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Configuration and documentation update due the release 1.6
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<title>pyFTS.models.ensemble package &#8212; pyFTS 1.6 documentation</title>
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Incremental module
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<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-1"><a href="modules.html" >pyFTS</a> &#187;</li>
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<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="#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>
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<p class="topless"><a href="pyFTS.models.html"
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Incremental module
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<p class="topless"><a href="pyFTS.models.incremental.html"
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<div class="document">
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<div class="body" role="main">
<div class="section" id="pyfts-models-ensemble-package">
<h1>pyFTS.models.ensemble package<a class="headerlink" href="#pyfts-models-ensemble-package" title="Permalink to this headline"></a></h1>
<div class="section" id="submodules">
<h2>Submodules<a class="headerlink" href="#submodules" title="Permalink to this headline"></a></h2>
</div>
<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>
Incremental module
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<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>
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<dl class="class">
<dt id="pyFTS.models.ensemble.ensemble.AllMethodEnsembleFTS">
New benchmarks.Tests module; Improvements on forecast_ahead_* function; Documentation update
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<em class="property">class </em><code class="descclassname">pyFTS.models.ensemble.ensemble.</code><code class="descname">AllMethodEnsembleFTS</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.models.ensemble.ensemble.AllMethodEnsembleFTS" title="Permalink to this definition"></a></dt>
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<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>
Incremental module
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<p>Creates an EnsembleFTS with all point forecast methods, sharing the same partitioner</p>
Sphinx documentation for ReadTheDocs site
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<dl class="method">
<dt id="pyFTS.models.ensemble.ensemble.AllMethodEnsembleFTS.set_transformations">
New benchmarks.Tests module; Improvements on forecast_ahead_* function; Documentation update
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<code class="descname">set_transformations</code><span class="sig-paren">(</span><em>model</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.models.ensemble.ensemble.AllMethodEnsembleFTS.set_transformations" title="Permalink to this definition"></a></dt>
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<dd></dd></dl>
<dl class="method">
<dt id="pyFTS.models.ensemble.ensemble.AllMethodEnsembleFTS.train">
New benchmarks.Tests module; Improvements on forecast_ahead_* function; Documentation update
2019-06-06 18:04:20 +04:00
<code class="descname">train</code><span class="sig-paren">(</span><em>data</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.models.ensemble.ensemble.AllMethodEnsembleFTS.train" title="Permalink to this definition"></a></dt>
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<dd><p>Method specific parameter fitting</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 last simple">
<li><strong>data</strong> – training time series data</li>
<li><strong>kwargs</strong> – Method specific parameters</li>
</ul>
</td>
</tr>
</tbody>
</table>
</dd></dl>
</dd></dl>
<dl class="class">
<dt id="pyFTS.models.ensemble.ensemble.EnsembleFTS">
New benchmarks.Tests module; Improvements on forecast_ahead_* function; Documentation update
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<em class="property">class </em><code class="descclassname">pyFTS.models.ensemble.ensemble.</code><code class="descname">EnsembleFTS</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.models.ensemble.ensemble.EnsembleFTS" title="Permalink to this definition"></a></dt>
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<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="method">
<dt id="pyFTS.models.ensemble.ensemble.EnsembleFTS.append_model">
New benchmarks.Tests module; Improvements on forecast_ahead_* function; Documentation update
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<code class="descname">append_model</code><span class="sig-paren">(</span><em>model</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.models.ensemble.ensemble.EnsembleFTS.append_model" title="Permalink to this definition"></a></dt>
Incremental module
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<dd><p>Append a new trained model to the ensemble</p>
Sphinx documentation for ReadTheDocs site
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<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"><strong>model</strong> – FTS model</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="pyFTS.models.ensemble.ensemble.EnsembleFTS.forecast">
New benchmarks.Tests module; Improvements on forecast_ahead_* function; Documentation update
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<code class="descname">forecast</code><span class="sig-paren">(</span><em>data</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.models.ensemble.ensemble.EnsembleFTS.forecast" title="Permalink to this definition"></a></dt>
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<dd><p>Point 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> – time series data 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 list with the forecasted values</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="pyFTS.models.ensemble.ensemble.EnsembleFTS.forecast_ahead_distribution">
New benchmarks.Tests module; Improvements on forecast_ahead_* function; Documentation update
2019-06-06 18:04:20 +04:00
<code class="descname">forecast_ahead_distribution</code><span class="sig-paren">(</span><em>data</em>, <em>steps</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.models.ensemble.ensemble.EnsembleFTS.forecast_ahead_distribution" title="Permalink to this definition"></a></dt>
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<dd><p>Probabilistic forecast n steps 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> – time series data 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>
New benchmarks.Tests module; Improvements on forecast_ahead_* function; Documentation update
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<li><strong>start_at</strong> – in the multi step forecasting, the index of the data where to start forecasting (default: 0)</li>
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</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">a list with the forecasted Probability Distributions</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="pyFTS.models.ensemble.ensemble.EnsembleFTS.forecast_ahead_interval">
New benchmarks.Tests module; Improvements on forecast_ahead_* function; Documentation update
2019-06-06 18:04:20 +04:00
<code class="descname">forecast_ahead_interval</code><span class="sig-paren">(</span><em>data</em>, <em>steps</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.models.ensemble.ensemble.EnsembleFTS.forecast_ahead_interval" title="Permalink to this definition"></a></dt>
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<dd><p>Interval forecast n steps 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> – time series data 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>
New benchmarks.Tests module; Improvements on forecast_ahead_* function; Documentation update
2019-06-06 18:04:20 +04:00
<li><strong>start_at</strong> – in the multi step forecasting, the index of the data where to start forecasting (default: 0)</li>
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</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">a list with the forecasted intervals</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="pyFTS.models.ensemble.ensemble.EnsembleFTS.forecast_distribution">
New benchmarks.Tests module; Improvements on forecast_ahead_* function; Documentation update
2019-06-06 18:04:20 +04:00
<code class="descname">forecast_distribution</code><span class="sig-paren">(</span><em>data</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.models.ensemble.ensemble.EnsembleFTS.forecast_distribution" title="Permalink to this definition"></a></dt>
Sphinx documentation for ReadTheDocs site
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<dd><p>Probabilistic 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> – time series data with the minimal length equal to the max_lag of the model</li>
<li><strong>kwargs</strong> – model specific parameters</li>
</ul>
</td>
</tr>
Documentation update
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<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">a list with probabilistic.ProbabilityDistribution objects representing the forecasted Probability Distributions</p>
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</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="pyFTS.models.ensemble.ensemble.EnsembleFTS.forecast_interval">
New benchmarks.Tests module; Improvements on forecast_ahead_* function; Documentation update
2019-06-06 18:04:20 +04:00
<code class="descname">forecast_interval</code><span class="sig-paren">(</span><em>data</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.models.ensemble.ensemble.EnsembleFTS.forecast_interval" title="Permalink to this definition"></a></dt>
Sphinx documentation for ReadTheDocs site
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<dd><p>Interval 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> – time series data with the minimal length equal to the max_lag of the model</li>
<li><strong>kwargs</strong> – model specific parameters</li>
</ul>
</td>
</tr>
Documentation update
2018-10-29 23:48:05 +04:00
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">a list with the prediction intervals</p>
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</td>
</tr>
</tbody>
</table>
</dd></dl>
New benchmarks.Tests module; Improvements on forecast_ahead_* function; Documentation update
2019-06-06 18:04:20 +04:00
<dl class="method">
<dt id="pyFTS.models.ensemble.ensemble.EnsembleFTS.get_UoD">
<code class="descname">get_UoD</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.models.ensemble.ensemble.EnsembleFTS.get_UoD" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
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<dl class="method">
<dt id="pyFTS.models.ensemble.ensemble.EnsembleFTS.get_distribution_interquantile">
New benchmarks.Tests module; Improvements on forecast_ahead_* function; Documentation update
2019-06-06 18:04:20 +04:00
<code class="descname">get_distribution_interquantile</code><span class="sig-paren">(</span><em>forecasts</em>, <em>alpha</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.models.ensemble.ensemble.EnsembleFTS.get_distribution_interquantile" title="Permalink to this definition"></a></dt>
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<dd></dd></dl>
<dl class="method">
<dt id="pyFTS.models.ensemble.ensemble.EnsembleFTS.get_interval">
New benchmarks.Tests module; Improvements on forecast_ahead_* function; Documentation update
2019-06-06 18:04:20 +04:00
<code class="descname">get_interval</code><span class="sig-paren">(</span><em>forecasts</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.models.ensemble.ensemble.EnsembleFTS.get_interval" title="Permalink to this definition"></a></dt>
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<dd></dd></dl>
<dl class="method">
<dt id="pyFTS.models.ensemble.ensemble.EnsembleFTS.get_models_forecasts">
New benchmarks.Tests module; Improvements on forecast_ahead_* function; Documentation update
2019-06-06 18:04:20 +04:00
<code class="descname">get_models_forecasts</code><span class="sig-paren">(</span><em>data</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.models.ensemble.ensemble.EnsembleFTS.get_models_forecasts" title="Permalink to this definition"></a></dt>
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<dd></dd></dl>
<dl class="method">
<dt id="pyFTS.models.ensemble.ensemble.EnsembleFTS.get_point">
New benchmarks.Tests module; Improvements on forecast_ahead_* function; Documentation update
2019-06-06 18:04:20 +04:00
<code class="descname">get_point</code><span class="sig-paren">(</span><em>forecasts</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.models.ensemble.ensemble.EnsembleFTS.get_point" title="Permalink to this definition"></a></dt>
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<dd></dd></dl>
New benchmarks.Tests module; Improvements on forecast_ahead_* function; Documentation update
2019-06-06 18:04:20 +04:00
<dl class="method">
<dt id="pyFTS.models.ensemble.ensemble.EnsembleFTS.train">
<code class="descname">train</code><span class="sig-paren">(</span><em>data</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.models.ensemble.ensemble.EnsembleFTS.train" title="Permalink to this definition"></a></dt>
<dd><p>Method specific parameter fitting</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 last simple">
<li><strong>data</strong> – training time series data</li>
<li><strong>kwargs</strong> – Method specific parameters</li>
</ul>
</td>
</tr>
</tbody>
</table>
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</dd></dl>
Incremental module
2018-11-01 18:11:20 +04:00
</dd></dl>
New benchmarks.Tests module; Improvements on forecast_ahead_* function; Documentation update
2019-06-06 18:04:20 +04:00
<dl class="class">
<dt id="pyFTS.models.ensemble.ensemble.SimpleEnsembleFTS">
<em class="property">class </em><code class="descclassname">pyFTS.models.ensemble.ensemble.</code><code class="descname">SimpleEnsembleFTS</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><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>
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<dl class="method">
New benchmarks.Tests module; Improvements on forecast_ahead_* function; Documentation update
2019-06-06 18:04:20 +04:00
<dt id="pyFTS.models.ensemble.ensemble.SimpleEnsembleFTS.train">
<code class="descname">train</code><span class="sig-paren">(</span><em>data</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.models.ensemble.ensemble.SimpleEnsembleFTS.train" title="Permalink to this definition"></a></dt>
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<dd><p>Method specific parameter fitting</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 last simple">
<li><strong>data</strong> – training time series data</li>
<li><strong>kwargs</strong> – Method specific parameters</li>
</ul>
</td>
</tr>
</tbody>
</table>
</dd></dl>
</dd></dl>
<dl class="function">
<dt id="pyFTS.models.ensemble.ensemble.sampler">
New benchmarks.Tests module; Improvements on forecast_ahead_* function; Documentation update
2019-06-06 18:04:20 +04:00
<code class="descclassname">pyFTS.models.ensemble.ensemble.</code><code class="descname">sampler</code><span class="sig-paren">(</span><em>data</em>, <em>quantiles</em>, <em>bounds=False</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.models.ensemble.ensemble.sampler" title="Permalink to this definition"></a></dt>
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<dd></dd></dl>
</div>
<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>
Incremental module
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<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>
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<dl class="class">
<dt id="pyFTS.models.ensemble.multiseasonal.SeasonalEnsembleFTS">
New benchmarks.Tests module; Improvements on forecast_ahead_* function; Documentation update
2019-06-06 18:04:20 +04:00
<em class="property">class </em><code class="descclassname">pyFTS.models.ensemble.multiseasonal.</code><code class="descname">SeasonalEnsembleFTS</code><span class="sig-paren">(</span><em>name</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.models.ensemble.multiseasonal.SeasonalEnsembleFTS" title="Permalink to this definition"></a></dt>
Sphinx documentation for ReadTheDocs site
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<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="method">
<dt id="pyFTS.models.ensemble.multiseasonal.SeasonalEnsembleFTS.forecast_distribution">
New benchmarks.Tests module; Improvements on forecast_ahead_* function; Documentation update
2019-06-06 18:04:20 +04:00
<code class="descname">forecast_distribution</code><span class="sig-paren">(</span><em>data</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.models.ensemble.multiseasonal.SeasonalEnsembleFTS.forecast_distribution" title="Permalink to this definition"></a></dt>
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<dd><p>Probabilistic 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> – time series data with the minimal length equal to the max_lag of the model</li>
<li><strong>kwargs</strong> – model specific parameters</li>
</ul>
</td>
</tr>
Documentation update
2018-10-29 23:48:05 +04:00
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">a list with probabilistic.ProbabilityDistribution objects representing the forecasted Probability Distributions</p>
Sphinx documentation for ReadTheDocs site
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</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="pyFTS.models.ensemble.multiseasonal.SeasonalEnsembleFTS.train">
New benchmarks.Tests module; Improvements on forecast_ahead_* function; Documentation update
2019-06-06 18:04:20 +04:00
<code class="descname">train</code><span class="sig-paren">(</span><em>data</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.models.ensemble.multiseasonal.SeasonalEnsembleFTS.train" title="Permalink to this definition"></a></dt>
Sphinx documentation for ReadTheDocs site
2018-08-30 09:05:29 +04:00
<dd><p>Method specific parameter fitting</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 last simple">
<li><strong>data</strong> – training time series data</li>
<li><strong>kwargs</strong> – Method specific parameters</li>
</ul>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="pyFTS.models.ensemble.multiseasonal.SeasonalEnsembleFTS.update_uod">
New benchmarks.Tests module; Improvements on forecast_ahead_* function; Documentation update
2019-06-06 18:04:20 +04:00
<code class="descname">update_uod</code><span class="sig-paren">(</span><em>data</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.models.ensemble.multiseasonal.SeasonalEnsembleFTS.update_uod" title="Permalink to this definition"></a></dt>
Sphinx documentation for ReadTheDocs site
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<dd></dd></dl>
</dd></dl>
<dl class="function">
<dt id="pyFTS.models.ensemble.multiseasonal.train_individual_model">
New benchmarks.Tests module; Improvements on forecast_ahead_* function; Documentation update
2019-06-06 18:04:20 +04:00
<code class="descclassname">pyFTS.models.ensemble.multiseasonal.</code><code class="descname">train_individual_model</code><span class="sig-paren">(</span><em>partitioner</em>, <em>train_data</em>, <em>indexer</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.models.ensemble.multiseasonal.train_individual_model" title="Permalink to this definition"></a></dt>
Sphinx documentation for ReadTheDocs site
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<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>
<p>Meta FTS that aggregates other FTS methods</p>
</div>
</div>
</div>
</div>
</div>
<div class="clearer"></div>
</div>
Updating the documentation
2018-08-30 23:04:52 +04:00
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