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<li><a class="reference internal" href="#">pyFTS.benchmarks package</a><ul>
<li><a class="reference internal" href="#module-pyFTS.benchmarks">Module contents</a></li>
<li><a class="reference internal" href="#submodules">Submodules</a></li>
<li><a class="reference internal" href="#module-pyFTS.benchmarks.benchmarks">pyFTS.benchmarks.benchmarks module</a></li>
<li><a class="reference internal" href="#module-pyFTS.benchmarks.Measures">pyFTS.benchmarks.Measures module</a></li>
<li><a class="reference internal" href="#module-pyFTS.benchmarks.ResidualAnalysis">pyFTS.benchmarks.ResidualAnalysis module</a></li>
New benchmarks.Tests module; Improvements on forecast_ahead_* function; Documentation update
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<li><a class="reference internal" href="#module-pyFTS.benchmarks.Tests">pyFTS.benchmarks.Tests module</a></li>
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<li><a class="reference internal" href="#module-pyFTS.benchmarks.Util">pyFTS.benchmarks.Util module</a></li>
<li><a class="reference internal" href="#module-pyFTS.benchmarks.arima">pyFTS.benchmarks.arima module</a></li>
<li><a class="reference internal" href="#module-pyFTS.benchmarks.knn">pyFTS.benchmarks.knn module</a></li>
<li><a class="reference internal" href="#module-pyFTS.benchmarks.naive">pyFTS.benchmarks.naive module</a></li>
<li><a class="reference internal" href="#module-pyFTS.benchmarks.quantreg">pyFTS.benchmarks.quantreg module</a></li>
New benchmarks.Tests module; Improvements on forecast_ahead_* function; Documentation update
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<li><a class="reference internal" href="#module-pyFTS.benchmarks.gaussianproc">pyFTS.benchmarks.gaussianproc module</a></li>
<li><a class="reference internal" href="#module-pyFTS.benchmarks.BSTS">pyFTS.benchmarks.BSTS module</a></li>
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<div class="section" id="pyfts-benchmarks-package">
<h1>pyFTS.benchmarks package<a class="headerlink" href="#pyfts-benchmarks-package" title="Permalink to this headline"></a></h1>
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<div class="section" id="module-pyFTS.benchmarks">
<span id="module-contents"></span><h2>Module contents<a class="headerlink" href="#module-pyFTS.benchmarks" title="Permalink to this headline"></a></h2>
<p>pyFTS module for benchmarking the FTS models</p>
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<div class="section" id="module-pyFTS.benchmarks.benchmarks">
<span id="pyfts-benchmarks-benchmarks-module"></span><h2>pyFTS.benchmarks.benchmarks module<a class="headerlink" href="#module-pyFTS.benchmarks.benchmarks" title="Permalink to this headline"></a></h2>
<p>Benchmarks methods for FTS methods</p>
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<dl class="function">
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<dt id="pyFTS.benchmarks.benchmarks.SelecaoSimples_MenorRMSE">
New benchmarks.Tests module; Improvements on forecast_ahead_* function; Documentation update
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<code class="descclassname">pyFTS.benchmarks.benchmarks.</code><code class="descname">SelecaoSimples_MenorRMSE</code><span class="sig-paren">(</span><em>original</em>, <em>parameters</em>, <em>modelo</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.SelecaoSimples_MenorRMSE" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="function">
<dt id="pyFTS.benchmarks.benchmarks.common_process_interval_jobs">
<code class="descclassname">pyFTS.benchmarks.benchmarks.</code><code class="descname">common_process_interval_jobs</code><span class="sig-paren">(</span><em>conn</em>, <em>data</em>, <em>job</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.common_process_interval_jobs" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="function">
<dt id="pyFTS.benchmarks.benchmarks.common_process_point_jobs">
<code class="descclassname">pyFTS.benchmarks.benchmarks.</code><code class="descname">common_process_point_jobs</code><span class="sig-paren">(</span><em>conn</em>, <em>data</em>, <em>job</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.common_process_point_jobs" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="function">
<dt id="pyFTS.benchmarks.benchmarks.common_process_probabilistic_jobs">
<code class="descclassname">pyFTS.benchmarks.benchmarks.</code><code class="descname">common_process_probabilistic_jobs</code><span class="sig-paren">(</span><em>conn</em>, <em>data</em>, <em>job</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.common_process_probabilistic_jobs" title="Permalink to this definition"></a></dt>
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<dd></dd></dl>
Documentation update
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<dl class="function">
<dt id="pyFTS.benchmarks.benchmarks.common_process_time_jobs">
<code class="descclassname">pyFTS.benchmarks.benchmarks.</code><code class="descname">common_process_time_jobs</code><span class="sig-paren">(</span><em>conn</em>, <em>data</em>, <em>job</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.common_process_time_jobs" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
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<dl class="function">
<dt id="pyFTS.benchmarks.benchmarks.compareModelsPlot">
New benchmarks.Tests module; Improvements on forecast_ahead_* function; Documentation update
2019-06-06 18:04:20 +04:00
<code class="descclassname">pyFTS.benchmarks.benchmarks.</code><code class="descname">compareModelsPlot</code><span class="sig-paren">(</span><em>original</em>, <em>models_fo</em>, <em>models_ho</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.compareModelsPlot" title="Permalink to this definition"></a></dt>
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<dd></dd></dl>
<dl class="function">
<dt id="pyFTS.benchmarks.benchmarks.compareModelsTable">
New benchmarks.Tests module; Improvements on forecast_ahead_* function; Documentation update
2019-06-06 18:04:20 +04:00
<code class="descclassname">pyFTS.benchmarks.benchmarks.</code><code class="descname">compareModelsTable</code><span class="sig-paren">(</span><em>original</em>, <em>models_fo</em>, <em>models_ho</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.compareModelsTable" title="Permalink to this definition"></a></dt>
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<dd></dd></dl>
<dl class="function">
<dt id="pyFTS.benchmarks.benchmarks.get_benchmark_interval_methods">
New benchmarks.Tests module; Improvements on forecast_ahead_* function; Documentation update
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<code class="descclassname">pyFTS.benchmarks.benchmarks.</code><code class="descname">get_benchmark_interval_methods</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.get_benchmark_interval_methods" title="Permalink to this definition"></a></dt>
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<dd><p>Return all non FTS methods for point_to_interval forecasting</p>
</dd></dl>
<dl class="function">
<dt id="pyFTS.benchmarks.benchmarks.get_benchmark_point_methods">
New benchmarks.Tests module; Improvements on forecast_ahead_* function; Documentation update
2019-06-06 18:04:20 +04:00
<code class="descclassname">pyFTS.benchmarks.benchmarks.</code><code class="descname">get_benchmark_point_methods</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.get_benchmark_point_methods" title="Permalink to this definition"></a></dt>
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<dd><p>Return all non FTS methods for point forecasting</p>
</dd></dl>
<dl class="function">
<dt id="pyFTS.benchmarks.benchmarks.get_benchmark_probabilistic_methods">
New benchmarks.Tests module; Improvements on forecast_ahead_* function; Documentation update
2019-06-06 18:04:20 +04:00
<code class="descclassname">pyFTS.benchmarks.benchmarks.</code><code class="descname">get_benchmark_probabilistic_methods</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.get_benchmark_probabilistic_methods" title="Permalink to this definition"></a></dt>
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<dd><p>Return all FTS methods for probabilistic forecasting</p>
</dd></dl>
<dl class="function">
<dt id="pyFTS.benchmarks.benchmarks.get_interval_methods">
New benchmarks.Tests module; Improvements on forecast_ahead_* function; Documentation update
2019-06-06 18:04:20 +04:00
<code class="descclassname">pyFTS.benchmarks.benchmarks.</code><code class="descname">get_interval_methods</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.get_interval_methods" title="Permalink to this definition"></a></dt>
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<dd><p>Return all FTS methods for point_to_interval forecasting</p>
</dd></dl>
<dl class="function">
<dt id="pyFTS.benchmarks.benchmarks.get_point_methods">
New benchmarks.Tests module; Improvements on forecast_ahead_* function; Documentation update
2019-06-06 18:04:20 +04:00
<code class="descclassname">pyFTS.benchmarks.benchmarks.</code><code class="descname">get_point_methods</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.get_point_methods" title="Permalink to this definition"></a></dt>
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<dd><p>Return all FTS methods for point forecasting</p>
</dd></dl>
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<dl class="function">
<dt id="pyFTS.benchmarks.benchmarks.get_point_multivariate_methods">
New benchmarks.Tests module; Improvements on forecast_ahead_* function; Documentation update
2019-06-06 18:04:20 +04:00
<code class="descclassname">pyFTS.benchmarks.benchmarks.</code><code class="descname">get_point_multivariate_methods</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.get_point_multivariate_methods" title="Permalink to this definition"></a></dt>
Documentation update
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<dd><p>Return all multivariate FTS methods por point forecasting</p>
</dd></dl>
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<dl class="function">
<dt id="pyFTS.benchmarks.benchmarks.get_probabilistic_methods">
New benchmarks.Tests module; Improvements on forecast_ahead_* function; Documentation update
2019-06-06 18:04:20 +04:00
<code class="descclassname">pyFTS.benchmarks.benchmarks.</code><code class="descname">get_probabilistic_methods</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.get_probabilistic_methods" title="Permalink to this definition"></a></dt>
Updating the documentation
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<dd><p>Return all FTS methods for probabilistic forecasting</p>
</dd></dl>
Documentation update
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<dl class="function">
<dt id="pyFTS.benchmarks.benchmarks.multivariate_sliding_window_benchmarks2">
<code class="descclassname">pyFTS.benchmarks.benchmarks.</code><code class="descname">multivariate_sliding_window_benchmarks2</code><span class="sig-paren">(</span><em>data</em>, <em>windowsize</em>, <em>train=0.8</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.multivariate_sliding_window_benchmarks2" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="function">
<dt id="pyFTS.benchmarks.benchmarks.mv_run_interval2">
<code class="descclassname">pyFTS.benchmarks.benchmarks.</code><code class="descname">mv_run_interval2</code><span class="sig-paren">(</span><em>mfts</em>, <em>train_data</em>, <em>test_data</em>, <em>window_key=None</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.mv_run_interval2" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="function">
<dt id="pyFTS.benchmarks.benchmarks.mv_run_point2">
<code class="descclassname">pyFTS.benchmarks.benchmarks.</code><code class="descname">mv_run_point2</code><span class="sig-paren">(</span><em>mfts</em>, <em>train_data</em>, <em>test_data</em>, <em>window_key=None</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.mv_run_point2" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="function">
<dt id="pyFTS.benchmarks.benchmarks.mv_run_probabilistic2">
<code class="descclassname">pyFTS.benchmarks.benchmarks.</code><code class="descname">mv_run_probabilistic2</code><span class="sig-paren">(</span><em>mfts</em>, <em>train_data</em>, <em>test_data</em>, <em>window_key=None</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.mv_run_probabilistic2" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
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<dl class="function">
<dt id="pyFTS.benchmarks.benchmarks.pftsExploreOrderAndPartitions">
New benchmarks.Tests module; Improvements on forecast_ahead_* function; Documentation update
2019-06-06 18:04:20 +04:00
<code class="descclassname">pyFTS.benchmarks.benchmarks.</code><code class="descname">pftsExploreOrderAndPartitions</code><span class="sig-paren">(</span><em>data</em>, <em>save=False</em>, <em>file=None</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.pftsExploreOrderAndPartitions" title="Permalink to this definition"></a></dt>
Updating the documentation
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<dd></dd></dl>
<dl class="function">
<dt id="pyFTS.benchmarks.benchmarks.plotCompared">
New benchmarks.Tests module; Improvements on forecast_ahead_* function; Documentation update
2019-06-06 18:04:20 +04:00
<code class="descclassname">pyFTS.benchmarks.benchmarks.</code><code class="descname">plotCompared</code><span class="sig-paren">(</span><em>original</em>, <em>forecasts</em>, <em>labels</em>, <em>title</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.plotCompared" title="Permalink to this definition"></a></dt>
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<dd></dd></dl>
Sphinx documentation for ReadTheDocs site
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<dl class="function">
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<dt id="pyFTS.benchmarks.benchmarks.plot_compared_series">
New benchmarks.Tests module; Improvements on forecast_ahead_* function; Documentation update
2019-06-06 18:04:20 +04:00
<code class="descclassname">pyFTS.benchmarks.benchmarks.</code><code class="descname">plot_compared_series</code><span class="sig-paren">(</span><em>original, models, colors, typeonlegend=False, save=False, file=None, tam=[20, 5], points=True, intervals=True, linewidth=1.5</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.plot_compared_series" title="Permalink to this definition"></a></dt>
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<dd><p>Plot the forecasts of several one step ahead models, by point or by interval</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"><ul class="first simple">
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<li><strong>original</strong> – Original time series data (list)</li>
<li><strong>models</strong> – List of models to compare</li>
<li><strong>colors</strong> – List of models colors</li>
<li><strong>typeonlegend</strong> – Add the type of forecast (point / interval) on legend</li>
<li><strong>save</strong> – Save the picture on file</li>
<li><strong>file</strong> – Filename to save the picture</li>
<li><strong>tam</strong> – Size of the picture</li>
<li><strong>points</strong> – True to plot the point forecasts, False otherwise</li>
<li><strong>intervals</strong> – True to plot the interval forecasts, False otherwise</li>
<li><strong>linewidth</strong> – </li>
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</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>
Updating the documentation
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<dl class="function">
<dt id="pyFTS.benchmarks.benchmarks.plot_point">
New benchmarks.Tests module; Improvements on forecast_ahead_* function; Documentation update
2019-06-06 18:04:20 +04:00
<code class="descclassname">pyFTS.benchmarks.benchmarks.</code><code class="descname">plot_point</code><span class="sig-paren">(</span><em>axis</em>, <em>points</em>, <em>order</em>, <em>label</em>, <em>color='red'</em>, <em>ls='-'</em>, <em>linewidth=1</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.plot_point" title="Permalink to this definition"></a></dt>
Updating the documentation
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<dd></dd></dl>
<dl class="function">
<dt id="pyFTS.benchmarks.benchmarks.print_distribution_statistics">
New benchmarks.Tests module; Improvements on forecast_ahead_* function; Documentation update
2019-06-06 18:04:20 +04:00
<code class="descclassname">pyFTS.benchmarks.benchmarks.</code><code class="descname">print_distribution_statistics</code><span class="sig-paren">(</span><em>original</em>, <em>models</em>, <em>steps</em>, <em>resolution</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.print_distribution_statistics" title="Permalink to this definition"></a></dt>
Updating the documentation
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<dd><p>Run probabilistic benchmarks on given models and data and print the results</p>
Sphinx documentation for ReadTheDocs site
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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"><ul class="first simple">
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<li><strong>data</strong> – test data</li>
<li><strong>models</strong> – a list of FTS models to benchmark</li>
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</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>
<dl class="function">
Updating the documentation
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<dt id="pyFTS.benchmarks.benchmarks.print_interval_statistics">
New benchmarks.Tests module; Improvements on forecast_ahead_* function; Documentation update
2019-06-06 18:04:20 +04:00
<code class="descclassname">pyFTS.benchmarks.benchmarks.</code><code class="descname">print_interval_statistics</code><span class="sig-paren">(</span><em>original</em>, <em>models</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.print_interval_statistics" title="Permalink to this definition"></a></dt>
Updating the documentation
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<dd><p>Run interval benchmarks on given models and data and print the results</p>
Sphinx documentation for ReadTheDocs site
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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"><ul class="first simple">
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<li><strong>data</strong> – test data</li>
<li><strong>models</strong> – a list of FTS models to benchmark</li>
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</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>
<dl class="function">
Updating the documentation
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<dt id="pyFTS.benchmarks.benchmarks.print_point_statistics">
New benchmarks.Tests module; Improvements on forecast_ahead_* function; Documentation update
2019-06-06 18:04:20 +04:00
<code class="descclassname">pyFTS.benchmarks.benchmarks.</code><code class="descname">print_point_statistics</code><span class="sig-paren">(</span><em>data</em>, <em>models</em>, <em>externalmodels=None</em>, <em>externalforecasts=None</em>, <em>indexers=None</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.print_point_statistics" title="Permalink to this definition"></a></dt>
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<dd><p>Run point benchmarks on given models and data and print the results</p>
Sphinx documentation for ReadTheDocs site
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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"><ul class="first simple">
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<li><strong>data</strong> – test data</li>
<li><strong>models</strong> – a list of FTS models to benchmark</li>
<li><strong>externalmodels</strong> – a list with benchmark models (façades for other methods)</li>
<li><strong>externalforecasts</strong> – </li>
<li><strong>indexers</strong> – </li>
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</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>
<dl class="function">
Updating the documentation
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<dt id="pyFTS.benchmarks.benchmarks.process_interval_jobs">
New benchmarks.Tests module; Improvements on forecast_ahead_* function; Documentation update
2019-06-06 18:04:20 +04:00
<code class="descclassname">pyFTS.benchmarks.benchmarks.</code><code class="descname">process_interval_jobs</code><span class="sig-paren">(</span><em>dataset</em>, <em>tag</em>, <em>job</em>, <em>conn</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.process_interval_jobs" title="Permalink to this definition"></a></dt>
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<dd><p>Extract information from an dictionary with interval benchmark results and save it on a database</p>
Sphinx documentation for ReadTheDocs site
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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"><ul class="first simple">
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<li><strong>dataset</strong> – the benchmark dataset name</li>
<li><strong>tag</strong> – alias for the benchmark group being executed</li>
<li><strong>job</strong> – a dictionary with the benchmark results</li>
<li><strong>conn</strong> – a connection to a Sqlite database</li>
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</ul>
</td>
</tr>
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<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last"></p>
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</td>
</tr>
</tbody>
</table>
</dd></dl>
New benchmarks.Tests module; Improvements on forecast_ahead_* function; Documentation update
2019-06-06 18:04:20 +04:00
<dl class="function">
<dt id="pyFTS.benchmarks.benchmarks.process_interval_jobs2">
<code class="descclassname">pyFTS.benchmarks.benchmarks.</code><code class="descname">process_interval_jobs2</code><span class="sig-paren">(</span><em>dataset</em>, <em>tag</em>, <em>job</em>, <em>conn</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.process_interval_jobs2" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
Sphinx documentation for ReadTheDocs site
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<dl class="function">
Updating the documentation
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<dt id="pyFTS.benchmarks.benchmarks.process_point_jobs">
New benchmarks.Tests module; Improvements on forecast_ahead_* function; Documentation update
2019-06-06 18:04:20 +04:00
<code class="descclassname">pyFTS.benchmarks.benchmarks.</code><code class="descname">process_point_jobs</code><span class="sig-paren">(</span><em>dataset</em>, <em>tag</em>, <em>job</em>, <em>conn</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.process_point_jobs" title="Permalink to this definition"></a></dt>
<dd><p>Extract information from a dictionary with point benchmark results and save it on a database</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>dataset</strong> – the benchmark dataset name</li>
<li><strong>tag</strong> – alias for the benchmark group being executed</li>
<li><strong>job</strong> – a dictionary with the benchmark results</li>
<li><strong>conn</strong> – a connection to a Sqlite database</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>
<dl class="function">
<dt id="pyFTS.benchmarks.benchmarks.process_point_jobs2">
<code class="descclassname">pyFTS.benchmarks.benchmarks.</code><code class="descname">process_point_jobs2</code><span class="sig-paren">(</span><em>dataset</em>, <em>tag</em>, <em>job</em>, <em>conn</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.process_point_jobs2" title="Permalink to this definition"></a></dt>
Updating the documentation
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<dd><p>Extract information from a dictionary with point benchmark results and save it on a database</p>
Sphinx documentation for ReadTheDocs site
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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"><ul class="first simple">
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<li><strong>dataset</strong> – the benchmark dataset name</li>
<li><strong>tag</strong> – alias for the benchmark group being executed</li>
<li><strong>job</strong> – a dictionary with the benchmark results</li>
<li><strong>conn</strong> – a connection to a Sqlite database</li>
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</ul>
</td>
</tr>
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<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last"></p>
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</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="function">
Updating the documentation
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<dt id="pyFTS.benchmarks.benchmarks.process_probabilistic_jobs">
New benchmarks.Tests module; Improvements on forecast_ahead_* function; Documentation update
2019-06-06 18:04:20 +04:00
<code class="descclassname">pyFTS.benchmarks.benchmarks.</code><code class="descname">process_probabilistic_jobs</code><span class="sig-paren">(</span><em>dataset</em>, <em>tag</em>, <em>job</em>, <em>conn</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.process_probabilistic_jobs" title="Permalink to this definition"></a></dt>
<dd><p>Extract information from an dictionary with probabilistic benchmark results and save it on a database</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>dataset</strong> – the benchmark dataset name</li>
<li><strong>tag</strong> – alias for the benchmark group being executed</li>
<li><strong>job</strong> – a dictionary with the benchmark results</li>
<li><strong>conn</strong> – a connection to a Sqlite database</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>
<dl class="function">
<dt id="pyFTS.benchmarks.benchmarks.process_probabilistic_jobs2">
<code class="descclassname">pyFTS.benchmarks.benchmarks.</code><code class="descname">process_probabilistic_jobs2</code><span class="sig-paren">(</span><em>dataset</em>, <em>tag</em>, <em>job</em>, <em>conn</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.process_probabilistic_jobs2" title="Permalink to this definition"></a></dt>
Updating the documentation
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<dd><p>Extract information from an dictionary with probabilistic benchmark results and save it on a database</p>
Sphinx documentation for ReadTheDocs site
2018-08-30 09:05:29 +04:00
<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">
Updating the documentation
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<li><strong>dataset</strong> – the benchmark dataset name</li>
<li><strong>tag</strong> – alias for the benchmark group being executed</li>
<li><strong>job</strong> – a dictionary with the benchmark results</li>
<li><strong>conn</strong> – a connection to a Sqlite database</li>
Sphinx documentation for ReadTheDocs site
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</ul>
</td>
</tr>
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<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last"></p>
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</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="function">
Updating the documentation
2018-08-30 23:04:52 +04:00
<dt id="pyFTS.benchmarks.benchmarks.run_interval">
New benchmarks.Tests module; Improvements on forecast_ahead_* function; Documentation update
2019-06-06 18:04:20 +04:00
<code class="descclassname">pyFTS.benchmarks.benchmarks.</code><code class="descname">run_interval</code><span class="sig-paren">(</span><em>mfts</em>, <em>partitioner</em>, <em>train_data</em>, <em>test_data</em>, <em>window_key=None</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.run_interval" title="Permalink to this definition"></a></dt>
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<dd><p>Run the interval forecasting benchmarks</p>
Sphinx documentation for ReadTheDocs site
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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"><ul class="first simple">
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<li><strong>mfts</strong> – FTS model</li>
<li><strong>partitioner</strong> – Universe of Discourse partitioner</li>
<li><strong>train_data</strong> – data used to train the model</li>
<li><strong>test_data</strong> – ata used to test the model</li>
<li><strong>window_key</strong> – id of the sliding window</li>
<li><strong>transformation</strong> – data transformation</li>
<li><strong>indexer</strong> – seasonal indexer</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">a dictionary with the benchmark results</p>
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</td>
</tr>
</tbody>
</table>
</dd></dl>
New benchmarks.Tests module; Improvements on forecast_ahead_* function; Documentation update
2019-06-06 18:04:20 +04:00
<dl class="function">
<dt id="pyFTS.benchmarks.benchmarks.run_interval2">
<code class="descclassname">pyFTS.benchmarks.benchmarks.</code><code class="descname">run_interval2</code><span class="sig-paren">(</span><em>fts_method</em>, <em>order</em>, <em>partitioner_method</em>, <em>partitions</em>, <em>transformation</em>, <em>train_data</em>, <em>test_data</em>, <em>window_key=None</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.run_interval2" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
Sphinx documentation for ReadTheDocs site
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<dl class="function">
Updating the documentation
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<dt id="pyFTS.benchmarks.benchmarks.run_point">
New benchmarks.Tests module; Improvements on forecast_ahead_* function; Documentation update
2019-06-06 18:04:20 +04:00
<code class="descclassname">pyFTS.benchmarks.benchmarks.</code><code class="descname">run_point</code><span class="sig-paren">(</span><em>mfts</em>, <em>partitioner</em>, <em>train_data</em>, <em>test_data</em>, <em>window_key=None</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.run_point" title="Permalink to this definition"></a></dt>
Updating the documentation
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<dd><p>Run the point forecasting benchmarks</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>mfts</strong> – FTS model</li>
<li><strong>partitioner</strong> – Universe of Discourse partitioner</li>
<li><strong>train_data</strong> – data used to train the model</li>
<li><strong>test_data</strong> – ata used to test the model</li>
<li><strong>window_key</strong> – id of the sliding window</li>
<li><strong>transformation</strong> – data transformation</li>
<li><strong>indexer</strong> – seasonal indexer</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">a dictionary with the benchmark results</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>
Sphinx documentation for ReadTheDocs site
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New benchmarks.Tests module; Improvements on forecast_ahead_* function; Documentation update
2019-06-06 18:04:20 +04:00
<dl class="function">
<dt id="pyFTS.benchmarks.benchmarks.run_point2">
<code class="descclassname">pyFTS.benchmarks.benchmarks.</code><code class="descname">run_point2</code><span class="sig-paren">(</span><em>fts_method</em>, <em>order</em>, <em>partitioner_method</em>, <em>partitions</em>, <em>transformation</em>, <em>train_data</em>, <em>test_data</em>, <em>window_key=None</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.run_point2" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
Sphinx documentation for ReadTheDocs site
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<dl class="function">
Updating the documentation
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<dt id="pyFTS.benchmarks.benchmarks.run_probabilistic">
New benchmarks.Tests module; Improvements on forecast_ahead_* function; Documentation update
2019-06-06 18:04:20 +04:00
<code class="descclassname">pyFTS.benchmarks.benchmarks.</code><code class="descname">run_probabilistic</code><span class="sig-paren">(</span><em>mfts</em>, <em>partitioner</em>, <em>train_data</em>, <em>test_data</em>, <em>window_key=None</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.run_probabilistic" title="Permalink to this definition"></a></dt>
Updating the documentation
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<dd><p>Run the probabilistic forecasting benchmarks</p>
Sphinx documentation for ReadTheDocs site
2018-08-30 09:05:29 +04:00
<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">
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<li><strong>mfts</strong> – FTS model</li>
<li><strong>partitioner</strong> – Universe of Discourse partitioner</li>
<li><strong>train_data</strong> – data used to train the model</li>
<li><strong>test_data</strong> – ata used to test the model</li>
<li><strong>steps</strong> – </li>
<li><strong>resolution</strong> – </li>
<li><strong>window_key</strong> – id of the sliding window</li>
<li><strong>transformation</strong> – data transformation</li>
<li><strong>indexer</strong> – seasonal indexer</li>
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</ul>
</td>
</tr>
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<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">a dictionary with the benchmark results</p>
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</td>
</tr>
</tbody>
</table>
</dd></dl>
New benchmarks.Tests module; Improvements on forecast_ahead_* function; Documentation update
2019-06-06 18:04:20 +04:00
<dl class="function">
<dt id="pyFTS.benchmarks.benchmarks.run_probabilistic2">
<code class="descclassname">pyFTS.benchmarks.benchmarks.</code><code class="descname">run_probabilistic2</code><span class="sig-paren">(</span><em>fts_method</em>, <em>order</em>, <em>partitioner_method</em>, <em>partitions</em>, <em>transformation</em>, <em>train_data</em>, <em>test_data</em>, <em>window_key=None</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.run_probabilistic2" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
Sphinx documentation for ReadTheDocs site
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<dl class="function">
Updating the documentation
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<dt id="pyFTS.benchmarks.benchmarks.simpleSearch_RMSE">
New benchmarks.Tests module; Improvements on forecast_ahead_* function; Documentation update
2019-06-06 18:04:20 +04:00
<code class="descclassname">pyFTS.benchmarks.benchmarks.</code><code class="descname">simpleSearch_RMSE</code><span class="sig-paren">(</span><em>train, test, model, partitions, orders, save=False, file=None, tam=[10, 15], plotforecasts=False, elev=30, azim=144, intervals=False, parameters=None, partitioner=&lt;class 'pyFTS.partitioners.Grid.GridPartitioner'&gt;, transformation=None, indexer=None</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.simpleSearch_RMSE" title="Permalink to this definition"></a></dt>
Sphinx documentation for ReadTheDocs site
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<dd></dd></dl>
<dl class="function">
Updating the documentation
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<dt id="pyFTS.benchmarks.benchmarks.sliding_window_benchmarks">
New benchmarks.Tests module; Improvements on forecast_ahead_* function; Documentation update
2019-06-06 18:04:20 +04:00
<code class="descclassname">pyFTS.benchmarks.benchmarks.</code><code class="descname">sliding_window_benchmarks</code><span class="sig-paren">(</span><em>data</em>, <em>windowsize</em>, <em>train=0.8</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.sliding_window_benchmarks" title="Permalink to this definition"></a></dt>
Updating the documentation
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<dd><p>Sliding window benchmarks for FTS forecasters.</p>
<p>For each data window, a train and test datasets will be splitted. For each train split, number of
partitions and partitioning method will be created a partitioner model. And for each partitioner, order,
steps ahead and FTS method a foreasting model will be trained.</p>
<p>Then all trained models are benchmarked on the test data and the metrics are stored on a sqlite3 database
(identified by the ‘file’ parameter) for posterior analysis.</p>
<p>All these process can be distributed on a dispy cluster, setting the atributed ‘distributed’ to true and
informing the list of dispy nodes on ‘nodes’ parameter.</p>
<p>The number of experiments is determined by ‘windowsize’ and ‘inc’ parameters.</p>
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<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>data</strong> – test data</li>
<li><strong>windowsize</strong> – size of sliding window</li>
<li><strong>train</strong> – percentual of sliding window data used to train the models</li>
<li><strong>kwargs</strong> – dict, optional arguments</li>
<li><strong>benchmark_methods</strong> – a list with Non FTS models to benchmark. The default is None.</li>
<li><strong>benchmark_methods_parameters</strong> – a list with Non FTS models parameters. The default is None.</li>
<li><strong>benchmark_models</strong> – A boolean value indicating if external FTS methods will be used on benchmark. The default is False.</li>
<li><strong>build_methods</strong> – A boolean value indicating if the default FTS methods will be used on benchmark. The default is True.</li>
<li><strong>dataset</strong> – the dataset name to identify the current set of benchmarks results on database.</li>
<li><strong>distributed</strong> – A boolean value indicating if the forecasting procedure will be distributed in a dispy cluster. . The default is False</li>
<li><strong>file</strong> – file path to save the results. The default is benchmarks.db.</li>
<li><strong>inc</strong> – a float on interval [0,1] indicating the percentage of the windowsize to move the window</li>
<li><strong>methods</strong> – a list with FTS class names. The default depends on the forecasting type and contains the list of all FTS methods.</li>
<li><strong>models</strong> – a list with prebuilt FTS objects. The default is None.</li>
<li><strong>nodes</strong> – a list with the dispy cluster nodes addresses. The default is [127.0.0.1].</li>
<li><strong>orders</strong> – a list with orders of the models (for high order models). The default is [1,2,3].</li>
<li><strong>partitions</strong> – a list with the numbers of partitions on the Universe of Discourse. The default is [10].</li>
<li><strong>partitioners_models</strong> – a list with prebuilt Universe of Discourse partitioners objects. The default is None.</li>
<li><strong>partitioners_methods</strong> – a list with Universe of Discourse partitioners class names. The default is [partitioners.Grid.GridPartitioner].</li>
<li><strong>progress</strong> – If true a progress bar will be displayed during the benchmarks. The default is False.</li>
<li><strong>start</strong> – in the multi step forecasting, the index of the data where to start forecasting. The default is 0.</li>
<li><strong>steps_ahead</strong> – a list with the forecasting horizons, i. e., the number of steps ahead to forecast. The default is 1.</li>
<li><strong>tag</strong> – a name to identify the current set of benchmarks results on database.</li>
<li><strong>type</strong> – the forecasting type, one of these values: point(default), interval or distribution. The default is point.</li>
<li><strong>transformations</strong> – a list with data transformations do apply . The default is [None].</li>
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</td>
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<dl class="function">
<dt id="pyFTS.benchmarks.benchmarks.sliding_window_benchmarks2">
<code class="descclassname">pyFTS.benchmarks.benchmarks.</code><code class="descname">sliding_window_benchmarks2</code><span class="sig-paren">(</span><em>data</em>, <em>windowsize</em>, <em>train=0.8</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.sliding_window_benchmarks2" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
Documentation update
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<dl class="function">
<dt id="pyFTS.benchmarks.benchmarks.train_test_time">
<code class="descclassname">pyFTS.benchmarks.benchmarks.</code><code class="descname">train_test_time</code><span class="sig-paren">(</span><em>data</em>, <em>windowsize</em>, <em>train=0.8</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.train_test_time" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
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</div>
<div class="section" id="module-pyFTS.benchmarks.Measures">
<span id="pyfts-benchmarks-measures-module"></span><h2>pyFTS.benchmarks.Measures module<a class="headerlink" href="#module-pyFTS.benchmarks.Measures" title="Permalink to this headline"></a></h2>
<p>pyFTS module for common benchmark metrics</p>
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<dl class="function">
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<dt id="pyFTS.benchmarks.Measures.TheilsInequality">
<code class="descclassname">pyFTS.benchmarks.Measures.</code><code class="descname">TheilsInequality</code><span class="sig-paren">(</span><em>targets</em>, <em>forecasts</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.Measures.TheilsInequality" title="Permalink to this definition"></a></dt>
<dd><p>Theil’s Inequality Coefficient</p>
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<li><strong>targets</strong> – </li>
<li><strong>forecasts</strong> – </li>
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<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last"></p>
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</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="function">
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<dt id="pyFTS.benchmarks.Measures.UStatistic">
<code class="descclassname">pyFTS.benchmarks.Measures.</code><code class="descname">UStatistic</code><span class="sig-paren">(</span><em>targets</em>, <em>forecasts</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.Measures.UStatistic" title="Permalink to this definition"></a></dt>
<dd><p>Theil’s U Statistic</p>
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<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
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<li><strong>targets</strong> – </li>
<li><strong>forecasts</strong> – </li>
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</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>
<dl class="function">
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<dt id="pyFTS.benchmarks.Measures.acf">
<code class="descclassname">pyFTS.benchmarks.Measures.</code><code class="descname">acf</code><span class="sig-paren">(</span><em>data</em>, <em>k</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.Measures.acf" title="Permalink to this definition"></a></dt>
<dd><p>Autocorrelation function estimative</p>
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<li><strong>data</strong> – </li>
<li><strong>k</strong> – </li>
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</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>
<dl class="function">
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<dt id="pyFTS.benchmarks.Measures.brier_score">
<code class="descclassname">pyFTS.benchmarks.Measures.</code><code class="descname">brier_score</code><span class="sig-paren">(</span><em>targets</em>, <em>densities</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.Measures.brier_score" title="Permalink to this definition"></a></dt>
<dd><p>Brier Score for probabilistic forecasts.
Brier (1950). “Verification of Forecasts Expressed in Terms of Probability”. Monthly Weather Review. 78: 1–3.</p>
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<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
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<li><strong>targets</strong> – a list with the target values</li>
<li><strong>densities</strong> – a list with pyFTS.probabil objectsistic.ProbabilityDistribution</li>
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</ul>
</td>
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<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">float</p>
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</tbody>
</table>
</dd></dl>
<dl class="function">
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<dt id="pyFTS.benchmarks.Measures.coverage">
<code class="descclassname">pyFTS.benchmarks.Measures.</code><code class="descname">coverage</code><span class="sig-paren">(</span><em>targets</em>, <em>forecasts</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.Measures.coverage" title="Permalink to this definition"></a></dt>
<dd><p>Percent of target values that fall inside forecasted interval</p>
</dd></dl>
<dl class="function">
<dt id="pyFTS.benchmarks.Measures.crps">
<code class="descclassname">pyFTS.benchmarks.Measures.</code><code class="descname">crps</code><span class="sig-paren">(</span><em>targets</em>, <em>densities</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.Measures.crps" title="Permalink to this definition"></a></dt>
<dd><p>Continuous Ranked Probability Score</p>
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<li><strong>targets</strong> – a list with the target values</li>
<li><strong>densities</strong> – a list with pyFTS.probabil objectsistic.ProbabilityDistribution</li>
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</ul>
</td>
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<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">float</p>
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</dd></dl>
<dl class="function">
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<dt id="pyFTS.benchmarks.Measures.get_distribution_ahead_statistics">
<code class="descclassname">pyFTS.benchmarks.Measures.</code><code class="descname">get_distribution_ahead_statistics</code><span class="sig-paren">(</span><em>data</em>, <em>distributions</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.Measures.get_distribution_ahead_statistics" title="Permalink to this definition"></a></dt>
<dd><p>Get CRPS statistic and time for a forecasting model</p>
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<table class="docutils field-list" frame="void" rules="none">
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<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
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<li><strong>data</strong> – test data</li>
<li><strong>model</strong> – FTS model with probabilistic forecasting capability</li>
<li><strong>kwargs</strong> – </li>
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</ul>
</td>
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<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">a list with the CRPS and execution time</p>
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</dd></dl>
<dl class="function">
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<dt id="pyFTS.benchmarks.Measures.get_distribution_statistics">
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<code class="descclassname">pyFTS.benchmarks.Measures.</code><code class="descname">get_distribution_statistics</code><span class="sig-paren">(</span><em>data</em>, <em>model</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.Measures.get_distribution_statistics" title="Permalink to this definition"></a></dt>
Updating the documentation
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<dd><p>Get CRPS statistic and time for a forecasting model</p>
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<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> – test data</li>
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<li><strong>model</strong> – FTS model with probabilistic forecasting capability</li>
<li><strong>kwargs</strong> – </li>
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</ul>
</td>
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<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">a list with the CRPS and execution time</p>
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</tbody>
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</dd></dl>
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<dl class="function">
<dt id="pyFTS.benchmarks.Measures.get_interval_ahead_statistics">
<code class="descclassname">pyFTS.benchmarks.Measures.</code><code class="descname">get_interval_ahead_statistics</code><span class="sig-paren">(</span><em>data</em>, <em>intervals</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.Measures.get_interval_ahead_statistics" title="Permalink to this definition"></a></dt>
<dd><p>Condensate all measures for point interval forecasters</p>
<table class="docutils field-list" frame="void" rules="none">
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<li><strong>data</strong> – test data</li>
Documentation update
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<li><strong>intervals</strong> – predicted intervals for each datapoint</li>
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<li><strong>kwargs</strong> – </li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">a list with the sharpness, resolution, coverage, .05 pinball mean,
.25 pinball mean, .75 pinball mean and .95 pinball mean.</p>
</td>
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</tbody>
</table>
</dd></dl>
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<dt id="pyFTS.benchmarks.Measures.get_interval_statistics">
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<code class="descclassname">pyFTS.benchmarks.Measures.</code><code class="descname">get_interval_statistics</code><span class="sig-paren">(</span><em>data</em>, <em>model</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.Measures.get_interval_statistics" title="Permalink to this definition"></a></dt>
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<dd><p>Condensate all measures for point interval forecasters</p>
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<li><strong>data</strong> – test data</li>
<li><strong>model</strong> – FTS model with interval forecasting capability</li>
<li><strong>kwargs</strong> – </li>
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</ul>
</td>
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<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">a list with the sharpness, resolution, coverage, .05 pinball mean,
.25 pinball mean, .75 pinball mean and .95 pinball mean.</p>
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</td>
</tr>
</tbody>
</table>
</dd></dl>
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<dl class="function">
<dt id="pyFTS.benchmarks.Measures.get_point_ahead_statistics">
<code class="descclassname">pyFTS.benchmarks.Measures.</code><code class="descname">get_point_ahead_statistics</code><span class="sig-paren">(</span><em>data</em>, <em>forecasts</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.Measures.get_point_ahead_statistics" title="Permalink to this definition"></a></dt>
<dd><p>Condensate all measures for point forecasters</p>
<table class="docutils field-list" frame="void" rules="none">
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<li><strong>data</strong> – test data</li>
<li><strong>model</strong> – FTS model with point forecasting capability</li>
<li><strong>kwargs</strong> – </li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">a list with the RMSE, SMAPE and U Statistic</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>
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<dl class="function">
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<dt id="pyFTS.benchmarks.Measures.get_point_statistics">
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<code class="descclassname">pyFTS.benchmarks.Measures.</code><code class="descname">get_point_statistics</code><span class="sig-paren">(</span><em>data</em>, <em>model</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.Measures.get_point_statistics" title="Permalink to this definition"></a></dt>
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<dd><p>Condensate all measures for point forecasters</p>
<table class="docutils field-list" frame="void" rules="none">
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<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> – test data</li>
<li><strong>model</strong> – FTS model with point forecasting capability</li>
<li><strong>kwargs</strong> – </li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">a list with the RMSE, SMAPE and U Statistic</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>
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<dt id="pyFTS.benchmarks.Measures.logarithm_score">
<code class="descclassname">pyFTS.benchmarks.Measures.</code><code class="descname">logarithm_score</code><span class="sig-paren">(</span><em>targets</em>, <em>densities</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.Measures.logarithm_score" title="Permalink to this definition"></a></dt>
<dd><p>Logarithm Score for probabilistic forecasts.
Good IJ (1952). “Rational Decisions.”Journal of the Royal Statistical Society B,14(1),107–114. URLhttps://www.jstor.org/stable/2984087.</p>
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<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>targets</strong> – a list with the target values</li>
<li><strong>densities</strong> – a list with pyFTS.probabil objectsistic.ProbabilityDistribution</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">float</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>
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<code class="descclassname">pyFTS.benchmarks.Measures.</code><code class="descname">mape</code><span class="sig-paren">(</span><em>targets</em>, <em>forecasts</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.Measures.mape" title="Permalink to this definition"></a></dt>
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<dd><p>Mean Average Percentual Error</p>
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<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>targets</strong> – </li>
<li><strong>forecasts</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>
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<code class="descclassname">pyFTS.benchmarks.Measures.</code><code class="descname">mape_interval</code><span class="sig-paren">(</span><em>targets</em>, <em>forecasts</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.Measures.mape_interval" title="Permalink to this definition"></a></dt>
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<dd></dd></dl>
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<code class="descclassname">pyFTS.benchmarks.Measures.</code><code class="descname">pinball</code><span class="sig-paren">(</span><em>tau</em>, <em>target</em>, <em>forecast</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.Measures.pinball" title="Permalink to this definition"></a></dt>
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<dd><p>Pinball loss function. Measure the distance of forecast to the tau-quantile of the target</p>
<table class="docutils field-list" frame="void" rules="none">
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<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>tau</strong> – quantile value in the range (0,1)</li>
<li><strong>target</strong> – </li>
<li><strong>forecast</strong> – </li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">float, distance of forecast to the tau-quantile of the target</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>
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<code class="descclassname">pyFTS.benchmarks.Measures.</code><code class="descname">pinball_mean</code><span class="sig-paren">(</span><em>tau</em>, <em>targets</em>, <em>forecasts</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.Measures.pinball_mean" title="Permalink to this definition"></a></dt>
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<dd><p>Mean pinball loss value of the forecast for a given tau-quantile of the targets</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>tau</strong> – quantile value in the range (0,1)</li>
<li><strong>targets</strong> – list of target values</li>
<li><strong>forecasts</strong> – list of prediction intervals</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">float, the pinball loss mean for tau quantile</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>
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<code class="descclassname">pyFTS.benchmarks.Measures.</code><code class="descname">resolution</code><span class="sig-paren">(</span><em>forecasts</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.Measures.resolution" title="Permalink to this definition"></a></dt>
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<dd><p>Resolution - Standard deviation of the intervals</p>
</dd></dl>
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<code class="descclassname">pyFTS.benchmarks.Measures.</code><code class="descname">rmse</code><span class="sig-paren">(</span><em>targets</em>, <em>forecasts</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.Measures.rmse" title="Permalink to this definition"></a></dt>
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<dd><p>Root Mean Squared Error</p>
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<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>targets</strong> – </li>
<li><strong>forecasts</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>
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<code class="descclassname">pyFTS.benchmarks.Measures.</code><code class="descname">rmse_interval</code><span class="sig-paren">(</span><em>targets</em>, <em>forecasts</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.Measures.rmse_interval" title="Permalink to this definition"></a></dt>
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<dd><p>Root Mean Squared Error</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>targets</strong> – </li>
<li><strong>forecasts</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>
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<code class="descclassname">pyFTS.benchmarks.Measures.</code><code class="descname">sharpness</code><span class="sig-paren">(</span><em>forecasts</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.Measures.sharpness" title="Permalink to this definition"></a></dt>
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<dd><p>Sharpness - Mean size of the intervals</p>
</dd></dl>
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<code class="descclassname">pyFTS.benchmarks.Measures.</code><code class="descname">smape</code><span class="sig-paren">(</span><em>targets</em>, <em>forecasts</em>, <em>type=2</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.Measures.smape" title="Permalink to this definition"></a></dt>
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<dd><p>Symmetric Mean Average Percentual Error</p>
<table class="docutils field-list" frame="void" rules="none">
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<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>targets</strong> – </li>
<li><strong>forecasts</strong> – </li>
<li><strong>type</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>
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<code class="descclassname">pyFTS.benchmarks.Measures.</code><code class="descname">winkler_mean</code><span class="sig-paren">(</span><em>tau</em>, <em>targets</em>, <em>forecasts</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.Measures.winkler_mean" title="Permalink to this definition"></a></dt>
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<dd><p>Mean Winkler score value of the forecast for a given tau-quantile of the targets</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>tau</strong> – quantile value in the range (0,1)</li>
<li><strong>targets</strong> – list of target values</li>
<li><strong>forecasts</strong> – list of prediction intervals</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">float, the Winkler score mean for tau quantile</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>
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<code class="descclassname">pyFTS.benchmarks.Measures.</code><code class="descname">winkler_score</code><span class="sig-paren">(</span><em>tau</em>, <em>target</em>, <em>forecast</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.Measures.winkler_score" title="Permalink to this definition"></a></dt>
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<dd><ol class="upperalpha simple" start="18">
<li><ol class="first upperalpha" start="12">
<li>Winkler, A Decision-Theoretic Approach to Interval Estimation, J. Am. Stat. Assoc. 67 (337) (1972) 187–191. doi:10.2307/2284720.</li>
</ol>
</li>
</ol>
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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"><ul class="first simple">
<li><strong>tau</strong> – </li>
<li><strong>target</strong> – </li>
<li><strong>forecast</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>
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</dd></dl>
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</div>
<div class="section" id="module-pyFTS.benchmarks.ResidualAnalysis">
<span id="pyfts-benchmarks-residualanalysis-module"></span><h2>pyFTS.benchmarks.ResidualAnalysis module<a class="headerlink" href="#module-pyFTS.benchmarks.ResidualAnalysis" title="Permalink to this headline"></a></h2>
<p>Residual Analysis methods</p>
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<code class="descclassname">pyFTS.benchmarks.ResidualAnalysis.</code><code class="descname">compare_residuals</code><span class="sig-paren">(</span><em>data</em>, <em>models</em>, <em>alpha=0.05</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.ResidualAnalysis.compare_residuals" title="Permalink to this definition"></a></dt>
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<dd><p>Compare residual’s statistics of several models</p>
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<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> – test data</li>
<li><strong>models</strong> – </li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">a Pandas dataframe with the Box-Ljung statistic for each model</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>
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<dt id="pyFTS.benchmarks.ResidualAnalysis.ljung_box_test">
<code class="descclassname">pyFTS.benchmarks.ResidualAnalysis.</code><code class="descname">ljung_box_test</code><span class="sig-paren">(</span><em>residuals, lags=[1, 2, 3], alpha=0.5</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.ResidualAnalysis.ljung_box_test" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
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<dt id="pyFTS.benchmarks.ResidualAnalysis.plot_residuals_by_model">
<code class="descclassname">pyFTS.benchmarks.ResidualAnalysis.</code><code class="descname">plot_residuals_by_model</code><span class="sig-paren">(</span><em>targets, models, tam=[8, 8], save=False, file=None</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.ResidualAnalysis.plot_residuals_by_model" title="Permalink to this definition"></a></dt>
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<dd></dd></dl>
<dl class="function">
<dt id="pyFTS.benchmarks.ResidualAnalysis.residuals">
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<code class="descclassname">pyFTS.benchmarks.ResidualAnalysis.</code><code class="descname">residuals</code><span class="sig-paren">(</span><em>targets</em>, <em>forecasts</em>, <em>order=1</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.ResidualAnalysis.residuals" title="Permalink to this definition"></a></dt>
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<dd><p>First order residuals</p>
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<code class="descclassname">pyFTS.benchmarks.ResidualAnalysis.</code><code class="descname">single_plot_residuals</code><span class="sig-paren">(</span><em>res, order, tam=[10, 7], save=False, file=None</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.ResidualAnalysis.single_plot_residuals" title="Permalink to this definition"></a></dt>
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<dd></dd></dl>
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<div class="section" id="module-pyFTS.benchmarks.Tests">
<span id="pyfts-benchmarks-tests-module"></span><h2>pyFTS.benchmarks.Tests module<a class="headerlink" href="#module-pyFTS.benchmarks.Tests" title="Permalink to this headline"></a></h2>
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<dl class="function">
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<dt id="pyFTS.benchmarks.Tests.BoxLjungStatistic">
<code class="descclassname">pyFTS.benchmarks.Tests.</code><code class="descname">BoxLjungStatistic</code><span class="sig-paren">(</span><em>data</em>, <em>h</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.Tests.BoxLjungStatistic" title="Permalink to this definition"></a></dt>
<dd><p>Q Statistic for Ljung–Box test</p>
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<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> – </li>
<li><strong>h</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>
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</tr>
</tbody>
</table>
</dd></dl>
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<dl class="function">
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<dt id="pyFTS.benchmarks.Tests.BoxPierceStatistic">
<code class="descclassname">pyFTS.benchmarks.Tests.</code><code class="descname">BoxPierceStatistic</code><span class="sig-paren">(</span><em>data</em>, <em>h</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.Tests.BoxPierceStatistic" title="Permalink to this definition"></a></dt>
<dd><p>Q Statistic for Box-Pierce test</p>
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<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
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<li><strong>data</strong> – </li>
<li><strong>h</strong> – </li>
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</ul>
</td>
</tr>
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<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>
<dl class="function">
<dt id="pyFTS.benchmarks.Tests.format_experiment_table">
<code class="descclassname">pyFTS.benchmarks.Tests.</code><code class="descname">format_experiment_table</code><span class="sig-paren">(</span><em>df</em>, <em>exclude=[]</em>, <em>replace={}</em>, <em>csv=True</em>, <em>std=False</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.Tests.format_experiment_table" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="function">
<dt id="pyFTS.benchmarks.Tests.post_hoc_tests">
<code class="descclassname">pyFTS.benchmarks.Tests.</code><code class="descname">post_hoc_tests</code><span class="sig-paren">(</span><em>post_hoc</em>, <em>control_method</em>, <em>alpha=0.05</em>, <em>method='finner'</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.Tests.post_hoc_tests" title="Permalink to this definition"></a></dt>
<dd><p>Finner paired post-hoc test with NSFTS as control method.</p>
<p>$H_0$: There is no significant difference between the means</p>
<p>$H_1$: There is a significant difference between the means</p>
<table class="docutils field-list" frame="void" rules="none">
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<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>post_hoc</strong> – </li>
<li><strong>control_method</strong> – </li>
<li><strong>alpha</strong> – </li>
<li><strong>method</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>
<dl class="function">
<dt id="pyFTS.benchmarks.Tests.test_mean_equality">
<code class="descclassname">pyFTS.benchmarks.Tests.</code><code class="descname">test_mean_equality</code><span class="sig-paren">(</span><em>tests</em>, <em>alpha=0.05</em>, <em>method='friedman'</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.Tests.test_mean_equality" title="Permalink to this definition"></a></dt>
<dd><p>Test for the equality of the means, with alpha confidence level.</p>
<p>H_0: There’s no significant difference between the means
H_1: There is at least one significant difference between the means</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>tests</strong> – </li>
<li><strong>alpha</strong> – </li>
<li><strong>method</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>
</div>
<div class="section" id="module-pyFTS.benchmarks.Util">
<span id="pyfts-benchmarks-util-module"></span><h2>pyFTS.benchmarks.Util module<a class="headerlink" href="#module-pyFTS.benchmarks.Util" title="Permalink to this headline"></a></h2>
<p>Facilities for pyFTS Benchmark module</p>
<dl class="function">
<dt id="pyFTS.benchmarks.Util.analytic_tabular_dataframe">
<code class="descclassname">pyFTS.benchmarks.Util.</code><code class="descname">analytic_tabular_dataframe</code><span class="sig-paren">(</span><em>dataframe</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.Util.analytic_tabular_dataframe" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="function">
<dt id="pyFTS.benchmarks.Util.analytical_data_columns">
<code class="descclassname">pyFTS.benchmarks.Util.</code><code class="descname">analytical_data_columns</code><span class="sig-paren">(</span><em>experiments</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.Util.analytical_data_columns" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="function">
<dt id="pyFTS.benchmarks.Util.base_dataframe_columns">
<code class="descclassname">pyFTS.benchmarks.Util.</code><code class="descname">base_dataframe_columns</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.Util.base_dataframe_columns" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="function">
<dt id="pyFTS.benchmarks.Util.cast_dataframe_to_synthetic">
<code class="descclassname">pyFTS.benchmarks.Util.</code><code class="descname">cast_dataframe_to_synthetic</code><span class="sig-paren">(</span><em>infile</em>, <em>outfile</em>, <em>experiments</em>, <em>type</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.Util.cast_dataframe_to_synthetic" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="function">
<dt id="pyFTS.benchmarks.Util.cast_dataframe_to_synthetic_interval">
<code class="descclassname">pyFTS.benchmarks.Util.</code><code class="descname">cast_dataframe_to_synthetic_interval</code><span class="sig-paren">(</span><em>df</em>, <em>data_columns</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.Util.cast_dataframe_to_synthetic_interval" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="function">
<dt id="pyFTS.benchmarks.Util.cast_dataframe_to_synthetic_point">
<code class="descclassname">pyFTS.benchmarks.Util.</code><code class="descname">cast_dataframe_to_synthetic_point</code><span class="sig-paren">(</span><em>df</em>, <em>data_columns</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.Util.cast_dataframe_to_synthetic_point" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="function">
<dt id="pyFTS.benchmarks.Util.cast_dataframe_to_synthetic_probabilistic">
<code class="descclassname">pyFTS.benchmarks.Util.</code><code class="descname">cast_dataframe_to_synthetic_probabilistic</code><span class="sig-paren">(</span><em>df</em>, <em>data_columns</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.Util.cast_dataframe_to_synthetic_probabilistic" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="function">
<dt id="pyFTS.benchmarks.Util.check_ignore_list">
<code class="descclassname">pyFTS.benchmarks.Util.</code><code class="descname">check_ignore_list</code><span class="sig-paren">(</span><em>b</em>, <em>ignore</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.Util.check_ignore_list" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="function">
<dt id="pyFTS.benchmarks.Util.check_replace_list">
<code class="descclassname">pyFTS.benchmarks.Util.</code><code class="descname">check_replace_list</code><span class="sig-paren">(</span><em>m</em>, <em>replace</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.Util.check_replace_list" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="function">
<dt id="pyFTS.benchmarks.Util.create_benchmark_tables">
<code class="descclassname">pyFTS.benchmarks.Util.</code><code class="descname">create_benchmark_tables</code><span class="sig-paren">(</span><em>conn</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.Util.create_benchmark_tables" title="Permalink to this definition"></a></dt>
<dd><p>Create a sqlite3 table designed to store benchmark results.</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>conn</strong> – a sqlite3 database connection</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="function">
<dt id="pyFTS.benchmarks.Util.extract_measure">
<code class="descclassname">pyFTS.benchmarks.Util.</code><code class="descname">extract_measure</code><span class="sig-paren">(</span><em>dataframe</em>, <em>measure</em>, <em>data_columns</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.Util.extract_measure" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="function">
<dt id="pyFTS.benchmarks.Util.find_best">
<code class="descclassname">pyFTS.benchmarks.Util.</code><code class="descname">find_best</code><span class="sig-paren">(</span><em>dataframe</em>, <em>criteria</em>, <em>ascending</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.Util.find_best" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="function">
<dt id="pyFTS.benchmarks.Util.get_dataframe_from_bd">
<code class="descclassname">pyFTS.benchmarks.Util.</code><code class="descname">get_dataframe_from_bd</code><span class="sig-paren">(</span><em>file</em>, <em>filter</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.Util.get_dataframe_from_bd" title="Permalink to this definition"></a></dt>
<dd><p>Query the sqlite benchmark database and return a pandas dataframe with the results</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>file</strong> – the url of the benchmark database</li>
<li><strong>filter</strong> – sql conditions to filter</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">pandas dataframe with the query results</p>
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</td>
</tr>
</tbody>
</table>
</dd></dl>
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<dl class="function">
<dt id="pyFTS.benchmarks.Util.insert_benchmark">
New benchmarks.Tests module; Improvements on forecast_ahead_* function; Documentation update
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<code class="descclassname">pyFTS.benchmarks.Util.</code><code class="descname">insert_benchmark</code><span class="sig-paren">(</span><em>data</em>, <em>conn</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.Util.insert_benchmark" title="Permalink to this definition"></a></dt>
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<dd><p>Insert benchmark data on database</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">
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<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>data</strong> – a tuple with the benchmark data with format:</td>
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</tr>
</tbody>
</table>
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<p>ID: integer incremental primary key
Date: Date/hour of benchmark execution
Dataset: Identify on which dataset the dataset was performed
Tag: a user defined word that indentify a benchmark set
Type: forecasting type (point, interval, distribution)
Model: FTS model
Transformation: The name of data transformation, if one was used
Order: the order of the FTS method
Scheme: UoD partitioning scheme
Partitions: Number of partitions
Size: Number of rules of the FTS model
Steps: prediction horizon, i. e., the number of steps ahead
Measure: accuracy measure
Value: the measure value</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">
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<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>conn</strong> – a sqlite3 database connection</td>
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</tr>
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<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"></td>
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</tr>
</tbody>
</table>
</dd></dl>
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<dl class="function">
<dt id="pyFTS.benchmarks.Util.interval_dataframe_analytic_columns">
New benchmarks.Tests module; Improvements on forecast_ahead_* function; Documentation update
2019-06-06 18:04:20 +04:00
<code class="descclassname">pyFTS.benchmarks.Util.</code><code class="descname">interval_dataframe_analytic_columns</code><span class="sig-paren">(</span><em>experiments</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.Util.interval_dataframe_analytic_columns" title="Permalink to this definition"></a></dt>
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<dd></dd></dl>
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<dl class="function">
<dt id="pyFTS.benchmarks.Util.interval_dataframe_synthetic_columns">
New benchmarks.Tests module; Improvements on forecast_ahead_* function; Documentation update
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<code class="descclassname">pyFTS.benchmarks.Util.</code><code class="descname">interval_dataframe_synthetic_columns</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.Util.interval_dataframe_synthetic_columns" title="Permalink to this definition"></a></dt>
Updating the documentation
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<dd></dd></dl>
<dl class="function">
<dt id="pyFTS.benchmarks.Util.open_benchmark_db">
New benchmarks.Tests module; Improvements on forecast_ahead_* function; Documentation update
2019-06-06 18:04:20 +04:00
<code class="descclassname">pyFTS.benchmarks.Util.</code><code class="descname">open_benchmark_db</code><span class="sig-paren">(</span><em>name</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.Util.open_benchmark_db" title="Permalink to this definition"></a></dt>
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<dd><p>Open a connection with a Sqlite database designed to store benchmark results.</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">
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<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>name</strong> – database filenem</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body">a sqlite3 database connection</td>
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</tr>
</tbody>
</table>
</dd></dl>
<dl class="function">
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<dt id="pyFTS.benchmarks.Util.plot_dataframe_interval">
New benchmarks.Tests module; Improvements on forecast_ahead_* function; Documentation update
2019-06-06 18:04:20 +04:00
<code class="descclassname">pyFTS.benchmarks.Util.</code><code class="descname">plot_dataframe_interval</code><span class="sig-paren">(</span><em>file_synthetic, file_analytic, experiments, tam, save=False, file=None, sort_columns=['COVAVG', 'SHARPAVG', 'COVSTD', 'SHARPSTD'], sort_ascend=[True, False, True, True], save_best=False, ignore=None, replace=None</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.Util.plot_dataframe_interval" title="Permalink to this definition"></a></dt>
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<dd></dd></dl>
<dl class="function">
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<dt id="pyFTS.benchmarks.Util.plot_dataframe_interval_pinball">
New benchmarks.Tests module; Improvements on forecast_ahead_* function; Documentation update
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<code class="descclassname">pyFTS.benchmarks.Util.</code><code class="descname">plot_dataframe_interval_pinball</code><span class="sig-paren">(</span><em>file_synthetic, file_analytic, experiments, tam, save=False, file=None, sort_columns=['COVAVG', 'SHARPAVG', 'COVSTD', 'SHARPSTD'], sort_ascend=[True, False, True, True], save_best=False, ignore=None, replace=None</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.Util.plot_dataframe_interval_pinball" title="Permalink to this definition"></a></dt>
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<dd></dd></dl>
<dl class="function">
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<dt id="pyFTS.benchmarks.Util.plot_dataframe_point">
New benchmarks.Tests module; Improvements on forecast_ahead_* function; Documentation update
2019-06-06 18:04:20 +04:00
<code class="descclassname">pyFTS.benchmarks.Util.</code><code class="descname">plot_dataframe_point</code><span class="sig-paren">(</span><em>file_synthetic, file_analytic, experiments, tam, save=False, file=None, sort_columns=['UAVG', 'RMSEAVG', 'USTD', 'RMSESTD'], sort_ascend=[1, 1, 1, 1], save_best=False, ignore=None, replace=None</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.Util.plot_dataframe_point" title="Permalink to this definition"></a></dt>
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<dd></dd></dl>
<dl class="function">
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<dt id="pyFTS.benchmarks.Util.plot_dataframe_probabilistic">
New benchmarks.Tests module; Improvements on forecast_ahead_* function; Documentation update
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<code class="descclassname">pyFTS.benchmarks.Util.</code><code class="descname">plot_dataframe_probabilistic</code><span class="sig-paren">(</span><em>file_synthetic, file_analytic, experiments, tam, save=False, file=None, sort_columns=['CRPS1AVG', 'CRPS2AVG', 'CRPS1STD', 'CRPS2STD'], sort_ascend=[True, True, True, True], save_best=False, ignore=None, replace=None</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.Util.plot_dataframe_probabilistic" title="Permalink to this definition"></a></dt>
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<dd></dd></dl>
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<dl class="function">
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<dt id="pyFTS.benchmarks.Util.point_dataframe_analytic_columns">
New benchmarks.Tests module; Improvements on forecast_ahead_* function; Documentation update
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<code class="descclassname">pyFTS.benchmarks.Util.</code><code class="descname">point_dataframe_analytic_columns</code><span class="sig-paren">(</span><em>experiments</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.Util.point_dataframe_analytic_columns" title="Permalink to this definition"></a></dt>
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<dd></dd></dl>
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<dl class="function">
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<dt id="pyFTS.benchmarks.Util.point_dataframe_synthetic_columns">
New benchmarks.Tests module; Improvements on forecast_ahead_* function; Documentation update
2019-06-06 18:04:20 +04:00
<code class="descclassname">pyFTS.benchmarks.Util.</code><code class="descname">point_dataframe_synthetic_columns</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.Util.point_dataframe_synthetic_columns" title="Permalink to this definition"></a></dt>
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<dd></dd></dl>
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<dl class="function">
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<dt id="pyFTS.benchmarks.Util.probabilistic_dataframe_analytic_columns">
New benchmarks.Tests module; Improvements on forecast_ahead_* function; Documentation update
2019-06-06 18:04:20 +04:00
<code class="descclassname">pyFTS.benchmarks.Util.</code><code class="descname">probabilistic_dataframe_analytic_columns</code><span class="sig-paren">(</span><em>experiments</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.Util.probabilistic_dataframe_analytic_columns" title="Permalink to this definition"></a></dt>
Sphinx documentation for ReadTheDocs site
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<dd></dd></dl>
<dl class="function">
Updating the documentation
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<dt id="pyFTS.benchmarks.Util.probabilistic_dataframe_synthetic_columns">
New benchmarks.Tests module; Improvements on forecast_ahead_* function; Documentation update
2019-06-06 18:04:20 +04:00
<code class="descclassname">pyFTS.benchmarks.Util.</code><code class="descname">probabilistic_dataframe_synthetic_columns</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.Util.probabilistic_dataframe_synthetic_columns" title="Permalink to this definition"></a></dt>
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<dd></dd></dl>
<dl class="function">
Updating the documentation
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<dt id="pyFTS.benchmarks.Util.process_common_data">
New benchmarks.Tests module; Improvements on forecast_ahead_* function; Documentation update
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<code class="descclassname">pyFTS.benchmarks.Util.</code><code class="descname">process_common_data</code><span class="sig-paren">(</span><em>dataset</em>, <em>tag</em>, <em>type</em>, <em>job</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.Util.process_common_data" title="Permalink to this definition"></a></dt>
<dd><p>Wraps benchmark information on a tuple for sqlite database</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>dataset</strong> – benchmark dataset</li>
<li><strong>tag</strong> – benchmark set alias</li>
<li><strong>type</strong> – forecasting type</li>
<li><strong>job</strong> – a dictionary with benchmark data</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">tuple for sqlite database</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="function">
<dt id="pyFTS.benchmarks.Util.process_common_data2">
<code class="descclassname">pyFTS.benchmarks.Util.</code><code class="descname">process_common_data2</code><span class="sig-paren">(</span><em>dataset</em>, <em>tag</em>, <em>type</em>, <em>job</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.Util.process_common_data2" title="Permalink to this definition"></a></dt>
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<dd><p>Wraps benchmark information on a tuple for sqlite database</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"><ul class="first simple">
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<li><strong>dataset</strong> – benchmark dataset</li>
<li><strong>tag</strong> – benchmark set alias</li>
<li><strong>type</strong> – forecasting type</li>
<li><strong>job</strong> – a dictionary with benchmark data</li>
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</ul>
</td>
</tr>
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<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">tuple for sqlite database</p>
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</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="function">
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<dt id="pyFTS.benchmarks.Util.save_dataframe_interval">
New benchmarks.Tests module; Improvements on forecast_ahead_* function; Documentation update
2019-06-06 18:04:20 +04:00
<code class="descclassname">pyFTS.benchmarks.Util.</code><code class="descname">save_dataframe_interval</code><span class="sig-paren">(</span><em>coverage</em>, <em>experiments</em>, <em>file</em>, <em>objs</em>, <em>resolution</em>, <em>save</em>, <em>sharpness</em>, <em>synthetic</em>, <em>times</em>, <em>q05</em>, <em>q25</em>, <em>q75</em>, <em>q95</em>, <em>steps</em>, <em>method</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.Util.save_dataframe_interval" title="Permalink to this definition"></a></dt>
Updating the documentation
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<dd></dd></dl>
<dl class="function">
<dt id="pyFTS.benchmarks.Util.save_dataframe_point">
New benchmarks.Tests module; Improvements on forecast_ahead_* function; Documentation update
2019-06-06 18:04:20 +04:00
<code class="descclassname">pyFTS.benchmarks.Util.</code><code class="descname">save_dataframe_point</code><span class="sig-paren">(</span><em>experiments</em>, <em>file</em>, <em>objs</em>, <em>rmse</em>, <em>save</em>, <em>synthetic</em>, <em>smape</em>, <em>times</em>, <em>u</em>, <em>steps</em>, <em>method</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.Util.save_dataframe_point" title="Permalink to this definition"></a></dt>
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<dd><p>Create a dataframe to store the benchmark results</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"><ul class="first simple">
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<li><strong>experiments</strong> – dictionary with the execution results</li>
<li><strong>file</strong> – </li>
<li><strong>objs</strong> – </li>
<li><strong>rmse</strong> – </li>
<li><strong>save</strong> – </li>
<li><strong>synthetic</strong> – </li>
<li><strong>smape</strong> – </li>
<li><strong>times</strong> – </li>
<li><strong>u</strong> – </li>
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</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>
<dl class="function">
Updating the documentation
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<dt id="pyFTS.benchmarks.Util.save_dataframe_probabilistic">
New benchmarks.Tests module; Improvements on forecast_ahead_* function; Documentation update
2019-06-06 18:04:20 +04:00
<code class="descclassname">pyFTS.benchmarks.Util.</code><code class="descname">save_dataframe_probabilistic</code><span class="sig-paren">(</span><em>experiments</em>, <em>file</em>, <em>objs</em>, <em>crps</em>, <em>times</em>, <em>save</em>, <em>synthetic</em>, <em>steps</em>, <em>method</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.Util.save_dataframe_probabilistic" title="Permalink to this definition"></a></dt>
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<dd><p>Save benchmark results for m-step ahead probabilistic forecasters
:param experiments:
:param file:
:param objs:
:param crps_interval:
:param crps_distr:
:param times:
:param times2:
:param save:
:param synthetic:
:return:</p>
</dd></dl>
<dl class="function">
<dt id="pyFTS.benchmarks.Util.scale">
New benchmarks.Tests module; Improvements on forecast_ahead_* function; Documentation update
2019-06-06 18:04:20 +04:00
<code class="descclassname">pyFTS.benchmarks.Util.</code><code class="descname">scale</code><span class="sig-paren">(</span><em>data</em>, <em>params</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.Util.scale" title="Permalink to this definition"></a></dt>
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<dd></dd></dl>
<dl class="function">
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<dt id="pyFTS.benchmarks.Util.scale_params">
New benchmarks.Tests module; Improvements on forecast_ahead_* function; Documentation update
2019-06-06 18:04:20 +04:00
<code class="descclassname">pyFTS.benchmarks.Util.</code><code class="descname">scale_params</code><span class="sig-paren">(</span><em>data</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.Util.scale_params" title="Permalink to this definition"></a></dt>
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<dd></dd></dl>
New benchmarks.Tests module; Improvements on forecast_ahead_* function; Documentation update
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<dl class="function">
<dt id="pyFTS.benchmarks.Util.simple_synthetic_dataframe">
<code class="descclassname">pyFTS.benchmarks.Util.</code><code class="descname">simple_synthetic_dataframe</code><span class="sig-paren">(</span><em>file</em>, <em>tag</em>, <em>measure</em>, <em>sql=None</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.Util.simple_synthetic_dataframe" title="Permalink to this definition"></a></dt>
<dd><p>Read experiments results from sqlite3 database in ‘file’, make a synthesis of the results
of the metric ‘measure’ with the same ‘tag’, returning a Pandas DataFrame with the mean results.</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>file</strong> – sqlite3 database file name</li>
<li><strong>tag</strong> – common tag of the experiments</li>
<li><strong>measure</strong> – metric to synthetize</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">Pandas DataFrame with the mean results</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>
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<dl class="function">
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<dt id="pyFTS.benchmarks.Util.stats">
New benchmarks.Tests module; Improvements on forecast_ahead_* function; Documentation update
2019-06-06 18:04:20 +04:00
<code class="descclassname">pyFTS.benchmarks.Util.</code><code class="descname">stats</code><span class="sig-paren">(</span><em>measure</em>, <em>data</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.Util.stats" title="Permalink to this definition"></a></dt>
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<dd></dd></dl>
<dl class="function">
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<dt id="pyFTS.benchmarks.Util.tabular_dataframe_columns">
New benchmarks.Tests module; Improvements on forecast_ahead_* function; Documentation update
2019-06-06 18:04:20 +04:00
<code class="descclassname">pyFTS.benchmarks.Util.</code><code class="descname">tabular_dataframe_columns</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.Util.tabular_dataframe_columns" title="Permalink to this definition"></a></dt>
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<dd></dd></dl>
<dl class="function">
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<dt id="pyFTS.benchmarks.Util.unified_scaled_interval">
New benchmarks.Tests module; Improvements on forecast_ahead_* function; Documentation update
2019-06-06 18:04:20 +04:00
<code class="descclassname">pyFTS.benchmarks.Util.</code><code class="descname">unified_scaled_interval</code><span class="sig-paren">(</span><em>experiments, tam, save=False, file=None, sort_columns=['COVAVG', 'SHARPAVG', 'COVSTD', 'SHARPSTD'], sort_ascend=[True, False, True, True], save_best=False, ignore=None, replace=None</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.Util.unified_scaled_interval" title="Permalink to this definition"></a></dt>
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<dd></dd></dl>
<dl class="function">
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<dt id="pyFTS.benchmarks.Util.unified_scaled_interval_pinball">
New benchmarks.Tests module; Improvements on forecast_ahead_* function; Documentation update
2019-06-06 18:04:20 +04:00
<code class="descclassname">pyFTS.benchmarks.Util.</code><code class="descname">unified_scaled_interval_pinball</code><span class="sig-paren">(</span><em>experiments, tam, save=False, file=None, sort_columns=['COVAVG', 'SHARPAVG', 'COVSTD', 'SHARPSTD'], sort_ascend=[True, False, True, True], save_best=False, ignore=None, replace=None</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.Util.unified_scaled_interval_pinball" title="Permalink to this definition"></a></dt>
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<dd></dd></dl>
<dl class="function">
Updating the documentation
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<dt id="pyFTS.benchmarks.Util.unified_scaled_point">
New benchmarks.Tests module; Improvements on forecast_ahead_* function; Documentation update
2019-06-06 18:04:20 +04:00
<code class="descclassname">pyFTS.benchmarks.Util.</code><code class="descname">unified_scaled_point</code><span class="sig-paren">(</span><em>experiments, tam, save=False, file=None, sort_columns=['UAVG', 'RMSEAVG', 'USTD', 'RMSESTD'], sort_ascend=[1, 1, 1, 1], save_best=False, ignore=None, replace=None</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.Util.unified_scaled_point" title="Permalink to this definition"></a></dt>
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<dd></dd></dl>
<dl class="function">
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<dt id="pyFTS.benchmarks.Util.unified_scaled_probabilistic">
New benchmarks.Tests module; Improvements on forecast_ahead_* function; Documentation update
2019-06-06 18:04:20 +04:00
<code class="descclassname">pyFTS.benchmarks.Util.</code><code class="descname">unified_scaled_probabilistic</code><span class="sig-paren">(</span><em>experiments, tam, save=False, file=None, sort_columns=['CRPSAVG', 'CRPSSTD'], sort_ascend=[True, True], save_best=False, ignore=None, replace=None</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.Util.unified_scaled_probabilistic" title="Permalink to this definition"></a></dt>
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<dd></dd></dl>
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</div>
<div class="section" id="module-pyFTS.benchmarks.arima">
<span id="pyfts-benchmarks-arima-module"></span><h2>pyFTS.benchmarks.arima module<a class="headerlink" href="#module-pyFTS.benchmarks.arima" title="Permalink to this headline"></a></h2>
<dl class="class">
<dt id="pyFTS.benchmarks.arima.ARIMA">
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.benchmarks.arima.</code><code class="descname">ARIMA</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.arima.ARIMA" title="Permalink to this definition"></a></dt>
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<dd><p>Bases: <a class="reference internal" href="pyFTS.common.html#pyFTS.common.fts.FTS" title="pyFTS.common.fts.FTS"><code class="xref py py-class docutils literal notranslate"><span class="pre">pyFTS.common.fts.FTS</span></code></a></p>
<p>Façade for statsmodels.tsa.arima_model</p>
<dl class="method">
<dt id="pyFTS.benchmarks.arima.ARIMA.ar">
New benchmarks.Tests module; Improvements on forecast_ahead_* function; Documentation update
2019-06-06 18:04:20 +04:00
<code class="descname">ar</code><span class="sig-paren">(</span><em>data</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.arima.ARIMA.ar" title="Permalink to this definition"></a></dt>
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<dd></dd></dl>
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<dl class="method">
<dt id="pyFTS.benchmarks.arima.ARIMA.forecast">
New benchmarks.Tests module; Improvements on forecast_ahead_* function; Documentation update
2019-06-06 18:04:20 +04:00
<code class="descname">forecast</code><span class="sig-paren">(</span><em>ndata</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.arima.ARIMA.forecast" title="Permalink to this definition"></a></dt>
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<dd><p>Point forecast one step ahead</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> – time series data with the minimal length equal to the max_lag of the model</li>
<li><strong>kwargs</strong> – model specific parameters</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">a list with the forecasted values</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>
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<dl class="method">
<dt id="pyFTS.benchmarks.arima.ARIMA.forecast_ahead_distribution">
New benchmarks.Tests module; Improvements on forecast_ahead_* function; Documentation update
2019-06-06 18:04:20 +04:00
<code class="descname">forecast_ahead_distribution</code><span class="sig-paren">(</span><em>data</em>, <em>steps</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.arima.ARIMA.forecast_ahead_distribution" title="Permalink to this definition"></a></dt>
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<dd><p>Probabilistic forecast n steps ahead</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> – time series data with the minimal length equal to the max_lag of the model</li>
<li><strong>steps</strong> – the number of steps ahead to forecast</li>
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<li><strong>start_at</strong> – in the multi step forecasting, the index of the data where to start forecasting (default: 0)</li>
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</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">a list with the forecasted Probability Distributions</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>
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<dl class="method">
<dt id="pyFTS.benchmarks.arima.ARIMA.forecast_ahead_interval">
New benchmarks.Tests module; Improvements on forecast_ahead_* function; Documentation update
2019-06-06 18:04:20 +04:00
<code class="descname">forecast_ahead_interval</code><span class="sig-paren">(</span><em>ndata</em>, <em>steps</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.arima.ARIMA.forecast_ahead_interval" title="Permalink to this definition"></a></dt>
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<dd><p>Interval forecast n steps ahead</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"><ul class="first simple">
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<li><strong>data</strong> – time series data with the minimal length equal to the max_lag of the model</li>
<li><strong>steps</strong> – the number of steps ahead to forecast</li>
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<li><strong>start_at</strong> – in the multi step forecasting, the index of the data where to start forecasting (default: 0)</li>
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</ul>
</td>
</tr>
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<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">a list with the forecasted intervals</p>
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</td>
</tr>
</tbody>
</table>
</dd></dl>
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<dl class="method">
<dt id="pyFTS.benchmarks.arima.ARIMA.forecast_distribution">
New benchmarks.Tests module; Improvements on forecast_ahead_* function; Documentation update
2019-06-06 18:04:20 +04:00
<code class="descname">forecast_distribution</code><span class="sig-paren">(</span><em>data</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.arima.ARIMA.forecast_distribution" title="Permalink to this definition"></a></dt>
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<dd><p>Probabilistic forecast one step ahead</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"><ul class="first simple">
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<li><strong>data</strong> – time series data with the minimal length equal to the max_lag of the model</li>
<li><strong>kwargs</strong> – model specific parameters</li>
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</ul>
</td>
</tr>
Improving documentation for the Interval and Probabilistic forecasts
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<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">a list with probabilistic.ProbabilityDistribution objects representing the forecasted Probability Distributions</p>
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</td>
</tr>
</tbody>
</table>
</dd></dl>
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<dl class="method">
<dt id="pyFTS.benchmarks.arima.ARIMA.forecast_interval">
New benchmarks.Tests module; Improvements on forecast_ahead_* function; Documentation update
2019-06-06 18:04:20 +04:00
<code class="descname">forecast_interval</code><span class="sig-paren">(</span><em>data</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.arima.ARIMA.forecast_interval" title="Permalink to this definition"></a></dt>
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<dd><p>Interval forecast one step ahead</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"><ul class="first simple">
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<li><strong>data</strong> – time series data with the minimal length equal to the max_lag of the model</li>
<li><strong>kwargs</strong> – model specific parameters</li>
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</ul>
</td>
</tr>
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<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">a list with the prediction intervals</p>
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</td>
</tr>
</tbody>
</table>
</dd></dl>
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<dl class="method">
<dt id="pyFTS.benchmarks.arima.ARIMA.ma">
New benchmarks.Tests module; Improvements on forecast_ahead_* function; Documentation update
2019-06-06 18:04:20 +04:00
<code class="descname">ma</code><span class="sig-paren">(</span><em>data</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.arima.ARIMA.ma" title="Permalink to this definition"></a></dt>
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<dd></dd></dl>
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<dl class="method">
<dt id="pyFTS.benchmarks.arima.ARIMA.train">
New benchmarks.Tests module; Improvements on forecast_ahead_* function; Documentation update
2019-06-06 18:04:20 +04:00
<code class="descname">train</code><span class="sig-paren">(</span><em>data</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.arima.ARIMA.train" title="Permalink to this definition"></a></dt>
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<dd><p>Method specific parameter fitting</p>
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<table class="docutils field-list" frame="void" rules="none">
<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">
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<li><strong>data</strong> – training time series data</li>
<li><strong>kwargs</strong> – Method specific parameters</li>
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</ul>
</td>
</tr>
</tbody>
</table>
</dd></dl>
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</dd></dl>
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</div>
<div class="section" id="module-pyFTS.benchmarks.knn">
<span id="pyfts-benchmarks-knn-module"></span><h2>pyFTS.benchmarks.knn module<a class="headerlink" href="#module-pyFTS.benchmarks.knn" title="Permalink to this headline"></a></h2>
<dl class="class">
<dt id="pyFTS.benchmarks.knn.KNearestNeighbors">
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.benchmarks.knn.</code><code class="descname">KNearestNeighbors</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.knn.KNearestNeighbors" title="Permalink to this definition"></a></dt>
Sphinx documentation for ReadTheDocs site
2018-08-30 09:05:29 +04:00
<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>
New benchmarks.Tests module; Improvements on forecast_ahead_* function; Documentation update
2019-06-06 18:04:20 +04:00
<p>A façade for sklearn.neighbors</p>
<dl class="method">
<dt id="pyFTS.benchmarks.knn.KNearestNeighbors.forecast">
<code class="descname">forecast</code><span class="sig-paren">(</span><em>data</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.knn.KNearestNeighbors.forecast" title="Permalink to this definition"></a></dt>
<dd><p>Point forecast one step ahead</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> – time series data with the minimal length equal to the max_lag of the model</li>
<li><strong>kwargs</strong> – model specific parameters</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">a list with the forecasted values</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="pyFTS.benchmarks.knn.KNearestNeighbors.forecast_ahead_distribution">
<code class="descname">forecast_ahead_distribution</code><span class="sig-paren">(</span><em>data</em>, <em>steps</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.knn.KNearestNeighbors.forecast_ahead_distribution" title="Permalink to this definition"></a></dt>
<dd><p>Probabilistic forecast n steps ahead</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> – time series data with the minimal length equal to the max_lag of the model</li>
<li><strong>steps</strong> – the number of steps ahead to forecast</li>
<li><strong>start_at</strong> – in the multi step forecasting, the index of the data where to start forecasting (default: 0)</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">a list with the forecasted Probability Distributions</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="pyFTS.benchmarks.knn.KNearestNeighbors.forecast_ahead_interval">
<code class="descname">forecast_ahead_interval</code><span class="sig-paren">(</span><em>data</em>, <em>steps</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.knn.KNearestNeighbors.forecast_ahead_interval" title="Permalink to this definition"></a></dt>
<dd><p>Interval forecast n steps ahead</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> – time series data with the minimal length equal to the max_lag of the model</li>
<li><strong>steps</strong> – the number of steps ahead to forecast</li>
<li><strong>start_at</strong> – in the multi step forecasting, the index of the data where to start forecasting (default: 0)</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">a list with the forecasted intervals</p>
</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.benchmarks.knn.KNearestNeighbors.forecast_distribution">
New benchmarks.Tests module; Improvements on forecast_ahead_* function; Documentation update
2019-06-06 18:04:20 +04:00
<code class="descname">forecast_distribution</code><span class="sig-paren">(</span><em>data</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.knn.KNearestNeighbors.forecast_distribution" title="Permalink to this definition"></a></dt>
Sphinx documentation for ReadTheDocs site
2018-08-30 09:05:29 +04:00
<dd><p>Probabilistic forecast one step ahead</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> – time series data with the minimal length equal to the max_lag of the model</li>
<li><strong>kwargs</strong> – model specific parameters</li>
</ul>
</td>
</tr>
Improving documentation for the Interval and Probabilistic forecasts
2018-09-29 02:35:07 +04:00
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">a list with probabilistic.ProbabilityDistribution objects representing the forecasted Probability Distributions</p>
Sphinx documentation for ReadTheDocs site
2018-08-30 09:05:29 +04:00
</td>
</tr>
</tbody>
</table>
</dd></dl>
New benchmarks.Tests module; Improvements on forecast_ahead_* function; Documentation update
2019-06-06 18:04:20 +04:00
<dl class="method">
<dt id="pyFTS.benchmarks.knn.KNearestNeighbors.forecast_interval">
<code class="descname">forecast_interval</code><span class="sig-paren">(</span><em>data</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.knn.KNearestNeighbors.forecast_interval" title="Permalink to this definition"></a></dt>
<dd><p>Interval forecast one step ahead</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> – time series data with the minimal length equal to the max_lag of the model</li>
<li><strong>kwargs</strong> – model specific parameters</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">a list with the prediction intervals</p>
</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.benchmarks.knn.KNearestNeighbors.knn">
New benchmarks.Tests module; Improvements on forecast_ahead_* function; Documentation update
2019-06-06 18:04:20 +04:00
<code class="descname">knn</code><span class="sig-paren">(</span><em>sample</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.knn.KNearestNeighbors.knn" title="Permalink to this definition"></a></dt>
Sphinx documentation for ReadTheDocs site
2018-08-30 09:05:29 +04:00
<dd></dd></dl>
<dl class="method">
<dt id="pyFTS.benchmarks.knn.KNearestNeighbors.train">
New benchmarks.Tests module; Improvements on forecast_ahead_* function; Documentation update
2019-06-06 18:04:20 +04:00
<code class="descname">train</code><span class="sig-paren">(</span><em>data</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.knn.KNearestNeighbors.train" title="Permalink to this definition"></a></dt>
Sphinx documentation for ReadTheDocs site
2018-08-30 09:05:29 +04:00
<dd><p>Method specific parameter fitting</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>data</strong> – training time series data</li>
<li><strong>kwargs</strong> – Method specific parameters</li>
</ul>
</td>
</tr>
</tbody>
</table>
</dd></dl>
</dd></dl>
</div>
<div class="section" id="module-pyFTS.benchmarks.naive">
<span id="pyfts-benchmarks-naive-module"></span><h2>pyFTS.benchmarks.naive module<a class="headerlink" href="#module-pyFTS.benchmarks.naive" title="Permalink to this headline"></a></h2>
<dl class="class">
<dt id="pyFTS.benchmarks.naive.Naive">
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.benchmarks.naive.</code><code class="descname">Naive</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.naive.Naive" title="Permalink to this definition"></a></dt>
Sphinx documentation for ReadTheDocs site
2018-08-30 09:05:29 +04:00
<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>Naïve Forecasting method</p>
<dl class="method">
<dt id="pyFTS.benchmarks.naive.Naive.forecast">
New benchmarks.Tests module; Improvements on forecast_ahead_* function; Documentation update
2019-06-06 18:04:20 +04:00
<code class="descname">forecast</code><span class="sig-paren">(</span><em>data</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.naive.Naive.forecast" title="Permalink to this definition"></a></dt>
Sphinx documentation for ReadTheDocs site
2018-08-30 09:05:29 +04:00
<dd><p>Point forecast one step ahead</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> – time series data with the minimal length equal to the max_lag of the model</li>
<li><strong>kwargs</strong> – model specific parameters</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">a list with the forecasted values</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>
</dd></dl>
</div>
<div class="section" id="module-pyFTS.benchmarks.quantreg">
<span id="pyfts-benchmarks-quantreg-module"></span><h2>pyFTS.benchmarks.quantreg module<a class="headerlink" href="#module-pyFTS.benchmarks.quantreg" title="Permalink to this headline"></a></h2>
<dl class="class">
<dt id="pyFTS.benchmarks.quantreg.QuantileRegression">
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.benchmarks.quantreg.</code><code class="descname">QuantileRegression</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.quantreg.QuantileRegression" title="Permalink to this definition"></a></dt>
Sphinx documentation for ReadTheDocs site
2018-08-30 09:05:29 +04:00
<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>Façade for statsmodels.regression.quantile_regression</p>
<dl class="method">
<dt id="pyFTS.benchmarks.quantreg.QuantileRegression.forecast">
New benchmarks.Tests module; Improvements on forecast_ahead_* function; Documentation update
2019-06-06 18:04:20 +04:00
<code class="descname">forecast</code><span class="sig-paren">(</span><em>ndata</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.quantreg.QuantileRegression.forecast" title="Permalink to this definition"></a></dt>
Sphinx documentation for ReadTheDocs site
2018-08-30 09:05:29 +04:00
<dd><p>Point forecast one step ahead</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> – time series data with the minimal length equal to the max_lag of the model</li>
<li><strong>kwargs</strong> – model specific parameters</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">a list with the forecasted values</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="pyFTS.benchmarks.quantreg.QuantileRegression.forecast_ahead_distribution">
New benchmarks.Tests module; Improvements on forecast_ahead_* function; Documentation update
2019-06-06 18:04:20 +04:00
<code class="descname">forecast_ahead_distribution</code><span class="sig-paren">(</span><em>ndata</em>, <em>steps</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.quantreg.QuantileRegression.forecast_ahead_distribution" title="Permalink to this definition"></a></dt>
Sphinx documentation for ReadTheDocs site
2018-08-30 09:05:29 +04:00
<dd><p>Probabilistic forecast n steps ahead</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> – time series data with the minimal length equal to the max_lag of the model</li>
<li><strong>steps</strong> – the number of steps ahead to forecast</li>
New benchmarks.Tests module; Improvements on forecast_ahead_* function; Documentation update
2019-06-06 18:04:20 +04:00
<li><strong>start_at</strong> – in the multi step forecasting, the index of the data where to start forecasting (default: 0)</li>
Sphinx documentation for ReadTheDocs site
2018-08-30 09:05:29 +04:00
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">a list with the forecasted Probability Distributions</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="pyFTS.benchmarks.quantreg.QuantileRegression.forecast_ahead_interval">
New benchmarks.Tests module; Improvements on forecast_ahead_* function; Documentation update
2019-06-06 18:04:20 +04:00
<code class="descname">forecast_ahead_interval</code><span class="sig-paren">(</span><em>ndata</em>, <em>steps</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.quantreg.QuantileRegression.forecast_ahead_interval" title="Permalink to this definition"></a></dt>
Sphinx documentation for ReadTheDocs site
2018-08-30 09:05:29 +04:00
<dd><p>Interval forecast n steps ahead</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> – time series data with the minimal length equal to the max_lag of the model</li>
<li><strong>steps</strong> – the number of steps ahead to forecast</li>
New benchmarks.Tests module; Improvements on forecast_ahead_* function; Documentation update
2019-06-06 18:04:20 +04:00
<li><strong>start_at</strong> – in the multi step forecasting, the index of the data where to start forecasting (default: 0)</li>
Sphinx documentation for ReadTheDocs site
2018-08-30 09:05:29 +04:00
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">a list with the forecasted intervals</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="pyFTS.benchmarks.quantreg.QuantileRegression.forecast_distribution">
New benchmarks.Tests module; Improvements on forecast_ahead_* function; Documentation update
2019-06-06 18:04:20 +04:00
<code class="descname">forecast_distribution</code><span class="sig-paren">(</span><em>ndata</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.quantreg.QuantileRegression.forecast_distribution" title="Permalink to this definition"></a></dt>
Sphinx documentation for ReadTheDocs site
2018-08-30 09:05:29 +04:00
<dd><p>Probabilistic forecast one step ahead</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> – time series data with the minimal length equal to the max_lag of the model</li>
<li><strong>kwargs</strong> – model specific parameters</li>
</ul>
</td>
</tr>
Improving documentation for the Interval and Probabilistic forecasts
2018-09-29 02:35:07 +04:00
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">a list with probabilistic.ProbabilityDistribution objects representing the forecasted Probability Distributions</p>
Sphinx documentation for ReadTheDocs site
2018-08-30 09:05:29 +04:00
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="pyFTS.benchmarks.quantreg.QuantileRegression.forecast_interval">
New benchmarks.Tests module; Improvements on forecast_ahead_* function; Documentation update
2019-06-06 18:04:20 +04:00
<code class="descname">forecast_interval</code><span class="sig-paren">(</span><em>ndata</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.quantreg.QuantileRegression.forecast_interval" title="Permalink to this definition"></a></dt>
Sphinx documentation for ReadTheDocs site
2018-08-30 09:05:29 +04:00
<dd><p>Interval forecast one step ahead</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> – time series data with the minimal length equal to the max_lag of the model</li>
<li><strong>kwargs</strong> – model specific parameters</li>
</ul>
</td>
</tr>
Improving documentation for the Interval and Probabilistic forecasts
2018-09-29 02:35:07 +04:00
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">a list with the prediction intervals</p>
Sphinx documentation for ReadTheDocs site
2018-08-30 09:05:29 +04:00
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="pyFTS.benchmarks.quantreg.QuantileRegression.interval_to_interval">
New benchmarks.Tests module; Improvements on forecast_ahead_* function; Documentation update
2019-06-06 18:04:20 +04:00
<code class="descname">interval_to_interval</code><span class="sig-paren">(</span><em>data</em>, <em>lo_params</em>, <em>up_params</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.quantreg.QuantileRegression.interval_to_interval" title="Permalink to this definition"></a></dt>
Sphinx documentation for ReadTheDocs site
2018-08-30 09:05:29 +04:00
<dd></dd></dl>
<dl class="method">
<dt id="pyFTS.benchmarks.quantreg.QuantileRegression.linearmodel">
New benchmarks.Tests module; Improvements on forecast_ahead_* function; Documentation update
2019-06-06 18:04:20 +04:00
<code class="descname">linearmodel</code><span class="sig-paren">(</span><em>data</em>, <em>params</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.quantreg.QuantileRegression.linearmodel" title="Permalink to this definition"></a></dt>
Sphinx documentation for ReadTheDocs site
2018-08-30 09:05:29 +04:00
<dd></dd></dl>
<dl class="method">
<dt id="pyFTS.benchmarks.quantreg.QuantileRegression.point_to_interval">
New benchmarks.Tests module; Improvements on forecast_ahead_* function; Documentation update
2019-06-06 18:04:20 +04:00
<code class="descname">point_to_interval</code><span class="sig-paren">(</span><em>data</em>, <em>lo_params</em>, <em>up_params</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.quantreg.QuantileRegression.point_to_interval" title="Permalink to this definition"></a></dt>
Sphinx documentation for ReadTheDocs site
2018-08-30 09:05:29 +04:00
<dd></dd></dl>
<dl class="method">
<dt id="pyFTS.benchmarks.quantreg.QuantileRegression.train">
New benchmarks.Tests module; Improvements on forecast_ahead_* function; Documentation update
2019-06-06 18:04:20 +04:00
<code class="descname">train</code><span class="sig-paren">(</span><em>data</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.quantreg.QuantileRegression.train" title="Permalink to this definition"></a></dt>
<dd><p>Method specific parameter fitting</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>data</strong> – training time series data</li>
<li><strong>kwargs</strong> – Method specific parameters</li>
</ul>
</td>
</tr>
</tbody>
</table>
</dd></dl>
</dd></dl>
</div>
<div class="section" id="module-pyFTS.benchmarks.gaussianproc">
<span id="pyfts-benchmarks-gaussianproc-module"></span><h2>pyFTS.benchmarks.gaussianproc module<a class="headerlink" href="#module-pyFTS.benchmarks.gaussianproc" title="Permalink to this headline"></a></h2>
<dl class="class">
<dt id="pyFTS.benchmarks.gaussianproc.GPR">
<em class="property">class </em><code class="descclassname">pyFTS.benchmarks.gaussianproc.</code><code class="descname">GPR</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.gaussianproc.GPR" 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>Façade for sklearn.gaussian_proces</p>
<dl class="method">
<dt id="pyFTS.benchmarks.gaussianproc.GPR.forecast">
<code class="descname">forecast</code><span class="sig-paren">(</span><em>data</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.gaussianproc.GPR.forecast" title="Permalink to this definition"></a></dt>
<dd><p>Point forecast one step ahead</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> – time series data with the minimal length equal to the max_lag of the model</li>
<li><strong>kwargs</strong> – model specific parameters</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">a list with the forecasted values</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="pyFTS.benchmarks.gaussianproc.GPR.forecast_ahead">
<code class="descname">forecast_ahead</code><span class="sig-paren">(</span><em>data</em>, <em>steps</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.gaussianproc.GPR.forecast_ahead" title="Permalink to this definition"></a></dt>
<dd><p>Point forecast n steps ahead</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> – time series data with the minimal length equal to the max_lag of the model</li>
<li><strong>steps</strong> – the number of steps ahead to forecast (default: 1)</li>
<li><strong>start_at</strong> – in the multi step forecasting, the index of the data where to start forecasting (default: 0)</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">a list with the forecasted values</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="pyFTS.benchmarks.gaussianproc.GPR.forecast_ahead_distribution">
<code class="descname">forecast_ahead_distribution</code><span class="sig-paren">(</span><em>data</em>, <em>steps</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.gaussianproc.GPR.forecast_ahead_distribution" title="Permalink to this definition"></a></dt>
<dd><p>Probabilistic forecast n steps ahead</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> – time series data with the minimal length equal to the max_lag of the model</li>
<li><strong>steps</strong> – the number of steps ahead to forecast</li>
<li><strong>start_at</strong> – in the multi step forecasting, the index of the data where to start forecasting (default: 0)</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">a list with the forecasted Probability Distributions</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="pyFTS.benchmarks.gaussianproc.GPR.forecast_ahead_interval">
<code class="descname">forecast_ahead_interval</code><span class="sig-paren">(</span><em>data</em>, <em>steps</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.gaussianproc.GPR.forecast_ahead_interval" title="Permalink to this definition"></a></dt>
<dd><p>Interval forecast n steps ahead</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> – time series data with the minimal length equal to the max_lag of the model</li>
<li><strong>steps</strong> – the number of steps ahead to forecast</li>
<li><strong>start_at</strong> – in the multi step forecasting, the index of the data where to start forecasting (default: 0)</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">a list with the forecasted intervals</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="pyFTS.benchmarks.gaussianproc.GPR.forecast_distribution">
<code class="descname">forecast_distribution</code><span class="sig-paren">(</span><em>data</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.gaussianproc.GPR.forecast_distribution" title="Permalink to this definition"></a></dt>
<dd><p>Probabilistic forecast one step ahead</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> – time series data with the minimal length equal to the max_lag of the model</li>
<li><strong>kwargs</strong> – model specific parameters</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">a list with probabilistic.ProbabilityDistribution objects representing the forecasted Probability Distributions</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="pyFTS.benchmarks.gaussianproc.GPR.forecast_interval">
<code class="descname">forecast_interval</code><span class="sig-paren">(</span><em>data</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.gaussianproc.GPR.forecast_interval" title="Permalink to this definition"></a></dt>
<dd><p>Interval forecast one step ahead</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> – time series data with the minimal length equal to the max_lag of the model</li>
<li><strong>kwargs</strong> – model specific parameters</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">a list with the prediction intervals</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="pyFTS.benchmarks.gaussianproc.GPR.train">
<code class="descname">train</code><span class="sig-paren">(</span><em>data</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.gaussianproc.GPR.train" title="Permalink to this definition"></a></dt>
<dd><p>Method specific parameter fitting</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>data</strong> – training time series data</li>
<li><strong>kwargs</strong> – Method specific parameters</li>
</ul>
</td>
</tr>
</tbody>
</table>
</dd></dl>
</dd></dl>
</div>
<div class="section" id="module-pyFTS.benchmarks.BSTS">
<span id="pyfts-benchmarks-bsts-module"></span><h2>pyFTS.benchmarks.BSTS module<a class="headerlink" href="#module-pyFTS.benchmarks.BSTS" title="Permalink to this headline"></a></h2>
<dl class="class">
<dt id="pyFTS.benchmarks.BSTS.ARIMA">
<em class="property">class </em><code class="descclassname">pyFTS.benchmarks.BSTS.</code><code class="descname">ARIMA</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.BSTS.ARIMA" 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>Façade for statsmodels.tsa.arima_model</p>
<dl class="method">
<dt id="pyFTS.benchmarks.BSTS.ARIMA.forecast">
<code class="descname">forecast</code><span class="sig-paren">(</span><em>ndata</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.BSTS.ARIMA.forecast" title="Permalink to this definition"></a></dt>
<dd><p>Point forecast one step ahead</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> – time series data with the minimal length equal to the max_lag of the model</li>
<li><strong>kwargs</strong> – model specific parameters</li>
</ul>
Documentation update
2019-06-21 18:34:49 +04:00
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">a list with the forecasted values</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="pyFTS.benchmarks.BSTS.ARIMA.forecast_ahead">
<code class="descname">forecast_ahead</code><span class="sig-paren">(</span><em>data</em>, <em>steps</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.BSTS.ARIMA.forecast_ahead" title="Permalink to this definition"></a></dt>
<dd><p>Point forecast n steps ahead</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> – time series data with the minimal length equal to the max_lag of the model</li>
<li><strong>steps</strong> – the number of steps ahead to forecast (default: 1)</li>
<li><strong>start_at</strong> – in the multi step forecasting, the index of the data where to start forecasting (default: 0)</li>
</ul>
New benchmarks.Tests module; Improvements on forecast_ahead_* function; Documentation update
2019-06-06 18:04:20 +04:00
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">a list with the forecasted values</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="pyFTS.benchmarks.BSTS.ARIMA.forecast_ahead_distribution">
<code class="descname">forecast_ahead_distribution</code><span class="sig-paren">(</span><em>data</em>, <em>steps</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.BSTS.ARIMA.forecast_ahead_distribution" title="Permalink to this definition"></a></dt>
<dd><p>Probabilistic forecast n steps ahead</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> – time series data with the minimal length equal to the max_lag of the model</li>
<li><strong>steps</strong> – the number of steps ahead to forecast</li>
<li><strong>start_at</strong> – in the multi step forecasting, the index of the data where to start forecasting (default: 0)</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">a list with the forecasted Probability Distributions</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="pyFTS.benchmarks.BSTS.ARIMA.forecast_ahead_interval">
<code class="descname">forecast_ahead_interval</code><span class="sig-paren">(</span><em>ndata</em>, <em>steps</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.BSTS.ARIMA.forecast_ahead_interval" title="Permalink to this definition"></a></dt>
<dd><p>Interval forecast n steps ahead</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> – time series data with the minimal length equal to the max_lag of the model</li>
<li><strong>steps</strong> – the number of steps ahead to forecast</li>
<li><strong>start_at</strong> – in the multi step forecasting, the index of the data where to start forecasting (default: 0)</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">a list with the forecasted intervals</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="pyFTS.benchmarks.BSTS.ARIMA.forecast_distribution">
<code class="descname">forecast_distribution</code><span class="sig-paren">(</span><em>data</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.BSTS.ARIMA.forecast_distribution" title="Permalink to this definition"></a></dt>
<dd><p>Probabilistic forecast one step ahead</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> – time series data with the minimal length equal to the max_lag of the model</li>
<li><strong>kwargs</strong> – model specific parameters</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">a list with probabilistic.ProbabilityDistribution objects representing the forecasted Probability Distributions</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="pyFTS.benchmarks.BSTS.ARIMA.forecast_interval">
<code class="descname">forecast_interval</code><span class="sig-paren">(</span><em>data</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.BSTS.ARIMA.forecast_interval" title="Permalink to this definition"></a></dt>
<dd><p>Interval forecast one step ahead</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> – time series data with the minimal length equal to the max_lag of the model</li>
<li><strong>kwargs</strong> – model specific parameters</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">a list with the prediction intervals</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="pyFTS.benchmarks.BSTS.ARIMA.inference">
<code class="descname">inference</code><span class="sig-paren">(</span><em>steps</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.BSTS.ARIMA.inference" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="method">
<dt id="pyFTS.benchmarks.BSTS.ARIMA.train">
<code class="descname">train</code><span class="sig-paren">(</span><em>data</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.BSTS.ARIMA.train" title="Permalink to this definition"></a></dt>
Sphinx documentation for ReadTheDocs site
2018-08-30 09:05:29 +04:00
<dd><p>Method specific parameter fitting</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>data</strong> – training time series data</li>
<li><strong>kwargs</strong> – Method specific parameters</li>
</ul>
</td>
</tr>
</tbody>
</table>
</dd></dl>
</dd></dl>
</div>
</div>
</div>
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Updating the documentation
2018-08-30 23:04:52 +04:00
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