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<title>pyFTS.probabilistic package &#8212; pyFTS 1.6 documentation</title>
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<li><a class="reference internal" href="#module-pyFTS.probabilistic.ProbabilityDistribution">pyFTS.probabilistic.ProbabilityDistribution module</a></li>
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<div class="document">
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<div class="section" id="pyfts-probabilistic-package">
<h1>pyFTS.probabilistic package<a class="headerlink" href="#pyfts-probabilistic-package" title="Permalink to this headline"></a></h1>
Updating the documentation
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<div class="section" id="module-pyFTS.probabilistic">
<span id="module-contents"></span><h2>Module contents<a class="headerlink" href="#module-pyFTS.probabilistic" title="Permalink to this headline"></a></h2>
<p>Probability Distribution objects</p>
</div>
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<div class="section" id="submodules">
<h2>Submodules<a class="headerlink" href="#submodules" title="Permalink to this headline"></a></h2>
</div>
<div class="section" id="module-pyFTS.probabilistic.ProbabilityDistribution">
<span id="pyfts-probabilistic-probabilitydistribution-module"></span><h2>pyFTS.probabilistic.ProbabilityDistribution module<a class="headerlink" href="#module-pyFTS.probabilistic.ProbabilityDistribution" title="Permalink to this headline"></a></h2>
<dl class="class">
<dt id="pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution">
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<em class="property">class </em><code class="descclassname">pyFTS.probabilistic.ProbabilityDistribution.</code><code class="descname">ProbabilityDistribution</code><span class="sig-paren">(</span><em>type='KDE'</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution" title="Permalink to this definition"></a></dt>
is_time_variant flag on FTS; offset() function in FTS; Documentation update
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<dd><p>Bases: <a class="reference external" href="https://docs.python.org/3/library/functions.html#object" title="(in Python v3.8)"><code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></a></p>
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<p>Represents a discrete or continous probability distribution
If type is histogram, the PDF is discrete
If type is KDE the PDF is continuous</p>
<dl class="method">
<dt id="pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution.append">
New benchmarks.Tests module; Improvements on forecast_ahead_* function; Documentation update
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<code class="descname">append</code><span class="sig-paren">(</span><em>values</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution.append" title="Permalink to this definition"></a></dt>
Improving documentation for the Interval and Probabilistic forecasts
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<dd><p>Increment the frequency count for the values</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>values</strong> – A list of values to account the frequency</td>
</tr>
</tbody>
</table>
</dd></dl>
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<dl class="method">
<dt id="pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution.append_interval">
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<code class="descname">append_interval</code><span class="sig-paren">(</span><em>intervals</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution.append_interval" title="Permalink to this definition"></a></dt>
Improving documentation for the Interval and Probabilistic forecasts
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<dd><p>Increment the frequency count for all values inside an interval</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>intervals</strong> – A list of intervals do increment the frequency</td>
</tr>
</tbody>
</table>
</dd></dl>
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<dl class="method">
<dt id="pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution.averageloglikelihood">
New benchmarks.Tests module; Improvements on forecast_ahead_* function; Documentation update
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<code class="descname">averageloglikelihood</code><span class="sig-paren">(</span><em>data</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution.averageloglikelihood" title="Permalink to this definition"></a></dt>
Improving documentation for the Interval and Probabilistic forecasts
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<dd><p>Average log likelihood of the probability distribution with respect to data</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>data</strong> – </td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"></td>
</tr>
</tbody>
</table>
</dd></dl>
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<dl class="method">
<dt id="pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution.build_cdf_qtl">
New benchmarks.Tests module; Improvements on forecast_ahead_* function; Documentation update
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<code class="descname">build_cdf_qtl</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution.build_cdf_qtl" title="Permalink to this definition"></a></dt>
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<dd></dd></dl>
<dl class="method">
<dt id="pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution.crossentropy">
New benchmarks.Tests module; Improvements on forecast_ahead_* function; Documentation update
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<code class="descname">crossentropy</code><span class="sig-paren">(</span><em>q</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution.crossentropy" title="Permalink to this definition"></a></dt>
Incremental module
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<dd><p>Cross entropy between the actual probability distribution and the informed one,
H(P,Q) = - ∑ P(x) log ( Q(x) )</p>
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<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>q</strong> – a probabilistic.ProbabilityDistribution object</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body">Cross entropy between this probability distribution and the given distribution</td>
</tr>
</tbody>
</table>
</dd></dl>
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<dl class="method">
Improving documentation for the Interval and Probabilistic forecasts
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<dt id="pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution.cumulative">
New benchmarks.Tests module; Improvements on forecast_ahead_* function; Documentation update
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<code class="descname">cumulative</code><span class="sig-paren">(</span><em>values</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution.cumulative" title="Permalink to this definition"></a></dt>
<dd><p>Return the cumulative probability densities for the input values,
Incremental module
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such that F(x) = P(X &lt;= x)</p>
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<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>values</strong> – A list of input values</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body">The cumulative probability densities for the input values</td>
</tr>
</tbody>
</table>
</dd></dl>
Sphinx documentation for ReadTheDocs site
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<dl class="method">
<dt id="pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution.density">
New benchmarks.Tests module; Improvements on forecast_ahead_* function; Documentation update
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<code class="descname">density</code><span class="sig-paren">(</span><em>values</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution.density" title="Permalink to this definition"></a></dt>
Improving documentation for the Interval and Probabilistic forecasts
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<dd><p>Return the probability densities for the input values</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>values</strong> – List of values to return the densities</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body">List of probability densities for the input values</td>
</tr>
</tbody>
</table>
</dd></dl>
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<dl class="method">
<dt id="pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution.differential_offset">
New benchmarks.Tests module; Improvements on forecast_ahead_* function; Documentation update
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<code class="descname">differential_offset</code><span class="sig-paren">(</span><em>value</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution.differential_offset" title="Permalink to this definition"></a></dt>
Improving documentation for the Interval and Probabilistic forecasts
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<dd><p>Auxiliary function for probability distributions of differentiated data</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>value</strong> – </td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"></td>
</tr>
</tbody>
</table>
</dd></dl>
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<dl class="method">
<dt id="pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution.empiricalloglikelihood">
New benchmarks.Tests module; Improvements on forecast_ahead_* function; Documentation update
2019-06-06 18:04:20 +04:00
<code class="descname">empiricalloglikelihood</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution.empiricalloglikelihood" title="Permalink to this definition"></a></dt>
Incremental module
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<dd><p>Empirical Log Likelihood of the probability distribution, L(P) = ∑ log( P(x) )</p>
Improving documentation for the Interval and Probabilistic forecasts
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<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body"></td>
</tr>
</tbody>
</table>
</dd></dl>
Sphinx documentation for ReadTheDocs site
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<dl class="method">
<dt id="pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution.entropy">
New benchmarks.Tests module; Improvements on forecast_ahead_* function; Documentation update
2019-06-06 18:04:20 +04:00
<code class="descname">entropy</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution.entropy" title="Permalink to this definition"></a></dt>
Incremental module
2018-11-01 18:11:20 +04:00
<dd><p>Return the entropy of the probability distribution, H(P) = E[ -ln P(X) ] = - ∑ P(x) log ( P(x) )</p>
Improving documentation for the Interval and Probabilistic forecasts
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<p>:return:the entropy of the probability distribution</p>
</dd></dl>
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<dl class="method">
<dt id="pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution.expected_value">
New benchmarks.Tests module; Improvements on forecast_ahead_* function; Documentation update
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<code class="descname">expected_value</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution.expected_value" title="Permalink to this definition"></a></dt>
Improving documentation for the Interval and Probabilistic forecasts
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<dd><p>Return the expected value of the distribution, as E[X] = ∑ x * P(x)</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body">The expected value of the distribution</td>
</tr>
</tbody>
</table>
</dd></dl>
Sphinx documentation for ReadTheDocs site
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<dl class="method">
<dt id="pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution.kullbackleiblerdivergence">
New benchmarks.Tests module; Improvements on forecast_ahead_* function; Documentation update
2019-06-06 18:04:20 +04:00
<code class="descname">kullbackleiblerdivergence</code><span class="sig-paren">(</span><em>q</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution.kullbackleiblerdivergence" title="Permalink to this definition"></a></dt>
Incremental module
2018-11-01 18:11:20 +04:00
<dd><p>Kullback-Leibler divergence between the actual probability distribution and the informed one.
DKL(P || Q) = - ∑ P(x) log( P(X) / Q(x) )</p>
Improving documentation for the Interval and Probabilistic forecasts
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<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>q</strong> – a probabilistic.ProbabilityDistribution object</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body">Kullback-Leibler divergence</td>
</tr>
</tbody>
</table>
</dd></dl>
Sphinx documentation for ReadTheDocs site
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<dl class="method">
<dt id="pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution.plot">
New benchmarks.Tests module; Improvements on forecast_ahead_* function; Documentation update
2019-06-06 18:04:20 +04:00
<code class="descname">plot</code><span class="sig-paren">(</span><em>axis=None, color='black', tam=[10, 6], title=None</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution.plot" title="Permalink to this definition"></a></dt>
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<dd></dd></dl>
<dl class="method">
<dt id="pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution.pseudologlikelihood">
New benchmarks.Tests module; Improvements on forecast_ahead_* function; Documentation update
2019-06-06 18:04:20 +04:00
<code class="descname">pseudologlikelihood</code><span class="sig-paren">(</span><em>data</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution.pseudologlikelihood" title="Permalink to this definition"></a></dt>
Improving documentation for the Interval and Probabilistic forecasts
2018-09-29 02:35:07 +04:00
<dd><p>Pseudo log likelihood of the probability distribution with respect to data</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>data</strong> – </td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"></td>
</tr>
</tbody>
</table>
</dd></dl>
Sphinx documentation for ReadTheDocs site
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<dl class="method">
<dt id="pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution.quantile">
New benchmarks.Tests module; Improvements on forecast_ahead_* function; Documentation update
2019-06-06 18:04:20 +04:00
<code class="descname">quantile</code><span class="sig-paren">(</span><em>values</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution.quantile" title="Permalink to this definition"></a></dt>
<dd><p>Return the Universe of Discourse values in relation to the quantile input values,
Incremental module
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such that Q(tau) = min( {x | F(x) &gt;= tau })</p>
Improving documentation for the Interval and Probabilistic forecasts
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<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>values</strong> – input values</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body">The list of the quantile values for the input values</td>
</tr>
</tbody>
</table>
</dd></dl>
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<dl class="method">
<dt id="pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution.set">
New benchmarks.Tests module; Improvements on forecast_ahead_* function; Documentation update
2019-06-06 18:04:20 +04:00
<code class="descname">set</code><span class="sig-paren">(</span><em>value</em>, <em>density</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution.set" title="Permalink to this definition"></a></dt>
Improving documentation for the Interval and Probabilistic forecasts
2018-09-29 02:35:07 +04:00
<dd><p>Assert a probability ‘density’ for a certain value ‘value’, such that P(value) = density</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>value</strong> – A value in the universe of discourse from the distribution</li>
<li><strong>density</strong> – The probability density to assign to the value</li>
</ul>
</td>
</tr>
</tbody>
</table>
</dd></dl>
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</dd></dl>
Documentation update
2019-09-24 06:16:06 +04:00
<dl class="function">
<dt id="pyFTS.probabilistic.ProbabilityDistribution.from_point">
<code class="descclassname">pyFTS.probabilistic.ProbabilityDistribution.</code><code class="descname">from_point</code><span class="sig-paren">(</span><em>x</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.probabilistic.ProbabilityDistribution.from_point" title="Permalink to this definition"></a></dt>
<dd><p>Create a probability distribution from a scalar value</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>x</strong> – scalar value</li>
<li><strong>kwargs</strong> – common parameters of the distribution</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">the ProbabilityDistribution object</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>
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</div>
<div class="section" id="module-pyFTS.probabilistic.kde">
<span id="pyfts-probabilistic-kde-module"></span><h2>pyFTS.probabilistic.kde module<a class="headerlink" href="#module-pyFTS.probabilistic.kde" title="Permalink to this headline"></a></h2>
<p>Kernel Density Estimation</p>
<dl class="class">
<dt id="pyFTS.probabilistic.kde.KernelSmoothing">
New benchmarks.Tests module; Improvements on forecast_ahead_* function; Documentation update
2019-06-06 18:04:20 +04:00
<em class="property">class </em><code class="descclassname">pyFTS.probabilistic.kde.</code><code class="descname">KernelSmoothing</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.probabilistic.kde.KernelSmoothing" title="Permalink to this definition"></a></dt>
is_time_variant flag on FTS; offset() function in FTS; Documentation update
2019-11-15 23:48:47 +04:00
<dd><p>Bases: <a class="reference external" href="https://docs.python.org/3/library/functions.html#object" title="(in Python v3.8)"><code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></a></p>
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<p>Kernel Density Estimation</p>
<dl class="method">
<dt id="pyFTS.probabilistic.kde.KernelSmoothing.kernel_function">
New benchmarks.Tests module; Improvements on forecast_ahead_* function; Documentation update
2019-06-06 18:04:20 +04:00
<code class="descname">kernel_function</code><span class="sig-paren">(</span><em>u</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.probabilistic.kde.KernelSmoothing.kernel_function" title="Permalink to this definition"></a></dt>
Improving documentation for the Interval and Probabilistic forecasts
2018-09-29 02:35:07 +04:00
<dd><p>Apply the kernel</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>u</strong> – </td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"></td>
</tr>
</tbody>
</table>
</dd></dl>
Sphinx documentation for ReadTheDocs site
2018-08-30 09:05:29 +04:00
<dl class="method">
<dt id="pyFTS.probabilistic.kde.KernelSmoothing.probability">
New benchmarks.Tests module; Improvements on forecast_ahead_* function; Documentation update
2019-06-06 18:04:20 +04:00
<code class="descname">probability</code><span class="sig-paren">(</span><em>x</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.probabilistic.kde.KernelSmoothing.probability" title="Permalink to this definition"></a></dt>
Sphinx documentation for ReadTheDocs site
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<dd><p>Probability of the point x on data</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>x</strong> – </li>
<li><strong>data</strong> – </li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last"></p>
</td>
</tr>
</tbody>
</table>
</dd></dl>
</dd></dl>
</div>
</div>
</div>
</div>
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</div>
Updating the documentation
2018-08-30 23:04:52 +04:00
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Updating the documentation
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