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<h1>pyFTS.models package<a class="headerlink" href="#pyfts-models-package" title="Permalink to this headline"></a></h1>
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<p>Fuzzy Time Series methods</p>
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<li class="toctree-l2"><a class="reference internal" href="pyFTS.models.ensemble.html#module-pyFTS.models.ensemble.multiseasonal">pyFTS.models.ensemble.multiseasonal module</a></li>
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<div class="section" id="module-pyFTS.models.song">
<span id="pyfts-models-song-module"></span><h2>pyFTS.models.song module<a class="headerlink" href="#module-pyFTS.models.song" title="Permalink to this headline"></a></h2>
<p>First Order Traditional Fuzzy Time Series method by Song &amp; Chissom (1993)</p>
<ol class="upperalpha simple" start="17">
<li><p>Song and B. S. Chissom, “Fuzzy time series and its models,” Fuzzy Sets Syst., vol. 54, no. 3, pp. 269–277, 1993.</p></li>
</ol>
<dl class="py class">
<dt id="pyFTS.models.song.ConventionalFTS">
<em class="property">class </em><code class="sig-prename descclassname">pyFTS.models.song.</code><code class="sig-name descname">ConventionalFTS</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/song.html#ConventionalFTS"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.song.ConventionalFTS" 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>Traditional Fuzzy Time Series</p>
<dl class="py method">
<dt id="pyFTS.models.song.ConventionalFTS.flr_membership_matrix">
<code class="sig-name descname">flr_membership_matrix</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">flr</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/song.html#ConventionalFTS.flr_membership_matrix"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.song.ConventionalFTS.flr_membership_matrix" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="py method">
<dt id="pyFTS.models.song.ConventionalFTS.forecast">
<code class="sig-name descname">forecast</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">ndata</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/song.html#ConventionalFTS.forecast"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.song.ConventionalFTS.forecast" title="Permalink to this definition"></a></dt>
<dd><p>Point forecast one step ahead</p>
<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>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>a list with the forecasted values</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt id="pyFTS.models.song.ConventionalFTS.operation_matrix">
<code class="sig-name descname">operation_matrix</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">flrs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/song.html#ConventionalFTS.operation_matrix"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.song.ConventionalFTS.operation_matrix" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="py method">
<dt id="pyFTS.models.song.ConventionalFTS.train">
<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/song.html#ConventionalFTS.train"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.song.ConventionalFTS.train" title="Permalink to this definition"></a></dt>
<dd><p>Method specific parameter fitting</p>
<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>
</ul>
</dd>
</dl>
</dd></dl>
</dd></dl>
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Documentation update
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<div class="section" id="module-pyFTS.models.chen">
<span id="pyfts-models-chen-module"></span><h2>pyFTS.models.chen module<a class="headerlink" href="#module-pyFTS.models.chen" title="Permalink to this headline"></a></h2>
<p>First Order Conventional Fuzzy Time Series by Chen (1996)</p>
<p>S.-M. Chen, “Forecasting enrollments based on fuzzy time series,” Fuzzy Sets Syst., vol. 81, no. 3, pp. 311–319, 1996.</p>
<dl class="py class">
<dt id="pyFTS.models.chen.ConventionalFLRG">
<em class="property">class </em><code class="sig-prename descclassname">pyFTS.models.chen.</code><code class="sig-name descname">ConventionalFLRG</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">LHS</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/chen.html#ConventionalFLRG"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.chen.ConventionalFLRG" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <a class="reference internal" href="pyFTS.common.html#pyFTS.common.flrg.FLRG" title="pyFTS.common.flrg.FLRG"><code class="xref py py-class docutils literal notranslate"><span class="pre">pyFTS.common.flrg.FLRG</span></code></a></p>
<p>First Order Conventional Fuzzy Logical Relationship Group</p>
<dl class="py method">
<dt id="pyFTS.models.chen.ConventionalFLRG.append_rhs">
<code class="sig-name descname">append_rhs</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">c</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/chen.html#ConventionalFLRG.append_rhs"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.chen.ConventionalFLRG.append_rhs" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="py method">
<dt id="pyFTS.models.chen.ConventionalFLRG.get_key">
<code class="sig-name descname">get_key</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">sets</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/chen.html#ConventionalFLRG.get_key"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.chen.ConventionalFLRG.get_key" title="Permalink to this definition"></a></dt>
<dd><p>Returns a unique identifier for this FLRG</p>
</dd></dl>
</dd></dl>
<dl class="py class">
<dt id="pyFTS.models.chen.ConventionalFTS">
<em class="property">class </em><code class="sig-prename descclassname">pyFTS.models.chen.</code><code class="sig-name descname">ConventionalFTS</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/chen.html#ConventionalFTS"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.chen.ConventionalFTS" 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>Conventional Fuzzy Time Series</p>
<dl class="py method">
<dt id="pyFTS.models.chen.ConventionalFTS.forecast">
<code class="sig-name descname">forecast</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">ndata</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/chen.html#ConventionalFTS.forecast"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.chen.ConventionalFTS.forecast" title="Permalink to this definition"></a></dt>
<dd><p>Point forecast one step ahead</p>
<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>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>a list with the forecasted values</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt id="pyFTS.models.chen.ConventionalFTS.generate_flrg">
<code class="sig-name descname">generate_flrg</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">flrs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/chen.html#ConventionalFTS.generate_flrg"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.chen.ConventionalFTS.generate_flrg" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="py method">
<dt id="pyFTS.models.chen.ConventionalFTS.train">
<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/chen.html#ConventionalFTS.train"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.chen.ConventionalFTS.train" title="Permalink to this definition"></a></dt>
<dd><p>Method specific parameter fitting</p>
<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>
</ul>
</dd>
</dl>
</dd></dl>
</dd></dl>
Updating the documentation
2018-08-30 23:04:52 +04:00
</div>
Documentation update
2021-01-13 01:04:42 +04:00
<div class="section" id="module-pyFTS.models.yu">
<span id="pyfts-models-yu-module"></span><h2>pyFTS.models.yu module<a class="headerlink" href="#module-pyFTS.models.yu" title="Permalink to this headline"></a></h2>
<p>First Order Weighted Fuzzy Time Series by Yu(2005)</p>
<p>H.-K. Yu, “Weighted fuzzy time series models for TAIEX forecasting,”
Phys. A Stat. Mech. its Appl., vol. 349, no. 3, pp. 609–624, 2005.</p>
<dl class="py class">
<dt id="pyFTS.models.yu.WeightedFLRG">
<em class="property">class </em><code class="sig-prename descclassname">pyFTS.models.yu.</code><code class="sig-name descname">WeightedFLRG</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">LHS</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/yu.html#WeightedFLRG"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.yu.WeightedFLRG" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <a class="reference internal" href="pyFTS.common.html#pyFTS.common.flrg.FLRG" title="pyFTS.common.flrg.FLRG"><code class="xref py py-class docutils literal notranslate"><span class="pre">pyFTS.common.flrg.FLRG</span></code></a></p>
<p>First Order Weighted Fuzzy Logical Relationship Group</p>
<dl class="py method">
<dt id="pyFTS.models.yu.WeightedFLRG.append_rhs">
<code class="sig-name descname">append_rhs</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">c</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/yu.html#WeightedFLRG.append_rhs"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.yu.WeightedFLRG.append_rhs" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="py method">
<dt id="pyFTS.models.yu.WeightedFLRG.weights">
<code class="sig-name descname">weights</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">sets</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/yu.html#WeightedFLRG.weights"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.yu.WeightedFLRG.weights" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
</dd></dl>
<dl class="py class">
<dt id="pyFTS.models.yu.WeightedFTS">
<em class="property">class </em><code class="sig-prename descclassname">pyFTS.models.yu.</code><code class="sig-name descname">WeightedFTS</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/yu.html#WeightedFTS"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.yu.WeightedFTS" 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>First Order Weighted Fuzzy Time Series</p>
<dl class="py method">
<dt id="pyFTS.models.yu.WeightedFTS.forecast">
<code class="sig-name descname">forecast</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">ndata</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/yu.html#WeightedFTS.forecast"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.yu.WeightedFTS.forecast" title="Permalink to this definition"></a></dt>
<dd><p>Point forecast one step ahead</p>
<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>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>a list with the forecasted values</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt id="pyFTS.models.yu.WeightedFTS.generate_FLRG">
<code class="sig-name descname">generate_FLRG</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">flrs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/yu.html#WeightedFTS.generate_FLRG"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.yu.WeightedFTS.generate_FLRG" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="py method">
<dt id="pyFTS.models.yu.WeightedFTS.train">
<code class="sig-name descname">train</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">ndata</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/yu.html#WeightedFTS.train"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.yu.WeightedFTS.train" title="Permalink to this definition"></a></dt>
<dd><p>Method specific parameter fitting</p>
<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>
</ul>
</dd>
</dl>
</dd></dl>
</dd></dl>
Sphinx documentation for ReadTheDocs site
2018-08-30 09:05:29 +04:00
</div>
Documentation update
2021-01-13 01:04:42 +04:00
<div class="section" id="module-pyFTS.models.cheng">
<span id="pyfts-models-cheng-module"></span><h2>pyFTS.models.cheng module<a class="headerlink" href="#module-pyFTS.models.cheng" title="Permalink to this headline"></a></h2>
<p>Trend Weighted Fuzzy Time Series by Cheng, Chen and Wu (2009)</p>
<p>C.-H. Cheng, Y.-S. Chen, and Y.-L. Wu, “Forecasting innovation diffusion of products using trend-weighted fuzzy time-series model,”
Expert Syst. Appl., vol. 36, no. 2, pp. 1826–1832, 2009.</p>
<dl class="py class">
<dt id="pyFTS.models.cheng.TrendWeightedFLRG">
<em class="property">class </em><code class="sig-prename descclassname">pyFTS.models.cheng.</code><code class="sig-name descname">TrendWeightedFLRG</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">LHS</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/cheng.html#TrendWeightedFLRG"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.cheng.TrendWeightedFLRG" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <a class="reference internal" href="#pyFTS.models.yu.WeightedFLRG" title="pyFTS.models.yu.WeightedFLRG"><code class="xref py py-class docutils literal notranslate"><span class="pre">pyFTS.models.yu.WeightedFLRG</span></code></a></p>
<p>First Order Trend Weighted Fuzzy Logical Relationship Group</p>
<dl class="py method">
<dt id="pyFTS.models.cheng.TrendWeightedFLRG.weights">
<code class="sig-name descname">weights</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">sets</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/cheng.html#TrendWeightedFLRG.weights"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.cheng.TrendWeightedFLRG.weights" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
</dd></dl>
<dl class="py class">
<dt id="pyFTS.models.cheng.TrendWeightedFTS">
<em class="property">class </em><code class="sig-prename descclassname">pyFTS.models.cheng.</code><code class="sig-name descname">TrendWeightedFTS</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/cheng.html#TrendWeightedFTS"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.cheng.TrendWeightedFTS" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <a class="reference internal" href="#pyFTS.models.yu.WeightedFTS" title="pyFTS.models.yu.WeightedFTS"><code class="xref py py-class docutils literal notranslate"><span class="pre">pyFTS.models.yu.WeightedFTS</span></code></a></p>
<p>First Order Trend Weighted Fuzzy Time Series</p>
<dl class="py method">
<dt id="pyFTS.models.cheng.TrendWeightedFTS.generate_FLRG">
<code class="sig-name descname">generate_FLRG</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">flrs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/cheng.html#TrendWeightedFTS.generate_FLRG"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.cheng.TrendWeightedFTS.generate_FLRG" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
</dd></dl>
Sphinx documentation for ReadTheDocs site
2018-08-30 09:05:29 +04:00
</div>
Documentation update
2021-01-13 01:04:42 +04:00
<div class="section" id="module-pyFTS.models.hofts">
<span id="pyfts-models-hofts-module"></span><h2>pyFTS.models.hofts module<a class="headerlink" href="#module-pyFTS.models.hofts" title="Permalink to this headline"></a></h2>
<p>High Order FTS</p>
<p>Severiano, S. A. Jr; Silva, P. C. L.; Sadaei, H. J.; Guimarães, F. G. Very Short-term Solar Forecasting
using Fuzzy Time Series. 2017 IEEE International Conference on Fuzzy Systems. DOI10.1109/FUZZ-IEEE.2017.8015732</p>
<dl class="py class">
<dt id="pyFTS.models.hofts.HighOrderFLRG">
<em class="property">class </em><code class="sig-prename descclassname">pyFTS.models.hofts.</code><code class="sig-name descname">HighOrderFLRG</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">order</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/hofts.html#HighOrderFLRG"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.hofts.HighOrderFLRG" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <a class="reference internal" href="pyFTS.common.html#pyFTS.common.flrg.FLRG" title="pyFTS.common.flrg.FLRG"><code class="xref py py-class docutils literal notranslate"><span class="pre">pyFTS.common.flrg.FLRG</span></code></a></p>
<p>Conventional High Order Fuzzy Logical Relationship Group</p>
<dl class="py method">
<dt id="pyFTS.models.hofts.HighOrderFLRG.append_lhs">
<code class="sig-name descname">append_lhs</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">c</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/hofts.html#HighOrderFLRG.append_lhs"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.hofts.HighOrderFLRG.append_lhs" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="py method">
<dt id="pyFTS.models.hofts.HighOrderFLRG.append_rhs">
<code class="sig-name descname">append_rhs</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">c</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/hofts.html#HighOrderFLRG.append_rhs"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.hofts.HighOrderFLRG.append_rhs" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
</dd></dl>
<dl class="py class">
<dt id="pyFTS.models.hofts.HighOrderFTS">
<em class="property">class </em><code class="sig-prename descclassname">pyFTS.models.hofts.</code><code class="sig-name descname">HighOrderFTS</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/hofts.html#HighOrderFTS"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.hofts.HighOrderFTS" 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>Conventional High Order Fuzzy Time Series</p>
<dl class="py method">
<dt id="pyFTS.models.hofts.HighOrderFTS.configure_lags">
<code class="sig-name descname">configure_lags</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/hofts.html#HighOrderFTS.configure_lags"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.hofts.HighOrderFTS.configure_lags" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="py method">
<dt id="pyFTS.models.hofts.HighOrderFTS.forecast">
<code class="sig-name descname">forecast</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">ndata</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/hofts.html#HighOrderFTS.forecast"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.hofts.HighOrderFTS.forecast" title="Permalink to this definition"></a></dt>
<dd><p>Point forecast one step ahead</p>
<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>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>a list with the forecasted values</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt id="pyFTS.models.hofts.HighOrderFTS.generate_flrg">
<code class="sig-name descname">generate_flrg</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/hofts.html#HighOrderFTS.generate_flrg"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.hofts.HighOrderFTS.generate_flrg" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="py method">
<dt id="pyFTS.models.hofts.HighOrderFTS.generate_flrg_fuzzyfied">
<code class="sig-name descname">generate_flrg_fuzzyfied</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/hofts.html#HighOrderFTS.generate_flrg_fuzzyfied"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.hofts.HighOrderFTS.generate_flrg_fuzzyfied" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="py method">
<dt id="pyFTS.models.hofts.HighOrderFTS.generate_lhs_flrg">
<code class="sig-name descname">generate_lhs_flrg</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">sample</span></em>, <em class="sig-param"><span class="n">explain</span><span class="o">=</span><span class="default_value">False</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/hofts.html#HighOrderFTS.generate_lhs_flrg"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.hofts.HighOrderFTS.generate_lhs_flrg" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="py method">
<dt id="pyFTS.models.hofts.HighOrderFTS.generate_lhs_flrg_fuzzyfied">
<code class="sig-name descname">generate_lhs_flrg_fuzzyfied</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">sample</span></em>, <em class="sig-param"><span class="n">explain</span><span class="o">=</span><span class="default_value">False</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/hofts.html#HighOrderFTS.generate_lhs_flrg_fuzzyfied"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.hofts.HighOrderFTS.generate_lhs_flrg_fuzzyfied" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="py method">
<dt id="pyFTS.models.hofts.HighOrderFTS.train">
<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/hofts.html#HighOrderFTS.train"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.hofts.HighOrderFTS.train" title="Permalink to this definition"></a></dt>
<dd><p>Method specific parameter fitting</p>
<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>
</ul>
</dd>
</dl>
</dd></dl>
</dd></dl>
<dl class="py class">
<dt id="pyFTS.models.hofts.WeightedHighOrderFLRG">
<em class="property">class </em><code class="sig-prename descclassname">pyFTS.models.hofts.</code><code class="sig-name descname">WeightedHighOrderFLRG</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">order</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/hofts.html#WeightedHighOrderFLRG"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.hofts.WeightedHighOrderFLRG" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <a class="reference internal" href="pyFTS.common.html#pyFTS.common.flrg.FLRG" title="pyFTS.common.flrg.FLRG"><code class="xref py py-class docutils literal notranslate"><span class="pre">pyFTS.common.flrg.FLRG</span></code></a></p>
<p>Weighted High Order Fuzzy Logical Relationship Group</p>
<dl class="py method">
<dt id="pyFTS.models.hofts.WeightedHighOrderFLRG.append_lhs">
<code class="sig-name descname">append_lhs</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">c</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/hofts.html#WeightedHighOrderFLRG.append_lhs"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.hofts.WeightedHighOrderFLRG.append_lhs" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="py method">
<dt id="pyFTS.models.hofts.WeightedHighOrderFLRG.append_rhs">
<code class="sig-name descname">append_rhs</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">fset</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/hofts.html#WeightedHighOrderFLRG.append_rhs"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.hofts.WeightedHighOrderFLRG.append_rhs" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="py method">
<dt id="pyFTS.models.hofts.WeightedHighOrderFLRG.get_lower">
<code class="sig-name descname">get_lower</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">sets</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/hofts.html#WeightedHighOrderFLRG.get_lower"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.hofts.WeightedHighOrderFLRG.get_lower" title="Permalink to this definition"></a></dt>
<dd><p>Returns the lower bound value for the RHS fuzzy sets</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>sets</strong> – fuzzy sets</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>lower bound value</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt id="pyFTS.models.hofts.WeightedHighOrderFLRG.get_midpoint">
<code class="sig-name descname">get_midpoint</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">sets</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/hofts.html#WeightedHighOrderFLRG.get_midpoint"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.hofts.WeightedHighOrderFLRG.get_midpoint" title="Permalink to this definition"></a></dt>
<dd><p>Returns the midpoint value for the RHS fuzzy sets</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>sets</strong> – fuzzy sets</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>the midpoint value</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt id="pyFTS.models.hofts.WeightedHighOrderFLRG.get_upper">
<code class="sig-name descname">get_upper</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">sets</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/hofts.html#WeightedHighOrderFLRG.get_upper"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.hofts.WeightedHighOrderFLRG.get_upper" title="Permalink to this definition"></a></dt>
<dd><p>Returns the upper bound value for the RHS fuzzy sets</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>sets</strong> – fuzzy sets</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>upper bound value</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt id="pyFTS.models.hofts.WeightedHighOrderFLRG.weights">
<code class="sig-name descname">weights</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/hofts.html#WeightedHighOrderFLRG.weights"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.hofts.WeightedHighOrderFLRG.weights" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
</dd></dl>
<dl class="py class">
<dt id="pyFTS.models.hofts.WeightedHighOrderFTS">
<em class="property">class </em><code class="sig-prename descclassname">pyFTS.models.hofts.</code><code class="sig-name descname">WeightedHighOrderFTS</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/hofts.html#WeightedHighOrderFTS"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.hofts.WeightedHighOrderFTS" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <a class="reference internal" href="#pyFTS.models.hofts.HighOrderFTS" title="pyFTS.models.hofts.HighOrderFTS"><code class="xref py py-class docutils literal notranslate"><span class="pre">pyFTS.models.hofts.HighOrderFTS</span></code></a></p>
<p>Weighted High Order Fuzzy Time Series</p>
<dl class="py method">
<dt id="pyFTS.models.hofts.WeightedHighOrderFTS.generate_lhs_flrg_fuzzyfied">
<code class="sig-name descname">generate_lhs_flrg_fuzzyfied</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">sample</span></em>, <em class="sig-param"><span class="n">explain</span><span class="o">=</span><span class="default_value">False</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/hofts.html#WeightedHighOrderFTS.generate_lhs_flrg_fuzzyfied"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.hofts.WeightedHighOrderFTS.generate_lhs_flrg_fuzzyfied" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
</dd></dl>
Sphinx documentation for ReadTheDocs site
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</div>
Documentation update
2021-01-13 01:04:42 +04:00
<div class="section" id="module-pyFTS.models.hwang">
<span id="pyfts-models-hwang-module"></span><h2>pyFTS.models.hwang module<a class="headerlink" href="#module-pyFTS.models.hwang" title="Permalink to this headline"></a></h2>
<p>High Order Fuzzy Time Series by Hwang, Chen and Lee (1998)</p>
<p>Jeng-Ren Hwang, Shyi-Ming Chen, and Chia-Hoang Lee, “Handling forecasting problems using fuzzy time series,”
Fuzzy Sets Syst., no. 100, pp. 217–228, 1998.</p>
<dl class="py class">
<dt id="pyFTS.models.hwang.HighOrderFTS">
<em class="property">class </em><code class="sig-prename descclassname">pyFTS.models.hwang.</code><code class="sig-name descname">HighOrderFTS</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/hwang.html#HighOrderFTS"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.hwang.HighOrderFTS" 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>
<dl class="py method">
<dt id="pyFTS.models.hwang.HighOrderFTS.configure_lags">
<code class="sig-name descname">configure_lags</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/hwang.html#HighOrderFTS.configure_lags"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.hwang.HighOrderFTS.configure_lags" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="py method">
<dt id="pyFTS.models.hwang.HighOrderFTS.forecast">
<code class="sig-name descname">forecast</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">ndata</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/hwang.html#HighOrderFTS.forecast"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.hwang.HighOrderFTS.forecast" title="Permalink to this definition"></a></dt>
<dd><p>Point forecast one step ahead</p>
<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>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>a list with the forecasted values</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt id="pyFTS.models.hwang.HighOrderFTS.train">
<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/hwang.html#HighOrderFTS.train"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.hwang.HighOrderFTS.train" title="Permalink to this definition"></a></dt>
<dd><p>Method specific parameter fitting</p>
<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>
</ul>
</dd>
</dl>
</dd></dl>
</dd></dl>
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Documentation update
2021-01-13 01:04:42 +04:00
<div class="section" id="module-pyFTS.models.ifts">
<span id="pyfts-models-ifts-module"></span><h2>pyFTS.models.ifts module<a class="headerlink" href="#module-pyFTS.models.ifts" title="Permalink to this headline"></a></h2>
<p>High Order Interval Fuzzy Time Series</p>
<p>SILVA, Petrônio CL; SADAEI, Hossein Javedani; GUIMARÃES, Frederico Gadelha. Interval Forecasting with Fuzzy Time Series.
In: Computational Intelligence (SSCI), 2016 IEEE Symposium Series on. IEEE, 2016. p. 1-8.</p>
<dl class="py class">
<dt id="pyFTS.models.ifts.IntervalFTS">
<em class="property">class </em><code class="sig-prename descclassname">pyFTS.models.ifts.</code><code class="sig-name descname">IntervalFTS</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/ifts.html#IntervalFTS"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.ifts.IntervalFTS" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <a class="reference internal" href="#pyFTS.models.hofts.HighOrderFTS" title="pyFTS.models.hofts.HighOrderFTS"><code class="xref py py-class docutils literal notranslate"><span class="pre">pyFTS.models.hofts.HighOrderFTS</span></code></a></p>
<p>High Order Interval Fuzzy Time Series</p>
<dl class="py method">
<dt id="pyFTS.models.ifts.IntervalFTS.forecast_ahead_interval">
<code class="sig-name descname">forecast_ahead_interval</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/ifts.html#IntervalFTS.forecast_ahead_interval"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.ifts.IntervalFTS.forecast_ahead_interval" title="Permalink to this definition"></a></dt>
<dd><p>Interval forecast from 1 to H steps ahead, where H is given by the steps parameter</p>
<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</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>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>a list with the forecasted intervals</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt id="pyFTS.models.ifts.IntervalFTS.forecast_interval">
<code class="sig-name descname">forecast_interval</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">ndata</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/ifts.html#IntervalFTS.forecast_interval"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.ifts.IntervalFTS.forecast_interval" title="Permalink to this definition"></a></dt>
<dd><p>Interval forecast one step ahead</p>
<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>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>a list with the prediction intervals</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt id="pyFTS.models.ifts.IntervalFTS.get_lower">
<code class="sig-name descname">get_lower</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">flrg</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/ifts.html#IntervalFTS.get_lower"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.ifts.IntervalFTS.get_lower" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="py method">
<dt id="pyFTS.models.ifts.IntervalFTS.get_sequence_membership">
<code class="sig-name descname">get_sequence_membership</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="n">fuzzySets</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/ifts.html#IntervalFTS.get_sequence_membership"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.ifts.IntervalFTS.get_sequence_membership" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="py method">
<dt id="pyFTS.models.ifts.IntervalFTS.get_upper">
<code class="sig-name descname">get_upper</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">flrg</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/ifts.html#IntervalFTS.get_upper"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.ifts.IntervalFTS.get_upper" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
</dd></dl>
<dl class="py class">
<dt id="pyFTS.models.ifts.WeightedIntervalFTS">
<em class="property">class </em><code class="sig-prename descclassname">pyFTS.models.ifts.</code><code class="sig-name descname">WeightedIntervalFTS</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/ifts.html#WeightedIntervalFTS"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.ifts.WeightedIntervalFTS" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <a class="reference internal" href="#pyFTS.models.hofts.WeightedHighOrderFTS" title="pyFTS.models.hofts.WeightedHighOrderFTS"><code class="xref py py-class docutils literal notranslate"><span class="pre">pyFTS.models.hofts.WeightedHighOrderFTS</span></code></a></p>
<p>Weighted High Order Interval Fuzzy Time Series</p>
<dl class="py method">
<dt id="pyFTS.models.ifts.WeightedIntervalFTS.forecast_ahead_interval">
<code class="sig-name descname">forecast_ahead_interval</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/ifts.html#WeightedIntervalFTS.forecast_ahead_interval"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.ifts.WeightedIntervalFTS.forecast_ahead_interval" title="Permalink to this definition"></a></dt>
<dd><p>Interval forecast from 1 to H steps ahead, where H is given by the steps parameter</p>
<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</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>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>a list with the forecasted intervals</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt id="pyFTS.models.ifts.WeightedIntervalFTS.forecast_interval">
<code class="sig-name descname">forecast_interval</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">ndata</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/ifts.html#WeightedIntervalFTS.forecast_interval"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.ifts.WeightedIntervalFTS.forecast_interval" title="Permalink to this definition"></a></dt>
<dd><p>Interval forecast one step ahead</p>
<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>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>a list with the prediction intervals</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt id="pyFTS.models.ifts.WeightedIntervalFTS.get_lower">
<code class="sig-name descname">get_lower</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">flrg</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/ifts.html#WeightedIntervalFTS.get_lower"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.ifts.WeightedIntervalFTS.get_lower" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="py method">
<dt id="pyFTS.models.ifts.WeightedIntervalFTS.get_sequence_membership">
<code class="sig-name descname">get_sequence_membership</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="n">fuzzySets</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/ifts.html#WeightedIntervalFTS.get_sequence_membership"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.ifts.WeightedIntervalFTS.get_sequence_membership" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="py method">
<dt id="pyFTS.models.ifts.WeightedIntervalFTS.get_upper">
<code class="sig-name descname">get_upper</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">flrg</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/ifts.html#WeightedIntervalFTS.get_upper"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.ifts.WeightedIntervalFTS.get_upper" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
</dd></dl>
Sphinx documentation for ReadTheDocs site
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Documentation update
2021-01-13 01:04:42 +04:00
<div class="section" id="module-pyFTS.models.ismailefendi">
<span id="pyfts-models-ismailefendi-module"></span><h2>pyFTS.models.ismailefendi module<a class="headerlink" href="#module-pyFTS.models.ismailefendi" title="Permalink to this headline"></a></h2>
<p>First Order Improved Weighted Fuzzy Time Series by Efendi, Ismail and Deris (2013)</p>
<p>R. Efendi, Z. Ismail, and M. M. Deris, “Improved weight Fuzzy Time Series as used in the exchange rates forecasting of
US Dollar to Ringgit Malaysia,” Int. J. Comput. Intell. Appl., vol. 12, no. 1, p. 1350005, 2013.</p>
<dl class="py class">
<dt id="pyFTS.models.ismailefendi.ImprovedWeightedFLRG">
<em class="property">class </em><code class="sig-prename descclassname">pyFTS.models.ismailefendi.</code><code class="sig-name descname">ImprovedWeightedFLRG</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">LHS</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/ismailefendi.html#ImprovedWeightedFLRG"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.ismailefendi.ImprovedWeightedFLRG" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <a class="reference internal" href="pyFTS.common.html#pyFTS.common.flrg.FLRG" title="pyFTS.common.flrg.FLRG"><code class="xref py py-class docutils literal notranslate"><span class="pre">pyFTS.common.flrg.FLRG</span></code></a></p>
<p>First Order Improved Weighted Fuzzy Logical Relationship Group</p>
<dl class="py method">
<dt id="pyFTS.models.ismailefendi.ImprovedWeightedFLRG.append_rhs">
<code class="sig-name descname">append_rhs</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">c</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/ismailefendi.html#ImprovedWeightedFLRG.append_rhs"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.ismailefendi.ImprovedWeightedFLRG.append_rhs" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="py method">
<dt id="pyFTS.models.ismailefendi.ImprovedWeightedFLRG.weights">
<code class="sig-name descname">weights</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/ismailefendi.html#ImprovedWeightedFLRG.weights"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.ismailefendi.ImprovedWeightedFLRG.weights" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
</dd></dl>
<dl class="py class">
<dt id="pyFTS.models.ismailefendi.ImprovedWeightedFTS">
<em class="property">class </em><code class="sig-prename descclassname">pyFTS.models.ismailefendi.</code><code class="sig-name descname">ImprovedWeightedFTS</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/ismailefendi.html#ImprovedWeightedFTS"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.ismailefendi.ImprovedWeightedFTS" 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>First Order Improved Weighted Fuzzy Time Series</p>
<dl class="py method">
<dt id="pyFTS.models.ismailefendi.ImprovedWeightedFTS.forecast">
<code class="sig-name descname">forecast</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">ndata</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/ismailefendi.html#ImprovedWeightedFTS.forecast"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.ismailefendi.ImprovedWeightedFTS.forecast" title="Permalink to this definition"></a></dt>
<dd><p>Point forecast one step ahead</p>
<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>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>a list with the forecasted values</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt id="pyFTS.models.ismailefendi.ImprovedWeightedFTS.generate_flrg">
<code class="sig-name descname">generate_flrg</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">flrs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/ismailefendi.html#ImprovedWeightedFTS.generate_flrg"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.ismailefendi.ImprovedWeightedFTS.generate_flrg" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="py method">
<dt id="pyFTS.models.ismailefendi.ImprovedWeightedFTS.train">
<code class="sig-name descname">train</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">ndata</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/ismailefendi.html#ImprovedWeightedFTS.train"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.ismailefendi.ImprovedWeightedFTS.train" title="Permalink to this definition"></a></dt>
<dd><p>Method specific parameter fitting</p>
<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>
</ul>
</dd>
</dl>
</dd></dl>
</dd></dl>
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</div>
Documentation update
2021-01-13 01:04:42 +04:00
<div class="section" id="module-pyFTS.models.pwfts">
<span id="pyfts-models-pwfts-module"></span><h2>pyFTS.models.pwfts module<a class="headerlink" href="#module-pyFTS.models.pwfts" title="Permalink to this headline"></a></h2>
<dl class="py class">
<dt id="pyFTS.models.pwfts.ProbabilisticWeightedFLRG">
<em class="property">class </em><code class="sig-prename descclassname">pyFTS.models.pwfts.</code><code class="sig-name descname">ProbabilisticWeightedFLRG</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">order</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/pwfts.html#ProbabilisticWeightedFLRG"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.pwfts.ProbabilisticWeightedFLRG" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <a class="reference internal" href="#pyFTS.models.hofts.HighOrderFLRG" title="pyFTS.models.hofts.HighOrderFLRG"><code class="xref py py-class docutils literal notranslate"><span class="pre">pyFTS.models.hofts.HighOrderFLRG</span></code></a></p>
<p>High Order Probabilistic Weighted Fuzzy Logical Relationship Group</p>
<dl class="py method">
<dt id="pyFTS.models.pwfts.ProbabilisticWeightedFLRG.append_rhs">
<code class="sig-name descname">append_rhs</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">c</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/pwfts.html#ProbabilisticWeightedFLRG.append_rhs"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.pwfts.ProbabilisticWeightedFLRG.append_rhs" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="py method">
<dt id="pyFTS.models.pwfts.ProbabilisticWeightedFLRG.get_lower">
<code class="sig-name descname">get_lower</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">sets</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/pwfts.html#ProbabilisticWeightedFLRG.get_lower"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.pwfts.ProbabilisticWeightedFLRG.get_lower" title="Permalink to this definition"></a></dt>
<dd><p>Returns the lower bound value for the RHS fuzzy sets</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>sets</strong> – fuzzy sets</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>lower bound value</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt id="pyFTS.models.pwfts.ProbabilisticWeightedFLRG.get_membership">
<code class="sig-name descname">get_membership</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="n">sets</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/pwfts.html#ProbabilisticWeightedFLRG.get_membership"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.pwfts.ProbabilisticWeightedFLRG.get_membership" title="Permalink to this definition"></a></dt>
<dd><p>Returns the membership value of the FLRG for the input data</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>data</strong> – input data</p></li>
<li><p><strong>sets</strong> – fuzzy sets</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>the membership value</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt id="pyFTS.models.pwfts.ProbabilisticWeightedFLRG.get_midpoint">
<code class="sig-name descname">get_midpoint</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">sets</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/pwfts.html#ProbabilisticWeightedFLRG.get_midpoint"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.pwfts.ProbabilisticWeightedFLRG.get_midpoint" title="Permalink to this definition"></a></dt>
<dd><p>Return the expectation of the PWFLRG, the weighted sum</p>
</dd></dl>
<dl class="py method">
<dt id="pyFTS.models.pwfts.ProbabilisticWeightedFLRG.get_upper">
<code class="sig-name descname">get_upper</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">sets</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/pwfts.html#ProbabilisticWeightedFLRG.get_upper"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.pwfts.ProbabilisticWeightedFLRG.get_upper" title="Permalink to this definition"></a></dt>
<dd><p>Returns the upper bound value for the RHS fuzzy sets</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>sets</strong> – fuzzy sets</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>upper bound value</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt id="pyFTS.models.pwfts.ProbabilisticWeightedFLRG.lhs_conditional_probability">
<code class="sig-name descname">lhs_conditional_probability</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">x</span></em>, <em class="sig-param"><span class="n">sets</span></em>, <em class="sig-param"><span class="n">norm</span></em>, <em class="sig-param"><span class="n">uod</span></em>, <em class="sig-param"><span class="n">nbins</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/pwfts.html#ProbabilisticWeightedFLRG.lhs_conditional_probability"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.pwfts.ProbabilisticWeightedFLRG.lhs_conditional_probability" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="py method">
<dt id="pyFTS.models.pwfts.ProbabilisticWeightedFLRG.lhs_conditional_probability_fuzzyfied">
<code class="sig-name descname">lhs_conditional_probability_fuzzyfied</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">lhs_mv</span></em>, <em class="sig-param"><span class="n">sets</span></em>, <em class="sig-param"><span class="n">norm</span></em>, <em class="sig-param"><span class="n">uod</span></em>, <em class="sig-param"><span class="n">nbins</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/pwfts.html#ProbabilisticWeightedFLRG.lhs_conditional_probability_fuzzyfied"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.pwfts.ProbabilisticWeightedFLRG.lhs_conditional_probability_fuzzyfied" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="py method">
<dt id="pyFTS.models.pwfts.ProbabilisticWeightedFLRG.partition_function">
<code class="sig-name descname">partition_function</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">sets</span></em>, <em class="sig-param"><span class="n">uod</span></em>, <em class="sig-param"><span class="n">nbins</span><span class="o">=</span><span class="default_value">100</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/pwfts.html#ProbabilisticWeightedFLRG.partition_function"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.pwfts.ProbabilisticWeightedFLRG.partition_function" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="py method">
<dt id="pyFTS.models.pwfts.ProbabilisticWeightedFLRG.rhs_conditional_probability">
<code class="sig-name descname">rhs_conditional_probability</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">x</span></em>, <em class="sig-param"><span class="n">sets</span></em>, <em class="sig-param"><span class="n">uod</span></em>, <em class="sig-param"><span class="n">nbins</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/pwfts.html#ProbabilisticWeightedFLRG.rhs_conditional_probability"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.pwfts.ProbabilisticWeightedFLRG.rhs_conditional_probability" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="py method">
<dt id="pyFTS.models.pwfts.ProbabilisticWeightedFLRG.rhs_unconditional_probability">
<code class="sig-name descname">rhs_unconditional_probability</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">c</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/pwfts.html#ProbabilisticWeightedFLRG.rhs_unconditional_probability"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.pwfts.ProbabilisticWeightedFLRG.rhs_unconditional_probability" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
</dd></dl>
<dl class="py class">
<dt id="pyFTS.models.pwfts.ProbabilisticWeightedFTS">
<em class="property">class </em><code class="sig-prename descclassname">pyFTS.models.pwfts.</code><code class="sig-name descname">ProbabilisticWeightedFTS</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/pwfts.html#ProbabilisticWeightedFTS"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.pwfts.ProbabilisticWeightedFTS" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <a class="reference internal" href="#pyFTS.models.ifts.IntervalFTS" title="pyFTS.models.ifts.IntervalFTS"><code class="xref py py-class docutils literal notranslate"><span class="pre">pyFTS.models.ifts.IntervalFTS</span></code></a></p>
<p>High Order Probabilistic Weighted Fuzzy Time Series</p>
<dl class="py method">
<dt id="pyFTS.models.pwfts.ProbabilisticWeightedFTS.add_new_PWFLGR">
<code class="sig-name descname">add_new_PWFLGR</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">flrg</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/pwfts.html#ProbabilisticWeightedFTS.add_new_PWFLGR"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.pwfts.ProbabilisticWeightedFTS.add_new_PWFLGR" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="py method">
<dt id="pyFTS.models.pwfts.ProbabilisticWeightedFTS.flrg_lhs_conditional_probability">
<code class="sig-name descname">flrg_lhs_conditional_probability</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">x</span></em>, <em class="sig-param"><span class="n">flrg</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/pwfts.html#ProbabilisticWeightedFTS.flrg_lhs_conditional_probability"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.pwfts.ProbabilisticWeightedFTS.flrg_lhs_conditional_probability" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="py method">
<dt id="pyFTS.models.pwfts.ProbabilisticWeightedFTS.flrg_lhs_conditional_probability_fuzzyfied">
<code class="sig-name descname">flrg_lhs_conditional_probability_fuzzyfied</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">x</span></em>, <em class="sig-param"><span class="n">flrg</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/pwfts.html#ProbabilisticWeightedFTS.flrg_lhs_conditional_probability_fuzzyfied"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.pwfts.ProbabilisticWeightedFTS.flrg_lhs_conditional_probability_fuzzyfied" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="py method">
<dt id="pyFTS.models.pwfts.ProbabilisticWeightedFTS.flrg_lhs_unconditional_probability">
<code class="sig-name descname">flrg_lhs_unconditional_probability</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">flrg</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/pwfts.html#ProbabilisticWeightedFTS.flrg_lhs_unconditional_probability"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.pwfts.ProbabilisticWeightedFTS.flrg_lhs_unconditional_probability" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="py method">
<dt id="pyFTS.models.pwfts.ProbabilisticWeightedFTS.flrg_rhs_conditional_probability">
<code class="sig-name descname">flrg_rhs_conditional_probability</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">x</span></em>, <em class="sig-param"><span class="n">flrg</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/pwfts.html#ProbabilisticWeightedFTS.flrg_rhs_conditional_probability"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.pwfts.ProbabilisticWeightedFTS.flrg_rhs_conditional_probability" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="py method">
<dt id="pyFTS.models.pwfts.ProbabilisticWeightedFTS.forecast">
<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/pwfts.html#ProbabilisticWeightedFTS.forecast"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.pwfts.ProbabilisticWeightedFTS.forecast" title="Permalink to this definition"></a></dt>
<dd><p>Point forecast one step ahead</p>
<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>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>a list with the forecasted values</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt id="pyFTS.models.pwfts.ProbabilisticWeightedFTS.forecast_ahead">
<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/pwfts.html#ProbabilisticWeightedFTS.forecast_ahead"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.pwfts.ProbabilisticWeightedFTS.forecast_ahead" title="Permalink to this definition"></a></dt>
<dd><p>Point forecast from 1 to H steps ahead, where H is given by the steps parameter</p>
<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>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>a list with the forecasted values</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt id="pyFTS.models.pwfts.ProbabilisticWeightedFTS.forecast_ahead_distribution">
<code class="sig-name descname">forecast_ahead_distribution</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">ndata</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/pwfts.html#ProbabilisticWeightedFTS.forecast_ahead_distribution"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.pwfts.ProbabilisticWeightedFTS.forecast_ahead_distribution" title="Permalink to this definition"></a></dt>
<dd><p>Probabilistic forecast from 1 to H steps ahead, where H is given by the steps parameter</p>
<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</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>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>a list with the forecasted Probability Distributions</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt id="pyFTS.models.pwfts.ProbabilisticWeightedFTS.forecast_ahead_interval">
<code class="sig-name descname">forecast_ahead_interval</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/pwfts.html#ProbabilisticWeightedFTS.forecast_ahead_interval"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.pwfts.ProbabilisticWeightedFTS.forecast_ahead_interval" title="Permalink to this definition"></a></dt>
<dd><p>Interval forecast from 1 to H steps ahead, where H is given by the steps parameter</p>
<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</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>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>a list with the forecasted intervals</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt id="pyFTS.models.pwfts.ProbabilisticWeightedFTS.forecast_distribution">
<code class="sig-name descname">forecast_distribution</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">ndata</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/pwfts.html#ProbabilisticWeightedFTS.forecast_distribution"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.pwfts.ProbabilisticWeightedFTS.forecast_distribution" title="Permalink to this definition"></a></dt>
<dd><p>Probabilistic forecast one step ahead</p>
<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>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>a list with probabilistic.ProbabilityDistribution objects representing the forecasted Probability Distributions</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt id="pyFTS.models.pwfts.ProbabilisticWeightedFTS.forecast_distribution_from_distribution">
<code class="sig-name descname">forecast_distribution_from_distribution</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">previous_dist</span></em>, <em class="sig-param"><span class="n">smooth</span></em>, <em class="sig-param"><span class="n">uod</span></em>, <em class="sig-param"><span class="n">bins</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/pwfts.html#ProbabilisticWeightedFTS.forecast_distribution_from_distribution"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.pwfts.ProbabilisticWeightedFTS.forecast_distribution_from_distribution" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="py method">
<dt id="pyFTS.models.pwfts.ProbabilisticWeightedFTS.forecast_interval">
<code class="sig-name descname">forecast_interval</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">ndata</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/pwfts.html#ProbabilisticWeightedFTS.forecast_interval"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.pwfts.ProbabilisticWeightedFTS.forecast_interval" title="Permalink to this definition"></a></dt>
<dd><p>Interval forecast one step ahead</p>
<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>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>a list with the prediction intervals</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt id="pyFTS.models.pwfts.ProbabilisticWeightedFTS.generate_flrg">
<code class="sig-name descname">generate_flrg</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/pwfts.html#ProbabilisticWeightedFTS.generate_flrg"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.pwfts.ProbabilisticWeightedFTS.generate_flrg" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="py method">
<dt id="pyFTS.models.pwfts.ProbabilisticWeightedFTS.generate_flrg2">
<code class="sig-name descname">generate_flrg2</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/pwfts.html#ProbabilisticWeightedFTS.generate_flrg2"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.pwfts.ProbabilisticWeightedFTS.generate_flrg2" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="py method">
<dt id="pyFTS.models.pwfts.ProbabilisticWeightedFTS.generate_flrg_fuzzyfied">
<code class="sig-name descname">generate_flrg_fuzzyfied</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/pwfts.html#ProbabilisticWeightedFTS.generate_flrg_fuzzyfied"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.pwfts.ProbabilisticWeightedFTS.generate_flrg_fuzzyfied" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="py method">
<dt id="pyFTS.models.pwfts.ProbabilisticWeightedFTS.generate_lhs_flrg">
<code class="sig-name descname">generate_lhs_flrg</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">sample</span></em>, <em class="sig-param"><span class="n">explain</span><span class="o">=</span><span class="default_value">False</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/pwfts.html#ProbabilisticWeightedFTS.generate_lhs_flrg"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.pwfts.ProbabilisticWeightedFTS.generate_lhs_flrg" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="py method">
<dt id="pyFTS.models.pwfts.ProbabilisticWeightedFTS.generate_lhs_flrg_fuzzyfied">
<code class="sig-name descname">generate_lhs_flrg_fuzzyfied</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">sample</span></em>, <em class="sig-param"><span class="n">explain</span><span class="o">=</span><span class="default_value">False</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/pwfts.html#ProbabilisticWeightedFTS.generate_lhs_flrg_fuzzyfied"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.pwfts.ProbabilisticWeightedFTS.generate_lhs_flrg_fuzzyfied" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="py method">
<dt id="pyFTS.models.pwfts.ProbabilisticWeightedFTS.get_lower">
<code class="sig-name descname">get_lower</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">flrg</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/pwfts.html#ProbabilisticWeightedFTS.get_lower"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.pwfts.ProbabilisticWeightedFTS.get_lower" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="py method">
<dt id="pyFTS.models.pwfts.ProbabilisticWeightedFTS.get_midpoint">
<code class="sig-name descname">get_midpoint</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">flrg</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/pwfts.html#ProbabilisticWeightedFTS.get_midpoint"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.pwfts.ProbabilisticWeightedFTS.get_midpoint" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="py method">
<dt id="pyFTS.models.pwfts.ProbabilisticWeightedFTS.get_sets_from_both_fuzzyfication">
<code class="sig-name descname">get_sets_from_both_fuzzyfication</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">sample</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/pwfts.html#ProbabilisticWeightedFTS.get_sets_from_both_fuzzyfication"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.pwfts.ProbabilisticWeightedFTS.get_sets_from_both_fuzzyfication" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="py method">
<dt id="pyFTS.models.pwfts.ProbabilisticWeightedFTS.get_upper">
<code class="sig-name descname">get_upper</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">flrg</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/pwfts.html#ProbabilisticWeightedFTS.get_upper"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.pwfts.ProbabilisticWeightedFTS.get_upper" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="py method">
<dt id="pyFTS.models.pwfts.ProbabilisticWeightedFTS.interval_heuristic">
<code class="sig-name descname">interval_heuristic</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">sample</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/pwfts.html#ProbabilisticWeightedFTS.interval_heuristic"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.pwfts.ProbabilisticWeightedFTS.interval_heuristic" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="py method">
<dt id="pyFTS.models.pwfts.ProbabilisticWeightedFTS.interval_quantile">
<code class="sig-name descname">interval_quantile</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">ndata</span></em>, <em class="sig-param"><span class="n">alpha</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/pwfts.html#ProbabilisticWeightedFTS.interval_quantile"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.pwfts.ProbabilisticWeightedFTS.interval_quantile" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="py method">
<dt id="pyFTS.models.pwfts.ProbabilisticWeightedFTS.point_expected_value">
<code class="sig-name descname">point_expected_value</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">sample</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/pwfts.html#ProbabilisticWeightedFTS.point_expected_value"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.pwfts.ProbabilisticWeightedFTS.point_expected_value" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="py method">
<dt id="pyFTS.models.pwfts.ProbabilisticWeightedFTS.point_heuristic">
<code class="sig-name descname">point_heuristic</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">sample</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/pwfts.html#ProbabilisticWeightedFTS.point_heuristic"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.pwfts.ProbabilisticWeightedFTS.point_heuristic" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="py method">
<dt id="pyFTS.models.pwfts.ProbabilisticWeightedFTS.pwflrg_lhs_memberhip_fuzzyfied">
<code class="sig-name descname">pwflrg_lhs_memberhip_fuzzyfied</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">flrg</span></em>, <em class="sig-param"><span class="n">sample</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/pwfts.html#ProbabilisticWeightedFTS.pwflrg_lhs_memberhip_fuzzyfied"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.pwfts.ProbabilisticWeightedFTS.pwflrg_lhs_memberhip_fuzzyfied" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="py method">
<dt id="pyFTS.models.pwfts.ProbabilisticWeightedFTS.train">
<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/pwfts.html#ProbabilisticWeightedFTS.train"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.pwfts.ProbabilisticWeightedFTS.train" title="Permalink to this definition"></a></dt>
<dd><p>Method specific parameter fitting</p>
<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>
</ul>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt id="pyFTS.models.pwfts.ProbabilisticWeightedFTS.update_model">
<code class="sig-name descname">update_model</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/pwfts.html#ProbabilisticWeightedFTS.update_model"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.pwfts.ProbabilisticWeightedFTS.update_model" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
</dd></dl>
<dl class="py function">
<dt id="pyFTS.models.pwfts.visualize_distributions">
<code class="sig-prename descclassname">pyFTS.models.pwfts.</code><code class="sig-name descname">visualize_distributions</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">model</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/pwfts.html#visualize_distributions"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.pwfts.visualize_distributions" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
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<div class="section" id="module-pyFTS.models.sadaei">
<span id="pyfts-models-sadaei-module"></span><h2>pyFTS.models.sadaei module<a class="headerlink" href="#module-pyFTS.models.sadaei" title="Permalink to this headline"></a></h2>
<p>First Order Exponentialy Weighted Fuzzy Time Series by Sadaei et al. (2013)</p>
<p>H. J. Sadaei, R. Enayatifar, A. H. Abdullah, and A. Gani, “Short-term load forecasting using a hybrid model with a
refined exponentially weighted fuzzy time series and an improved harmony search,” Int. J. Electr. Power Energy Syst., vol. 62, no. from 2005, pp. 118–129, 2014.</p>
<dl class="py class">
<dt id="pyFTS.models.sadaei.ExponentialyWeightedFLRG">
<em class="property">class </em><code class="sig-prename descclassname">pyFTS.models.sadaei.</code><code class="sig-name descname">ExponentialyWeightedFLRG</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">LHS</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/sadaei.html#ExponentialyWeightedFLRG"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.sadaei.ExponentialyWeightedFLRG" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <a class="reference internal" href="pyFTS.common.html#pyFTS.common.flrg.FLRG" title="pyFTS.common.flrg.FLRG"><code class="xref py py-class docutils literal notranslate"><span class="pre">pyFTS.common.flrg.FLRG</span></code></a></p>
<p>First Order Exponentialy Weighted Fuzzy Logical Relationship Group</p>
<dl class="py method">
<dt id="pyFTS.models.sadaei.ExponentialyWeightedFLRG.append_rhs">
<code class="sig-name descname">append_rhs</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">c</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/sadaei.html#ExponentialyWeightedFLRG.append_rhs"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.sadaei.ExponentialyWeightedFLRG.append_rhs" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="py method">
<dt id="pyFTS.models.sadaei.ExponentialyWeightedFLRG.weights">
<code class="sig-name descname">weights</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/sadaei.html#ExponentialyWeightedFLRG.weights"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.sadaei.ExponentialyWeightedFLRG.weights" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
</dd></dl>
<dl class="py class">
<dt id="pyFTS.models.sadaei.ExponentialyWeightedFTS">
<em class="property">class </em><code class="sig-prename descclassname">pyFTS.models.sadaei.</code><code class="sig-name descname">ExponentialyWeightedFTS</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/sadaei.html#ExponentialyWeightedFTS"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.sadaei.ExponentialyWeightedFTS" 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>First Order Exponentialy Weighted Fuzzy Time Series</p>
<dl class="py method">
<dt id="pyFTS.models.sadaei.ExponentialyWeightedFTS.forecast">
<code class="sig-name descname">forecast</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">ndata</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/sadaei.html#ExponentialyWeightedFTS.forecast"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.sadaei.ExponentialyWeightedFTS.forecast" title="Permalink to this definition"></a></dt>
<dd><p>Point forecast one step ahead</p>
<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>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>a list with the forecasted values</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt id="pyFTS.models.sadaei.ExponentialyWeightedFTS.generate_flrg">
<code class="sig-name descname">generate_flrg</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">flrs</span></em>, <em class="sig-param"><span class="n">c</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/sadaei.html#ExponentialyWeightedFTS.generate_flrg"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.sadaei.ExponentialyWeightedFTS.generate_flrg" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="py method">
<dt id="pyFTS.models.sadaei.ExponentialyWeightedFTS.train">
<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/sadaei.html#ExponentialyWeightedFTS.train"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.sadaei.ExponentialyWeightedFTS.train" title="Permalink to this definition"></a></dt>
<dd><p>Method specific parameter fitting</p>
<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>
</ul>
</dd>
</dl>
</dd></dl>
</dd></dl>
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<h3><a href="index.html">Table of Contents</a></h3>
<ul>
<li><a class="reference internal" href="#">pyFTS.models package</a><ul>
<li><a class="reference internal" href="#module-pyFTS.models">Module contents</a></li>
<li><a class="reference internal" href="#subpackages">Subpackages</a></li>
<li><a class="reference internal" href="#submodules">Submodules</a></li>
Documentation update
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<li><a class="reference internal" href="#module-pyFTS.models.song">pyFTS.models.song module</a></li>
<li><a class="reference internal" href="#module-pyFTS.models.chen">pyFTS.models.chen module</a></li>
<li><a class="reference internal" href="#module-pyFTS.models.yu">pyFTS.models.yu module</a></li>
<li><a class="reference internal" href="#module-pyFTS.models.cheng">pyFTS.models.cheng module</a></li>
<li><a class="reference internal" href="#module-pyFTS.models.hofts">pyFTS.models.hofts module</a></li>
<li><a class="reference internal" href="#module-pyFTS.models.hwang">pyFTS.models.hwang module</a></li>
<li><a class="reference internal" href="#module-pyFTS.models.ifts">pyFTS.models.ifts module</a></li>
<li><a class="reference internal" href="#module-pyFTS.models.ismailefendi">pyFTS.models.ismailefendi module</a></li>
<li><a class="reference internal" href="#module-pyFTS.models.pwfts">pyFTS.models.pwfts module</a></li>
<li><a class="reference internal" href="#module-pyFTS.models.sadaei">pyFTS.models.sadaei module</a></li>
Documentation update
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