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Incremental module
2018-11-01 18:11:20 +04:00
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Documentation update
2018-12-11 23:22:05 +04:00
<title>pyFTS.models.incremental package &#8212; pyFTS 1.4 documentation</title>
Incremental module
2018-11-01 18:11:20 +04:00
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Documentation update
2018-12-11 23:22:05 +04:00
<li class="nav-item nav-item-0"><a href="index.html">pyFTS 1.4 documentation</a> &#187;</li>
Incremental module
2018-11-01 18:11:20 +04:00
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<li><a class="reference internal" href="#">pyFTS.models.incremental package</a><ul>
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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>
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<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="pyfts-models-incremental-retrainer-module">
<h2>pyFTS.models.incremental.Retrainer module<a class="headerlink" href="#pyfts-models-incremental-retrainer-module" title="Permalink to this headline"></a></h2>
<span class="target" id="module-pyFTS.models.incremental.Retrainer"></span><p>Meta model that wraps another FTS method and continously retrain it using a data window with the most recent data</p>
<dl class="class">
<dt id="pyFTS.models.incremental.Retrainer.Retrainer">
<em class="property">class </em><code class="descclassname">pyFTS.models.incremental.Retrainer.</code><code class="descname">Retrainer</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/incremental/Retrainer.html#Retrainer"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.incremental.Retrainer.Retrainer" 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>Meta model for incremental/online learning</p>
<dl class="attribute">
<dt id="pyFTS.models.incremental.Retrainer.Retrainer.auto_update">
<code class="descname">auto_update</code><em class="property"> = None</em><a class="headerlink" href="#pyFTS.models.incremental.Retrainer.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>
Updating the documentation
2018-11-07 17:31:46 +04:00
<dl class="attribute">
<dt id="pyFTS.models.incremental.Retrainer.Retrainer.batch_size">
<code class="descname">batch_size</code><em class="property"> = None</em><a class="headerlink" href="#pyFTS.models.incremental.Retrainer.Retrainer.batch_size" title="Permalink to this definition"></a></dt>
<dd><p>The batch interval between each retraining</p>
</dd></dl>
Incremental module
2018-11-01 18:11:20 +04:00
<dl class="method">
<dt id="pyFTS.models.incremental.Retrainer.Retrainer.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/incremental/Retrainer.html#Retrainer.forecast"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.incremental.Retrainer.Retrainer.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="attribute">
<dt id="pyFTS.models.incremental.Retrainer.Retrainer.fts_method">
<code class="descname">fts_method</code><em class="property"> = None</em><a class="headerlink" href="#pyFTS.models.incremental.Retrainer.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="attribute">
<dt id="pyFTS.models.incremental.Retrainer.Retrainer.fts_params">
<code class="descname">fts_params</code><em class="property"> = None</em><a class="headerlink" href="#pyFTS.models.incremental.Retrainer.Retrainer.fts_params" title="Permalink to this definition"></a></dt>
<dd><p>The FTS method specific parameters</p>
</dd></dl>
<dl class="attribute">
<dt id="pyFTS.models.incremental.Retrainer.Retrainer.model">
<code class="descname">model</code><em class="property"> = None</em><a class="headerlink" href="#pyFTS.models.incremental.Retrainer.Retrainer.model" title="Permalink to this definition"></a></dt>
<dd><p>The most recent trained model</p>
</dd></dl>
<dl class="attribute">
<dt id="pyFTS.models.incremental.Retrainer.Retrainer.partitioner">
<code class="descname">partitioner</code><em class="property"> = None</em><a class="headerlink" href="#pyFTS.models.incremental.Retrainer.Retrainer.partitioner" title="Permalink to this definition"></a></dt>
<dd><p>The most recent trained partitioner</p>
</dd></dl>
<dl class="attribute">
<dt id="pyFTS.models.incremental.Retrainer.Retrainer.partitioner_method">
<code class="descname">partitioner_method</code><em class="property"> = None</em><a class="headerlink" href="#pyFTS.models.incremental.Retrainer.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="attribute">
<dt id="pyFTS.models.incremental.Retrainer.Retrainer.partitioner_params">
<code class="descname">partitioner_params</code><em class="property"> = None</em><a class="headerlink" href="#pyFTS.models.incremental.Retrainer.Retrainer.partitioner_params" title="Permalink to this definition"></a></dt>
<dd><p>The partitioner method parameters</p>
</dd></dl>
<dl class="method">
<dt id="pyFTS.models.incremental.Retrainer.Retrainer.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/incremental/Retrainer.html#Retrainer.train"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.incremental.Retrainer.Retrainer.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="attribute">
<dt id="pyFTS.models.incremental.Retrainer.Retrainer.window_length">
<code class="descname">window_length</code><em class="property"> = None</em><a class="headerlink" href="#pyFTS.models.incremental.Retrainer.Retrainer.window_length" title="Permalink to this definition"></a></dt>
<dd><p>The memory window length</p>
</dd></dl>
</dd></dl>
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