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<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>
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<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>
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<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>
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<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>
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