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Incremental module
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Incremental module
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<div class="document">
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<div class="section" id="pyfts-models-incremental-package">
<h1>pyFTS.models.incremental package<a class="headerlink" href="#pyfts-models-incremental-package" title="Permalink to this headline"></a></h1>
<div class="section" id="module-pyFTS.models.incremental">
<span id="module-contents"></span><h2>Module contents<a class="headerlink" href="#module-pyFTS.models.incremental" title="Permalink to this headline"></a></h2>
<p>FTS methods with incremental/online learning</p>
</div>
<div class="section" id="submodules">
<h2>Submodules<a class="headerlink" href="#submodules" title="Permalink to this headline"></a></h2>
</div>
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<div class="section" id="module-pyFTS.models.incremental.TimeVariant">
<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="py class">
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<dt id="pyFTS.models.incremental.TimeVariant.Retrainer">
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<em class="property">class </em><code class="sig-prename descclassname">pyFTS.models.incremental.TimeVariant.</code><code class="sig-name descname">Retrainer</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/incremental/TimeVariant.html#Retrainer"><span class="viewcode-link">[source]</span></a><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 that retrain its internal model after
data windows controlled by the parameter ‘batch_size’, using as the training data a
window of recent lags, whose size is controlled by the parameter ‘window_length’.</p>
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<dl class="py attribute">
<dt id="pyFTS.models.incremental.TimeVariant.Retrainer.auto_update">
<code class="sig-name descname">auto_update</code><a class="headerlink" href="#pyFTS.models.incremental.TimeVariant.Retrainer.auto_update" title="Permalink to this definition"></a></dt>
<dd><p>If true the model is updated at each time and not recreated</p>
</dd></dl>
<dl class="py attribute">
<dt id="pyFTS.models.incremental.TimeVariant.Retrainer.batch_size">
<code class="sig-name descname">batch_size</code><a class="headerlink" href="#pyFTS.models.incremental.TimeVariant.Retrainer.batch_size" title="Permalink to this definition"></a></dt>
<dd><p>The batch interval between each retraining</p>
</dd></dl>
<dl class="py method">
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<dt id="pyFTS.models.incremental.TimeVariant.Retrainer.forecast">
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<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/incremental/TimeVariant.html#Retrainer.forecast"><span class="viewcode-link">[source]</span></a><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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<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>
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</ul>
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</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>a list with the forecasted values</p>
</dd>
</dl>
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</dd></dl>
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<dl class="py method">
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<dt id="pyFTS.models.incremental.TimeVariant.Retrainer.forecast_ahead">
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<code class="sig-name descname">forecast_ahead</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/incremental/TimeVariant.html#Retrainer.forecast_ahead"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.incremental.TimeVariant.Retrainer.forecast_ahead" title="Permalink to this definition"></a></dt>
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<dd><p>Point forecast n steps ahead</p>
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<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 (default: 1)</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>
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</ul>
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</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>a list with the forecasted values</p>
</dd>
</dl>
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</dd></dl>
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<dl class="py attribute">
<dt id="pyFTS.models.incremental.TimeVariant.Retrainer.fts_method">
<code class="sig-name descname">fts_method</code><a class="headerlink" href="#pyFTS.models.incremental.TimeVariant.Retrainer.fts_method" title="Permalink to this definition"></a></dt>
<dd><p>The FTS method to be called when a new model is build</p>
</dd></dl>
<dl class="py attribute">
<dt id="pyFTS.models.incremental.TimeVariant.Retrainer.fts_params">
<code class="sig-name descname">fts_params</code><a class="headerlink" href="#pyFTS.models.incremental.TimeVariant.Retrainer.fts_params" title="Permalink to this definition"></a></dt>
<dd><p>The FTS method specific parameters</p>
</dd></dl>
<dl class="py attribute">
<dt id="pyFTS.models.incremental.TimeVariant.Retrainer.model">
<code class="sig-name descname">model</code><a class="headerlink" href="#pyFTS.models.incremental.TimeVariant.Retrainer.model" title="Permalink to this definition"></a></dt>
<dd><p>The most recent trained model</p>
</dd></dl>
<dl class="py method">
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<dt id="pyFTS.models.incremental.TimeVariant.Retrainer.offset">
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<code class="sig-name descname">offset</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/incremental/TimeVariant.html#Retrainer.offset"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.incremental.TimeVariant.Retrainer.offset" title="Permalink to this definition"></a></dt>
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<dd><p>Returns the number of lags to skip in the input test data in order to synchronize it with
the forecasted values given by the predict function. This is necessary due to the order of the
model, among other parameters.</p>
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<dl class="field-list simple">
<dt class="field-odd">Returns</dt>
<dd class="field-odd"><p>An integer with the number of lags to skip</p>
</dd>
</dl>
</dd></dl>
<dl class="py attribute">
<dt id="pyFTS.models.incremental.TimeVariant.Retrainer.partitioner">
<code class="sig-name descname">partitioner</code><a class="headerlink" href="#pyFTS.models.incremental.TimeVariant.Retrainer.partitioner" title="Permalink to this definition"></a></dt>
<dd><p>The most recent trained partitioner</p>
</dd></dl>
<dl class="py attribute">
<dt id="pyFTS.models.incremental.TimeVariant.Retrainer.partitioner_method">
<code class="sig-name descname">partitioner_method</code><a class="headerlink" href="#pyFTS.models.incremental.TimeVariant.Retrainer.partitioner_method" title="Permalink to this definition"></a></dt>
<dd><p>The partitioner method to be called when a new model is build</p>
</dd></dl>
<dl class="py attribute">
<dt id="pyFTS.models.incremental.TimeVariant.Retrainer.partitioner_params">
<code class="sig-name descname">partitioner_params</code><a class="headerlink" href="#pyFTS.models.incremental.TimeVariant.Retrainer.partitioner_params" title="Permalink to this definition"></a></dt>
<dd><p>The partitioner method parameters</p>
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</dd></dl>
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<dl class="py method">
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<dt id="pyFTS.models.incremental.TimeVariant.Retrainer.train">
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<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/incremental/TimeVariant.html#Retrainer.train"><span class="viewcode-link">[source]</span></a><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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<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>
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</ul>
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</dd>
</dl>
</dd></dl>
<dl class="py attribute">
<dt id="pyFTS.models.incremental.TimeVariant.Retrainer.window_length">
<code class="sig-name descname">window_length</code><a class="headerlink" href="#pyFTS.models.incremental.TimeVariant.Retrainer.window_length" title="Permalink to this definition"></a></dt>
<dd><p>The memory window length</p>
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</dd></dl>
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</dd></dl>
</div>
<div class="section" id="module-pyFTS.models.incremental.IncrementalEnsemble">
<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>
<p>Time Variant/Incremental Ensemble of FTS methods</p>
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<dl class="py class">
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<dt id="pyFTS.models.incremental.IncrementalEnsemble.IncrementalEnsembleFTS">
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<em class="property">class </em><code class="sig-prename descclassname">pyFTS.models.incremental.IncrementalEnsemble.</code><code class="sig-name descname">IncrementalEnsembleFTS</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/incremental/IncrementalEnsemble.html#IncrementalEnsembleFTS"><span class="viewcode-link">[source]</span></a><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>
<p>Time Variant/Incremental Ensemble of FTS methods</p>
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<dl class="py attribute">
<dt id="pyFTS.models.incremental.IncrementalEnsemble.IncrementalEnsembleFTS.batch_size">
<code class="sig-name descname">batch_size</code><a class="headerlink" href="#pyFTS.models.incremental.IncrementalEnsemble.IncrementalEnsembleFTS.batch_size" title="Permalink to this definition"></a></dt>
<dd><p>The batch interval between each retraining</p>
</dd></dl>
<dl class="py method">
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<dt id="pyFTS.models.incremental.IncrementalEnsemble.IncrementalEnsembleFTS.forecast">
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<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/incremental/IncrementalEnsemble.html#IncrementalEnsembleFTS.forecast"><span class="viewcode-link">[source]</span></a><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>
Documentation update
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<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>
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</ul>
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</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>a list with the forecasted values</p>
</dd>
</dl>
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</dd></dl>
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<dl class="py method">
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<dt id="pyFTS.models.incremental.IncrementalEnsemble.IncrementalEnsembleFTS.forecast_ahead">
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<code class="sig-name descname">forecast_ahead</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/incremental/IncrementalEnsemble.html#IncrementalEnsembleFTS.forecast_ahead"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.incremental.IncrementalEnsemble.IncrementalEnsembleFTS.forecast_ahead" title="Permalink to this definition"></a></dt>
Documentation update
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<dd><p>Point forecast n steps ahead</p>
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<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 (default: 1)</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>
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</ul>
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</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>a list with the forecasted values</p>
</dd>
</dl>
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</dd></dl>
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<dl class="py attribute">
<dt id="pyFTS.models.incremental.IncrementalEnsemble.IncrementalEnsembleFTS.fts_method">
<code class="sig-name descname">fts_method</code><a class="headerlink" href="#pyFTS.models.incremental.IncrementalEnsemble.IncrementalEnsembleFTS.fts_method" title="Permalink to this definition"></a></dt>
<dd><p>The FTS method to be called when a new model is build</p>
</dd></dl>
<dl class="py attribute">
<dt id="pyFTS.models.incremental.IncrementalEnsemble.IncrementalEnsembleFTS.fts_params">
<code class="sig-name descname">fts_params</code><a class="headerlink" href="#pyFTS.models.incremental.IncrementalEnsemble.IncrementalEnsembleFTS.fts_params" title="Permalink to this definition"></a></dt>
<dd><p>The FTS method specific parameters</p>
</dd></dl>
<dl class="py attribute">
<dt id="pyFTS.models.incremental.IncrementalEnsemble.IncrementalEnsembleFTS.num_models">
<code class="sig-name descname">num_models</code><a class="headerlink" href="#pyFTS.models.incremental.IncrementalEnsemble.IncrementalEnsembleFTS.num_models" title="Permalink to this definition"></a></dt>
<dd><p>The number of models to hold in the ensemble</p>
</dd></dl>
<dl class="py method">
Documentation update
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<dt id="pyFTS.models.incremental.IncrementalEnsemble.IncrementalEnsembleFTS.offset">
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<code class="sig-name descname">offset</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/incremental/IncrementalEnsemble.html#IncrementalEnsembleFTS.offset"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.incremental.IncrementalEnsemble.IncrementalEnsembleFTS.offset" title="Permalink to this definition"></a></dt>
Documentation update
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<dd><p>Returns the number of lags to skip in the input test data in order to synchronize it with
the forecasted values given by the predict function. This is necessary due to the order of the
model, among other parameters.</p>
Documentation update
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<dl class="field-list simple">
<dt class="field-odd">Returns</dt>
<dd class="field-odd"><p>An integer with the number of lags to skip</p>
</dd>
</dl>
Documentation update
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</dd></dl>
Documentation update
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<dl class="py attribute">
<dt id="pyFTS.models.incremental.IncrementalEnsemble.IncrementalEnsembleFTS.partitioner_method">
<code class="sig-name descname">partitioner_method</code><a class="headerlink" href="#pyFTS.models.incremental.IncrementalEnsemble.IncrementalEnsembleFTS.partitioner_method" title="Permalink to this definition"></a></dt>
<dd><p>The partitioner method to be called when a new model is build</p>
</dd></dl>
<dl class="py attribute">
<dt id="pyFTS.models.incremental.IncrementalEnsemble.IncrementalEnsembleFTS.partitioner_params">
<code class="sig-name descname">partitioner_params</code><a class="headerlink" href="#pyFTS.models.incremental.IncrementalEnsemble.IncrementalEnsembleFTS.partitioner_params" title="Permalink to this definition"></a></dt>
<dd><p>The partitioner method parameters</p>
</dd></dl>
<dl class="py method">
Updating the documentation
2019-02-21 19:00:09 +04:00
<dt id="pyFTS.models.incremental.IncrementalEnsemble.IncrementalEnsembleFTS.train">
Documentation update
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<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/incremental/IncrementalEnsemble.html#IncrementalEnsembleFTS.train"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.incremental.IncrementalEnsemble.IncrementalEnsembleFTS.train" title="Permalink to this definition"></a></dt>
Updating the documentation
2019-02-21 19:00:09 +04:00
<dd><p>Method specific parameter fitting</p>
Documentation update
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<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>
Updating the documentation
2019-02-21 19:00:09 +04:00
</ul>
Documentation update
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</dd>
</dl>
</dd></dl>
<dl class="py attribute">
<dt id="pyFTS.models.incremental.IncrementalEnsemble.IncrementalEnsembleFTS.window_length">
<code class="sig-name descname">window_length</code><a class="headerlink" href="#pyFTS.models.incremental.IncrementalEnsemble.IncrementalEnsembleFTS.window_length" title="Permalink to this definition"></a></dt>
<dd><p>The memory window length</p>
Updating the documentation
2019-02-21 19:00:09 +04:00
</dd></dl>
Incremental module
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</dd></dl>
</div>
</div>
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