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<li><a class="reference internal" href="#">pyFTS.models.incremental package</a><ul>
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<li><a class="reference internal" href="#module-pyFTS.models.incremental">Module contents</a></li>
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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.incremental.TimeVariant">pyFTS.models.incremental.TimeVariant module</a></li>
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<li><a class="reference internal" href="#module-pyFTS.models.incremental.IncrementalEnsemble">pyFTS.models.incremental.IncrementalEnsemble module</a></li>
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<div class="section" id="pyfts-models-incremental-package">
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<h1>pyFTS.models.incremental package<a class="headerlink" href="#pyfts-models-incremental-package" title="Permalink to this headline">¶</a></h1>
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<div class="section" id="module-pyFTS.models.incremental">
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<span id="module-contents"></span><h2>Module contents<a class="headerlink" href="#module-pyFTS.models.incremental" title="Permalink to this headline">¶</a></h2>
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<p>FTS methods with incremental/online learning</p>
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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.incremental.TimeVariant">
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<span id="pyfts-models-incremental-timevariant-module"></span><h2>pyFTS.models.incremental.TimeVariant module<a class="headerlink" href="#module-pyFTS.models.incremental.TimeVariant" title="Permalink to this headline">¶</a></h2>
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<p>Meta model that wraps another FTS method and continously retrain it using a data window with the most recent data</p>
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<dl class="class">
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<dt id="pyFTS.models.incremental.TimeVariant.Retrainer">
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<em class="property">class </em><code class="descclassname">pyFTS.models.incremental.TimeVariant.</code><code class="descname">Retrainer</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.models.incremental.TimeVariant.Retrainer" 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>Meta model for incremental/online learning</p>
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<dl class="method">
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<dt id="pyFTS.models.incremental.TimeVariant.Retrainer.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="headerlink" href="#pyFTS.models.incremental.TimeVariant.Retrainer.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.incremental.TimeVariant.Retrainer.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="headerlink" href="#pyFTS.models.incremental.TimeVariant.Retrainer.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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</div>
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<div class="section" id="module-pyFTS.models.incremental.IncrementalEnsemble">
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<span id="pyfts-models-incremental-incrementalensemble-module"></span><h2>pyFTS.models.incremental.IncrementalEnsemble module<a class="headerlink" href="#module-pyFTS.models.incremental.IncrementalEnsemble" title="Permalink to this headline">¶</a></h2>
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<p>Time Variant/Incremental Ensemble of FTS methods</p>
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<dl class="class">
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<dt id="pyFTS.models.incremental.IncrementalEnsemble.IncrementalEnsembleFTS">
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<em class="property">class </em><code class="descclassname">pyFTS.models.incremental.IncrementalEnsemble.</code><code class="descname">IncrementalEnsembleFTS</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.models.incremental.IncrementalEnsemble.IncrementalEnsembleFTS" title="Permalink to this definition">¶</a></dt>
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<dd><p>Bases: <a class="reference internal" href="pyFTS.models.ensemble.html#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>Time Variant/Incremental Ensemble of FTS methods</p>
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<dl class="method">
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<dt id="pyFTS.models.incremental.IncrementalEnsemble.IncrementalEnsembleFTS.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="headerlink" href="#pyFTS.models.incremental.IncrementalEnsemble.IncrementalEnsembleFTS.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.incremental.IncrementalEnsemble.IncrementalEnsembleFTS.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="headerlink" href="#pyFTS.models.incremental.IncrementalEnsemble.IncrementalEnsembleFTS.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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