<!doctype html> <html xmlns="http://www.w3.org/1999/xhtml"> <head> <meta http-equiv="X-UA-Compatible" content="IE=Edge" /> <meta http-equiv="Content-Type" content="text/html; charset=utf-8" /><script type="text/javascript"> var _gaq = _gaq || []; _gaq.push(['_setAccount', 'UA-55120145-3']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 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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.ensemble.AllMethodEnsembleFTS"> <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> <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="method"> <dt id="pyFTS.models.ensemble.ensemble.AllMethodEnsembleFTS.set_transformations"> <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> <dd></dd></dl> <dl class="method"> <dt id="pyFTS.models.ensemble.ensemble.AllMethodEnsembleFTS.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#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> <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"> <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> <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="attribute"> <dt id="pyFTS.models.ensemble.ensemble.EnsembleFTS.alpha"> <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> <dd><p>The quantiles</p> </dd></dl> <dl class="method"> <dt id="pyFTS.models.ensemble.ensemble.EnsembleFTS.append_model"> <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> <dd><p>Append a new trained model to the ensemble</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"><strong>model</strong> – FTS model</td> </tr> </tbody> </table> </dd></dl> <dl class="method"> <dt id="pyFTS.models.ensemble.ensemble.EnsembleFTS.forecast"> <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> <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"> <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> <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> <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 Probability Distributions</p> </td> </tr> </tbody> </table> </dd></dl> <dl class="method"> <dt id="pyFTS.models.ensemble.ensemble.EnsembleFTS.forecast_ahead_interval"> <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> <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> <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 intervals</p> </td> </tr> </tbody> </table> </dd></dl> <dl class="method"> <dt id="pyFTS.models.ensemble.ensemble.EnsembleFTS.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/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> <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.ensemble.EnsembleFTS.forecast_interval"> <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> <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> <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> </td> </tr> </tbody> </table> </dd></dl> <dl class="method"> <dt id="pyFTS.models.ensemble.ensemble.EnsembleFTS.get_distribution_interquantile"> <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> <dd></dd></dl> <dl class="method"> <dt id="pyFTS.models.ensemble.ensemble.EnsembleFTS.get_interval"> <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> <dd></dd></dl> <dl class="method"> <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> </div> <div class="clearer"></div> </div> <div class="related" role="navigation" aria-label="related navigation"> <h3>Navigation</h3> <ul> <li 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