¶</a></dt>
+<dd><p>Returns the upper bound value for the RHS fuzzy sets</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>sets</strong> – fuzzy sets</p>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>upper bound value</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.multivariate.wmvfts.WeightedFLRG.weights">
+<code class="sig-name descname">weights</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/multivariate/wmvfts.html#WeightedFLRG.weights"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.multivariate.wmvfts.WeightedFLRG.weights" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt id="pyFTS.models.multivariate.wmvfts.WeightedMVFTS">
+<em class="property">class </em><code class="sig-prename descclassname">pyFTS.models.multivariate.wmvfts.</code><code class="sig-name descname">WeightedMVFTS</code><span class="sig-paren">(</span><em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/multivariate/wmvfts.html#WeightedMVFTS"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.multivariate.wmvfts.WeightedMVFTS" title="Permalink to this definition">¶</a></dt>
+<dd><p>Bases: <a class="reference internal" href="#pyFTS.models.multivariate.mvfts.MVFTS" title="pyFTS.models.multivariate.mvfts.MVFTS"><code class="xref py py-class docutils literal notranslate"><span class="pre">pyFTS.models.multivariate.mvfts.MVFTS</span></code></a></p>
+<p>Weighted Multivariate FTS</p>
+<dl class="py method">
+<dt id="pyFTS.models.multivariate.wmvfts.WeightedMVFTS.generate_flrg">
+<code class="sig-name descname">generate_flrg</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">flrs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/multivariate/wmvfts.html#WeightedMVFTS.generate_flrg"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.multivariate.wmvfts.WeightedMVFTS.generate_flrg" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
 </div>
-<div class="section" id="pyfts-models-multivariate-cmvfts-module">
-<h2>pyFTS.models.multivariate.cmvfts module<a class="headerlink" href="#pyfts-models-multivariate-cmvfts-module" title="Permalink to this headline">¶</a></h2>
+<div class="section" id="module-pyFTS.models.multivariate.cmvfts">
+<span id="pyfts-models-multivariate-cmvfts-module"></span><h2>pyFTS.models.multivariate.cmvfts module<a class="headerlink" href="#module-pyFTS.models.multivariate.cmvfts" title="Permalink to this headline">¶</a></h2>
+<dl class="py class">
+<dt id="pyFTS.models.multivariate.cmvfts.ClusteredMVFTS">
+<em class="property">class </em><code class="sig-prename descclassname">pyFTS.models.multivariate.cmvfts.</code><code class="sig-name descname">ClusteredMVFTS</code><span class="sig-paren">(</span><em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/multivariate/cmvfts.html#ClusteredMVFTS"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.multivariate.cmvfts.ClusteredMVFTS" title="Permalink to this definition">¶</a></dt>
+<dd><p>Bases: <a class="reference internal" href="#pyFTS.models.multivariate.mvfts.MVFTS" title="pyFTS.models.multivariate.mvfts.MVFTS"><code class="xref py py-class docutils literal notranslate"><span class="pre">pyFTS.models.multivariate.mvfts.MVFTS</span></code></a></p>
+<p>Meta model for high order, clustered multivariate FTS</p>
+<dl class="py method">
+<dt id="pyFTS.models.multivariate.cmvfts.ClusteredMVFTS.check_data">
+<code class="sig-name descname">check_data</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/multivariate/cmvfts.html#ClusteredMVFTS.check_data"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.multivariate.cmvfts.ClusteredMVFTS.check_data" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.multivariate.cmvfts.ClusteredMVFTS.forecast">
+<code class="sig-name descname">forecast</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/multivariate/cmvfts.html#ClusteredMVFTS.forecast"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.multivariate.cmvfts.ClusteredMVFTS.forecast" title="Permalink to this definition">¶</a></dt>
+<dd><p>Point forecast one step ahead</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> – time series data with the minimal length equal to the max_lag of the model</p></li>
+<li><p><strong>kwargs</strong> – model specific parameters</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>a list with the forecasted values</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.multivariate.cmvfts.ClusteredMVFTS.forecast_ahead_distribution">
+<code class="sig-name descname">forecast_ahead_distribution</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="n">steps</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/multivariate/cmvfts.html#ClusteredMVFTS.forecast_ahead_distribution"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.multivariate.cmvfts.ClusteredMVFTS.forecast_ahead_distribution" title="Permalink to this definition">¶</a></dt>
+<dd><p>Probabilistic forecast from 1 to H steps ahead, where H is given by the steps parameter</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> – time series data with the minimal length equal to the max_lag of the model</p></li>
+<li><p><strong>steps</strong> – the number of steps ahead to forecast</p></li>
+<li><p><strong>start_at</strong> – in the multi step forecasting, the index of the data where to start forecasting (default: 0)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>a list with the forecasted Probability Distributions</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.multivariate.cmvfts.ClusteredMVFTS.forecast_ahead_multivariate">
+<code class="sig-name descname">forecast_ahead_multivariate</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="n">steps</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/multivariate/cmvfts.html#ClusteredMVFTS.forecast_ahead_multivariate"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.multivariate.cmvfts.ClusteredMVFTS.forecast_ahead_multivariate" title="Permalink to this definition">¶</a></dt>
+<dd><p>Multivariate forecast n step ahead</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> – Pandas dataframe with one column for each variable and with the minimal length equal to the max_lag of the model</p></li>
+<li><p><strong>steps</strong> – the number of steps ahead to forecast</p></li>
+<li><p><strong>start_at</strong> – in the multi step forecasting, the index of the data where to start forecasting (default: 0)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>a Pandas Dataframe object representing the forecasted values for each variable</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.multivariate.cmvfts.ClusteredMVFTS.forecast_distribution">
+<code class="sig-name descname">forecast_distribution</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/multivariate/cmvfts.html#ClusteredMVFTS.forecast_distribution"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.multivariate.cmvfts.ClusteredMVFTS.forecast_distribution" title="Permalink to this definition">¶</a></dt>
+<dd><p>Probabilistic forecast one step ahead</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> – time series data with the minimal length equal to the max_lag of the model</p></li>
+<li><p><strong>kwargs</strong> – model specific parameters</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>a list with probabilistic.ProbabilityDistribution objects representing the forecasted Probability Distributions</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.multivariate.cmvfts.ClusteredMVFTS.forecast_interval">
+<code class="sig-name descname">forecast_interval</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/multivariate/cmvfts.html#ClusteredMVFTS.forecast_interval"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.multivariate.cmvfts.ClusteredMVFTS.forecast_interval" title="Permalink to this definition">¶</a></dt>
+<dd><p>Interval forecast one step ahead</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> – time series data with the minimal length equal to the max_lag of the model</p></li>
+<li><p><strong>kwargs</strong> – model specific parameters</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>a list with the prediction intervals</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.multivariate.cmvfts.ClusteredMVFTS.forecast_multivariate">
+<code class="sig-name descname">forecast_multivariate</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/multivariate/cmvfts.html#ClusteredMVFTS.forecast_multivariate"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.multivariate.cmvfts.ClusteredMVFTS.forecast_multivariate" title="Permalink to this definition">¶</a></dt>
+<dd><p>Multivariate forecast one step ahead</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> – Pandas dataframe with one column for each variable and with the minimal length equal to the max_lag of the model</p></li>
+<li><p><strong>kwargs</strong> – model specific parameters</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>a Pandas Dataframe object representing the forecasted values for each variable</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt id="pyFTS.models.multivariate.cmvfts.ClusteredMVFTS.fts_method">
+<code class="sig-name descname">fts_method</code><a class="headerlink" href="#pyFTS.models.multivariate.cmvfts.ClusteredMVFTS.fts_method" title="Permalink to this definition">¶</a></dt>
+<dd><p>The FTS method to be called when a new model is build</p>
+</dd></dl>
+
+<dl class="py attribute">
+<dt id="pyFTS.models.multivariate.cmvfts.ClusteredMVFTS.fts_params">
+<code class="sig-name descname">fts_params</code><a class="headerlink" href="#pyFTS.models.multivariate.cmvfts.ClusteredMVFTS.fts_params" title="Permalink to this definition">¶</a></dt>
+<dd><p>The FTS method specific parameters</p>
+</dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.multivariate.cmvfts.ClusteredMVFTS.fuzzyfy">
+<code class="sig-name descname">fuzzyfy</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/multivariate/cmvfts.html#ClusteredMVFTS.fuzzyfy"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.multivariate.cmvfts.ClusteredMVFTS.fuzzyfy" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py attribute">
+<dt id="pyFTS.models.multivariate.cmvfts.ClusteredMVFTS.model">
+<code class="sig-name descname">model</code><a class="headerlink" href="#pyFTS.models.multivariate.cmvfts.ClusteredMVFTS.model" title="Permalink to this definition">¶</a></dt>
+<dd><p>The most recent trained model</p>
+</dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.multivariate.cmvfts.ClusteredMVFTS.train">
+<code class="sig-name descname">train</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/multivariate/cmvfts.html#ClusteredMVFTS.train"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.multivariate.cmvfts.ClusteredMVFTS.train" title="Permalink to this definition">¶</a></dt>
+<dd><p>Method specific parameter fitting</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> – training time series data</p></li>
+<li><p><strong>kwargs</strong> – Method specific parameters</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
 </div>
-<div class="section" id="pyfts-models-multivariate-granular-module">
-<h2>pyFTS.models.multivariate.granular module<a class="headerlink" href="#pyfts-models-multivariate-granular-module" title="Permalink to this headline">¶</a></h2>
+<div class="section" id="module-pyFTS.models.multivariate.granular">
+<span id="pyfts-models-multivariate-granular-module"></span><h2>pyFTS.models.multivariate.granular module<a class="headerlink" href="#module-pyFTS.models.multivariate.granular" title="Permalink to this headline">¶</a></h2>
+<dl class="py class">
+<dt id="pyFTS.models.multivariate.granular.GranularWMVFTS">
+<em class="property">class </em><code class="sig-prename descclassname">pyFTS.models.multivariate.granular.</code><code class="sig-name descname">GranularWMVFTS</code><span class="sig-paren">(</span><em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/multivariate/granular.html#GranularWMVFTS"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.multivariate.granular.GranularWMVFTS" title="Permalink to this definition">¶</a></dt>
+<dd><p>Bases: <a class="reference internal" href="#pyFTS.models.multivariate.cmvfts.ClusteredMVFTS" title="pyFTS.models.multivariate.cmvfts.ClusteredMVFTS"><code class="xref py py-class docutils literal notranslate"><span class="pre">pyFTS.models.multivariate.cmvfts.ClusteredMVFTS</span></code></a></p>
+<p>Granular multivariate weighted high order FTS</p>
+<dl class="py attribute">
+<dt id="pyFTS.models.multivariate.granular.GranularWMVFTS.model">
+<code class="sig-name descname">model</code><a class="headerlink" href="#pyFTS.models.multivariate.granular.GranularWMVFTS.model" title="Permalink to this definition">¶</a></dt>
+<dd><p>The most recent trained model</p>
+</dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.multivariate.granular.GranularWMVFTS.train">
+<code class="sig-name descname">train</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/multivariate/granular.html#GranularWMVFTS.train"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.multivariate.granular.GranularWMVFTS.train" title="Permalink to this definition">¶</a></dt>
+<dd><p>Method specific parameter fitting</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> – training time series data</p></li>
+<li><p><strong>kwargs</strong> – Method specific parameters</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
 </div>
 </div>
 
@@ -392,14 +890,14 @@ multivariate fuzzy set base.</p>
 <li><a class="reference internal" href="#submodules">Submodules</a></li>
 <li><a class="reference internal" href="#module-pyFTS.models.multivariate.FLR">pyFTS.models.multivariate.FLR module</a></li>
 <li><a class="reference internal" href="#module-pyFTS.models.multivariate.common">pyFTS.models.multivariate.common module</a></li>
-<li><a class="reference internal" href="#pyfts-models-multivariate-variable-module">pyFTS.models.multivariate.variable module</a></li>
+<li><a class="reference internal" href="#module-pyFTS.models.multivariate.variable">pyFTS.models.multivariate.variable module</a></li>
 <li><a class="reference internal" href="#module-pyFTS.models.multivariate.flrg">pyFTS.models.multivariate.flrg module</a></li>
 <li><a class="reference internal" href="#module-pyFTS.models.multivariate.partitioner">pyFTS.models.multivariate.partitioner module</a></li>
 <li><a class="reference internal" href="#module-pyFTS.models.multivariate.grid">pyFTS.models.multivariate.grid module</a></li>
-<li><a class="reference internal" href="#pyfts-models-multivariate-mvfts-module">pyFTS.models.multivariate.mvfts module</a></li>
-<li><a class="reference internal" href="#pyfts-models-multivariate-wmvfts-module">pyFTS.models.multivariate.wmvfts module</a></li>
-<li><a class="reference internal" href="#pyfts-models-multivariate-cmvfts-module">pyFTS.models.multivariate.cmvfts module</a></li>
-<li><a class="reference internal" href="#pyfts-models-multivariate-granular-module">pyFTS.models.multivariate.granular module</a></li>
+<li><a class="reference internal" href="#module-pyFTS.models.multivariate.mvfts">pyFTS.models.multivariate.mvfts module</a></li>
+<li><a class="reference internal" href="#module-pyFTS.models.multivariate.wmvfts">pyFTS.models.multivariate.wmvfts module</a></li>
+<li><a class="reference internal" href="#module-pyFTS.models.multivariate.cmvfts">pyFTS.models.multivariate.cmvfts module</a></li>
+<li><a class="reference internal" href="#module-pyFTS.models.multivariate.granular">pyFTS.models.multivariate.granular module</a></li>
 </ul>
 </li>
 </ul>
diff --git a/docs/build/html/pyFTS.models.nonstationary.html b/docs/build/html/pyFTS.models.nonstationary.html
index 539a7fd..8807ad0 100644
--- a/docs/build/html/pyFTS.models.nonstationary.html
+++ b/docs/build/html/pyFTS.models.nonstationary.html
@@ -203,8 +203,94 @@ IEEE Transactions on Fuzzy Systems, v. 16, n. 4, p. 1072-1086, 2008.</p>
 <dd></dd></dl>
 
 </div>
-<div class="section" id="pyfts-models-nonstationary-cvfts-module">
-<h2>pyFTS.models.nonstationary.cvfts module<a class="headerlink" href="#pyfts-models-nonstationary-cvfts-module" title="Permalink to this headline">¶</a></h2>
+<div class="section" id="module-pyFTS.models.nonstationary.cvfts">
+<span id="pyfts-models-nonstationary-cvfts-module"></span><h2>pyFTS.models.nonstationary.cvfts module<a class="headerlink" href="#module-pyFTS.models.nonstationary.cvfts" title="Permalink to this headline">¶</a></h2>
+<dl class="py class">
+<dt id="pyFTS.models.nonstationary.cvfts.ConditionalVarianceFTS">
+<em class="property">class </em><code class="sig-prename descclassname">pyFTS.models.nonstationary.cvfts.</code><code class="sig-name descname">ConditionalVarianceFTS</code><span class="sig-paren">(</span><em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/nonstationary/cvfts.html#ConditionalVarianceFTS"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.nonstationary.cvfts.ConditionalVarianceFTS" title="Permalink to this definition">¶</a></dt>
+<dd><p>Bases: <a class="reference internal" href="pyFTS.models.html#pyFTS.models.hofts.HighOrderFTS" title="pyFTS.models.hofts.HighOrderFTS"><code class="xref py py-class docutils literal notranslate"><span class="pre">pyFTS.models.hofts.HighOrderFTS</span></code></a></p>
+<dl class="py method">
+<dt id="pyFTS.models.nonstationary.cvfts.ConditionalVarianceFTS.forecast">
+<code class="sig-name descname">forecast</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">ndata</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/nonstationary/cvfts.html#ConditionalVarianceFTS.forecast"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.nonstationary.cvfts.ConditionalVarianceFTS.forecast" title="Permalink to this definition">¶</a></dt>
+<dd><p>Point forecast one step ahead</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> – time series data with the minimal length equal to the max_lag of the model</p></li>
+<li><p><strong>kwargs</strong> – model specific parameters</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>a list with the forecasted values</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.nonstationary.cvfts.ConditionalVarianceFTS.forecast_interval">
+<code class="sig-name descname">forecast_interval</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">ndata</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/nonstationary/cvfts.html#ConditionalVarianceFTS.forecast_interval"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.nonstationary.cvfts.ConditionalVarianceFTS.forecast_interval" title="Permalink to this definition">¶</a></dt>
+<dd><p>Interval forecast one step ahead</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> – time series data with the minimal length equal to the max_lag of the model</p></li>
+<li><p><strong>kwargs</strong> – model specific parameters</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>a list with the prediction intervals</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.nonstationary.cvfts.ConditionalVarianceFTS.generate_flrg">
+<code class="sig-name descname">generate_flrg</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">flrs</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/nonstationary/cvfts.html#ConditionalVarianceFTS.generate_flrg"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.nonstationary.cvfts.ConditionalVarianceFTS.generate_flrg" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.nonstationary.cvfts.ConditionalVarianceFTS.perturbation_factors">
+<code class="sig-name descname">perturbation_factors</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/nonstationary/cvfts.html#ConditionalVarianceFTS.perturbation_factors"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.nonstationary.cvfts.ConditionalVarianceFTS.perturbation_factors" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.nonstationary.cvfts.ConditionalVarianceFTS.perturbation_factors__old">
+<code class="sig-name descname">perturbation_factors__old</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/nonstationary/cvfts.html#ConditionalVarianceFTS.perturbation_factors__old"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.nonstationary.cvfts.ConditionalVarianceFTS.perturbation_factors__old" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.nonstationary.cvfts.ConditionalVarianceFTS.train">
+<code class="sig-name descname">train</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">ndata</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/nonstationary/cvfts.html#ConditionalVarianceFTS.train"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.nonstationary.cvfts.ConditionalVarianceFTS.train" title="Permalink to this definition">¶</a></dt>
+<dd><p>Method specific parameter fitting</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> – training time series data</p></li>
+<li><p><strong>kwargs</strong> – Method specific parameters</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt id="pyFTS.models.nonstationary.cvfts.HighOrderNonstationaryFLRG">
+<em class="property">class </em><code class="sig-prename descclassname">pyFTS.models.nonstationary.cvfts.</code><code class="sig-name descname">HighOrderNonstationaryFLRG</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">order</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/nonstationary/cvfts.html#HighOrderNonstationaryFLRG"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.nonstationary.cvfts.HighOrderNonstationaryFLRG" title="Permalink to this definition">¶</a></dt>
+<dd><p>Bases: <a class="reference internal" href="pyFTS.models.html#pyFTS.models.hofts.HighOrderFTS" title="pyFTS.models.hofts.HighOrderFTS"><code class="xref py py-class docutils literal notranslate"><span class="pre">pyFTS.models.hofts.HighOrderFTS</span></code></a></p>
+<p>Conventional High Order Fuzzy Logical Relationship Group</p>
+<dl class="py method">
+<dt id="pyFTS.models.nonstationary.cvfts.HighOrderNonstationaryFLRG.append_lhs">
+<code class="sig-name descname">append_lhs</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">c</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/nonstationary/cvfts.html#HighOrderNonstationaryFLRG.append_lhs"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.nonstationary.cvfts.HighOrderNonstationaryFLRG.append_lhs" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.nonstationary.cvfts.HighOrderNonstationaryFLRG.append_rhs">
+<code class="sig-name descname">append_rhs</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">c</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/nonstationary/cvfts.html#HighOrderNonstationaryFLRG.append_rhs"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.nonstationary.cvfts.HighOrderNonstationaryFLRG.append_rhs" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
 </div>
 <div class="section" id="module-pyFTS.models.nonstationary.flrg">
 <span id="pyfts-models-nonstationary-flrg-module"></span><h2>pyFTS.models.nonstationary.flrg module<a class="headerlink" href="#module-pyFTS.models.nonstationary.flrg" title="Permalink to this headline">¶</a></h2>
@@ -285,11 +371,269 @@ IEEE Transactions on Fuzzy Systems, v. 16, n. 4, p. 1072-1086, 2008.</p>
 </dd></dl>
 
 </div>
-<div class="section" id="pyfts-models-nonstationary-honsfts-module">
-<h2>pyFTS.models.nonstationary.honsfts module<a class="headerlink" href="#pyfts-models-nonstationary-honsfts-module" title="Permalink to this headline">¶</a></h2>
+<div class="section" id="module-pyFTS.models.nonstationary.honsfts">
+<span id="pyfts-models-nonstationary-honsfts-module"></span><h2>pyFTS.models.nonstationary.honsfts module<a class="headerlink" href="#module-pyFTS.models.nonstationary.honsfts" title="Permalink to this headline">¶</a></h2>
+<dl class="py class">
+<dt id="pyFTS.models.nonstationary.honsfts.HighOrderNonStationaryFLRG">
+<em class="property">class </em><code class="sig-prename descclassname">pyFTS.models.nonstationary.honsfts.</code><code class="sig-name descname">HighOrderNonStationaryFLRG</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">order</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/nonstationary/honsfts.html#HighOrderNonStationaryFLRG"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.nonstationary.honsfts.HighOrderNonStationaryFLRG" title="Permalink to this definition">¶</a></dt>
+<dd><p>Bases: <a class="reference internal" href="#pyFTS.models.nonstationary.flrg.NonStationaryFLRG" title="pyFTS.models.nonstationary.flrg.NonStationaryFLRG"><code class="xref py py-class docutils literal notranslate"><span class="pre">pyFTS.models.nonstationary.flrg.NonStationaryFLRG</span></code></a></p>
+<p>First Order NonStationary Fuzzy Logical Relationship Group</p>
+<dl class="py method">
+<dt id="pyFTS.models.nonstationary.honsfts.HighOrderNonStationaryFLRG.append_lhs">
+<code class="sig-name descname">append_lhs</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">c</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/nonstationary/honsfts.html#HighOrderNonStationaryFLRG.append_lhs"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.nonstationary.honsfts.HighOrderNonStationaryFLRG.append_lhs" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.nonstationary.honsfts.HighOrderNonStationaryFLRG.append_rhs">
+<code class="sig-name descname">append_rhs</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">fset</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/nonstationary/honsfts.html#HighOrderNonStationaryFLRG.append_rhs"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.nonstationary.honsfts.HighOrderNonStationaryFLRG.append_rhs" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.nonstationary.honsfts.HighOrderNonStationaryFLRG.get_lower">
+<code class="sig-name descname">get_lower</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">sets</span></em>, <em class="sig-param"><span class="n">perturb</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/nonstationary/honsfts.html#HighOrderNonStationaryFLRG.get_lower"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.nonstationary.honsfts.HighOrderNonStationaryFLRG.get_lower" title="Permalink to this definition">¶</a></dt>
+<dd><p>Returns the lower bound value for the RHS fuzzy sets</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>sets</strong> – fuzzy sets</p>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>lower bound value</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.nonstationary.honsfts.HighOrderNonStationaryFLRG.get_midpoint">
+<code class="sig-name descname">get_midpoint</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">sets</span></em>, <em class="sig-param"><span class="n">perturb</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/nonstationary/honsfts.html#HighOrderNonStationaryFLRG.get_midpoint"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.nonstationary.honsfts.HighOrderNonStationaryFLRG.get_midpoint" title="Permalink to this definition">¶</a></dt>
+<dd><p>Returns the midpoint value for the RHS fuzzy sets</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>sets</strong> – fuzzy sets</p>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>the midpoint value</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.nonstationary.honsfts.HighOrderNonStationaryFLRG.get_upper">
+<code class="sig-name descname">get_upper</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">sets</span></em>, <em class="sig-param"><span class="n">perturb</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/nonstationary/honsfts.html#HighOrderNonStationaryFLRG.get_upper"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.nonstationary.honsfts.HighOrderNonStationaryFLRG.get_upper" title="Permalink to this definition">¶</a></dt>
+<dd><p>Returns the upper bound value for the RHS fuzzy sets</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>sets</strong> – fuzzy sets</p>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>upper bound value</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.nonstationary.honsfts.HighOrderNonStationaryFLRG.weights">
+<code class="sig-name descname">weights</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/nonstationary/honsfts.html#HighOrderNonStationaryFLRG.weights"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.nonstationary.honsfts.HighOrderNonStationaryFLRG.weights" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt id="pyFTS.models.nonstationary.honsfts.HighOrderNonStationaryFTS">
+<em class="property">class </em><code class="sig-prename descclassname">pyFTS.models.nonstationary.honsfts.</code><code class="sig-name descname">HighOrderNonStationaryFTS</code><span class="sig-paren">(</span><em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/nonstationary/honsfts.html#HighOrderNonStationaryFTS"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.nonstationary.honsfts.HighOrderNonStationaryFTS" title="Permalink to this definition">¶</a></dt>
+<dd><p>Bases: <a class="reference internal" href="#pyFTS.models.nonstationary.nsfts.NonStationaryFTS" title="pyFTS.models.nonstationary.nsfts.NonStationaryFTS"><code class="xref py py-class docutils literal notranslate"><span class="pre">pyFTS.models.nonstationary.nsfts.NonStationaryFTS</span></code></a></p>
+<p>NonStationaryFTS Fuzzy Time Series</p>
+<dl class="py method">
+<dt id="pyFTS.models.nonstationary.honsfts.HighOrderNonStationaryFTS.configure_lags">
+<code class="sig-name descname">configure_lags</code><span class="sig-paren">(</span><em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/nonstationary/honsfts.html#HighOrderNonStationaryFTS.configure_lags"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.nonstationary.honsfts.HighOrderNonStationaryFTS.configure_lags" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.nonstationary.honsfts.HighOrderNonStationaryFTS.forecast">
+<code class="sig-name descname">forecast</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">ndata</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/nonstationary/honsfts.html#HighOrderNonStationaryFTS.forecast"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.nonstationary.honsfts.HighOrderNonStationaryFTS.forecast" title="Permalink to this definition">¶</a></dt>
+<dd><p>Point forecast one step ahead</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> – time series data with the minimal length equal to the max_lag of the model</p></li>
+<li><p><strong>kwargs</strong> – model specific parameters</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>a list with the forecasted values</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.nonstationary.honsfts.HighOrderNonStationaryFTS.generate_flrg">
+<code class="sig-name descname">generate_flrg</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/nonstationary/honsfts.html#HighOrderNonStationaryFTS.generate_flrg"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.nonstationary.honsfts.HighOrderNonStationaryFTS.generate_flrg" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.nonstationary.honsfts.HighOrderNonStationaryFTS.train">
+<code class="sig-name descname">train</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/nonstationary/honsfts.html#HighOrderNonStationaryFTS.train"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.nonstationary.honsfts.HighOrderNonStationaryFTS.train" title="Permalink to this definition">¶</a></dt>
+<dd><p>Method specific parameter fitting</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> – training time series data</p></li>
+<li><p><strong>kwargs</strong> – Method specific parameters</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
 </div>
-<div class="section" id="pyfts-models-nonstationary-nsfts-module">
-<h2>pyFTS.models.nonstationary.nsfts module<a class="headerlink" href="#pyfts-models-nonstationary-nsfts-module" title="Permalink to this headline">¶</a></h2>
+<div class="section" id="module-pyFTS.models.nonstationary.nsfts">
+<span id="pyfts-models-nonstationary-nsfts-module"></span><h2>pyFTS.models.nonstationary.nsfts module<a class="headerlink" href="#module-pyFTS.models.nonstationary.nsfts" title="Permalink to this headline">¶</a></h2>
+<dl class="py class">
+<dt id="pyFTS.models.nonstationary.nsfts.ConventionalNonStationaryFLRG">
+<em class="property">class </em><code class="sig-prename descclassname">pyFTS.models.nonstationary.nsfts.</code><code class="sig-name descname">ConventionalNonStationaryFLRG</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">LHS</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/nonstationary/nsfts.html#ConventionalNonStationaryFLRG"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.nonstationary.nsfts.ConventionalNonStationaryFLRG" title="Permalink to this definition">¶</a></dt>
+<dd><p>Bases: <a class="reference internal" href="#pyFTS.models.nonstationary.flrg.NonStationaryFLRG" title="pyFTS.models.nonstationary.flrg.NonStationaryFLRG"><code class="xref py py-class docutils literal notranslate"><span class="pre">pyFTS.models.nonstationary.flrg.NonStationaryFLRG</span></code></a></p>
+<p>First Order NonStationary Fuzzy Logical Relationship Group</p>
+<dl class="py method">
+<dt id="pyFTS.models.nonstationary.nsfts.ConventionalNonStationaryFLRG.append_rhs">
+<code class="sig-name descname">append_rhs</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">c</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/nonstationary/nsfts.html#ConventionalNonStationaryFLRG.append_rhs"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.nonstationary.nsfts.ConventionalNonStationaryFLRG.append_rhs" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.nonstationary.nsfts.ConventionalNonStationaryFLRG.get_key">
+<code class="sig-name descname">get_key</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/nonstationary/nsfts.html#ConventionalNonStationaryFLRG.get_key"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.nonstationary.nsfts.ConventionalNonStationaryFLRG.get_key" title="Permalink to this definition">¶</a></dt>
+<dd><p>Returns a unique identifier for this FLRG</p>
+</dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt id="pyFTS.models.nonstationary.nsfts.NonStationaryFTS">
+<em class="property">class </em><code class="sig-prename descclassname">pyFTS.models.nonstationary.nsfts.</code><code class="sig-name descname">NonStationaryFTS</code><span class="sig-paren">(</span><em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/nonstationary/nsfts.html#NonStationaryFTS"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.nonstationary.nsfts.NonStationaryFTS" title="Permalink to this definition">¶</a></dt>
+<dd><p>Bases: <a class="reference internal" href="pyFTS.common.html#pyFTS.common.fts.FTS" title="pyFTS.common.fts.FTS"><code class="xref py py-class docutils literal notranslate"><span class="pre">pyFTS.common.fts.FTS</span></code></a></p>
+<p>NonStationaryFTS Fuzzy Time Series</p>
+<dl class="py method">
+<dt id="pyFTS.models.nonstationary.nsfts.NonStationaryFTS.conditional_perturbation_factors">
+<code class="sig-name descname">conditional_perturbation_factors</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/nonstationary/nsfts.html#NonStationaryFTS.conditional_perturbation_factors"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.nonstationary.nsfts.NonStationaryFTS.conditional_perturbation_factors" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.nonstationary.nsfts.NonStationaryFTS.forecast">
+<code class="sig-name descname">forecast</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">ndata</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/nonstationary/nsfts.html#NonStationaryFTS.forecast"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.nonstationary.nsfts.NonStationaryFTS.forecast" title="Permalink to this definition">¶</a></dt>
+<dd><p>Point forecast one step ahead</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> – time series data with the minimal length equal to the max_lag of the model</p></li>
+<li><p><strong>kwargs</strong> – model specific parameters</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>a list with the forecasted values</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.nonstationary.nsfts.NonStationaryFTS.forecast_interval">
+<code class="sig-name descname">forecast_interval</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">ndata</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/nonstationary/nsfts.html#NonStationaryFTS.forecast_interval"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.nonstationary.nsfts.NonStationaryFTS.forecast_interval" title="Permalink to this definition">¶</a></dt>
+<dd><p>Interval forecast one step ahead</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> – time series data with the minimal length equal to the max_lag of the model</p></li>
+<li><p><strong>kwargs</strong> – model specific parameters</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>a list with the prediction intervals</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.nonstationary.nsfts.NonStationaryFTS.generate_flrg">
+<code class="sig-name descname">generate_flrg</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">flrs</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/nonstationary/nsfts.html#NonStationaryFTS.generate_flrg"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.nonstationary.nsfts.NonStationaryFTS.generate_flrg" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.nonstationary.nsfts.NonStationaryFTS.train">
+<code class="sig-name descname">train</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/nonstationary/nsfts.html#NonStationaryFTS.train"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.nonstationary.nsfts.NonStationaryFTS.train" title="Permalink to this definition">¶</a></dt>
+<dd><p>Method specific parameter fitting</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> – training time series data</p></li>
+<li><p><strong>kwargs</strong> – Method specific parameters</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt id="pyFTS.models.nonstationary.nsfts.WeightedNonStationaryFLRG">
+<em class="property">class </em><code class="sig-prename descclassname">pyFTS.models.nonstationary.nsfts.</code><code class="sig-name descname">WeightedNonStationaryFLRG</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">LHS</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/nonstationary/nsfts.html#WeightedNonStationaryFLRG"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.nonstationary.nsfts.WeightedNonStationaryFLRG" title="Permalink to this definition">¶</a></dt>
+<dd><p>Bases: <a class="reference internal" href="#pyFTS.models.nonstationary.flrg.NonStationaryFLRG" title="pyFTS.models.nonstationary.flrg.NonStationaryFLRG"><code class="xref py py-class docutils literal notranslate"><span class="pre">pyFTS.models.nonstationary.flrg.NonStationaryFLRG</span></code></a></p>
+<p>First Order NonStationary Fuzzy Logical Relationship Group</p>
+<dl class="py method">
+<dt id="pyFTS.models.nonstationary.nsfts.WeightedNonStationaryFLRG.append_rhs">
+<code class="sig-name descname">append_rhs</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">c</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/nonstationary/nsfts.html#WeightedNonStationaryFLRG.append_rhs"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.nonstationary.nsfts.WeightedNonStationaryFLRG.append_rhs" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.nonstationary.nsfts.WeightedNonStationaryFLRG.get_key">
+<code class="sig-name descname">get_key</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/nonstationary/nsfts.html#WeightedNonStationaryFLRG.get_key"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.nonstationary.nsfts.WeightedNonStationaryFLRG.get_key" title="Permalink to this definition">¶</a></dt>
+<dd><p>Returns a unique identifier for this FLRG</p>
+</dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.nonstationary.nsfts.WeightedNonStationaryFLRG.get_midpoint">
+<code class="sig-name descname">get_midpoint</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">sets</span></em>, <em class="sig-param"><span class="n">perturb</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/nonstationary/nsfts.html#WeightedNonStationaryFLRG.get_midpoint"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.nonstationary.nsfts.WeightedNonStationaryFLRG.get_midpoint" title="Permalink to this definition">¶</a></dt>
+<dd><p>Returns the midpoint value for the RHS fuzzy sets</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>sets</strong> – fuzzy sets</p>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>the midpoint value</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.nonstationary.nsfts.WeightedNonStationaryFLRG.weights">
+<code class="sig-name descname">weights</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/nonstationary/nsfts.html#WeightedNonStationaryFLRG.weights"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.nonstationary.nsfts.WeightedNonStationaryFLRG.weights" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt id="pyFTS.models.nonstationary.nsfts.WeightedNonStationaryFTS">
+<em class="property">class </em><code class="sig-prename descclassname">pyFTS.models.nonstationary.nsfts.</code><code class="sig-name descname">WeightedNonStationaryFTS</code><span class="sig-paren">(</span><em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/nonstationary/nsfts.html#WeightedNonStationaryFTS"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.nonstationary.nsfts.WeightedNonStationaryFTS" title="Permalink to this definition">¶</a></dt>
+<dd><p>Bases: <a class="reference internal" href="#pyFTS.models.nonstationary.nsfts.NonStationaryFTS" title="pyFTS.models.nonstationary.nsfts.NonStationaryFTS"><code class="xref py py-class docutils literal notranslate"><span class="pre">pyFTS.models.nonstationary.nsfts.NonStationaryFTS</span></code></a></p>
+<p>Weighted NonStationaryFTS Fuzzy Time Series</p>
+<dl class="py method">
+<dt id="pyFTS.models.nonstationary.nsfts.WeightedNonStationaryFTS.generate_flrg">
+<code class="sig-name descname">generate_flrg</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">flrs</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/nonstationary/nsfts.html#WeightedNonStationaryFTS.generate_flrg"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.nonstationary.nsfts.WeightedNonStationaryFTS.generate_flrg" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.nonstationary.nsfts.WeightedNonStationaryFTS.train">
+<code class="sig-name descname">train</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/nonstationary/nsfts.html#WeightedNonStationaryFTS.train"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.nonstationary.nsfts.WeightedNonStationaryFTS.train" title="Permalink to this definition">¶</a></dt>
+<dd><p>Method specific parameter fitting</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> – training time series data</p></li>
+<li><p><strong>kwargs</strong> – Method specific parameters</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
 </div>
 <div class="section" id="module-pyFTS.models.nonstationary.partitioners">
 <span id="pyfts-models-nonstationary-partitioners-module"></span><h2>pyFTS.models.nonstationary.partitioners module<a class="headerlink" href="#module-pyFTS.models.nonstationary.partitioners" title="Permalink to this headline">¶</a></h2>
@@ -385,8 +729,18 @@ IEEE Transactions on Fuzzy Systems, v. 16, n. 4, p. 1072-1086, 2008.</p>
 <dd></dd></dl>
 
 </div>
-<div class="section" id="pyfts-models-nonstationary-util-module">
-<h2>pyFTS.models.nonstationary.util module<a class="headerlink" href="#pyfts-models-nonstationary-util-module" title="Permalink to this headline">¶</a></h2>
+<div class="section" id="module-pyFTS.models.nonstationary.util">
+<span id="pyfts-models-nonstationary-util-module"></span><h2>pyFTS.models.nonstationary.util module<a class="headerlink" href="#module-pyFTS.models.nonstationary.util" title="Permalink to this headline">¶</a></h2>
+<dl class="py function">
+<dt id="pyFTS.models.nonstationary.util.plot_sets">
+<code class="sig-prename descclassname">pyFTS.models.nonstationary.util.</code><code class="sig-name descname">plot_sets</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">partitioner</span></em>, <em class="sig-param"><span class="n">start</span><span class="o">=</span><span class="default_value">0</span></em>, <em class="sig-param"><span class="n">end</span><span class="o">=</span><span class="default_value">10</span></em>, <em class="sig-param"><span class="n">step</span><span class="o">=</span><span class="default_value">1</span></em>, <em class="sig-param"><span class="n">tam</span><span class="o">=</span><span class="default_value">[5, 5]</span></em>, <em class="sig-param"><span class="n">colors</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">save</span><span class="o">=</span><span class="default_value">False</span></em>, <em class="sig-param"><span class="n">file</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">axes</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">data</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">window_size</span><span class="o">=</span><span class="default_value">1</span></em>, <em class="sig-param"><span class="n">only_lines</span><span class="o">=</span><span class="default_value">False</span></em>, <em class="sig-param"><span class="n">legend</span><span class="o">=</span><span class="default_value">True</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/nonstationary/util.html#plot_sets"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.nonstationary.util.plot_sets" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.models.nonstationary.util.plot_sets_conditional">
+<code class="sig-prename descclassname">pyFTS.models.nonstationary.util.</code><code class="sig-name descname">plot_sets_conditional</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">model</span></em>, <em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="n">step</span><span class="o">=</span><span class="default_value">1</span></em>, <em class="sig-param"><span class="n">size</span><span class="o">=</span><span class="default_value">[5, 5]</span></em>, <em class="sig-param"><span class="n">colors</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">save</span><span class="o">=</span><span class="default_value">False</span></em>, <em class="sig-param"><span class="n">file</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">axes</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">fig</span><span class="o">=</span><span class="default_value">None</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/nonstationary/util.html#plot_sets_conditional"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.nonstationary.util.plot_sets_conditional" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
 </div>
 <div class="section" id="module-pyFTS.models.nonstationary">
 <span id="module-contents"></span><h2>Module contents<a class="headerlink" href="#module-pyFTS.models.nonstationary" title="Permalink to this headline">¶</a></h2>
@@ -406,13 +760,13 @@ IEEE Transactions on Fuzzy Systems, v. 16, n. 4, p. 1072-1086, 2008.</p>
 <li><a class="reference internal" href="#">pyFTS.models.nonstationary package</a><ul>
 <li><a class="reference internal" href="#submodules">Submodules</a></li>
 <li><a class="reference internal" href="#module-pyFTS.models.nonstationary.common">pyFTS.models.nonstationary.common module</a></li>
-<li><a class="reference internal" href="#pyfts-models-nonstationary-cvfts-module">pyFTS.models.nonstationary.cvfts module</a></li>
+<li><a class="reference internal" href="#module-pyFTS.models.nonstationary.cvfts">pyFTS.models.nonstationary.cvfts module</a></li>
 <li><a class="reference internal" href="#module-pyFTS.models.nonstationary.flrg">pyFTS.models.nonstationary.flrg module</a></li>
-<li><a class="reference internal" href="#pyfts-models-nonstationary-honsfts-module">pyFTS.models.nonstationary.honsfts module</a></li>
-<li><a class="reference internal" href="#pyfts-models-nonstationary-nsfts-module">pyFTS.models.nonstationary.nsfts module</a></li>
+<li><a class="reference internal" href="#module-pyFTS.models.nonstationary.honsfts">pyFTS.models.nonstationary.honsfts module</a></li>
+<li><a class="reference internal" href="#module-pyFTS.models.nonstationary.nsfts">pyFTS.models.nonstationary.nsfts module</a></li>
 <li><a class="reference internal" href="#module-pyFTS.models.nonstationary.partitioners">pyFTS.models.nonstationary.partitioners module</a></li>
 <li><a class="reference internal" href="#module-pyFTS.models.nonstationary.perturbation">pyFTS.models.nonstationary.perturbation module</a></li>
-<li><a class="reference internal" href="#pyfts-models-nonstationary-util-module">pyFTS.models.nonstationary.util module</a></li>
+<li><a class="reference internal" href="#module-pyFTS.models.nonstationary.util">pyFTS.models.nonstationary.util module</a></li>
 <li><a class="reference internal" href="#module-pyFTS.models.nonstationary">Module contents</a></li>
 </ul>
 </li>
diff --git a/docs/build/html/pyFTS.models.seasonal.html b/docs/build/html/pyFTS.models.seasonal.html
index 76d3f01..1a8029f 100644
--- a/docs/build/html/pyFTS.models.seasonal.html
+++ b/docs/build/html/pyFTS.models.seasonal.html
@@ -215,8 +215,86 @@
 </dd></dl>
 
 </div>
-<div class="section" id="pyfts-models-seasonal-cmsfts-module">
-<h2>pyFTS.models.seasonal.cmsfts module<a class="headerlink" href="#pyfts-models-seasonal-cmsfts-module" title="Permalink to this headline">¶</a></h2>
+<div class="section" id="module-pyFTS.models.seasonal.cmsfts">
+<span id="pyfts-models-seasonal-cmsfts-module"></span><h2>pyFTS.models.seasonal.cmsfts module<a class="headerlink" href="#module-pyFTS.models.seasonal.cmsfts" title="Permalink to this headline">¶</a></h2>
+<dl class="py class">
+<dt id="pyFTS.models.seasonal.cmsfts.ContextualMultiSeasonalFTS">
+<em class="property">class </em><code class="sig-prename descclassname">pyFTS.models.seasonal.cmsfts.</code><code class="sig-name descname">ContextualMultiSeasonalFTS</code><span class="sig-paren">(</span><em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/seasonal/cmsfts.html#ContextualMultiSeasonalFTS"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.seasonal.cmsfts.ContextualMultiSeasonalFTS" title="Permalink to this definition">¶</a></dt>
+<dd><p>Bases: <a class="reference internal" href="#pyFTS.models.seasonal.sfts.SeasonalFTS" title="pyFTS.models.seasonal.sfts.SeasonalFTS"><code class="xref py py-class docutils literal notranslate"><span class="pre">pyFTS.models.seasonal.sfts.SeasonalFTS</span></code></a></p>
+<p>Contextual Multi-Seasonal Fuzzy Time Series</p>
+<dl class="py method">
+<dt id="pyFTS.models.seasonal.cmsfts.ContextualMultiSeasonalFTS.forecast">
+<code class="sig-name descname">forecast</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/seasonal/cmsfts.html#ContextualMultiSeasonalFTS.forecast"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.seasonal.cmsfts.ContextualMultiSeasonalFTS.forecast" title="Permalink to this definition">¶</a></dt>
+<dd><p>Point forecast one step ahead</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> – time series data with the minimal length equal to the max_lag of the model</p></li>
+<li><p><strong>kwargs</strong> – model specific parameters</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>a list with the forecasted values</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.seasonal.cmsfts.ContextualMultiSeasonalFTS.forecast_ahead">
+<code class="sig-name descname">forecast_ahead</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="n">steps</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/seasonal/cmsfts.html#ContextualMultiSeasonalFTS.forecast_ahead"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.seasonal.cmsfts.ContextualMultiSeasonalFTS.forecast_ahead" title="Permalink to this definition">¶</a></dt>
+<dd><p>Point forecast from 1 to H steps ahead, where H is given by the steps parameter</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> – time series data with the minimal length equal to the max_lag of the model</p></li>
+<li><p><strong>steps</strong> – the number of steps ahead to forecast (default: 1)</p></li>
+<li><p><strong>start_at</strong> – in the multi step forecasting, the index of the data where to start forecasting (default: 0)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>a list with the forecasted values</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.seasonal.cmsfts.ContextualMultiSeasonalFTS.generate_flrg">
+<code class="sig-name descname">generate_flrg</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">flrs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/seasonal/cmsfts.html#ContextualMultiSeasonalFTS.generate_flrg"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.seasonal.cmsfts.ContextualMultiSeasonalFTS.generate_flrg" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.seasonal.cmsfts.ContextualMultiSeasonalFTS.get_midpoints">
+<code class="sig-name descname">get_midpoints</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">flrg</span></em>, <em class="sig-param"><span class="n">data</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/seasonal/cmsfts.html#ContextualMultiSeasonalFTS.get_midpoints"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.seasonal.cmsfts.ContextualMultiSeasonalFTS.get_midpoints" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.seasonal.cmsfts.ContextualMultiSeasonalFTS.train">
+<code class="sig-name descname">train</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/seasonal/cmsfts.html#ContextualMultiSeasonalFTS.train"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.seasonal.cmsfts.ContextualMultiSeasonalFTS.train" title="Permalink to this definition">¶</a></dt>
+<dd><p>Method specific parameter fitting</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> – training time series data</p></li>
+<li><p><strong>kwargs</strong> – Method specific parameters</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt id="pyFTS.models.seasonal.cmsfts.ContextualSeasonalFLRG">
+<em class="property">class </em><code class="sig-prename descclassname">pyFTS.models.seasonal.cmsfts.</code><code class="sig-name descname">ContextualSeasonalFLRG</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">seasonality</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/seasonal/cmsfts.html#ContextualSeasonalFLRG"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.seasonal.cmsfts.ContextualSeasonalFLRG" title="Permalink to this definition">¶</a></dt>
+<dd><p>Bases: <a class="reference internal" href="#pyFTS.models.seasonal.sfts.SeasonalFLRG" title="pyFTS.models.seasonal.sfts.SeasonalFLRG"><code class="xref py py-class docutils literal notranslate"><span class="pre">pyFTS.models.seasonal.sfts.SeasonalFLRG</span></code></a></p>
+<p>Contextual Seasonal Fuzzy Logical Relationship Group</p>
+<dl class="py method">
+<dt id="pyFTS.models.seasonal.cmsfts.ContextualSeasonalFLRG.append_rhs">
+<code class="sig-name descname">append_rhs</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">flr</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/seasonal/cmsfts.html#ContextualSeasonalFLRG.append_rhs"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.seasonal.cmsfts.ContextualSeasonalFLRG.append_rhs" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
 </div>
 <div class="section" id="module-pyFTS.models.seasonal.common">
 <span id="pyfts-models-seasonal-common-module"></span><h2>pyFTS.models.seasonal.common module<a class="headerlink" href="#module-pyFTS.models.seasonal.common" title="Permalink to this headline">¶</a></h2>
@@ -374,8 +452,69 @@
 <dd></dd></dl>
 
 </div>
-<div class="section" id="pyfts-models-seasonal-msfts-module">
-<h2>pyFTS.models.seasonal.msfts module<a class="headerlink" href="#pyfts-models-seasonal-msfts-module" title="Permalink to this headline">¶</a></h2>
+<div class="section" id="module-pyFTS.models.seasonal.msfts">
+<span id="pyfts-models-seasonal-msfts-module"></span><h2>pyFTS.models.seasonal.msfts module<a class="headerlink" href="#module-pyFTS.models.seasonal.msfts" title="Permalink to this headline">¶</a></h2>
+<dl class="py class">
+<dt id="pyFTS.models.seasonal.msfts.MultiSeasonalFTS">
+<em class="property">class </em><code class="sig-prename descclassname">pyFTS.models.seasonal.msfts.</code><code class="sig-name descname">MultiSeasonalFTS</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">name</span></em>, <em class="sig-param"><span class="n">indexer</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/seasonal/msfts.html#MultiSeasonalFTS"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.seasonal.msfts.MultiSeasonalFTS" title="Permalink to this definition">¶</a></dt>
+<dd><p>Bases: <a class="reference internal" href="#pyFTS.models.seasonal.sfts.SeasonalFTS" title="pyFTS.models.seasonal.sfts.SeasonalFTS"><code class="xref py py-class docutils literal notranslate"><span class="pre">pyFTS.models.seasonal.sfts.SeasonalFTS</span></code></a></p>
+<p>Multi-Seasonal Fuzzy Time Series</p>
+<dl class="py method">
+<dt id="pyFTS.models.seasonal.msfts.MultiSeasonalFTS.forecast">
+<code class="sig-name descname">forecast</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/seasonal/msfts.html#MultiSeasonalFTS.forecast"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.seasonal.msfts.MultiSeasonalFTS.forecast" title="Permalink to this definition">¶</a></dt>
+<dd><p>Point forecast one step ahead</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> – time series data with the minimal length equal to the max_lag of the model</p></li>
+<li><p><strong>kwargs</strong> – model specific parameters</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>a list with the forecasted values</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.seasonal.msfts.MultiSeasonalFTS.forecast_ahead">
+<code class="sig-name descname">forecast_ahead</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="n">steps</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/seasonal/msfts.html#MultiSeasonalFTS.forecast_ahead"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.seasonal.msfts.MultiSeasonalFTS.forecast_ahead" title="Permalink to this definition">¶</a></dt>
+<dd><p>Point forecast from 1 to H steps ahead, where H is given by the steps parameter</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> – time series data with the minimal length equal to the max_lag of the model</p></li>
+<li><p><strong>steps</strong> – the number of steps ahead to forecast (default: 1)</p></li>
+<li><p><strong>start_at</strong> – in the multi step forecasting, the index of the data where to start forecasting (default: 0)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>a list with the forecasted values</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.seasonal.msfts.MultiSeasonalFTS.generate_flrg">
+<code class="sig-name descname">generate_flrg</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">flrs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/seasonal/msfts.html#MultiSeasonalFTS.generate_flrg"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.seasonal.msfts.MultiSeasonalFTS.generate_flrg" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.seasonal.msfts.MultiSeasonalFTS.train">
+<code class="sig-name descname">train</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/seasonal/msfts.html#MultiSeasonalFTS.train"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.seasonal.msfts.MultiSeasonalFTS.train" title="Permalink to this definition">¶</a></dt>
+<dd><p>Method specific parameter fitting</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> – training time series data</p></li>
+<li><p><strong>kwargs</strong> – Method specific parameters</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
 </div>
 <div class="section" id="module-pyFTS.models.seasonal.partitioner">
 <span id="pyfts-models-seasonal-partitioner-module"></span><h2>pyFTS.models.seasonal.partitioner module<a class="headerlink" href="#module-pyFTS.models.seasonal.partitioner" title="Permalink to this headline">¶</a></h2>
@@ -451,8 +590,79 @@ overlapped fuzzy sets.</p>
 </dd></dl>
 
 </div>
-<div class="section" id="pyfts-models-seasonal-sfts-module">
-<h2>pyFTS.models.seasonal.sfts module<a class="headerlink" href="#pyfts-models-seasonal-sfts-module" title="Permalink to this headline">¶</a></h2>
+<div class="section" id="module-pyFTS.models.seasonal.sfts">
+<span id="pyfts-models-seasonal-sfts-module"></span><h2>pyFTS.models.seasonal.sfts module<a class="headerlink" href="#module-pyFTS.models.seasonal.sfts" title="Permalink to this headline">¶</a></h2>
+<p>Simple First Order Seasonal Fuzzy Time Series implementation of Song (1999) based of Conventional FTS by Chen (1996)</p>
+<ol class="upperalpha simple" start="17">
+<li><p>Song, “Seasonal forecasting in fuzzy time series,” Fuzzy sets Syst., vol. 107, pp. 235–236, 1999.</p></li>
+</ol>
+<p>S.-M. Chen, “Forecasting enrollments based on fuzzy time series,” Fuzzy Sets Syst., vol. 81, no. 3, pp. 311–319, 1996.</p>
+<dl class="py class">
+<dt id="pyFTS.models.seasonal.sfts.SeasonalFLRG">
+<em class="property">class </em><code class="sig-prename descclassname">pyFTS.models.seasonal.sfts.</code><code class="sig-name descname">SeasonalFLRG</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">seasonality</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/seasonal/sfts.html#SeasonalFLRG"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.seasonal.sfts.SeasonalFLRG" title="Permalink to this definition">¶</a></dt>
+<dd><p>Bases: <a class="reference internal" href="pyFTS.common.html#pyFTS.common.flrg.FLRG" title="pyFTS.common.flrg.FLRG"><code class="xref py py-class docutils literal notranslate"><span class="pre">pyFTS.common.flrg.FLRG</span></code></a></p>
+<p>First Order Seasonal Fuzzy Logical Relationship Group</p>
+<dl class="py method">
+<dt id="pyFTS.models.seasonal.sfts.SeasonalFLRG.append_rhs">
+<code class="sig-name descname">append_rhs</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">c</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/seasonal/sfts.html#SeasonalFLRG.append_rhs"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.seasonal.sfts.SeasonalFLRG.append_rhs" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.seasonal.sfts.SeasonalFLRG.get_key">
+<code class="sig-name descname">get_key</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/seasonal/sfts.html#SeasonalFLRG.get_key"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.seasonal.sfts.SeasonalFLRG.get_key" title="Permalink to this definition">¶</a></dt>
+<dd><p>Returns a unique identifier for this FLRG</p>
+</dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt id="pyFTS.models.seasonal.sfts.SeasonalFTS">
+<em class="property">class </em><code class="sig-prename descclassname">pyFTS.models.seasonal.sfts.</code><code class="sig-name descname">SeasonalFTS</code><span class="sig-paren">(</span><em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/seasonal/sfts.html#SeasonalFTS"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.seasonal.sfts.SeasonalFTS" title="Permalink to this definition">¶</a></dt>
+<dd><p>Bases: <a class="reference internal" href="pyFTS.common.html#pyFTS.common.fts.FTS" title="pyFTS.common.fts.FTS"><code class="xref py py-class docutils literal notranslate"><span class="pre">pyFTS.common.fts.FTS</span></code></a></p>
+<p>First Order Seasonal Fuzzy Time Series</p>
+<dl class="py method">
+<dt id="pyFTS.models.seasonal.sfts.SeasonalFTS.forecast">
+<code class="sig-name descname">forecast</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/seasonal/sfts.html#SeasonalFTS.forecast"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.seasonal.sfts.SeasonalFTS.forecast" title="Permalink to this definition">¶</a></dt>
+<dd><p>Point forecast one step ahead</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> – time series data with the minimal length equal to the max_lag of the model</p></li>
+<li><p><strong>kwargs</strong> – model specific parameters</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>a list with the forecasted values</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.seasonal.sfts.SeasonalFTS.generate_flrg">
+<code class="sig-name descname">generate_flrg</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">flrs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/seasonal/sfts.html#SeasonalFTS.generate_flrg"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.seasonal.sfts.SeasonalFTS.generate_flrg" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.seasonal.sfts.SeasonalFTS.get_midpoints">
+<code class="sig-name descname">get_midpoints</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">flrg</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/seasonal/sfts.html#SeasonalFTS.get_midpoints"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.seasonal.sfts.SeasonalFTS.get_midpoints" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.models.seasonal.sfts.SeasonalFTS.train">
+<code class="sig-name descname">train</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/seasonal/sfts.html#SeasonalFTS.train"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.seasonal.sfts.SeasonalFTS.train" title="Permalink to this definition">¶</a></dt>
+<dd><p>Method specific parameter fitting</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> – training time series data</p></li>
+<li><p><strong>kwargs</strong> – Method specific parameters</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
 </div>
 <div class="section" id="module-pyFTS.models.seasonal">
 <span id="module-contents"></span><h2>Module contents<a class="headerlink" href="#module-pyFTS.models.seasonal" title="Permalink to this headline">¶</a></h2>
@@ -471,11 +681,11 @@ overlapped fuzzy sets.</p>
 <li><a class="reference internal" href="#">pyFTS.models.seasonal package</a><ul>
 <li><a class="reference internal" href="#submodules">Submodules</a></li>
 <li><a class="reference internal" href="#module-pyFTS.models.seasonal.SeasonalIndexer">pyFTS.models.seasonal.SeasonalIndexer module</a></li>
-<li><a class="reference internal" href="#pyfts-models-seasonal-cmsfts-module">pyFTS.models.seasonal.cmsfts module</a></li>
+<li><a class="reference internal" href="#module-pyFTS.models.seasonal.cmsfts">pyFTS.models.seasonal.cmsfts module</a></li>
 <li><a class="reference internal" href="#module-pyFTS.models.seasonal.common">pyFTS.models.seasonal.common module</a></li>
-<li><a class="reference internal" href="#pyfts-models-seasonal-msfts-module">pyFTS.models.seasonal.msfts module</a></li>
+<li><a class="reference internal" href="#module-pyFTS.models.seasonal.msfts">pyFTS.models.seasonal.msfts module</a></li>
 <li><a class="reference internal" href="#module-pyFTS.models.seasonal.partitioner">pyFTS.models.seasonal.partitioner module</a></li>
-<li><a class="reference internal" href="#pyfts-models-seasonal-sfts-module">pyFTS.models.seasonal.sfts module</a></li>
+<li><a class="reference internal" href="#module-pyFTS.models.seasonal.sfts">pyFTS.models.seasonal.sfts module</a></li>
 <li><a class="reference internal" href="#module-pyFTS.models.seasonal">Module contents</a></li>
 </ul>
 </li>
diff --git a/docs/build/html/pyFTS.partitioners.html b/docs/build/html/pyFTS.partitioners.html
index 4b9e906..e1f4121 100644
--- a/docs/build/html/pyFTS.partitioners.html
+++ b/docs/build/html/pyFTS.partitioners.html
@@ -465,8 +465,31 @@ Comput. Math. Appl., vol. 56, no. 12, pp. 3052–3063, Dec. 2008. DOI: 10.1016/j
 </dd></dl>
 
 </div>
-<div class="section" id="pyfts-partitioners-huarng-module">
-<h2>pyFTS.partitioners.Huarng module<a class="headerlink" href="#pyfts-partitioners-huarng-module" title="Permalink to this headline">¶</a></h2>
+<div class="section" id="module-pyFTS.partitioners.Huarng">
+<span id="pyfts-partitioners-huarng-module"></span><h2>pyFTS.partitioners.Huarng module<a class="headerlink" href="#module-pyFTS.partitioners.Huarng" title="Permalink to this headline">¶</a></h2>
+<p>K. H. Huarng, “Effective lengths of intervals to improve forecasting in fuzzy time series,”
+Fuzzy Sets Syst., vol. 123, no. 3, pp. 387–394, Nov. 2001.</p>
+<dl class="py class">
+<dt id="pyFTS.partitioners.Huarng.HuarngPartitioner">
+<em class="property">class </em><code class="sig-prename descclassname">pyFTS.partitioners.Huarng.</code><code class="sig-name descname">HuarngPartitioner</code><span class="sig-paren">(</span><em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/partitioners/Huarng.html#HuarngPartitioner"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.partitioners.Huarng.HuarngPartitioner" title="Permalink to this definition">¶</a></dt>
+<dd><p>Bases: <a class="reference internal" href="#pyFTS.partitioners.partitioner.Partitioner" title="pyFTS.partitioners.partitioner.Partitioner"><code class="xref py py-class docutils literal notranslate"><span class="pre">pyFTS.partitioners.partitioner.Partitioner</span></code></a></p>
+<p>Huarng Empirical Partitioner</p>
+<dl class="py method">
+<dt id="pyFTS.partitioners.Huarng.HuarngPartitioner.build">
+<code class="sig-name descname">build</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/partitioners/Huarng.html#HuarngPartitioner.build"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.partitioners.Huarng.HuarngPartitioner.build" title="Permalink to this definition">¶</a></dt>
+<dd><p>Perform the partitioning of the Universe of Discourse</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>data</strong> – training data</p>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p></p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
 </div>
 <div class="section" id="module-pyFTS.partitioners.Singleton">
 <span id="pyfts-partitioners-singleton-module"></span><h2>pyFTS.partitioners.Singleton module<a class="headerlink" href="#module-pyFTS.partitioners.Singleton" title="Permalink to this headline">¶</a></h2>
@@ -560,11 +583,42 @@ Comput. Math. Appl., vol. 56, no. 12, pp. 3052–3063, Dec. 2008. DOI: 10.1016/j
 <dd></dd></dl>
 
 </div>
-<div class="section" id="pyfts-partitioners-util-module">
-<h2>pyFTS.partitioners.Util module<a class="headerlink" href="#pyfts-partitioners-util-module" title="Permalink to this headline">¶</a></h2>
+<div class="section" id="module-pyFTS.partitioners.Util">
+<span id="pyfts-partitioners-util-module"></span><h2>pyFTS.partitioners.Util module<a class="headerlink" href="#module-pyFTS.partitioners.Util" title="Permalink to this headline">¶</a></h2>
+<p>Facility methods for pyFTS partitioners module</p>
+<dl class="py function">
+<dt id="pyFTS.partitioners.Util.explore_partitioners">
+<code class="sig-prename descclassname">pyFTS.partitioners.Util.</code><code class="sig-name descname">explore_partitioners</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="n">npart</span></em>, <em class="sig-param"><span class="n">methods</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">mf</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">transformation</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">size</span><span class="o">=</span><span class="default_value">[12, 10]</span></em>, <em class="sig-param"><span class="n">save</span><span class="o">=</span><span class="default_value">False</span></em>, <em class="sig-param"><span class="n">file</span><span class="o">=</span><span class="default_value">None</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/partitioners/Util.html#explore_partitioners"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.partitioners.Util.explore_partitioners" title="Permalink to this definition">¶</a></dt>
+<dd><p>Create partitioners for the mf membership functions and npart partitions and show the partitioning images.
+:data: Time series data
+:npart: Maximum number of partitions of the universe of discourse
+:methods: A list with the partitioning methods to be used
+:mf: A list with the membership functions to be used
+:transformation: a transformation to be used in partitioner
+:size: list, the size of the output image [width, height]
+:save: boolean, if the image will be saved on disk
+:file: string, the file path to save the image
+:return: the list of the built partitioners</p>
+</dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.partitioners.Util.plot_partitioners">
+<code class="sig-prename descclassname">pyFTS.partitioners.Util.</code><code class="sig-name descname">plot_partitioners</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="n">objs</span></em>, <em class="sig-param"><span class="n">tam</span><span class="o">=</span><span class="default_value">[12, 10]</span></em>, <em class="sig-param"><span class="n">save</span><span class="o">=</span><span class="default_value">False</span></em>, <em class="sig-param"><span class="n">file</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">axis</span><span class="o">=</span><span class="default_value">None</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/partitioners/Util.html#plot_partitioners"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.partitioners.Util.plot_partitioners" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.partitioners.Util.plot_sets">
+<code class="sig-prename descclassname">pyFTS.partitioners.Util.</code><code class="sig-name descname">plot_sets</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="n">sets</span></em>, <em class="sig-param"><span class="n">titles</span></em>, <em class="sig-param"><span class="n">size</span><span class="o">=</span><span class="default_value">[12, 10]</span></em>, <em class="sig-param"><span class="n">save</span><span class="o">=</span><span class="default_value">False</span></em>, <em class="sig-param"><span class="n">file</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">axis</span><span class="o">=</span><span class="default_value">None</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/partitioners/Util.html#plot_sets"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.partitioners.Util.plot_sets" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
 </div>
-<div class="section" id="pyfts-partitioners-parallel-util-module">
-<h2>pyFTS.partitioners.parallel_util module<a class="headerlink" href="#pyfts-partitioners-parallel-util-module" title="Permalink to this headline">¶</a></h2>
+<div class="section" id="module-pyFTS.partitioners.parallel_util">
+<span id="pyfts-partitioners-parallel-util-module"></span><h2>pyFTS.partitioners.parallel_util module<a class="headerlink" href="#module-pyFTS.partitioners.parallel_util" title="Permalink to this headline">¶</a></h2>
+<dl class="py function">
+<dt id="pyFTS.partitioners.parallel_util.explore_partitioners">
+<code class="sig-prename descclassname">pyFTS.partitioners.parallel_util.</code><code class="sig-name descname">explore_partitioners</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="n">npart</span></em>, <em class="sig-param"><span class="n">methods</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">mf</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">tam</span><span class="o">=</span><span class="default_value">[12, 10]</span></em>, <em class="sig-param"><span class="n">save</span><span class="o">=</span><span class="default_value">False</span></em>, <em class="sig-param"><span class="n">file</span><span class="o">=</span><span class="default_value">None</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/partitioners/parallel_util.html#explore_partitioners"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.partitioners.parallel_util.explore_partitioners" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
 </div>
 </div>
 
@@ -585,12 +639,12 @@ Comput. Math. Appl., vol. 56, no. 12, pp. 3052–3063, Dec. 2008. DOI: 10.1016/j
 <li><a class="reference internal" href="#module-pyFTS.partitioners.Entropy">pyFTS.partitioners.Entropy module</a></li>
 <li><a class="reference internal" href="#module-pyFTS.partitioners.FCM">pyFTS.partitioners.FCM module</a></li>
 <li><a class="reference internal" href="#module-pyFTS.partitioners.Grid">pyFTS.partitioners.Grid module</a></li>
-<li><a class="reference internal" href="#pyfts-partitioners-huarng-module">pyFTS.partitioners.Huarng module</a></li>
+<li><a class="reference internal" href="#module-pyFTS.partitioners.Huarng">pyFTS.partitioners.Huarng module</a></li>
 <li><a class="reference internal" href="#module-pyFTS.partitioners.Singleton">pyFTS.partitioners.Singleton module</a></li>
 <li><a class="reference internal" href="#module-pyFTS.partitioners.Simple">pyFTS.partitioners.Simple module</a></li>
 <li><a class="reference internal" href="#module-pyFTS.partitioners.SubClust">pyFTS.partitioners.SubClust module</a></li>
-<li><a class="reference internal" href="#pyfts-partitioners-util-module">pyFTS.partitioners.Util module</a></li>
-<li><a class="reference internal" href="#pyfts-partitioners-parallel-util-module">pyFTS.partitioners.parallel_util module</a></li>
+<li><a class="reference internal" href="#module-pyFTS.partitioners.Util">pyFTS.partitioners.Util module</a></li>
+<li><a class="reference internal" href="#module-pyFTS.partitioners.parallel_util">pyFTS.partitioners.parallel_util module</a></li>
 </ul>
 </li>
 </ul>
diff --git a/docs/build/html/pyFTS.probabilistic.html b/docs/build/html/pyFTS.probabilistic.html
index 80a5db9..1792e93 100644
--- a/docs/build/html/pyFTS.probabilistic.html
+++ b/docs/build/html/pyFTS.probabilistic.html
@@ -68,11 +68,304 @@
 <div class="section" id="submodules">
 <h2>Submodules<a class="headerlink" href="#submodules" title="Permalink to this headline">¶</a></h2>
 </div>
-<div class="section" id="pyfts-probabilistic-probabilitydistribution-module">
-<h2>pyFTS.probabilistic.ProbabilityDistribution module<a class="headerlink" href="#pyfts-probabilistic-probabilitydistribution-module" title="Permalink to this headline">¶</a></h2>
+<div class="section" id="module-pyFTS.probabilistic.ProbabilityDistribution">
+<span id="pyfts-probabilistic-probabilitydistribution-module"></span><h2>pyFTS.probabilistic.ProbabilityDistribution module<a class="headerlink" href="#module-pyFTS.probabilistic.ProbabilityDistribution" title="Permalink to this headline">¶</a></h2>
+<dl class="py class">
+<dt id="pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution">
+<em class="property">class </em><code class="sig-prename descclassname">pyFTS.probabilistic.ProbabilityDistribution.</code><code class="sig-name descname">ProbabilityDistribution</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">type</span><span class="o">=</span><span class="default_value">'KDE'</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/probabilistic/ProbabilityDistribution.html#ProbabilityDistribution"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution" title="Permalink to this definition">¶</a></dt>
+<dd><p>Bases: <a class="reference external" href="https://docs.python.org/3/library/functions.html#object" title="(in Python v3.9)"><code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></a></p>
+<p>Represents a discrete or continous probability distribution
+If type is histogram, the PDF is discrete
+If type is KDE the PDF is continuous</p>
+<dl class="py method">
+<dt id="pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution.append">
+<code class="sig-name descname">append</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">values</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/probabilistic/ProbabilityDistribution.html#ProbabilityDistribution.append"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution.append" title="Permalink to this definition">¶</a></dt>
+<dd><p>Increment the frequency count for the values</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>values</strong> – A list of values to account the frequency</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution.append_interval">
+<code class="sig-name descname">append_interval</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">intervals</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/probabilistic/ProbabilityDistribution.html#ProbabilityDistribution.append_interval"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution.append_interval" title="Permalink to this definition">¶</a></dt>
+<dd><p>Increment the frequency count for all values inside an interval</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>intervals</strong> – A list of intervals do increment the frequency</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution.averageloglikelihood">
+<code class="sig-name descname">averageloglikelihood</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/probabilistic/ProbabilityDistribution.html#ProbabilityDistribution.averageloglikelihood"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution.averageloglikelihood" title="Permalink to this definition">¶</a></dt>
+<dd><p>Average log likelihood of the probability distribution with respect to data</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>data</strong> – </p>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt id="pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution.bins">
+<code class="sig-name descname">bins</code><a class="headerlink" href="#pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution.bins" title="Permalink to this definition">¶</a></dt>
+<dd><p>Number of bins on a discrete PDF</p>
+</dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution.build_cdf_qtl">
+<code class="sig-name descname">build_cdf_qtl</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/probabilistic/ProbabilityDistribution.html#ProbabilityDistribution.build_cdf_qtl"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution.build_cdf_qtl" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution.crossentropy">
+<code class="sig-name descname">crossentropy</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">q</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/probabilistic/ProbabilityDistribution.html#ProbabilityDistribution.crossentropy"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution.crossentropy" title="Permalink to this definition">¶</a></dt>
+<dd><p>Cross entropy between the actual probability distribution and the informed one, 
+H(P,Q) = - ∑ P(x) log ( Q(x) )</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>q</strong> – a probabilistic.ProbabilityDistribution object</p>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>Cross entropy between this probability distribution and the given distribution</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution.cumulative">
+<code class="sig-name descname">cumulative</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">values</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/probabilistic/ProbabilityDistribution.html#ProbabilityDistribution.cumulative"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution.cumulative" title="Permalink to this definition">¶</a></dt>
+<dd><p>Return the cumulative probability densities for the input values,
+such that F(x) = P(X &lt;= x)</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>values</strong> – A list of input values</p>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>The cumulative probability densities for the input values</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution.density">
+<code class="sig-name descname">density</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">values</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/probabilistic/ProbabilityDistribution.html#ProbabilityDistribution.density"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution.density" title="Permalink to this definition">¶</a></dt>
+<dd><p>Return the probability densities for the input values</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>values</strong> – List of values to return the densities</p>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>List of probability densities for the input values</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution.differential_offset">
+<code class="sig-name descname">differential_offset</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">value</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/probabilistic/ProbabilityDistribution.html#ProbabilityDistribution.differential_offset"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution.differential_offset" title="Permalink to this definition">¶</a></dt>
+<dd><p>Auxiliary function for probability distributions of differentiated data</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>value</strong> – </p>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution.empiricalloglikelihood">
+<code class="sig-name descname">empiricalloglikelihood</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/probabilistic/ProbabilityDistribution.html#ProbabilityDistribution.empiricalloglikelihood"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution.empiricalloglikelihood" title="Permalink to this definition">¶</a></dt>
+<dd><p>Empirical Log Likelihood of the probability distribution, L(P) = ∑ log( P(x) )</p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns</dt>
+<dd class="field-odd"><p></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution.entropy">
+<code class="sig-name descname">entropy</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/probabilistic/ProbabilityDistribution.html#ProbabilityDistribution.entropy"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution.entropy" title="Permalink to this definition">¶</a></dt>
+<dd><p>Return the entropy of the probability distribution, H(P) = E[ -ln P(X) ] = - ∑ P(x) log ( P(x) )</p>
+<p>:return:the entropy of the probability distribution</p>
+</dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution.expected_value">
+<code class="sig-name descname">expected_value</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/probabilistic/ProbabilityDistribution.html#ProbabilityDistribution.expected_value"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution.expected_value" title="Permalink to this definition">¶</a></dt>
+<dd><p>Return the expected value of the distribution, as E[X] = ∑ x * P(x)</p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns</dt>
+<dd class="field-odd"><p>The expected value of the distribution</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution.kullbackleiblerdivergence">
+<code class="sig-name descname">kullbackleiblerdivergence</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">q</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/probabilistic/ProbabilityDistribution.html#ProbabilityDistribution.kullbackleiblerdivergence"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution.kullbackleiblerdivergence" title="Permalink to this definition">¶</a></dt>
+<dd><p>Kullback-Leibler divergence between the actual probability distribution and the informed one.
+DKL(P || Q) = - ∑ P(x) log( P(X) / Q(x) )</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>q</strong> – a probabilistic.ProbabilityDistribution object</p>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>Kullback-Leibler divergence</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt id="pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution.labels">
+<code class="sig-name descname">labels</code><a class="headerlink" href="#pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution.labels" title="Permalink to this definition">¶</a></dt>
+<dd><p>Bins labels on a discrete PDF</p>
+</dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution.plot">
+<code class="sig-name descname">plot</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">axis</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">color</span><span class="o">=</span><span class="default_value">'black'</span></em>, <em class="sig-param"><span class="n">tam</span><span class="o">=</span><span class="default_value">[10, 6]</span></em>, <em class="sig-param"><span class="n">title</span><span class="o">=</span><span class="default_value">None</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/probabilistic/ProbabilityDistribution.html#ProbabilityDistribution.plot"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution.plot" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution.pseudologlikelihood">
+<code class="sig-name descname">pseudologlikelihood</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/probabilistic/ProbabilityDistribution.html#ProbabilityDistribution.pseudologlikelihood"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution.pseudologlikelihood" title="Permalink to this definition">¶</a></dt>
+<dd><p>Pseudo log likelihood of the probability distribution with respect to data</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>data</strong> – </p>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution.quantile">
+<code class="sig-name descname">quantile</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">values</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/probabilistic/ProbabilityDistribution.html#ProbabilityDistribution.quantile"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution.quantile" title="Permalink to this definition">¶</a></dt>
+<dd><p>Return the Universe of Discourse values in relation to the quantile input values,
+such that Q(tau) = min( {x | F(x) &gt;= tau })</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>values</strong> – input values</p>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>The list of the quantile values for the input values</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution.set">
+<code class="sig-name descname">set</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">value</span></em>, <em class="sig-param"><span class="n">density</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/probabilistic/ProbabilityDistribution.html#ProbabilityDistribution.set"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution.set" title="Permalink to this definition">¶</a></dt>
+<dd><p>Assert a probability ‘density’ for a certain value ‘value’, such that P(value) = density</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>value</strong> – A value in the universe of discourse from the distribution</p></li>
+<li><p><strong>density</strong> – The probability density to assign to the value</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt id="pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution.type">
+<code class="sig-name descname">type</code><a class="headerlink" href="#pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution.type" title="Permalink to this definition">¶</a></dt>
+<dd><p>If type is histogram, the PDF is discrete
+If type is KDE the PDF is continuous</p>
+</dd></dl>
+
+<dl class="py attribute">
+<dt id="pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution.uod">
+<code class="sig-name descname">uod</code><a class="headerlink" href="#pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution.uod" title="Permalink to this definition">¶</a></dt>
+<dd><p>Universe of discourse</p>
+</dd></dl>
+
+</dd></dl>
+
+<dl class="py function">
+<dt id="pyFTS.probabilistic.ProbabilityDistribution.from_point">
+<code class="sig-prename descclassname">pyFTS.probabilistic.ProbabilityDistribution.</code><code class="sig-name descname">from_point</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">x</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/probabilistic/ProbabilityDistribution.html#from_point"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.probabilistic.ProbabilityDistribution.from_point" title="Permalink to this definition">¶</a></dt>
+<dd><p>Create a probability distribution from a scalar value</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>x</strong> – scalar value</p></li>
+<li><p><strong>kwargs</strong> – common parameters of the distribution</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>the ProbabilityDistribution object</p>
+</dd>
+</dl>
+</dd></dl>
+
 </div>
-<div class="section" id="pyfts-probabilistic-kde-module">
-<h2>pyFTS.probabilistic.kde module<a class="headerlink" href="#pyfts-probabilistic-kde-module" title="Permalink to this headline">¶</a></h2>
+<div class="section" id="module-pyFTS.probabilistic.kde">
+<span id="pyfts-probabilistic-kde-module"></span><h2>pyFTS.probabilistic.kde module<a class="headerlink" href="#module-pyFTS.probabilistic.kde" title="Permalink to this headline">¶</a></h2>
+<p>Kernel Density Estimation</p>
+<dl class="py class">
+<dt id="pyFTS.probabilistic.kde.KernelSmoothing">
+<em class="property">class </em><code class="sig-prename descclassname">pyFTS.probabilistic.kde.</code><code class="sig-name descname">KernelSmoothing</code><span class="sig-paren">(</span><em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/probabilistic/kde.html#KernelSmoothing"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.probabilistic.kde.KernelSmoothing" title="Permalink to this definition">¶</a></dt>
+<dd><p>Bases: <a class="reference external" href="https://docs.python.org/3/library/functions.html#object" title="(in Python v3.9)"><code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></a></p>
+<p>Kernel Density Estimation</p>
+<dl class="py attribute">
+<dt id="pyFTS.probabilistic.kde.KernelSmoothing.h">
+<code class="sig-name descname">h</code><a class="headerlink" href="#pyFTS.probabilistic.kde.KernelSmoothing.h" title="Permalink to this definition">¶</a></dt>
+<dd><p>Width parameter</p>
+</dd></dl>
+
+<dl class="py attribute">
+<dt id="pyFTS.probabilistic.kde.KernelSmoothing.kernel">
+<code class="sig-name descname">kernel</code><a class="headerlink" href="#pyFTS.probabilistic.kde.KernelSmoothing.kernel" title="Permalink to this definition">¶</a></dt>
+<dd><p>Kernel function</p>
+</dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.probabilistic.kde.KernelSmoothing.kernel_function">
+<code class="sig-name descname">kernel_function</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">u</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/probabilistic/kde.html#KernelSmoothing.kernel_function"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.probabilistic.kde.KernelSmoothing.kernel_function" title="Permalink to this definition">¶</a></dt>
+<dd><p>Apply the kernel</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>u</strong> – </p>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt id="pyFTS.probabilistic.kde.KernelSmoothing.probability">
+<code class="sig-name descname">probability</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">x</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/probabilistic/kde.html#KernelSmoothing.probability"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.probabilistic.kde.KernelSmoothing.probability" title="Permalink to this definition">¶</a></dt>
+<dd><p>Probability of the point x on data</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>x</strong> – </p></li>
+<li><p><strong>data</strong> – </p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p></p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
 </div>
 </div>
 
@@ -88,8 +381,8 @@
 <li><a class="reference internal" href="#">pyFTS.probabilistic package</a><ul>
 <li><a class="reference internal" href="#module-pyFTS.probabilistic">Module contents</a></li>
 <li><a class="reference internal" href="#submodules">Submodules</a></li>
-<li><a class="reference internal" href="#pyfts-probabilistic-probabilitydistribution-module">pyFTS.probabilistic.ProbabilityDistribution module</a></li>
-<li><a class="reference internal" href="#pyfts-probabilistic-kde-module">pyFTS.probabilistic.kde module</a></li>
+<li><a class="reference internal" href="#module-pyFTS.probabilistic.ProbabilityDistribution">pyFTS.probabilistic.ProbabilityDistribution module</a></li>
+<li><a class="reference internal" href="#module-pyFTS.probabilistic.kde">pyFTS.probabilistic.kde module</a></li>
 </ul>
 </li>
 </ul>
diff --git a/docs/build/html/searchindex.js b/docs/build/html/searchindex.js
index d28c025..585b492 100644
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Start"],titleterms:{"short":17,FTS:17,adapativeexpect:5,airpasseng:6,arima:3,artifici:6,benchmark:3,bitcoin:6,boxcox:5,bst:3,chaotic:6,chen:9,cheng:9,cmean:15,cmsft:14,cmvft:12,common:[4,5,6,12,13,14],composit:4,conf:2,content:[2,3,4,5,6,7,8,9,10,11,12,13,14,15,16],cvft:13,data:6,dataset:6,differenti:5,dispi:7,distribut:7,dowjon:6,enrol:6,ensembl:10,entropi:15,ethereum:6,eur:6,evolutionari:8,exampl:17,fcm:15,flr:[4,12],flrg:[4,12,13],fts:4,fuzzi:[0,17],fuzzyset:4,gaussianproc:3,gbp:6,gener:6,glass:6,granular:12,grid:[12,15],gridsearch:8,henon:6,hoft:9,honsft:13,how:[0,17],huarng:15,hwang:9,hyperparam:8,ift:9,increment:11,incrementalensembl:11,index:0,inmet:6,instal:17,ismailefendi:9,kde:16,knn:3,librari:0,logistic_map:6,lorentz:6,mackei:6,malaysia:6,measur:3,membership:4,model:[9,10,11,12,13,14],modul:[2,3,4,5,6,7,8,9,10,11,12,13,14,15,16],msft:14,multiseason:10,multivari:12,mvft:12,naiv:3,nasdaq:6,nonstationari:13,normal:5,nsft:13,packag:[2,3,4,5,6,7,8,9,10,11,12,13,14,15,16],parallel_util:15,partition:[12,13,14,15],perturb:13,probabilist:16,probabilitydistribut:16,pwft:9,pyft:[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17],python:0,quantreg:3,quick:17,refer:0,residualanalysi:3,roi:5,rossler:6,sadaei:9,scale:5,season:14,seasonalindex:14,seri:[0,6,17],sft:14,simpl:15,singleton:15,smooth:5,som:5,sonda:6,song:9,sortedcollect:4,spark:7,start:17,subclust:15,submodul:[2,3,4,5,6,7,8,9,10,11,12,13,14,15,16],subpackag:[2,9],sunspot:6,synthet:6,taiex:6,test:3,time:[0,6,17],timevari:11,transform:5,tree:4,trend:5,tutori:17,usag:17,usd:6,util:[3,4,8,13,15],variabl:12,what:[0,17],wmvft:12}})
\ No newline at end of file
diff --git a/docs/pyFTS.common.rst b/docs/pyFTS.common.rst
index 1e6db4b..412bd44 100644
--- a/docs/pyFTS.common.rst
+++ b/docs/pyFTS.common.rst
@@ -52,14 +52,6 @@ pyFTS.common.SortedCollection module
     :undoc-members:
     :show-inheritance:
 
-pyFTS.common.transformations module
------------------------------------
-
-.. automodule:: pyFTS.common.Transformations
-    :members:
-    :undoc-members:
-    :show-inheritance:
-
 pyFTS.common.Util module
 ------------------------
 
diff --git a/docs/pyFTS.common.transformations.rst b/docs/pyFTS.common.transformations.rst
index a604d7d..089f361 100644
--- a/docs/pyFTS.common.transformations.rst
+++ b/docs/pyFTS.common.transformations.rst
@@ -60,6 +60,14 @@ pyFTS.common.transformations.scale module
     :undoc-members:
     :show-inheritance:
 
+pyFTS.common.transformations.smoothing module
+---------------------------------------------
+
+.. automodule:: pyFTS.common.transformations.smoothing
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
 pyFTS.common.transformations.som module
 ---------------------------------------
 
diff --git a/pyFTS/common/Transformations.py b/pyFTS/common/Transformations.py
index 205251b..c911c1b 100644
--- a/pyFTS/common/Transformations.py
+++ b/pyFTS/common/Transformations.py
@@ -5,15 +5,15 @@ Common data transformation used on pre and post processing of the FTS
 import numpy as np
 import pandas as pd
 import math
-from pyFTS.common.transformations.transformation import Transformation
-from pyFTS.common.transformations.differential import Differential
-from pyFTS.common.transformations.scale import Scale
-from pyFTS.common.transformations.adaptiveexpectation import AdaptiveExpectation 
-from pyFTS.common.transformations.boxcox import BoxCox
-from pyFTS.common.transformations.roi import ROI
-from pyFTS.common.transformations.trend import LinearTrend
-from pyFTS.common.transformations.som import SOMTransformation
-from pyFTS.common.transformations.normalization import Normalization
+#from pyFTS.common.transformations.transformation import Transformation
+#from pyFTS.common.transformations.differential import Differential
+#from pyFTS.common.transformations.scale import Scale
+#from pyFTS.common.transformations.adaptiveexpectation import AdaptiveExpectation 
+#from pyFTS.common.transformations.boxcox import BoxCox
+#from pyFTS.common.transformations.roi import ROI
+#from pyFTS.common.transformations.trend import LinearTrend
+#from pyFTS.common.transformations.som import SOMTransformation
+#from pyFTS.common.transformations.normalization import Normalization
 
 
 
diff --git a/pyFTS/common/transformations/som.py b/pyFTS/common/transformations/som.py
index 7a703c9..e98b48b 100644
--- a/pyFTS/common/transformations/som.py
+++ b/pyFTS/common/transformations/som.py
@@ -2,7 +2,6 @@
 Kohonen Self Organizing Maps for Fuzzy Time Series
 """
 import pandas as pd
-import SimpSOM as sps
 from pyFTS.models.multivariate import wmvfts
 from typing import Tuple
 from pyFTS.common.Transformations import Transformation
@@ -14,6 +13,9 @@ class SOMTransformation(Transformation):
     def __init__(self,
                  grid_dimension: Tuple,
                  **kwargs):
+
+        import SimpSOM as sps
+        
         # SOM attributes
         self.load_file = kwargs.get('loadFile')
         self.net: sps.somNet = None