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<li><a class="reference internal" href="#">pyFTS.models.ensemble package</a><ul>
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<li><a class="reference internal" href="#submodules">Submodules</a></li>
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<li><a class="reference internal" href="#module-pyFTS.models.ensemble.ensemble">pyFTS.models.ensemble.ensemble module</a></li>
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<li><a class="reference internal" href="#module-pyFTS.models.ensemble.multiseasonal">pyFTS.models.ensemble.multiseasonal module</a></li>
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<li><a class="reference internal" href="#module-pyFTS.models.ensemble">Module contents</a></li>
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<div class="section" id="pyfts-models-ensemble-package">
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<h1>pyFTS.models.ensemble package<a class="headerlink" href="#pyfts-models-ensemble-package" title="Permalink to this headline">¶</a></h1>
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<div class="section" id="submodules">
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<h2>Submodules<a class="headerlink" href="#submodules" title="Permalink to this headline">¶</a></h2>
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</div>
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<div class="section" id="module-pyFTS.models.ensemble.ensemble">
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<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>
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<p>EnsembleFTS wraps several FTS methods to ensemble their forecasts, providing point,
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interval and probabilistic forecasting.</p>
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<p>Silva, P. C. L et al. Probabilistic Forecasting with Seasonal Ensemble Fuzzy Time-Series
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XIII Brazilian Congress on Computational Intelligence, 2017. Rio de Janeiro, Brazil.</p>
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<dl class="class">
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<dt id="pyFTS.models.ensemble.ensemble.AllMethodEnsembleFTS">
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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="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>
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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>
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<p>Creates an EnsembleFTS with all point forecast methods, sharing the same partitioner</p>
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<dl class="method">
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<dt id="pyFTS.models.ensemble.ensemble.AllMethodEnsembleFTS.set_transformations">
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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="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>
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<dd></dd></dl>
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<dl class="method">
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<dt id="pyFTS.models.ensemble.ensemble.AllMethodEnsembleFTS.train">
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<code class="descname">train</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/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>
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<dd><p>Method specific parameter fitting</p>
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<table class="docutils field-list" frame="void" rules="none">
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<col class="field-name" />
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<col class="field-body" />
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<tbody valign="top">
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<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
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<li><strong>data</strong> – training time series data</li>
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<li><strong>kwargs</strong> – Method specific parameters</li>
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</ul>
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</td>
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</tr>
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</tbody>
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</table>
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</dd></dl>
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</dd></dl>
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<dl class="class">
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<dt id="pyFTS.models.ensemble.ensemble.EnsembleFTS">
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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="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>
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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>
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<p>Ensemble FTS</p>
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<dl class="attribute">
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<dt id="pyFTS.models.ensemble.ensemble.EnsembleFTS.alpha">
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<code class="descname">alpha</code><em class="property"> = None</em><a class="headerlink" href="#pyFTS.models.ensemble.ensemble.EnsembleFTS.alpha" title="Permalink to this definition">¶</a></dt>
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<dd><p>The quantiles</p>
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</dd></dl>
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<dl class="method">
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<dt id="pyFTS.models.ensemble.ensemble.EnsembleFTS.append_model">
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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="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>
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<dd><p>Append a new trained model to the ensemble</p>
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<table class="docutils field-list" frame="void" rules="none">
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<col class="field-name" />
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<col class="field-body" />
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<tbody valign="top">
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<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>model</strong> – FTS model</td>
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</tr>
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</tbody>
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</table>
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</dd></dl>
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<dl class="method">
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<dt id="pyFTS.models.ensemble.ensemble.EnsembleFTS.forecast">
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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="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>
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<dd><p>Point forecast one step ahead</p>
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<table class="docutils field-list" frame="void" rules="none">
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<col class="field-name" />
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||
<col class="field-body" />
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<tbody valign="top">
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||
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
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<li><strong>data</strong> – time series data with the minimal length equal to the max_lag of the model</li>
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<li><strong>kwargs</strong> – model specific parameters</li>
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</ul>
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</td>
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</tr>
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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 the forecasted values</p>
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</td>
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</tr>
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</tbody>
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</table>
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</dd></dl>
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<dl class="method">
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<dt id="pyFTS.models.ensemble.ensemble.EnsembleFTS.forecast_ahead_distribution">
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<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="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>
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<dd><p>Probabilistic forecast n steps ahead</p>
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<table class="docutils field-list" frame="void" rules="none">
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<col class="field-name" />
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<col class="field-body" />
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<tbody valign="top">
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<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
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<li><strong>data</strong> – time series data with the minimal length equal to the max_lag of the model</li>
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<li><strong>steps</strong> – the number of steps ahead to forecast</li>
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<li><strong>kwargs</strong> – model specific parameters</li>
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</ul>
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</td>
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</tr>
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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 the forecasted Probability Distributions</p>
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</td>
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</tr>
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</tbody>
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</table>
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</dd></dl>
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<dl class="method">
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<dt id="pyFTS.models.ensemble.ensemble.EnsembleFTS.forecast_ahead_interval">
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<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="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>
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<dd><p>Interval forecast n steps ahead</p>
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<table class="docutils field-list" frame="void" rules="none">
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<col class="field-name" />
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<col class="field-body" />
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<tbody valign="top">
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<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
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<li><strong>data</strong> – time series data with the minimal length equal to the max_lag of the model</li>
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<li><strong>steps</strong> – the number of steps ahead to forecast</li>
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<li><strong>kwargs</strong> – model specific parameters</li>
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</ul>
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</td>
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</tr>
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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 the forecasted intervals</p>
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</td>
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</tr>
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</tbody>
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</table>
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</dd></dl>
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<dl class="method">
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<dt id="pyFTS.models.ensemble.ensemble.EnsembleFTS.forecast_distribution">
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<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/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>
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<dd><p>Probabilistic forecast one step ahead</p>
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||
<table class="docutils field-list" frame="void" rules="none">
|
||
<col class="field-name" />
|
||
<col class="field-body" />
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||
<tbody valign="top">
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<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
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<li><strong>data</strong> – time series data with the minimal length equal to the max_lag of the model</li>
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<li><strong>kwargs</strong> – model specific parameters</li>
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</ul>
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</td>
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</tr>
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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>
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</tr>
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</tbody>
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</table>
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</dd></dl>
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<dl class="method">
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<dt id="pyFTS.models.ensemble.ensemble.EnsembleFTS.forecast_interval">
|
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<code class="descname">forecast_interval</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/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>
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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" />
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||
<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">
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<li><strong>data</strong> – time series data with the minimal length equal to the max_lag of the model</li>
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<li><strong>kwargs</strong> – model specific parameters</li>
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</ul>
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</td>
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</tr>
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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 the prediction intervals</p>
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</td>
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</tr>
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</tbody>
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</table>
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</dd></dl>
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<dl class="method">
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<dt id="pyFTS.models.ensemble.ensemble.EnsembleFTS.get_distribution_interquantile">
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<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="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>
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<dd></dd></dl>
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<dl class="method">
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<dt id="pyFTS.models.ensemble.ensemble.EnsembleFTS.get_interval">
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<code class="descname">get_interval</code><span class="sig-paren">(</span><em>forecasts</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>
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<dd></dd></dl>
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|
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<dl class="method">
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<dt id="pyFTS.models.ensemble.ensemble.EnsembleFTS.get_models_forecasts">
|
||
<code class="descname">get_models_forecasts</code><span class="sig-paren">(</span><em>data</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="method">
|
||
<dt id="pyFTS.models.ensemble.ensemble.EnsembleFTS.get_point">
|
||
<code class="descname">get_point</code><span class="sig-paren">(</span><em>forecasts</em>, <em>**kwargs</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="attribute">
|
||
<dt id="pyFTS.models.ensemble.ensemble.EnsembleFTS.interval_method">
|
||
<code class="descname">interval_method</code><em class="property"> = None</em><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="attribute">
|
||
<dt id="pyFTS.models.ensemble.ensemble.EnsembleFTS.models">
|
||
<code class="descname">models</code><em class="property"> = None</em><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="attribute">
|
||
<dt id="pyFTS.models.ensemble.ensemble.EnsembleFTS.parameters">
|
||
<code class="descname">parameters</code><em class="property"> = None</em><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="attribute">
|
||
<dt id="pyFTS.models.ensemble.ensemble.EnsembleFTS.point_method">
|
||
<code class="descname">point_method</code><em class="property"> = None</em><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</p>
|
||
</dd></dl>
|
||
|
||
<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="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>
|
||
<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">
|
||
<code class="descclassname">pyFTS.models.ensemble.ensemble.</code><code class="descname">sampler</code><span class="sig-paren">(</span><em>data</em>, <em>quantiles</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="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="class">
|
||
<dt id="pyFTS.models.ensemble.multiseasonal.SeasonalEnsembleFTS">
|
||
<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="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="method">
|
||
<dt id="pyFTS.models.ensemble.multiseasonal.SeasonalEnsembleFTS.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/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>
|
||
<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 probabilistic.ProbabilityDistribution objects representing the forecasted Probability Distributions</p>
|
||
</td>
|
||
</tr>
|
||
</tbody>
|
||
</table>
|
||
</dd></dl>
|
||
|
||
<dl class="method">
|
||
<dt id="pyFTS.models.ensemble.multiseasonal.SeasonalEnsembleFTS.train">
|
||
<code class="descname">train</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/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>
|
||
<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">
|
||
<code class="descname">update_uod</code><span class="sig-paren">(</span><em>data</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="function">
|
||
<dt id="pyFTS.models.ensemble.multiseasonal.train_individual_model">
|
||
<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="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>
|
||
<p>Meta FTS that aggregates other FTS methods</p>
|
||
</div>
|
||
</div>
|
||
|
||
|
||
</div>
|
||
</div>
|
||
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|
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