Updating documentation

docs/build/html/_modules/pyFTS/common/fts.html

@@ -162,7 +162,7 @@
 
 <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 or distribution.</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 horizon, i. e., the number of steps ahead to forecast</span>
 <span class="sd">        :keyword start: in the multi step forecasting, the index of the data where to start forecasting</span>
 <span class="sd">        :keyword distributed: boolean, indicate if the forecasting procedure will be distributed in a dispy cluster</span>
@@ -204,6 +204,8 @@
                     <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">kwargs</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">kwargs</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">kwargs</span><span class="p">)</span>
             <span class="k">elif</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">kwargs</span><span class="p">)</span>
@@ -211,8 +213,10 @@
                     <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">kwargs</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">kwargs</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="o">**</span><span class="n">kwargs</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="o">.</span><span class="fm">__contains__</span><span class="p">(</span><span class="nb">type</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>
@@ -258,6 +262,16 @@
 <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 n steps ahead</span>
@@ -307,6 +321,17 @@
 <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">        :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.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>

docs/build/html/_modules/pyFTS/models/multivariate/cmvfts.html

@@ -74,6 +74,7 @@
   <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="k">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="k">import</span> <span class="n">hofts</span>
 <span class="kn">from</span> <span class="nn">pyFTS.models.multivariate</span> <span class="k">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>
@@ -129,24 +130,38 @@
         <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">order</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="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="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">cluster</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">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">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">ndata</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">ndata</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="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="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</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></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">ret</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="bp">self</span><span class="o">.</span><span class="n">cluster</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="n">ret</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="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">columns</span> <span class="o">=</span> <span class="n">ret</span><span class="o">.</span><span class="n">keys</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">columns</span><span class="p">)</span></div>
+
     <span class="k">def</span> <span class="nf">__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>

docs/build/html/genindex.html

@@ -322,6 +322,8 @@
         <li><a href="pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.common.check_bounds_index">(in module pyFTS.models.nonstationary.common)</a>
 </li>
       </ul></li>
+      <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>
@@ -345,11 +347,11 @@
       <li><a href="pyFTS.models.multivariate.html#pyFTS.models.multivariate.cmvfts.ClusteredMVFTS.cluster_params">cluster_params (pyFTS.models.multivariate.cmvfts.ClusteredMVFTS attribute)</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>
   </ul></td>
   <td style="width: 33%; vertical-align: top;"><ul>
+      <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.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>
@@ -614,6 +616,8 @@
         <li><a href="pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFTS.forecast_ahead_interval">(pyFTS.models.pwfts.ProbabilisticWeightedFTS method)</a>
 </li>
       </ul></li>
+      <li><a href="pyFTS.common.html#pyFTS.common.fts.FTS.forecast_ahead_multivariate">forecast_ahead_multivariate() (pyFTS.common.fts.FTS method)</a>
+</li>
       <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.arima.ARIMA.forecast_distribution">forecast_distribution() (pyFTS.benchmarks.arima.ARIMA method)</a>
 
       <ul>
@@ -648,6 +652,12 @@
         <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.models.multivariate.html#pyFTS.models.multivariate.mvfts.MVFTS.format_data">format_data() (pyFTS.models.multivariate.mvfts.MVFTS method)</a>

docs/build/html/pyFTS.common.html

@@ -1667,6 +1667,28 @@ when the LHS pattern is identified on time t.</p>
 </table>
 </dd></dl>
 
+<dl class="method">
+<dt id="pyFTS.common.fts.FTS.forecast_ahead_multivariate">
+<code class="descname">forecast_ahead_multivariate</code><span class="sig-paren">(</span><em>data</em>, <em>steps</em>, <em>**kwargs</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>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name" />
+<col class="field-body" />
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
+<li><strong>data</strong> – Pandas dataframe with one column for each variable and with the minimal length equal to the max_lag of the model</li>
+<li><strong>steps</strong> – the number of steps ahead to forecast</li>
+<li><strong>kwargs</strong> – model specific parameters</li>
+</ul>
+</td>
+</tr>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">a Pandas Dataframe object representing the forecasted values for each variable</p>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+
 <dl class="method">
 <dt id="pyFTS.common.fts.FTS.forecast_distribution">
 <code class="descname">forecast_distribution</code><span class="sig-paren">(</span><em>data</em>, <em>**kwargs</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>
@@ -1709,6 +1731,27 @@ when the LHS pattern is identified on time t.</p>
 </table>
 </dd></dl>
 
+<dl class="method">
+<dt id="pyFTS.common.fts.FTS.forecast_multivariate">
+<code class="descname">forecast_multivariate</code><span class="sig-paren">(</span><em>data</em>, <em>**kwargs</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>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name" />
+<col class="field-body" />
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
+<li><strong>data</strong> – Pandas dataframe with one column for each variable and with the minimal length equal to the max_lag of the model</li>
+<li><strong>kwargs</strong> – model specific parameters</li>
+</ul>
+</td>
+</tr>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">a Pandas Dataframe object representing the forecasted values for each variable</p>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+
 <dl class="method">
 <dt id="pyFTS.common.fts.FTS.fuzzy">
 <code class="descname">fuzzy</code><span class="sig-paren">(</span><em>data</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>
@@ -1846,7 +1889,7 @@ needed to forecast a single step ahead</p>
 <tbody valign="top">
 <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
 <li><strong>data</strong> – time series with minimal length to the order of the model</li>
-<li><strong>type</strong> – the forecasting type, one of these values: point(default), interval or distribution.</li>
+<li><strong>type</strong> – the forecasting type, one of these values: point(default), interval, distribution or multivariate.</li>
 <li><strong>steps_ahead</strong> – The forecasting horizon, i. e., the number of steps ahead to forecast</li>
 <li><strong>start</strong> – in the multi step forecasting, the index of the data where to start forecasting</li>
 <li><strong>distributed</strong> – boolean, indicate if the forecasting procedure will be distributed in a dispy cluster</li>

docs/build/html/pyFTS.models.multivariate.html

@@ -487,6 +487,11 @@ transformations and partitioners.</p>
 <em class="property">class </em><code class="descclassname">pyFTS.models.multivariate.cmvfts.</code><code class="descname">ClusteredMVFTS</code><span class="sig-paren">(</span><em>**kwargs</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 multivariate, high order, clustered multivariate FTS</p>
+<dl class="method">
+<dt id="pyFTS.models.multivariate.cmvfts.ClusteredMVFTS.check_data">
+<code class="descname">check_data</code><span class="sig-paren">(</span><em>data</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="attribute">
 <dt id="pyFTS.models.multivariate.cmvfts.ClusteredMVFTS.cluster">
 <code class="descname">cluster</code><em class="property"> = None</em><a class="headerlink" href="#pyFTS.models.multivariate.cmvfts.ClusteredMVFTS.cluster" title="Permalink to this definition">¶</a></dt>
@@ -526,6 +531,27 @@ transformations and partitioners.</p>
 </table>
 </dd></dl>
 
+<dl class="method">
+<dt id="pyFTS.models.multivariate.cmvfts.ClusteredMVFTS.forecast_multivariate">
+<code class="descname">forecast_multivariate</code><span class="sig-paren">(</span><em>data</em>, <em>**kwargs</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>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name" />
+<col class="field-body" />
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
+<li><strong>data</strong> – Pandas dataframe with one column for each variable and with the minimal length equal to the max_lag of the model</li>
+<li><strong>kwargs</strong> – model specific parameters</li>
+</ul>
+</td>
+</tr>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">a Pandas Dataframe object representing the forecasted values for each variable</p>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+
 <dl class="attribute">
 <dt id="pyFTS.models.multivariate.cmvfts.ClusteredMVFTS.fts_method">
 <code class="descname">fts_method</code><em class="property"> = None</em><a class="headerlink" href="#pyFTS.models.multivariate.cmvfts.ClusteredMVFTS.fts_method" title="Permalink to this definition">¶</a></dt>