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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>
</div>
<div class="section" id="submodules">
<h2>Submodules<a class="headerlink" href="#submodules" title="Permalink to this headline"></a></h2>
</div>
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<div class="section" id="module-pyFTS.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="py function">
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<dt id="pyFTS.benchmarks.benchmarks.SelecaoSimples_MenorRMSE">
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<code class="sig-prename descclassname">pyFTS.benchmarks.benchmarks.</code><code class="sig-name descname">SelecaoSimples_MenorRMSE</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">original</span></em>, <em class="sig-param"><span class="n">parameters</span></em>, <em class="sig-param"><span class="n">modelo</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/benchmarks.html#SelecaoSimples_MenorRMSE"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.SelecaoSimples_MenorRMSE" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
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<dl class="py function">
<dt id="pyFTS.benchmarks.benchmarks.common_process_interval_jobs">
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<code class="sig-prename descclassname">pyFTS.benchmarks.benchmarks.</code><code class="sig-name descname">common_process_interval_jobs</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">conn</span></em>, <em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="n">job</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/benchmarks.html#common_process_interval_jobs"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.common_process_interval_jobs" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
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<dl class="py function">
<dt id="pyFTS.benchmarks.benchmarks.common_process_point_jobs">
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<code class="sig-prename descclassname">pyFTS.benchmarks.benchmarks.</code><code class="sig-name descname">common_process_point_jobs</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">conn</span></em>, <em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="n">job</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/benchmarks.html#common_process_point_jobs"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.common_process_point_jobs" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
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<dl class="py function">
<dt id="pyFTS.benchmarks.benchmarks.common_process_probabilistic_jobs">
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<code class="sig-prename descclassname">pyFTS.benchmarks.benchmarks.</code><code class="sig-name descname">common_process_probabilistic_jobs</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">conn</span></em>, <em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="n">job</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/benchmarks.html#common_process_probabilistic_jobs"><span class="viewcode-link">[source]</span></a><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>
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<dl class="py function">
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<dt id="pyFTS.benchmarks.benchmarks.common_process_time_jobs">
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<code class="sig-prename descclassname">pyFTS.benchmarks.benchmarks.</code><code class="sig-name descname">common_process_time_jobs</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">conn</span></em>, <em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="n">job</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/benchmarks.html#common_process_time_jobs"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.common_process_time_jobs" title="Permalink to this definition"></a></dt>
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<dd></dd></dl>
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<dl class="py function">
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<dt id="pyFTS.benchmarks.benchmarks.compareModelsPlot">
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<code class="sig-prename descclassname">pyFTS.benchmarks.benchmarks.</code><code class="sig-name descname">compareModelsPlot</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">original</span></em>, <em class="sig-param"><span class="n">models_fo</span></em>, <em class="sig-param"><span class="n">models_ho</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/benchmarks.html#compareModelsPlot"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.compareModelsPlot" title="Permalink to this definition"></a></dt>
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<dd></dd></dl>
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<dl class="py function">
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<dt id="pyFTS.benchmarks.benchmarks.compareModelsTable">
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<code class="sig-prename descclassname">pyFTS.benchmarks.benchmarks.</code><code class="sig-name descname">compareModelsTable</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">original</span></em>, <em class="sig-param"><span class="n">models_fo</span></em>, <em class="sig-param"><span class="n">models_ho</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/benchmarks.html#compareModelsTable"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.compareModelsTable" title="Permalink to this definition"></a></dt>
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<dd></dd></dl>
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<dl class="py function">
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<dt id="pyFTS.benchmarks.benchmarks.distributed_model_train_test_time">
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<code class="sig-prename descclassname">pyFTS.benchmarks.benchmarks.</code><code class="sig-name descname">distributed_model_train_test_time</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">models</span></em>, <em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="n">windowsize</span></em>, <em class="sig-param"><span class="n">train</span><span class="o">=</span><span class="default_value">0.8</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/benchmarks.html#distributed_model_train_test_time"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.distributed_model_train_test_time" title="Permalink to this definition"></a></dt>
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<dd><p>Assess the train and test times for a given list of configured models and save the results on a database.</p>
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<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>models</strong> A list of FTS models already configured, but not yet trained,</p></li>
<li><p><strong>data</strong> time series data, including train and test data</p></li>
<li><p><strong>windowsize</strong> Train/test data windows</p></li>
<li><p><strong>train</strong> Percent of data window that will be used to train the models</p></li>
<li><p><strong>kwargs</strong> </p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p></p>
</dd>
</dl>
</dd></dl>
<dl class="py function">
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<dt id="pyFTS.benchmarks.benchmarks.get_benchmark_interval_methods">
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<code class="sig-prename descclassname">pyFTS.benchmarks.benchmarks.</code><code class="sig-name descname">get_benchmark_interval_methods</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/benchmarks.html#get_benchmark_interval_methods"><span class="viewcode-link">[source]</span></a><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>
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<dl class="py function">
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<dt id="pyFTS.benchmarks.benchmarks.get_benchmark_point_methods">
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<code class="sig-prename descclassname">pyFTS.benchmarks.benchmarks.</code><code class="sig-name descname">get_benchmark_point_methods</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/benchmarks.html#get_benchmark_point_methods"><span class="viewcode-link">[source]</span></a><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>
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<dl class="py function">
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<dt id="pyFTS.benchmarks.benchmarks.get_benchmark_probabilistic_methods">
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<code class="sig-prename descclassname">pyFTS.benchmarks.benchmarks.</code><code class="sig-name descname">get_benchmark_probabilistic_methods</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/benchmarks.html#get_benchmark_probabilistic_methods"><span class="viewcode-link">[source]</span></a><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>
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<dl class="py function">
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<dt id="pyFTS.benchmarks.benchmarks.get_interval_methods">
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<code class="sig-prename descclassname">pyFTS.benchmarks.benchmarks.</code><code class="sig-name descname">get_interval_methods</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/benchmarks.html#get_interval_methods"><span class="viewcode-link">[source]</span></a><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>
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<dl class="py function">
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<dt id="pyFTS.benchmarks.benchmarks.get_point_methods">
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<code class="sig-prename descclassname">pyFTS.benchmarks.benchmarks.</code><code class="sig-name descname">get_point_methods</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/benchmarks.html#get_point_methods"><span class="viewcode-link">[source]</span></a><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="py function">
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<dt id="pyFTS.benchmarks.benchmarks.get_point_multivariate_methods">
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<code class="sig-prename descclassname">pyFTS.benchmarks.benchmarks.</code><code class="sig-name descname">get_point_multivariate_methods</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/benchmarks.html#get_point_multivariate_methods"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.get_point_multivariate_methods" title="Permalink to this definition"></a></dt>
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<dd><p>Return all multivariate FTS methods por point forecasting</p>
</dd></dl>
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<dl class="py function">
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<dt id="pyFTS.benchmarks.benchmarks.get_probabilistic_methods">
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<code class="sig-prename descclassname">pyFTS.benchmarks.benchmarks.</code><code class="sig-name descname">get_probabilistic_methods</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/benchmarks.html#get_probabilistic_methods"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.get_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>
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<dl class="py function">
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<dt id="pyFTS.benchmarks.benchmarks.multivariate_sliding_window_benchmarks2">
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<code class="sig-prename descclassname">pyFTS.benchmarks.benchmarks.</code><code class="sig-name descname">multivariate_sliding_window_benchmarks2</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="n">windowsize</span></em>, <em class="sig-param"><span class="n">train</span><span class="o">=</span><span class="default_value">0.8</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/benchmarks.html#multivariate_sliding_window_benchmarks2"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.multivariate_sliding_window_benchmarks2" title="Permalink to this definition"></a></dt>
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<dd></dd></dl>
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<dl class="py function">
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<dt id="pyFTS.benchmarks.benchmarks.mv_run_interval2">
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<code class="sig-prename descclassname">pyFTS.benchmarks.benchmarks.</code><code class="sig-name descname">mv_run_interval2</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">mfts</span></em>, <em class="sig-param"><span class="n">train_data</span></em>, <em class="sig-param"><span class="n">test_data</span></em>, <em class="sig-param"><span class="n">window_key</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/benchmarks.html#mv_run_interval2"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.mv_run_interval2" title="Permalink to this definition"></a></dt>
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<dd></dd></dl>
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<dl class="py function">
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<dt id="pyFTS.benchmarks.benchmarks.mv_run_point2">
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<code class="sig-prename descclassname">pyFTS.benchmarks.benchmarks.</code><code class="sig-name descname">mv_run_point2</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">mfts</span></em>, <em class="sig-param"><span class="n">train_data</span></em>, <em class="sig-param"><span class="n">test_data</span></em>, <em class="sig-param"><span class="n">window_key</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/benchmarks.html#mv_run_point2"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.mv_run_point2" title="Permalink to this definition"></a></dt>
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<dd></dd></dl>
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<dl class="py function">
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<dt id="pyFTS.benchmarks.benchmarks.mv_run_probabilistic2">
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<code class="sig-prename descclassname">pyFTS.benchmarks.benchmarks.</code><code class="sig-name descname">mv_run_probabilistic2</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">mfts</span></em>, <em class="sig-param"><span class="n">train_data</span></em>, <em class="sig-param"><span class="n">test_data</span></em>, <em class="sig-param"><span class="n">window_key</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/benchmarks.html#mv_run_probabilistic2"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.mv_run_probabilistic2" title="Permalink to this definition"></a></dt>
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<dd></dd></dl>
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<dl class="py function">
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<dt id="pyFTS.benchmarks.benchmarks.pftsExploreOrderAndPartitions">
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<code class="sig-prename descclassname">pyFTS.benchmarks.benchmarks.</code><code class="sig-name descname">pftsExploreOrderAndPartitions</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="n">save</span><span class="o">=</span><span class="default_value">False</span></em>, <em class="sig-param"><span class="n">file</span><span class="o">=</span><span class="default_value">None</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/benchmarks.html#pftsExploreOrderAndPartitions"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.pftsExploreOrderAndPartitions" title="Permalink to this definition"></a></dt>
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<dd></dd></dl>
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<dl class="py function">
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<dt id="pyFTS.benchmarks.benchmarks.plotCompared">
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<code class="sig-prename descclassname">pyFTS.benchmarks.benchmarks.</code><code class="sig-name descname">plotCompared</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">original</span></em>, <em class="sig-param"><span class="n">forecasts</span></em>, <em class="sig-param"><span class="n">labels</span></em>, <em class="sig-param"><span class="n">title</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/benchmarks.html#plotCompared"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.plotCompared" title="Permalink to this definition"></a></dt>
2018-08-30 23:04:52 +04:00
<dd></dd></dl>
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<dl class="py function">
2018-08-30 23:04:52 +04:00
<dt id="pyFTS.benchmarks.benchmarks.plot_compared_series">
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<code class="sig-prename descclassname">pyFTS.benchmarks.benchmarks.</code><code class="sig-name descname">plot_compared_series</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">original</span></em>, <em class="sig-param"><span class="n">models</span></em>, <em class="sig-param"><span class="n">colors</span></em>, <em class="sig-param"><span class="n">typeonlegend</span><span class="o">=</span><span class="default_value">False</span></em>, <em class="sig-param"><span class="n">save</span><span class="o">=</span><span class="default_value">False</span></em>, <em class="sig-param"><span class="n">file</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">tam</span><span class="o">=</span><span class="default_value">[20, 5]</span></em>, <em class="sig-param"><span class="n">points</span><span class="o">=</span><span class="default_value">True</span></em>, <em class="sig-param"><span class="n">intervals</span><span class="o">=</span><span class="default_value">True</span></em>, <em class="sig-param"><span class="n">linewidth</span><span class="o">=</span><span class="default_value">1.5</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/benchmarks.html#plot_compared_series"><span class="viewcode-link">[source]</span></a><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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<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>original</strong> Original time series data (list)</p></li>
<li><p><strong>models</strong> List of models to compare</p></li>
<li><p><strong>colors</strong> List of models colors</p></li>
<li><p><strong>typeonlegend</strong> Add the type of forecast (point / interval) on legend</p></li>
<li><p><strong>save</strong> Save the picture on file</p></li>
<li><p><strong>file</strong> Filename to save the picture</p></li>
<li><p><strong>tam</strong> Size of the picture</p></li>
<li><p><strong>points</strong> True to plot the point forecasts, False otherwise</p></li>
<li><p><strong>intervals</strong> True to plot the interval forecasts, False otherwise</p></li>
<li><p><strong>linewidth</strong> </p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p></p>
</dd>
</dl>
</dd></dl>
<dl class="py function">
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<dt id="pyFTS.benchmarks.benchmarks.plot_point">
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<code class="sig-prename descclassname">pyFTS.benchmarks.benchmarks.</code><code class="sig-name descname">plot_point</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">axis</span></em>, <em class="sig-param"><span class="n">points</span></em>, <em class="sig-param"><span class="n">order</span></em>, <em class="sig-param"><span class="n">label</span></em>, <em class="sig-param"><span class="n">color</span><span class="o">=</span><span class="default_value">'red'</span></em>, <em class="sig-param"><span class="n">ls</span><span class="o">=</span><span class="default_value">'-'</span></em>, <em class="sig-param"><span class="n">linewidth</span><span class="o">=</span><span class="default_value">1</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/benchmarks.html#plot_point"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.plot_point" title="Permalink to this definition"></a></dt>
2018-08-30 23:04:52 +04:00
<dd></dd></dl>
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<dl class="py function">
2018-08-30 23:04:52 +04:00
<dt id="pyFTS.benchmarks.benchmarks.print_distribution_statistics">
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<code class="sig-prename descclassname">pyFTS.benchmarks.benchmarks.</code><code class="sig-name descname">print_distribution_statistics</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">original</span></em>, <em class="sig-param"><span class="n">models</span></em>, <em class="sig-param"><span class="n">steps</span></em>, <em class="sig-param"><span class="n">resolution</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/benchmarks.html#print_distribution_statistics"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.print_distribution_statistics" title="Permalink to this definition"></a></dt>
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<dd><p>Run probabilistic benchmarks on given models and data and print the results</p>
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<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>data</strong> test data</p></li>
<li><p><strong>models</strong> a list of FTS models to benchmark</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p></p>
</dd>
</dl>
</dd></dl>
<dl class="py function">
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<dt id="pyFTS.benchmarks.benchmarks.print_interval_statistics">
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<code class="sig-prename descclassname">pyFTS.benchmarks.benchmarks.</code><code class="sig-name descname">print_interval_statistics</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">original</span></em>, <em class="sig-param"><span class="n">models</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/benchmarks.html#print_interval_statistics"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.print_interval_statistics" title="Permalink to this definition"></a></dt>
2018-08-30 23:04:52 +04:00
<dd><p>Run interval benchmarks on given models and data and print the results</p>
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<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>data</strong> test data</p></li>
<li><p><strong>models</strong> a list of FTS models to benchmark</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p></p>
</dd>
</dl>
</dd></dl>
<dl class="py function">
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<dt id="pyFTS.benchmarks.benchmarks.print_point_statistics">
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<code class="sig-prename descclassname">pyFTS.benchmarks.benchmarks.</code><code class="sig-name descname">print_point_statistics</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="n">models</span></em>, <em class="sig-param"><span class="n">externalmodels</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">externalforecasts</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">indexers</span><span class="o">=</span><span class="default_value">None</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/benchmarks.html#print_point_statistics"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.print_point_statistics" title="Permalink to this definition"></a></dt>
2018-08-30 23:04:52 +04:00
<dd><p>Run point benchmarks on given models and data and print the results</p>
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<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>data</strong> test data</p></li>
<li><p><strong>models</strong> a list of FTS models to benchmark</p></li>
<li><p><strong>externalmodels</strong> a list with benchmark models (façades for other methods)</p></li>
<li><p><strong>externalforecasts</strong> </p></li>
<li><p><strong>indexers</strong> </p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p></p>
</dd>
</dl>
</dd></dl>
<dl class="py function">
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<dt id="pyFTS.benchmarks.benchmarks.process_interval_jobs">
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<code class="sig-prename descclassname">pyFTS.benchmarks.benchmarks.</code><code class="sig-name descname">process_interval_jobs</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">dataset</span></em>, <em class="sig-param"><span class="n">tag</span></em>, <em class="sig-param"><span class="n">job</span></em>, <em class="sig-param"><span class="n">conn</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/benchmarks.html#process_interval_jobs"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.process_interval_jobs" title="Permalink to this definition"></a></dt>
2018-08-30 23:04:52 +04:00
<dd><p>Extract information from an dictionary with interval benchmark results and save it on a database</p>
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<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>dataset</strong> the benchmark dataset name</p></li>
<li><p><strong>tag</strong> alias for the benchmark group being executed</p></li>
<li><p><strong>job</strong> a dictionary with the benchmark results</p></li>
<li><p><strong>conn</strong> a connection to a Sqlite database</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p></p>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt id="pyFTS.benchmarks.benchmarks.process_interval_jobs2">
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<code class="sig-prename descclassname">pyFTS.benchmarks.benchmarks.</code><code class="sig-name descname">process_interval_jobs2</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">dataset</span></em>, <em class="sig-param"><span class="n">tag</span></em>, <em class="sig-param"><span class="n">job</span></em>, <em class="sig-param"><span class="n">conn</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/benchmarks.html#process_interval_jobs2"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.process_interval_jobs2" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
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<dl class="py function">
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<dt id="pyFTS.benchmarks.benchmarks.process_point_jobs">
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<code class="sig-prename descclassname">pyFTS.benchmarks.benchmarks.</code><code class="sig-name descname">process_point_jobs</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">dataset</span></em>, <em class="sig-param"><span class="n">tag</span></em>, <em class="sig-param"><span class="n">job</span></em>, <em class="sig-param"><span class="n">conn</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/benchmarks.html#process_point_jobs"><span class="viewcode-link">[source]</span></a><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>
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<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>dataset</strong> the benchmark dataset name</p></li>
<li><p><strong>tag</strong> alias for the benchmark group being executed</p></li>
<li><p><strong>job</strong> a dictionary with the benchmark results</p></li>
<li><p><strong>conn</strong> a connection to a Sqlite database</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p></p>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt id="pyFTS.benchmarks.benchmarks.process_point_jobs2">
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<code class="sig-prename descclassname">pyFTS.benchmarks.benchmarks.</code><code class="sig-name descname">process_point_jobs2</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">dataset</span></em>, <em class="sig-param"><span class="n">tag</span></em>, <em class="sig-param"><span class="n">job</span></em>, <em class="sig-param"><span class="n">conn</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/benchmarks.html#process_point_jobs2"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.process_point_jobs2" title="Permalink to this definition"></a></dt>
2018-08-30 23:04:52 +04:00
<dd><p>Extract information from a dictionary with point benchmark results and save it on a database</p>
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<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>dataset</strong> the benchmark dataset name</p></li>
<li><p><strong>tag</strong> alias for the benchmark group being executed</p></li>
<li><p><strong>job</strong> a dictionary with the benchmark results</p></li>
<li><p><strong>conn</strong> a connection to a Sqlite database</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p></p>
</dd>
</dl>
</dd></dl>
<dl class="py function">
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<dt id="pyFTS.benchmarks.benchmarks.process_probabilistic_jobs">
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<code class="sig-prename descclassname">pyFTS.benchmarks.benchmarks.</code><code class="sig-name descname">process_probabilistic_jobs</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">dataset</span></em>, <em class="sig-param"><span class="n">tag</span></em>, <em class="sig-param"><span class="n">job</span></em>, <em class="sig-param"><span class="n">conn</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/benchmarks.html#process_probabilistic_jobs"><span class="viewcode-link">[source]</span></a><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>
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<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>dataset</strong> the benchmark dataset name</p></li>
<li><p><strong>tag</strong> alias for the benchmark group being executed</p></li>
<li><p><strong>job</strong> a dictionary with the benchmark results</p></li>
<li><p><strong>conn</strong> a connection to a Sqlite database</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p></p>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt id="pyFTS.benchmarks.benchmarks.process_probabilistic_jobs2">
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<code class="sig-prename descclassname">pyFTS.benchmarks.benchmarks.</code><code class="sig-name descname">process_probabilistic_jobs2</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">dataset</span></em>, <em class="sig-param"><span class="n">tag</span></em>, <em class="sig-param"><span class="n">job</span></em>, <em class="sig-param"><span class="n">conn</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/benchmarks.html#process_probabilistic_jobs2"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.process_probabilistic_jobs2" title="Permalink to this definition"></a></dt>
2018-08-30 23:04:52 +04:00
<dd><p>Extract information from an dictionary with probabilistic benchmark results and save it on a database</p>
2020-08-19 00:06:41 +04:00
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>dataset</strong> the benchmark dataset name</p></li>
<li><p><strong>tag</strong> alias for the benchmark group being executed</p></li>
<li><p><strong>job</strong> a dictionary with the benchmark results</p></li>
<li><p><strong>conn</strong> a connection to a Sqlite database</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p></p>
</dd>
</dl>
</dd></dl>
<dl class="py function">
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<dt id="pyFTS.benchmarks.benchmarks.run_interval">
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<code class="sig-prename descclassname">pyFTS.benchmarks.benchmarks.</code><code class="sig-name descname">run_interval</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">mfts</span></em>, <em class="sig-param"><span class="n">partitioner</span></em>, <em class="sig-param"><span class="n">train_data</span></em>, <em class="sig-param"><span class="n">test_data</span></em>, <em class="sig-param"><span class="n">window_key</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/benchmarks.html#run_interval"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.run_interval" title="Permalink to this definition"></a></dt>
2018-08-30 23:04:52 +04:00
<dd><p>Run the interval forecasting benchmarks</p>
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<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>mfts</strong> FTS model</p></li>
<li><p><strong>partitioner</strong> Universe of Discourse partitioner</p></li>
<li><p><strong>train_data</strong> data used to train the model</p></li>
<li><p><strong>test_data</strong> ata used to test the model</p></li>
<li><p><strong>window_key</strong> id of the sliding window</p></li>
<li><p><strong>transformation</strong> data transformation</p></li>
<li><p><strong>indexer</strong> seasonal indexer</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>a dictionary with the benchmark results</p>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt id="pyFTS.benchmarks.benchmarks.run_interval2">
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<code class="sig-prename descclassname">pyFTS.benchmarks.benchmarks.</code><code class="sig-name descname">run_interval2</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">fts_method</span></em>, <em class="sig-param"><span class="n">order</span></em>, <em class="sig-param"><span class="n">partitioner_method</span></em>, <em class="sig-param"><span class="n">partitions</span></em>, <em class="sig-param"><span class="n">transformation</span></em>, <em class="sig-param"><span class="n">train_data</span></em>, <em class="sig-param"><span class="n">test_data</span></em>, <em class="sig-param"><span class="n">window_key</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/benchmarks.html#run_interval2"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.run_interval2" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
2020-08-19 00:06:41 +04:00
<dl class="py function">
2018-08-30 23:04:52 +04:00
<dt id="pyFTS.benchmarks.benchmarks.run_point">
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<code class="sig-prename descclassname">pyFTS.benchmarks.benchmarks.</code><code class="sig-name descname">run_point</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">mfts</span></em>, <em class="sig-param"><span class="n">partitioner</span></em>, <em class="sig-param"><span class="n">train_data</span></em>, <em class="sig-param"><span class="n">test_data</span></em>, <em class="sig-param"><span class="n">window_key</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/benchmarks.html#run_point"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.run_point" title="Permalink to this definition"></a></dt>
2018-08-30 23:04:52 +04:00
<dd><p>Run the point forecasting benchmarks</p>
2020-08-19 00:06:41 +04:00
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>mfts</strong> FTS model</p></li>
<li><p><strong>partitioner</strong> Universe of Discourse partitioner</p></li>
<li><p><strong>train_data</strong> data used to train the model</p></li>
<li><p><strong>test_data</strong> ata used to test the model</p></li>
<li><p><strong>window_key</strong> id of the sliding window</p></li>
<li><p><strong>transformation</strong> data transformation</p></li>
<li><p><strong>indexer</strong> seasonal indexer</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>a dictionary with the benchmark results</p>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt id="pyFTS.benchmarks.benchmarks.run_point2">
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<code class="sig-prename descclassname">pyFTS.benchmarks.benchmarks.</code><code class="sig-name descname">run_point2</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">fts_method</span></em>, <em class="sig-param"><span class="n">order</span></em>, <em class="sig-param"><span class="n">partitioner_method</span></em>, <em class="sig-param"><span class="n">partitions</span></em>, <em class="sig-param"><span class="n">transformation</span></em>, <em class="sig-param"><span class="n">train_data</span></em>, <em class="sig-param"><span class="n">test_data</span></em>, <em class="sig-param"><span class="n">window_key</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/benchmarks.html#run_point2"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.run_point2" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
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<dl class="py function">
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<dt id="pyFTS.benchmarks.benchmarks.run_probabilistic">
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<code class="sig-prename descclassname">pyFTS.benchmarks.benchmarks.</code><code class="sig-name descname">run_probabilistic</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">mfts</span></em>, <em class="sig-param"><span class="n">partitioner</span></em>, <em class="sig-param"><span class="n">train_data</span></em>, <em class="sig-param"><span class="n">test_data</span></em>, <em class="sig-param"><span class="n">window_key</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/benchmarks.html#run_probabilistic"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.run_probabilistic" title="Permalink to this definition"></a></dt>
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<dd><p>Run the probabilistic forecasting benchmarks</p>
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<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>mfts</strong> FTS model</p></li>
<li><p><strong>partitioner</strong> Universe of Discourse partitioner</p></li>
<li><p><strong>train_data</strong> data used to train the model</p></li>
<li><p><strong>test_data</strong> ata used to test the model</p></li>
<li><p><strong>steps</strong> </p></li>
<li><p><strong>resolution</strong> </p></li>
<li><p><strong>window_key</strong> id of the sliding window</p></li>
<li><p><strong>transformation</strong> data transformation</p></li>
<li><p><strong>indexer</strong> seasonal indexer</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>a dictionary with the benchmark results</p>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt id="pyFTS.benchmarks.benchmarks.run_probabilistic2">
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<code class="sig-prename descclassname">pyFTS.benchmarks.benchmarks.</code><code class="sig-name descname">run_probabilistic2</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">fts_method</span></em>, <em class="sig-param"><span class="n">order</span></em>, <em class="sig-param"><span class="n">partitioner_method</span></em>, <em class="sig-param"><span class="n">partitions</span></em>, <em class="sig-param"><span class="n">transformation</span></em>, <em class="sig-param"><span class="n">train_data</span></em>, <em class="sig-param"><span class="n">test_data</span></em>, <em class="sig-param"><span class="n">window_key</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/benchmarks.html#run_probabilistic2"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.run_probabilistic2" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
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<dl class="py function">
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<dt id="pyFTS.benchmarks.benchmarks.simpleSearch_RMSE">
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<code class="sig-prename descclassname">pyFTS.benchmarks.benchmarks.</code><code class="sig-name descname">simpleSearch_RMSE</code><span class="sig-paren">(</span><em class="sig-param">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="reference internal" href="_modules/pyFTS/benchmarks/benchmarks.html#simpleSearch_RMSE"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.simpleSearch_RMSE" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
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<dl class="py function">
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<dt id="pyFTS.benchmarks.benchmarks.sliding_window_benchmarks">
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<code class="sig-prename descclassname">pyFTS.benchmarks.benchmarks.</code><code class="sig-name descname">sliding_window_benchmarks</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="n">windowsize</span></em>, <em class="sig-param"><span class="n">train</span><span class="o">=</span><span class="default_value">0.8</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/benchmarks.html#sliding_window_benchmarks"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.sliding_window_benchmarks" title="Permalink to this definition"></a></dt>
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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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<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>data</strong> test data</p></li>
<li><p><strong>windowsize</strong> size of sliding window</p></li>
<li><p><strong>train</strong> percentual of sliding window data used to train the models</p></li>
<li><p><strong>kwargs</strong> dict, optional arguments</p></li>
<li><p><strong>benchmark_methods</strong> a list with Non FTS models to benchmark. The default is None.</p></li>
<li><p><strong>benchmark_methods_parameters</strong> a list with Non FTS models parameters. The default is None.</p></li>
<li><p><strong>benchmark_models</strong> A boolean value indicating if external FTS methods will be used on benchmark. The default is False.</p></li>
<li><p><strong>build_methods</strong> A boolean value indicating if the default FTS methods will be used on benchmark. The default is True.</p></li>
<li><p><strong>dataset</strong> the dataset name to identify the current set of benchmarks results on database.</p></li>
<li><p><strong>distributed</strong> A boolean value indicating if the forecasting procedure will be distributed in a dispy cluster. . The default is False</p></li>
<li><p><strong>file</strong> file path to save the results. The default is benchmarks.db.</p></li>
<li><p><strong>inc</strong> a float on interval [0,1] indicating the percentage of the windowsize to move the window</p></li>
<li><p><strong>methods</strong> a list with FTS class names. The default depends on the forecasting type and contains the list of all FTS methods.</p></li>
<li><p><strong>models</strong> a list with prebuilt FTS objects. The default is None.</p></li>
<li><p><strong>nodes</strong> a list with the dispy cluster nodes addresses. The default is [127.0.0.1].</p></li>
<li><p><strong>orders</strong> a list with orders of the models (for high order models). The default is [1,2,3].</p></li>
<li><p><strong>partitions</strong> a list with the numbers of partitions on the Universe of Discourse. The default is [10].</p></li>
<li><p><strong>partitioners_models</strong> a list with prebuilt Universe of Discourse partitioners objects. The default is None.</p></li>
<li><p><strong>partitioners_methods</strong> a list with Universe of Discourse partitioners class names. The default is [partitioners.Grid.GridPartitioner].</p></li>
<li><p><strong>progress</strong> If true a progress bar will be displayed during the benchmarks. The default is False.</p></li>
<li><p><strong>start</strong> in the multi step forecasting, the index of the data where to start forecasting. The default is 0.</p></li>
<li><p><strong>steps_ahead</strong> a list with the forecasting horizons, i. e., the number of steps ahead to forecast. The default is 1.</p></li>
<li><p><strong>tag</strong> a name to identify the current set of benchmarks results on database.</p></li>
<li><p><strong>type</strong> the forecasting type, one of these values: point(default), interval or distribution. The default is point.</p></li>
<li><p><strong>transformations</strong> a list with data transformations do apply . The default is [None].</p></li>
</ul>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt id="pyFTS.benchmarks.benchmarks.sliding_window_benchmarks2">
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<code class="sig-prename descclassname">pyFTS.benchmarks.benchmarks.</code><code class="sig-name descname">sliding_window_benchmarks2</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="n">windowsize</span></em>, <em class="sig-param"><span class="n">train</span><span class="o">=</span><span class="default_value">0.8</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/benchmarks.html#sliding_window_benchmarks2"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.sliding_window_benchmarks2" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
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<dl class="py function">
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<dt id="pyFTS.benchmarks.benchmarks.train_test_time">
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<code class="sig-prename descclassname">pyFTS.benchmarks.benchmarks.</code><code class="sig-name descname">train_test_time</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="n">windowsize</span></em>, <em class="sig-param"><span class="n">train</span><span class="o">=</span><span class="default_value">0.8</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/benchmarks.html#train_test_time"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.train_test_time" title="Permalink to this definition"></a></dt>
2019-06-21 18:34:49 +04:00
<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="py function">
<dt id="pyFTS.benchmarks.Measures.TheilsInequality">
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<code class="sig-prename descclassname">pyFTS.benchmarks.Measures.</code><code class="sig-name descname">TheilsInequality</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">targets</span></em>, <em class="sig-param"><span class="n">forecasts</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Measures.html#TheilsInequality"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.Measures.TheilsInequality" title="Permalink to this definition"></a></dt>
<dd><p>Theils Inequality Coefficient</p>
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<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>targets</strong> </p></li>
<li><p><strong>forecasts</strong> </p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p></p>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt id="pyFTS.benchmarks.Measures.UStatistic">
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<code class="sig-prename descclassname">pyFTS.benchmarks.Measures.</code><code class="sig-name descname">UStatistic</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">targets</span></em>, <em class="sig-param"><span class="n">forecasts</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Measures.html#UStatistic"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.Measures.UStatistic" title="Permalink to this definition"></a></dt>
<dd><p>Theils U Statistic</p>
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<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>targets</strong> </p></li>
<li><p><strong>forecasts</strong> </p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p></p>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt id="pyFTS.benchmarks.Measures.acf">
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<code class="sig-prename descclassname">pyFTS.benchmarks.Measures.</code><code class="sig-name descname">acf</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="n">k</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Measures.html#acf"><span class="viewcode-link">[source]</span></a><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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<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>data</strong> </p></li>
<li><p><strong>k</strong> </p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p></p>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt id="pyFTS.benchmarks.Measures.brier_score">
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<code class="sig-prename descclassname">pyFTS.benchmarks.Measures.</code><code class="sig-name descname">brier_score</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">targets</span></em>, <em class="sig-param"><span class="n">densities</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Measures.html#brier_score"><span class="viewcode-link">[source]</span></a><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: 13.</p>
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<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>targets</strong> a list with the target values</p></li>
<li><p><strong>densities</strong> a list with pyFTS.probabil objectsistic.ProbabilityDistribution</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>float</p>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt id="pyFTS.benchmarks.Measures.coverage">
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<code class="sig-prename descclassname">pyFTS.benchmarks.Measures.</code><code class="sig-name descname">coverage</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">targets</span></em>, <em class="sig-param"><span class="n">forecasts</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Measures.html#coverage"><span class="viewcode-link">[source]</span></a><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>
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<dl class="py function">
<dt id="pyFTS.benchmarks.Measures.crps">
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<code class="sig-prename descclassname">pyFTS.benchmarks.Measures.</code><code class="sig-name descname">crps</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">targets</span></em>, <em class="sig-param"><span class="n">densities</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Measures.html#crps"><span class="viewcode-link">[source]</span></a><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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<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>targets</strong> a list with the target values</p></li>
<li><p><strong>densities</strong> a list with pyFTS.probabil objectsistic.ProbabilityDistribution</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>float</p>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt id="pyFTS.benchmarks.Measures.get_distribution_ahead_statistics">
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<code class="sig-prename descclassname">pyFTS.benchmarks.Measures.</code><code class="sig-name descname">get_distribution_ahead_statistics</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="n">distributions</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Measures.html#get_distribution_ahead_statistics"><span class="viewcode-link">[source]</span></a><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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<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>data</strong> test data</p></li>
<li><p><strong>model</strong> FTS model with probabilistic forecasting capability</p></li>
<li><p><strong>kwargs</strong> </p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>a list with the CRPS and execution time</p>
</dd>
</dl>
</dd></dl>
<dl class="py function">
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<dt id="pyFTS.benchmarks.Measures.get_distribution_statistics">
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<code class="sig-prename descclassname">pyFTS.benchmarks.Measures.</code><code class="sig-name descname">get_distribution_statistics</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="n">model</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Measures.html#get_distribution_statistics"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.Measures.get_distribution_statistics" title="Permalink to this definition"></a></dt>
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<dd><p>Get CRPS statistic and time for a forecasting model</p>
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<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>data</strong> test data</p></li>
<li><p><strong>model</strong> FTS model with probabilistic forecasting capability</p></li>
<li><p><strong>kwargs</strong> </p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>a list with the CRPS and execution time</p>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt id="pyFTS.benchmarks.Measures.get_interval_ahead_statistics">
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<code class="sig-prename descclassname">pyFTS.benchmarks.Measures.</code><code class="sig-name descname">get_interval_ahead_statistics</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="n">intervals</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Measures.html#get_interval_ahead_statistics"><span class="viewcode-link">[source]</span></a><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>
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<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>data</strong> test data</p></li>
<li><p><strong>intervals</strong> predicted intervals for each datapoint</p></li>
<li><p><strong>kwargs</strong> </p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>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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</dd>
</dl>
</dd></dl>
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<dl class="py function">
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<dt id="pyFTS.benchmarks.Measures.get_interval_statistics">
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<code class="sig-prename descclassname">pyFTS.benchmarks.Measures.</code><code class="sig-name descname">get_interval_statistics</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="n">model</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Measures.html#get_interval_statistics"><span class="viewcode-link">[source]</span></a><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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<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>data</strong> test data</p></li>
<li><p><strong>model</strong> FTS model with interval forecasting capability</p></li>
<li><p><strong>kwargs</strong> </p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>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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</dd>
</dl>
</dd></dl>
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<dl class="py function">
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<dt id="pyFTS.benchmarks.Measures.get_point_ahead_statistics">
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<code class="sig-prename descclassname">pyFTS.benchmarks.Measures.</code><code class="sig-name descname">get_point_ahead_statistics</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="n">forecasts</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Measures.html#get_point_ahead_statistics"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.Measures.get_point_ahead_statistics" title="Permalink to this definition"></a></dt>
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<dd><p>Condensate all measures for point forecasters</p>
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<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>data</strong> test data</p></li>
<li><p><strong>model</strong> FTS model with point forecasting capability</p></li>
<li><p><strong>kwargs</strong> </p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>a list with the RMSE, SMAPE and U Statistic</p>
</dd>
</dl>
</dd></dl>
<dl class="py function">
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<dt id="pyFTS.benchmarks.Measures.get_point_statistics">
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<code class="sig-prename descclassname">pyFTS.benchmarks.Measures.</code><code class="sig-name descname">get_point_statistics</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="n">model</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Measures.html#get_point_statistics"><span class="viewcode-link">[source]</span></a><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>
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<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>data</strong> test data</p></li>
<li><p><strong>model</strong> FTS model with point forecasting capability</p></li>
<li><p><strong>kwargs</strong> </p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>a list with the RMSE, SMAPE and U Statistic</p>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt id="pyFTS.benchmarks.Measures.logarithm_score">
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<code class="sig-prename descclassname">pyFTS.benchmarks.Measures.</code><code class="sig-name descname">logarithm_score</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">targets</span></em>, <em class="sig-param"><span class="n">densities</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Measures.html#logarithm_score"><span class="viewcode-link">[source]</span></a><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),107114. URLhttps://www.jstor.org/stable/2984087.</p>
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<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>targets</strong> a list with the target values</p></li>
<li><p><strong>densities</strong> a list with pyFTS.probabil objectsistic.ProbabilityDistribution</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>float</p>
</dd>
</dl>
</dd></dl>
<dl class="py function">
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<dt id="pyFTS.benchmarks.Measures.mape">
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<code class="sig-prename descclassname">pyFTS.benchmarks.Measures.</code><code class="sig-name descname">mape</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">targets</span></em>, <em class="sig-param"><span class="n">forecasts</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Measures.html#mape"><span class="viewcode-link">[source]</span></a><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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<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>targets</strong> </p></li>
<li><p><strong>forecasts</strong> </p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p></p>
</dd>
</dl>
</dd></dl>
<dl class="py function">
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<dt id="pyFTS.benchmarks.Measures.mape_interval">
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<code class="sig-prename descclassname">pyFTS.benchmarks.Measures.</code><code class="sig-name descname">mape_interval</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">targets</span></em>, <em class="sig-param"><span class="n">forecasts</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Measures.html#mape_interval"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.Measures.mape_interval" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
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<dl class="py function">
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<dt id="pyFTS.benchmarks.Measures.pinball">
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<code class="sig-prename descclassname">pyFTS.benchmarks.Measures.</code><code class="sig-name descname">pinball</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">tau</span></em>, <em class="sig-param"><span class="n">target</span></em>, <em class="sig-param"><span class="n">forecast</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Measures.html#pinball"><span class="viewcode-link">[source]</span></a><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>
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<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>tau</strong> quantile value in the range (0,1)</p></li>
<li><p><strong>target</strong> </p></li>
<li><p><strong>forecast</strong> </p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>float, distance of forecast to the tau-quantile of the target</p>
</dd>
</dl>
</dd></dl>
<dl class="py function">
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<dt id="pyFTS.benchmarks.Measures.pinball_mean">
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<code class="sig-prename descclassname">pyFTS.benchmarks.Measures.</code><code class="sig-name descname">pinball_mean</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">tau</span></em>, <em class="sig-param"><span class="n">targets</span></em>, <em class="sig-param"><span class="n">forecasts</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Measures.html#pinball_mean"><span class="viewcode-link">[source]</span></a><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>
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<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>tau</strong> quantile value in the range (0,1)</p></li>
<li><p><strong>targets</strong> list of target values</p></li>
<li><p><strong>forecasts</strong> list of prediction intervals</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>float, the pinball loss mean for tau quantile</p>
</dd>
</dl>
</dd></dl>
<dl class="py function">
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<dt id="pyFTS.benchmarks.Measures.resolution">
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<code class="sig-prename descclassname">pyFTS.benchmarks.Measures.</code><code class="sig-name descname">resolution</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">forecasts</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Measures.html#resolution"><span class="viewcode-link">[source]</span></a><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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<dl class="py function">
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<dt id="pyFTS.benchmarks.Measures.rmse">
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<code class="sig-prename descclassname">pyFTS.benchmarks.Measures.</code><code class="sig-name descname">rmse</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">targets</span></em>, <em class="sig-param"><span class="n">forecasts</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Measures.html#rmse"><span class="viewcode-link">[source]</span></a><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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<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>targets</strong> </p></li>
<li><p><strong>forecasts</strong> </p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p></p>
</dd>
</dl>
</dd></dl>
<dl class="py function">
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<dt id="pyFTS.benchmarks.Measures.rmse_interval">
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<code class="sig-prename descclassname">pyFTS.benchmarks.Measures.</code><code class="sig-name descname">rmse_interval</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">targets</span></em>, <em class="sig-param"><span class="n">forecasts</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Measures.html#rmse_interval"><span class="viewcode-link">[source]</span></a><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>
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<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>targets</strong> </p></li>
<li><p><strong>forecasts</strong> </p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p></p>
</dd>
</dl>
</dd></dl>
<dl class="py function">
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<dt id="pyFTS.benchmarks.Measures.sharpness">
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<code class="sig-prename descclassname">pyFTS.benchmarks.Measures.</code><code class="sig-name descname">sharpness</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">forecasts</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Measures.html#sharpness"><span class="viewcode-link">[source]</span></a><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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<dl class="py function">
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<dt id="pyFTS.benchmarks.Measures.smape">
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<code class="sig-prename descclassname">pyFTS.benchmarks.Measures.</code><code class="sig-name descname">smape</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">targets</span></em>, <em class="sig-param"><span class="n">forecasts</span></em>, <em class="sig-param"><span class="n">type</span><span class="o">=</span><span class="default_value">2</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Measures.html#smape"><span class="viewcode-link">[source]</span></a><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>
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<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>targets</strong> </p></li>
<li><p><strong>forecasts</strong> </p></li>
<li><p><strong>type</strong> </p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p></p>
</dd>
</dl>
</dd></dl>
<dl class="py function">
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<dt id="pyFTS.benchmarks.Measures.winkler_mean">
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<code class="sig-prename descclassname">pyFTS.benchmarks.Measures.</code><code class="sig-name descname">winkler_mean</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">tau</span></em>, <em class="sig-param"><span class="n">targets</span></em>, <em class="sig-param"><span class="n">forecasts</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Measures.html#winkler_mean"><span class="viewcode-link">[source]</span></a><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>
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<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>tau</strong> quantile value in the range (0,1)</p></li>
<li><p><strong>targets</strong> list of target values</p></li>
<li><p><strong>forecasts</strong> list of prediction intervals</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>float, the Winkler score mean for tau quantile</p>
</dd>
</dl>
</dd></dl>
<dl class="py function">
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<dt id="pyFTS.benchmarks.Measures.winkler_score">
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<code class="sig-prename descclassname">pyFTS.benchmarks.Measures.</code><code class="sig-name descname">winkler_score</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">tau</span></em>, <em class="sig-param"><span class="n">target</span></em>, <em class="sig-param"><span class="n">forecast</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Measures.html#winkler_score"><span class="viewcode-link">[source]</span></a><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">
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<li><ol class="upperalpha simple" start="12">
<li><p>Winkler, A Decision-Theoretic Approach to Interval Estimation, J. Am. Stat. Assoc. 67 (337) (1972) 187191. doi:10.2307/2284720.</p></li>
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</ol>
</li>
</ol>
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<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>tau</strong> </p></li>
<li><p><strong>target</strong> </p></li>
<li><p><strong>forecast</strong> </p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p></p>
</dd>
</dl>
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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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<dl class="py function">
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<dt id="pyFTS.benchmarks.ResidualAnalysis.compare_residuals">
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<code class="sig-prename descclassname">pyFTS.benchmarks.ResidualAnalysis.</code><code class="sig-name descname">compare_residuals</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="n">models</span></em>, <em class="sig-param"><span class="n">alpha</span><span class="o">=</span><span class="default_value">0.05</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/ResidualAnalysis.html#compare_residuals"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.ResidualAnalysis.compare_residuals" title="Permalink to this definition"></a></dt>
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<dd><p>Compare residuals statistics of several models</p>
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<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>data</strong> test data</p></li>
<li><p><strong>models</strong> </p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>a Pandas dataframe with the Box-Ljung statistic for each model</p>
</dd>
</dl>
</dd></dl>
<dl class="py function">
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<dt id="pyFTS.benchmarks.ResidualAnalysis.ljung_box_test">
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<code class="sig-prename descclassname">pyFTS.benchmarks.ResidualAnalysis.</code><code class="sig-name descname">ljung_box_test</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">residuals</span></em>, <em class="sig-param"><span class="n">lags</span><span class="o">=</span><span class="default_value">[1, 2, 3]</span></em>, <em class="sig-param"><span class="n">alpha</span><span class="o">=</span><span class="default_value">0.5</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/ResidualAnalysis.html#ljung_box_test"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.ResidualAnalysis.ljung_box_test" title="Permalink to this definition"></a></dt>
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<dd></dd></dl>
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<dl class="py function">
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<dt id="pyFTS.benchmarks.ResidualAnalysis.plot_residuals_by_model">
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<code class="sig-prename descclassname">pyFTS.benchmarks.ResidualAnalysis.</code><code class="sig-name descname">plot_residuals_by_model</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">targets</span></em>, <em class="sig-param"><span class="n">models</span></em>, <em class="sig-param"><span class="n">tam</span><span class="o">=</span><span class="default_value">[8, 8]</span></em>, <em class="sig-param"><span class="n">save</span><span class="o">=</span><span class="default_value">False</span></em>, <em class="sig-param"><span class="n">file</span><span class="o">=</span><span class="default_value">None</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/ResidualAnalysis.html#plot_residuals_by_model"><span class="viewcode-link">[source]</span></a><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>
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<dl class="py function">
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<dt id="pyFTS.benchmarks.ResidualAnalysis.residuals">
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<code class="sig-prename descclassname">pyFTS.benchmarks.ResidualAnalysis.</code><code class="sig-name descname">residuals</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">targets</span></em>, <em class="sig-param"><span class="n">forecasts</span></em>, <em class="sig-param"><span class="n">order</span><span class="o">=</span><span class="default_value">1</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/ResidualAnalysis.html#residuals"><span class="viewcode-link">[source]</span></a><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>
</dd></dl>
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<dl class="py function">
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<dt id="pyFTS.benchmarks.ResidualAnalysis.single_plot_residuals">
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<code class="sig-prename descclassname">pyFTS.benchmarks.ResidualAnalysis.</code><code class="sig-name descname">single_plot_residuals</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">res</span></em>, <em class="sig-param"><span class="n">order</span></em>, <em class="sig-param"><span class="n">tam</span><span class="o">=</span><span class="default_value">[10, 7]</span></em>, <em class="sig-param"><span class="n">save</span><span class="o">=</span><span class="default_value">False</span></em>, <em class="sig-param"><span class="n">file</span><span class="o">=</span><span class="default_value">None</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/ResidualAnalysis.html#single_plot_residuals"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.ResidualAnalysis.single_plot_residuals" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
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</div>
<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="py function">
<dt id="pyFTS.benchmarks.Tests.BoxLjungStatistic">
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<code class="sig-prename descclassname">pyFTS.benchmarks.Tests.</code><code class="sig-name descname">BoxLjungStatistic</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="n">h</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Tests.html#BoxLjungStatistic"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.Tests.BoxLjungStatistic" title="Permalink to this definition"></a></dt>
<dd><p>Q Statistic for LjungBox test</p>
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<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>data</strong> </p></li>
<li><p><strong>h</strong> </p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p></p>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt id="pyFTS.benchmarks.Tests.BoxPierceStatistic">
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<code class="sig-prename descclassname">pyFTS.benchmarks.Tests.</code><code class="sig-name descname">BoxPierceStatistic</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="n">h</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Tests.html#BoxPierceStatistic"><span class="viewcode-link">[source]</span></a><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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<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>data</strong> </p></li>
<li><p><strong>h</strong> </p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p></p>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt id="pyFTS.benchmarks.Tests.format_experiment_table">
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<code class="sig-prename descclassname">pyFTS.benchmarks.Tests.</code><code class="sig-name descname">format_experiment_table</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">df</span></em>, <em class="sig-param"><span class="n">exclude</span><span class="o">=</span><span class="default_value">[]</span></em>, <em class="sig-param"><span class="n">replace</span><span class="o">=</span><span class="default_value">{}</span></em>, <em class="sig-param"><span class="n">csv</span><span class="o">=</span><span class="default_value">True</span></em>, <em class="sig-param"><span class="n">std</span><span class="o">=</span><span class="default_value">False</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Tests.html#format_experiment_table"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.Tests.format_experiment_table" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
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<dl class="py function">
<dt id="pyFTS.benchmarks.Tests.post_hoc_tests">
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<code class="sig-prename descclassname">pyFTS.benchmarks.Tests.</code><code class="sig-name descname">post_hoc_tests</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">post_hoc</span></em>, <em class="sig-param"><span class="n">control_method</span></em>, <em class="sig-param"><span class="n">alpha</span><span class="o">=</span><span class="default_value">0.05</span></em>, <em class="sig-param"><span class="n">method</span><span class="o">=</span><span class="default_value">'finner'</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Tests.html#post_hoc_tests"><span class="viewcode-link">[source]</span></a><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>
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<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>post_hoc</strong> </p></li>
<li><p><strong>control_method</strong> </p></li>
<li><p><strong>alpha</strong> </p></li>
<li><p><strong>method</strong> </p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p></p>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt id="pyFTS.benchmarks.Tests.test_mean_equality">
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<code class="sig-prename descclassname">pyFTS.benchmarks.Tests.</code><code class="sig-name descname">test_mean_equality</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">tests</span></em>, <em class="sig-param"><span class="n">alpha</span><span class="o">=</span><span class="default_value">0.05</span></em>, <em class="sig-param"><span class="n">method</span><span class="o">=</span><span class="default_value">'friedman'</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Tests.html#test_mean_equality"><span class="viewcode-link">[source]</span></a><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: Theres no significant difference between the means
H_1: There is at least one significant difference between the means</p>
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<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>tests</strong> </p></li>
<li><p><strong>alpha</strong> </p></li>
<li><p><strong>method</strong> </p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p></p>
</dd>
</dl>
</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>
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<dl class="py function">
<dt id="pyFTS.benchmarks.Util.analytic_tabular_dataframe">
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<code class="sig-prename descclassname">pyFTS.benchmarks.Util.</code><code class="sig-name descname">analytic_tabular_dataframe</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">dataframe</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Util.html#analytic_tabular_dataframe"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.Util.analytic_tabular_dataframe" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
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<dl class="py function">
<dt id="pyFTS.benchmarks.Util.analytical_data_columns">
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<code class="sig-prename descclassname">pyFTS.benchmarks.Util.</code><code class="sig-name descname">analytical_data_columns</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">experiments</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Util.html#analytical_data_columns"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.Util.analytical_data_columns" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
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<dl class="py function">
<dt id="pyFTS.benchmarks.Util.base_dataframe_columns">
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<code class="sig-prename descclassname">pyFTS.benchmarks.Util.</code><code class="sig-name descname">base_dataframe_columns</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Util.html#base_dataframe_columns"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.Util.base_dataframe_columns" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
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<dl class="py function">
<dt id="pyFTS.benchmarks.Util.cast_dataframe_to_synthetic">
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<code class="sig-prename descclassname">pyFTS.benchmarks.Util.</code><code class="sig-name descname">cast_dataframe_to_synthetic</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">infile</span></em>, <em class="sig-param"><span class="n">outfile</span></em>, <em class="sig-param"><span class="n">experiments</span></em>, <em class="sig-param"><span class="n">type</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Util.html#cast_dataframe_to_synthetic"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.Util.cast_dataframe_to_synthetic" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
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<dl class="py function">
<dt id="pyFTS.benchmarks.Util.cast_dataframe_to_synthetic_interval">
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<code class="sig-prename descclassname">pyFTS.benchmarks.Util.</code><code class="sig-name descname">cast_dataframe_to_synthetic_interval</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">df</span></em>, <em class="sig-param"><span class="n">data_columns</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Util.html#cast_dataframe_to_synthetic_interval"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.Util.cast_dataframe_to_synthetic_interval" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
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<dl class="py function">
<dt id="pyFTS.benchmarks.Util.cast_dataframe_to_synthetic_point">
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<code class="sig-prename descclassname">pyFTS.benchmarks.Util.</code><code class="sig-name descname">cast_dataframe_to_synthetic_point</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">df</span></em>, <em class="sig-param"><span class="n">data_columns</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Util.html#cast_dataframe_to_synthetic_point"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.Util.cast_dataframe_to_synthetic_point" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
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<dl class="py function">
<dt id="pyFTS.benchmarks.Util.cast_dataframe_to_synthetic_probabilistic">
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<code class="sig-prename descclassname">pyFTS.benchmarks.Util.</code><code class="sig-name descname">cast_dataframe_to_synthetic_probabilistic</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">df</span></em>, <em class="sig-param"><span class="n">data_columns</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Util.html#cast_dataframe_to_synthetic_probabilistic"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.Util.cast_dataframe_to_synthetic_probabilistic" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
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<dl class="py function">
<dt id="pyFTS.benchmarks.Util.check_ignore_list">
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<code class="sig-prename descclassname">pyFTS.benchmarks.Util.</code><code class="sig-name descname">check_ignore_list</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">b</span></em>, <em class="sig-param"><span class="n">ignore</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Util.html#check_ignore_list"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.Util.check_ignore_list" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
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<dl class="py function">
<dt id="pyFTS.benchmarks.Util.check_replace_list">
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<code class="sig-prename descclassname">pyFTS.benchmarks.Util.</code><code class="sig-name descname">check_replace_list</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">m</span></em>, <em class="sig-param"><span class="n">replace</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Util.html#check_replace_list"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.Util.check_replace_list" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
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<dl class="py function">
<dt id="pyFTS.benchmarks.Util.create_benchmark_tables">
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<code class="sig-prename descclassname">pyFTS.benchmarks.Util.</code><code class="sig-name descname">create_benchmark_tables</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">conn</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Util.html#create_benchmark_tables"><span class="viewcode-link">[source]</span></a><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>
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<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>conn</strong> a sqlite3 database connection</p>
</dd>
</dl>
</dd></dl>
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<dl class="py function">
<dt id="pyFTS.benchmarks.Util.extract_measure">
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<code class="sig-prename descclassname">pyFTS.benchmarks.Util.</code><code class="sig-name descname">extract_measure</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">dataframe</span></em>, <em class="sig-param"><span class="n">measure</span></em>, <em class="sig-param"><span class="n">data_columns</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Util.html#extract_measure"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.Util.extract_measure" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
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<dl class="py function">
<dt id="pyFTS.benchmarks.Util.find_best">
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<code class="sig-prename descclassname">pyFTS.benchmarks.Util.</code><code class="sig-name descname">find_best</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">dataframe</span></em>, <em class="sig-param"><span class="n">criteria</span></em>, <em class="sig-param"><span class="n">ascending</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Util.html#find_best"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.Util.find_best" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
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<dl class="py function">
<dt id="pyFTS.benchmarks.Util.get_dataframe_from_bd">
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<code class="sig-prename descclassname">pyFTS.benchmarks.Util.</code><code class="sig-name descname">get_dataframe_from_bd</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">file</span></em>, <em class="sig-param"><span class="n">filter</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Util.html#get_dataframe_from_bd"><span class="viewcode-link">[source]</span></a><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>
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<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>file</strong> the url of the benchmark database</p></li>
<li><p><strong>filter</strong> sql conditions to filter</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>pandas dataframe with the query results</p>
</dd>
</dl>
</dd></dl>
<dl class="py function">
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<dt id="pyFTS.benchmarks.Util.insert_benchmark">
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<code class="sig-prename descclassname">pyFTS.benchmarks.Util.</code><code class="sig-name descname">insert_benchmark</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="n">conn</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Util.html#insert_benchmark"><span class="viewcode-link">[source]</span></a><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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<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>data</strong> a tuple with the benchmark data with format:</p>
</dd>
</dl>
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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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<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>conn</strong> a sqlite3 database connection</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p></p>
</dd>
</dl>
</dd></dl>
<dl class="py function">
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<dt id="pyFTS.benchmarks.Util.interval_dataframe_analytic_columns">
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<code class="sig-prename descclassname">pyFTS.benchmarks.Util.</code><code class="sig-name descname">interval_dataframe_analytic_columns</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">experiments</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Util.html#interval_dataframe_analytic_columns"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.Util.interval_dataframe_analytic_columns" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
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<dl class="py function">
2018-08-30 23:04:52 +04:00
<dt id="pyFTS.benchmarks.Util.interval_dataframe_synthetic_columns">
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<code class="sig-prename descclassname">pyFTS.benchmarks.Util.</code><code class="sig-name descname">interval_dataframe_synthetic_columns</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Util.html#interval_dataframe_synthetic_columns"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.Util.interval_dataframe_synthetic_columns" title="Permalink to this definition"></a></dt>
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<dd></dd></dl>
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<dl class="py function">
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<dt id="pyFTS.benchmarks.Util.open_benchmark_db">
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<code class="sig-prename descclassname">pyFTS.benchmarks.Util.</code><code class="sig-name descname">open_benchmark_db</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">name</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Util.html#open_benchmark_db"><span class="viewcode-link">[source]</span></a><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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<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>name</strong> database filenem</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>a sqlite3 database connection</p>
</dd>
</dl>
</dd></dl>
<dl class="py function">
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<dt id="pyFTS.benchmarks.Util.plot_dataframe_interval">
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<code class="sig-prename descclassname">pyFTS.benchmarks.Util.</code><code class="sig-name descname">plot_dataframe_interval</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">file_synthetic</span></em>, <em class="sig-param"><span class="n">file_analytic</span></em>, <em class="sig-param"><span class="n">experiments</span></em>, <em class="sig-param"><span class="n">tam</span></em>, <em class="sig-param"><span class="n">save</span><span class="o">=</span><span class="default_value">False</span></em>, <em class="sig-param"><span class="n">file</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">sort_columns</span><span class="o">=</span><span class="default_value">['COVAVG', 'SHARPAVG', 'COVSTD', 'SHARPSTD']</span></em>, <em class="sig-param"><span class="n">sort_ascend</span><span class="o">=</span><span class="default_value">[True, False, True, True]</span></em>, <em class="sig-param"><span class="n">save_best</span><span class="o">=</span><span class="default_value">False</span></em>, <em class="sig-param"><span class="n">ignore</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">replace</span><span class="o">=</span><span class="default_value">None</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Util.html#plot_dataframe_interval"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.Util.plot_dataframe_interval" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
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<dl class="py function">
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<dt id="pyFTS.benchmarks.Util.plot_dataframe_interval_pinball">
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<code class="sig-prename descclassname">pyFTS.benchmarks.Util.</code><code class="sig-name descname">plot_dataframe_interval_pinball</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">file_synthetic</span></em>, <em class="sig-param"><span class="n">file_analytic</span></em>, <em class="sig-param"><span class="n">experiments</span></em>, <em class="sig-param"><span class="n">tam</span></em>, <em class="sig-param"><span class="n">save</span><span class="o">=</span><span class="default_value">False</span></em>, <em class="sig-param"><span class="n">file</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">sort_columns</span><span class="o">=</span><span class="default_value">['COVAVG', 'SHARPAVG', 'COVSTD', 'SHARPSTD']</span></em>, <em class="sig-param"><span class="n">sort_ascend</span><span class="o">=</span><span class="default_value">[True, False, True, True]</span></em>, <em class="sig-param"><span class="n">save_best</span><span class="o">=</span><span class="default_value">False</span></em>, <em class="sig-param"><span class="n">ignore</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">replace</span><span class="o">=</span><span class="default_value">None</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Util.html#plot_dataframe_interval_pinball"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.Util.plot_dataframe_interval_pinball" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
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<dl class="py function">
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<dt id="pyFTS.benchmarks.Util.plot_dataframe_point">
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<code class="sig-prename descclassname">pyFTS.benchmarks.Util.</code><code class="sig-name descname">plot_dataframe_point</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">file_synthetic</span></em>, <em class="sig-param"><span class="n">file_analytic</span></em>, <em class="sig-param"><span class="n">experiments</span></em>, <em class="sig-param"><span class="n">tam</span></em>, <em class="sig-param"><span class="n">save</span><span class="o">=</span><span class="default_value">False</span></em>, <em class="sig-param"><span class="n">file</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">sort_columns</span><span class="o">=</span><span class="default_value">['UAVG', 'RMSEAVG', 'USTD', 'RMSESTD']</span></em>, <em class="sig-param"><span class="n">sort_ascend</span><span class="o">=</span><span class="default_value">[1, 1, 1, 1]</span></em>, <em class="sig-param"><span class="n">save_best</span><span class="o">=</span><span class="default_value">False</span></em>, <em class="sig-param"><span class="n">ignore</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">replace</span><span class="o">=</span><span class="default_value">None</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Util.html#plot_dataframe_point"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.Util.plot_dataframe_point" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
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<dl class="py function">
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<dt id="pyFTS.benchmarks.Util.plot_dataframe_probabilistic">
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<code class="sig-prename descclassname">pyFTS.benchmarks.Util.</code><code class="sig-name descname">plot_dataframe_probabilistic</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">file_synthetic</span></em>, <em class="sig-param"><span class="n">file_analytic</span></em>, <em class="sig-param"><span class="n">experiments</span></em>, <em class="sig-param"><span class="n">tam</span></em>, <em class="sig-param"><span class="n">save</span><span class="o">=</span><span class="default_value">False</span></em>, <em class="sig-param"><span class="n">file</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">sort_columns</span><span class="o">=</span><span class="default_value">['CRPS1AVG', 'CRPS2AVG', 'CRPS1STD', 'CRPS2STD']</span></em>, <em class="sig-param"><span class="n">sort_ascend</span><span class="o">=</span><span class="default_value">[True, True, True, True]</span></em>, <em class="sig-param"><span class="n">save_best</span><span class="o">=</span><span class="default_value">False</span></em>, <em class="sig-param"><span class="n">ignore</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">replace</span><span class="o">=</span><span class="default_value">None</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Util.html#plot_dataframe_probabilistic"><span class="viewcode-link">[source]</span></a><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="py function">
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<dt id="pyFTS.benchmarks.Util.point_dataframe_analytic_columns">
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<code class="sig-prename descclassname">pyFTS.benchmarks.Util.</code><code class="sig-name descname">point_dataframe_analytic_columns</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">experiments</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Util.html#point_dataframe_analytic_columns"><span class="viewcode-link">[source]</span></a><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="py function">
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<dt id="pyFTS.benchmarks.Util.point_dataframe_synthetic_columns">
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<code class="sig-prename descclassname">pyFTS.benchmarks.Util.</code><code class="sig-name descname">point_dataframe_synthetic_columns</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Util.html#point_dataframe_synthetic_columns"><span class="viewcode-link">[source]</span></a><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="py function">
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<dt id="pyFTS.benchmarks.Util.probabilistic_dataframe_analytic_columns">
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<code class="sig-prename descclassname">pyFTS.benchmarks.Util.</code><code class="sig-name descname">probabilistic_dataframe_analytic_columns</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">experiments</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Util.html#probabilistic_dataframe_analytic_columns"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.Util.probabilistic_dataframe_analytic_columns" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
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<dl class="py function">
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<dt id="pyFTS.benchmarks.Util.probabilistic_dataframe_synthetic_columns">
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<code class="sig-prename descclassname">pyFTS.benchmarks.Util.</code><code class="sig-name descname">probabilistic_dataframe_synthetic_columns</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Util.html#probabilistic_dataframe_synthetic_columns"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.Util.probabilistic_dataframe_synthetic_columns" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
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<dl class="py function">
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<dt id="pyFTS.benchmarks.Util.process_common_data">
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<code class="sig-prename descclassname">pyFTS.benchmarks.Util.</code><code class="sig-name descname">process_common_data</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">dataset</span></em>, <em class="sig-param"><span class="n">tag</span></em>, <em class="sig-param"><span class="n">type</span></em>, <em class="sig-param"><span class="n">job</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Util.html#process_common_data"><span class="viewcode-link">[source]</span></a><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>
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<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>dataset</strong> benchmark dataset</p></li>
<li><p><strong>tag</strong> benchmark set alias</p></li>
<li><p><strong>type</strong> forecasting type</p></li>
<li><p><strong>job</strong> a dictionary with benchmark data</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>tuple for sqlite database</p>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt id="pyFTS.benchmarks.Util.process_common_data2">
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<code class="sig-prename descclassname">pyFTS.benchmarks.Util.</code><code class="sig-name descname">process_common_data2</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">dataset</span></em>, <em class="sig-param"><span class="n">tag</span></em>, <em class="sig-param"><span class="n">type</span></em>, <em class="sig-param"><span class="n">job</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Util.html#process_common_data2"><span class="viewcode-link">[source]</span></a><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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<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>dataset</strong> benchmark dataset</p></li>
<li><p><strong>tag</strong> benchmark set alias</p></li>
<li><p><strong>type</strong> forecasting type</p></li>
<li><p><strong>job</strong> a dictionary with benchmark data</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>tuple for sqlite database</p>
</dd>
</dl>
</dd></dl>
<dl class="py function">
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<dt id="pyFTS.benchmarks.Util.save_dataframe_interval">
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<code class="sig-prename descclassname">pyFTS.benchmarks.Util.</code><code class="sig-name descname">save_dataframe_interval</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">coverage</span></em>, <em class="sig-param"><span class="n">experiments</span></em>, <em class="sig-param"><span class="n">file</span></em>, <em class="sig-param"><span class="n">objs</span></em>, <em class="sig-param"><span class="n">resolution</span></em>, <em class="sig-param"><span class="n">save</span></em>, <em class="sig-param"><span class="n">sharpness</span></em>, <em class="sig-param"><span class="n">synthetic</span></em>, <em class="sig-param"><span class="n">times</span></em>, <em class="sig-param"><span class="n">q05</span></em>, <em class="sig-param"><span class="n">q25</span></em>, <em class="sig-param"><span class="n">q75</span></em>, <em class="sig-param"><span class="n">q95</span></em>, <em class="sig-param"><span class="n">steps</span></em>, <em class="sig-param"><span class="n">method</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Util.html#save_dataframe_interval"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.Util.save_dataframe_interval" title="Permalink to this definition"></a></dt>
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<dd></dd></dl>
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<dl class="py function">
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<dt id="pyFTS.benchmarks.Util.save_dataframe_point">
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<code class="sig-prename descclassname">pyFTS.benchmarks.Util.</code><code class="sig-name descname">save_dataframe_point</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">experiments</span></em>, <em class="sig-param"><span class="n">file</span></em>, <em class="sig-param"><span class="n">objs</span></em>, <em class="sig-param"><span class="n">rmse</span></em>, <em class="sig-param"><span class="n">save</span></em>, <em class="sig-param"><span class="n">synthetic</span></em>, <em class="sig-param"><span class="n">smape</span></em>, <em class="sig-param"><span class="n">times</span></em>, <em class="sig-param"><span class="n">u</span></em>, <em class="sig-param"><span class="n">steps</span></em>, <em class="sig-param"><span class="n">method</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Util.html#save_dataframe_point"><span class="viewcode-link">[source]</span></a><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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<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>experiments</strong> dictionary with the execution results</p></li>
<li><p><strong>file</strong> </p></li>
<li><p><strong>objs</strong> </p></li>
<li><p><strong>rmse</strong> </p></li>
<li><p><strong>save</strong> </p></li>
<li><p><strong>synthetic</strong> </p></li>
<li><p><strong>smape</strong> </p></li>
<li><p><strong>times</strong> </p></li>
<li><p><strong>u</strong> </p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p></p>
</dd>
</dl>
</dd></dl>
<dl class="py function">
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<dt id="pyFTS.benchmarks.Util.save_dataframe_probabilistic">
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<code class="sig-prename descclassname">pyFTS.benchmarks.Util.</code><code class="sig-name descname">save_dataframe_probabilistic</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">experiments</span></em>, <em class="sig-param"><span class="n">file</span></em>, <em class="sig-param"><span class="n">objs</span></em>, <em class="sig-param"><span class="n">crps</span></em>, <em class="sig-param"><span class="n">times</span></em>, <em class="sig-param"><span class="n">save</span></em>, <em class="sig-param"><span class="n">synthetic</span></em>, <em class="sig-param"><span class="n">steps</span></em>, <em class="sig-param"><span class="n">method</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Util.html#save_dataframe_probabilistic"><span class="viewcode-link">[source]</span></a><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>
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<dl class="py function">
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<dt id="pyFTS.benchmarks.Util.scale">
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<code class="sig-prename descclassname">pyFTS.benchmarks.Util.</code><code class="sig-name descname">scale</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="n">params</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Util.html#scale"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.Util.scale" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
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<dl class="py function">
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<dt id="pyFTS.benchmarks.Util.scale_params">
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<code class="sig-prename descclassname">pyFTS.benchmarks.Util.</code><code class="sig-name descname">scale_params</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Util.html#scale_params"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.Util.scale_params" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
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<dl class="py function">
<dt id="pyFTS.benchmarks.Util.simple_synthetic_dataframe">
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<code class="sig-prename descclassname">pyFTS.benchmarks.Util.</code><code class="sig-name descname">simple_synthetic_dataframe</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">file</span></em>, <em class="sig-param"><span class="n">tag</span></em>, <em class="sig-param"><span class="n">measure</span></em>, <em class="sig-param"><span class="n">sql</span><span class="o">=</span><span class="default_value">None</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Util.html#simple_synthetic_dataframe"><span class="viewcode-link">[source]</span></a><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>
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<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>file</strong> sqlite3 database file name</p></li>
<li><p><strong>tag</strong> common tag of the experiments</p></li>
<li><p><strong>measure</strong> metric to synthetize</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>Pandas DataFrame with the mean results</p>
</dd>
</dl>
</dd></dl>
<dl class="py function">
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<dt id="pyFTS.benchmarks.Util.stats">
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<code class="sig-prename descclassname">pyFTS.benchmarks.Util.</code><code class="sig-name descname">stats</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">measure</span></em>, <em class="sig-param"><span class="n">data</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Util.html#stats"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.Util.stats" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
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<dl class="py function">
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<dt id="pyFTS.benchmarks.Util.tabular_dataframe_columns">
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<code class="sig-prename descclassname">pyFTS.benchmarks.Util.</code><code class="sig-name descname">tabular_dataframe_columns</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Util.html#tabular_dataframe_columns"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.Util.tabular_dataframe_columns" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
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<dl class="py function">
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<dt id="pyFTS.benchmarks.Util.unified_scaled_interval">
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<code class="sig-prename descclassname">pyFTS.benchmarks.Util.</code><code class="sig-name descname">unified_scaled_interval</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">experiments</span></em>, <em class="sig-param"><span class="n">tam</span></em>, <em class="sig-param"><span class="n">save</span><span class="o">=</span><span class="default_value">False</span></em>, <em class="sig-param"><span class="n">file</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">sort_columns</span><span class="o">=</span><span class="default_value">['COVAVG', 'SHARPAVG', 'COVSTD', 'SHARPSTD']</span></em>, <em class="sig-param"><span class="n">sort_ascend</span><span class="o">=</span><span class="default_value">[True, False, True, True]</span></em>, <em class="sig-param"><span class="n">save_best</span><span class="o">=</span><span class="default_value">False</span></em>, <em class="sig-param"><span class="n">ignore</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">replace</span><span class="o">=</span><span class="default_value">None</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Util.html#unified_scaled_interval"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.Util.unified_scaled_interval" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
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<dl class="py function">
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<dt id="pyFTS.benchmarks.Util.unified_scaled_interval_pinball">
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<code class="sig-prename descclassname">pyFTS.benchmarks.Util.</code><code class="sig-name descname">unified_scaled_interval_pinball</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">experiments</span></em>, <em class="sig-param"><span class="n">tam</span></em>, <em class="sig-param"><span class="n">save</span><span class="o">=</span><span class="default_value">False</span></em>, <em class="sig-param"><span class="n">file</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">sort_columns</span><span class="o">=</span><span class="default_value">['COVAVG', 'SHARPAVG', 'COVSTD', 'SHARPSTD']</span></em>, <em class="sig-param"><span class="n">sort_ascend</span><span class="o">=</span><span class="default_value">[True, False, True, True]</span></em>, <em class="sig-param"><span class="n">save_best</span><span class="o">=</span><span class="default_value">False</span></em>, <em class="sig-param"><span class="n">ignore</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">replace</span><span class="o">=</span><span class="default_value">None</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Util.html#unified_scaled_interval_pinball"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.Util.unified_scaled_interval_pinball" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
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<dl class="py function">
2018-08-30 23:04:52 +04:00
<dt id="pyFTS.benchmarks.Util.unified_scaled_point">
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<code class="sig-prename descclassname">pyFTS.benchmarks.Util.</code><code class="sig-name descname">unified_scaled_point</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">experiments</span></em>, <em class="sig-param"><span class="n">tam</span></em>, <em class="sig-param"><span class="n">save</span><span class="o">=</span><span class="default_value">False</span></em>, <em class="sig-param"><span class="n">file</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">sort_columns</span><span class="o">=</span><span class="default_value">['UAVG', 'RMSEAVG', 'USTD', 'RMSESTD']</span></em>, <em class="sig-param"><span class="n">sort_ascend</span><span class="o">=</span><span class="default_value">[1, 1, 1, 1]</span></em>, <em class="sig-param"><span class="n">save_best</span><span class="o">=</span><span class="default_value">False</span></em>, <em class="sig-param"><span class="n">ignore</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">replace</span><span class="o">=</span><span class="default_value">None</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Util.html#unified_scaled_point"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.Util.unified_scaled_point" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
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<dl class="py function">
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<dt id="pyFTS.benchmarks.Util.unified_scaled_probabilistic">
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<code class="sig-prename descclassname">pyFTS.benchmarks.Util.</code><code class="sig-name descname">unified_scaled_probabilistic</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">experiments</span></em>, <em class="sig-param"><span class="n">tam</span></em>, <em class="sig-param"><span class="n">save</span><span class="o">=</span><span class="default_value">False</span></em>, <em class="sig-param"><span class="n">file</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">sort_columns</span><span class="o">=</span><span class="default_value">['CRPSAVG', 'CRPSSTD']</span></em>, <em class="sig-param"><span class="n">sort_ascend</span><span class="o">=</span><span class="default_value">[True, True]</span></em>, <em class="sig-param"><span class="n">save_best</span><span class="o">=</span><span class="default_value">False</span></em>, <em class="sig-param"><span class="n">ignore</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">replace</span><span class="o">=</span><span class="default_value">None</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Util.html#unified_scaled_probabilistic"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.Util.unified_scaled_probabilistic" title="Permalink to this definition"></a></dt>
<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>
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<dl class="py class">
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<dt id="pyFTS.benchmarks.arima.ARIMA">
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<em class="property">class </em><code class="sig-prename descclassname">pyFTS.benchmarks.arima.</code><code class="sig-name descname">ARIMA</code><span class="sig-paren">(</span><em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/arima.html#ARIMA"><span class="viewcode-link">[source]</span></a><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>
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<dl class="py method">
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<dt id="pyFTS.benchmarks.arima.ARIMA.ar">
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<code class="sig-name descname">ar</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/arima.html#ARIMA.ar"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.arima.ARIMA.ar" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
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<dl class="py method">
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<dt id="pyFTS.benchmarks.arima.ARIMA.forecast">
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<code class="sig-name descname">forecast</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">ndata</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/arima.html#ARIMA.forecast"><span class="viewcode-link">[source]</span></a><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>
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<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>data</strong> time series data with the minimal length equal to the max_lag of the model</p></li>
<li><p><strong>kwargs</strong> model specific parameters</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>a list with the forecasted values</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
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<dt id="pyFTS.benchmarks.arima.ARIMA.forecast_ahead_distribution">
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<code class="sig-name descname">forecast_ahead_distribution</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="n">steps</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/arima.html#ARIMA.forecast_ahead_distribution"><span class="viewcode-link">[source]</span></a><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>
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<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>data</strong> time series data with the minimal length equal to the max_lag of the model</p></li>
<li><p><strong>steps</strong> the number of steps ahead to forecast</p></li>
<li><p><strong>start_at</strong> in the multi step forecasting, the index of the data where to start forecasting (default: 0)</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>a list with the forecasted Probability Distributions</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
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<dt id="pyFTS.benchmarks.arima.ARIMA.forecast_ahead_interval">
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<code class="sig-name descname">forecast_ahead_interval</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">ndata</span></em>, <em class="sig-param"><span class="n">steps</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/arima.html#ARIMA.forecast_ahead_interval"><span class="viewcode-link">[source]</span></a><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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<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>data</strong> time series data with the minimal length equal to the max_lag of the model</p></li>
<li><p><strong>steps</strong> the number of steps ahead to forecast</p></li>
<li><p><strong>start_at</strong> in the multi step forecasting, the index of the data where to start forecasting (default: 0)</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>a list with the forecasted intervals</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
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<dt id="pyFTS.benchmarks.arima.ARIMA.forecast_distribution">
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<code class="sig-name descname">forecast_distribution</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/arima.html#ARIMA.forecast_distribution"><span class="viewcode-link">[source]</span></a><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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<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>data</strong> time series data with the minimal length equal to the max_lag of the model</p></li>
<li><p><strong>kwargs</strong> model specific parameters</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>a list with probabilistic.ProbabilityDistribution objects representing the forecasted Probability Distributions</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
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<dt id="pyFTS.benchmarks.arima.ARIMA.forecast_interval">
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<code class="sig-name descname">forecast_interval</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/arima.html#ARIMA.forecast_interval"><span class="viewcode-link">[source]</span></a><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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<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>data</strong> time series data with the minimal length equal to the max_lag of the model</p></li>
<li><p><strong>kwargs</strong> model specific parameters</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>a list with the prediction intervals</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
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<dt id="pyFTS.benchmarks.arima.ARIMA.ma">
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<code class="sig-name descname">ma</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/arima.html#ARIMA.ma"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.arima.ARIMA.ma" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
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<dl class="py method">
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<dt id="pyFTS.benchmarks.arima.ARIMA.train">
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<code class="sig-name descname">train</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/arima.html#ARIMA.train"><span class="viewcode-link">[source]</span></a><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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<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>data</strong> training time series data</p></li>
<li><p><strong>kwargs</strong> Method specific parameters</p></li>
</ul>
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</dd>
</dl>
</dd></dl>
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</dd></dl>
</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>
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<dl class="py class">
<dt id="pyFTS.benchmarks.knn.KNearestNeighbors">
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<em class="property">class </em><code class="sig-prename descclassname">pyFTS.benchmarks.knn.</code><code class="sig-name descname">KNearestNeighbors</code><span class="sig-paren">(</span><em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/knn.html#KNearestNeighbors"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.knn.KNearestNeighbors" 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>A façade for sklearn.neighbors</p>
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<dl class="py method">
<dt id="pyFTS.benchmarks.knn.KNearestNeighbors.forecast">
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<code class="sig-name descname">forecast</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/knn.html#KNearestNeighbors.forecast"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.knn.KNearestNeighbors.forecast" title="Permalink to this definition"></a></dt>
<dd><p>Point forecast one step ahead</p>
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<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>data</strong> time series data with the minimal length equal to the max_lag of the model</p></li>
<li><p><strong>kwargs</strong> model specific parameters</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>a list with the forecasted values</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt id="pyFTS.benchmarks.knn.KNearestNeighbors.forecast_ahead_distribution">
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<code class="sig-name descname">forecast_ahead_distribution</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="n">steps</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/knn.html#KNearestNeighbors.forecast_ahead_distribution"><span class="viewcode-link">[source]</span></a><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>
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<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>data</strong> time series data with the minimal length equal to the max_lag of the model</p></li>
<li><p><strong>steps</strong> the number of steps ahead to forecast</p></li>
<li><p><strong>start_at</strong> in the multi step forecasting, the index of the data where to start forecasting (default: 0)</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>a list with the forecasted Probability Distributions</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt id="pyFTS.benchmarks.knn.KNearestNeighbors.forecast_ahead_interval">
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<code class="sig-name descname">forecast_ahead_interval</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="n">steps</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/knn.html#KNearestNeighbors.forecast_ahead_interval"><span class="viewcode-link">[source]</span></a><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>
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<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>data</strong> time series data with the minimal length equal to the max_lag of the model</p></li>
<li><p><strong>steps</strong> the number of steps ahead to forecast</p></li>
<li><p><strong>start_at</strong> in the multi step forecasting, the index of the data where to start forecasting (default: 0)</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>a list with the forecasted intervals</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt id="pyFTS.benchmarks.knn.KNearestNeighbors.forecast_distribution">
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<code class="sig-name descname">forecast_distribution</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/knn.html#KNearestNeighbors.forecast_distribution"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.knn.KNearestNeighbors.forecast_distribution" title="Permalink to this definition"></a></dt>
<dd><p>Probabilistic forecast one step ahead</p>
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<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>data</strong> time series data with the minimal length equal to the max_lag of the model</p></li>
<li><p><strong>kwargs</strong> model specific parameters</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>a list with probabilistic.ProbabilityDistribution objects representing the forecasted Probability Distributions</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt id="pyFTS.benchmarks.knn.KNearestNeighbors.forecast_interval">
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<code class="sig-name descname">forecast_interval</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/knn.html#KNearestNeighbors.forecast_interval"><span class="viewcode-link">[source]</span></a><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>
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<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>data</strong> time series data with the minimal length equal to the max_lag of the model</p></li>
<li><p><strong>kwargs</strong> model specific parameters</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>a list with the prediction intervals</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt id="pyFTS.benchmarks.knn.KNearestNeighbors.knn">
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<code class="sig-name descname">knn</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">sample</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/knn.html#KNearestNeighbors.knn"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.knn.KNearestNeighbors.knn" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
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<dl class="py method">
<dt id="pyFTS.benchmarks.knn.KNearestNeighbors.train">
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<code class="sig-name descname">train</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/knn.html#KNearestNeighbors.train"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.knn.KNearestNeighbors.train" title="Permalink to this definition"></a></dt>
<dd><p>Method specific parameter fitting</p>
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<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>data</strong> training time series data</p></li>
<li><p><strong>kwargs</strong> Method specific parameters</p></li>
</ul>
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</dd>
</dl>
</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>
2020-08-19 00:06:41 +04:00
<dl class="py class">
<dt id="pyFTS.benchmarks.naive.Naive">
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<em class="property">class </em><code class="sig-prename descclassname">pyFTS.benchmarks.naive.</code><code class="sig-name descname">Naive</code><span class="sig-paren">(</span><em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/naive.html#Naive"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.naive.Naive" 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>Naïve Forecasting method</p>
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<dl class="py method">
<dt id="pyFTS.benchmarks.naive.Naive.forecast">
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<code class="sig-name descname">forecast</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/naive.html#Naive.forecast"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.naive.Naive.forecast" title="Permalink to this definition"></a></dt>
<dd><p>Point forecast one step ahead</p>
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<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>data</strong> time series data with the minimal length equal to the max_lag of the model</p></li>
<li><p><strong>kwargs</strong> model specific parameters</p></li>
</ul>
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</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>a list with the forecasted values</p>
</dd>
</dl>
</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>
2020-08-19 00:06:41 +04:00
<dl class="py class">
<dt id="pyFTS.benchmarks.quantreg.QuantileRegression">
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<em class="property">class </em><code class="sig-prename descclassname">pyFTS.benchmarks.quantreg.</code><code class="sig-name descname">QuantileRegression</code><span class="sig-paren">(</span><em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/quantreg.html#QuantileRegression"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.quantreg.QuantileRegression" 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.regression.quantile_regression</p>
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<dl class="py method">
<dt id="pyFTS.benchmarks.quantreg.QuantileRegression.forecast">
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<code class="sig-name descname">forecast</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">ndata</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/quantreg.html#QuantileRegression.forecast"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.quantreg.QuantileRegression.forecast" title="Permalink to this definition"></a></dt>
<dd><p>Point forecast one step ahead</p>
2020-08-19 00:06:41 +04:00
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>data</strong> time series data with the minimal length equal to the max_lag of the model</p></li>
<li><p><strong>kwargs</strong> model specific parameters</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>a list with the forecasted values</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt id="pyFTS.benchmarks.quantreg.QuantileRegression.forecast_ahead_distribution">
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<code class="sig-name descname">forecast_ahead_distribution</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">ndata</span></em>, <em class="sig-param"><span class="n">steps</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/quantreg.html#QuantileRegression.forecast_ahead_distribution"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.quantreg.QuantileRegression.forecast_ahead_distribution" title="Permalink to this definition"></a></dt>
<dd><p>Probabilistic forecast n steps ahead</p>
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<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>data</strong> time series data with the minimal length equal to the max_lag of the model</p></li>
<li><p><strong>steps</strong> the number of steps ahead to forecast</p></li>
<li><p><strong>start_at</strong> in the multi step forecasting, the index of the data where to start forecasting (default: 0)</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>a list with the forecasted Probability Distributions</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt id="pyFTS.benchmarks.quantreg.QuantileRegression.forecast_ahead_interval">
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<code class="sig-name descname">forecast_ahead_interval</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">ndata</span></em>, <em class="sig-param"><span class="n">steps</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/quantreg.html#QuantileRegression.forecast_ahead_interval"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.quantreg.QuantileRegression.forecast_ahead_interval" title="Permalink to this definition"></a></dt>
<dd><p>Interval forecast n steps ahead</p>
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<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>data</strong> time series data with the minimal length equal to the max_lag of the model</p></li>
<li><p><strong>steps</strong> the number of steps ahead to forecast</p></li>
<li><p><strong>start_at</strong> in the multi step forecasting, the index of the data where to start forecasting (default: 0)</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>a list with the forecasted intervals</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt id="pyFTS.benchmarks.quantreg.QuantileRegression.forecast_distribution">
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<code class="sig-name descname">forecast_distribution</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">ndata</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/quantreg.html#QuantileRegression.forecast_distribution"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.quantreg.QuantileRegression.forecast_distribution" title="Permalink to this definition"></a></dt>
<dd><p>Probabilistic forecast one step ahead</p>
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<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>data</strong> time series data with the minimal length equal to the max_lag of the model</p></li>
<li><p><strong>kwargs</strong> model specific parameters</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>a list with probabilistic.ProbabilityDistribution objects representing the forecasted Probability Distributions</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt id="pyFTS.benchmarks.quantreg.QuantileRegression.forecast_interval">
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<code class="sig-name descname">forecast_interval</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">ndata</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/quantreg.html#QuantileRegression.forecast_interval"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.quantreg.QuantileRegression.forecast_interval" title="Permalink to this definition"></a></dt>
<dd><p>Interval forecast one step ahead</p>
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<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>data</strong> time series data with the minimal length equal to the max_lag of the model</p></li>
<li><p><strong>kwargs</strong> model specific parameters</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>a list with the prediction intervals</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt id="pyFTS.benchmarks.quantreg.QuantileRegression.interval_to_interval">
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<code class="sig-name descname">interval_to_interval</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="n">lo_params</span></em>, <em class="sig-param"><span class="n">up_params</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/quantreg.html#QuantileRegression.interval_to_interval"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.quantreg.QuantileRegression.interval_to_interval" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
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<dl class="py method">
<dt id="pyFTS.benchmarks.quantreg.QuantileRegression.linearmodel">
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<code class="sig-name descname">linearmodel</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="n">params</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/quantreg.html#QuantileRegression.linearmodel"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.quantreg.QuantileRegression.linearmodel" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
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<dl class="py method">
<dt id="pyFTS.benchmarks.quantreg.QuantileRegression.point_to_interval">
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<code class="sig-name descname">point_to_interval</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="n">lo_params</span></em>, <em class="sig-param"><span class="n">up_params</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/quantreg.html#QuantileRegression.point_to_interval"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.quantreg.QuantileRegression.point_to_interval" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
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<dl class="py method">
<dt id="pyFTS.benchmarks.quantreg.QuantileRegression.train">
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<code class="sig-name descname">train</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/quantreg.html#QuantileRegression.train"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.quantreg.QuantileRegression.train" title="Permalink to this definition"></a></dt>
<dd><p>Method specific parameter fitting</p>
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<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>data</strong> training time series data</p></li>
<li><p><strong>kwargs</strong> Method specific parameters</p></li>
</ul>
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</dd>
</dl>
</dd></dl>
</dd></dl>
</div>
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<div class="section" id="pyfts-benchmarks-gaussianproc-module">
<h2>pyFTS.benchmarks.gaussianproc module<a class="headerlink" href="#pyfts-benchmarks-gaussianproc-module" title="Permalink to this headline"></a></h2>
</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>
2020-08-19 00:06:41 +04:00
<dl class="py class">
<dt id="pyFTS.benchmarks.BSTS.ARIMA">
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<em class="property">class </em><code class="sig-prename descclassname">pyFTS.benchmarks.BSTS.</code><code class="sig-name descname">ARIMA</code><span class="sig-paren">(</span><em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/BSTS.html#ARIMA"><span class="viewcode-link">[source]</span></a><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>
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<dl class="py method">
<dt id="pyFTS.benchmarks.BSTS.ARIMA.forecast">
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<code class="sig-name descname">forecast</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">ndata</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/BSTS.html#ARIMA.forecast"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.BSTS.ARIMA.forecast" title="Permalink to this definition"></a></dt>
<dd><p>Point forecast one step ahead</p>
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<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>data</strong> time series data with the minimal length equal to the max_lag of the model</p></li>
<li><p><strong>kwargs</strong> model specific parameters</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>a list with the forecasted values</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
2019-06-21 18:34:49 +04:00
<dt id="pyFTS.benchmarks.BSTS.ARIMA.forecast_ahead">
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<code class="sig-name descname">forecast_ahead</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="n">steps</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/BSTS.html#ARIMA.forecast_ahead"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.BSTS.ARIMA.forecast_ahead" title="Permalink to this definition"></a></dt>
2019-06-21 18:34:49 +04:00
<dd><p>Point forecast n steps ahead</p>
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<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>data</strong> time series data with the minimal length equal to the max_lag of the model</p></li>
<li><p><strong>steps</strong> the number of steps ahead to forecast (default: 1)</p></li>
<li><p><strong>start_at</strong> in the multi step forecasting, the index of the data where to start forecasting (default: 0)</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>a list with the forecasted values</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt id="pyFTS.benchmarks.BSTS.ARIMA.forecast_ahead_distribution">
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<code class="sig-name descname">forecast_ahead_distribution</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="n">steps</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/BSTS.html#ARIMA.forecast_ahead_distribution"><span class="viewcode-link">[source]</span></a><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>
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<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>data</strong> time series data with the minimal length equal to the max_lag of the model</p></li>
<li><p><strong>steps</strong> the number of steps ahead to forecast</p></li>
<li><p><strong>start_at</strong> in the multi step forecasting, the index of the data where to start forecasting (default: 0)</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>a list with the forecasted Probability Distributions</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt id="pyFTS.benchmarks.BSTS.ARIMA.forecast_ahead_interval">
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<code class="sig-name descname">forecast_ahead_interval</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">ndata</span></em>, <em class="sig-param"><span class="n">steps</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/BSTS.html#ARIMA.forecast_ahead_interval"><span class="viewcode-link">[source]</span></a><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>
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<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>data</strong> time series data with the minimal length equal to the max_lag of the model</p></li>
<li><p><strong>steps</strong> the number of steps ahead to forecast</p></li>
<li><p><strong>start_at</strong> in the multi step forecasting, the index of the data where to start forecasting (default: 0)</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>a list with the forecasted intervals</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt id="pyFTS.benchmarks.BSTS.ARIMA.forecast_distribution">
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<code class="sig-name descname">forecast_distribution</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/BSTS.html#ARIMA.forecast_distribution"><span class="viewcode-link">[source]</span></a><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>
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<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>data</strong> time series data with the minimal length equal to the max_lag of the model</p></li>
<li><p><strong>kwargs</strong> model specific parameters</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>a list with probabilistic.ProbabilityDistribution objects representing the forecasted Probability Distributions</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt id="pyFTS.benchmarks.BSTS.ARIMA.forecast_interval">
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<code class="sig-name descname">forecast_interval</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/BSTS.html#ARIMA.forecast_interval"><span class="viewcode-link">[source]</span></a><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>
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<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>data</strong> time series data with the minimal length equal to the max_lag of the model</p></li>
<li><p><strong>kwargs</strong> model specific parameters</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>a list with the prediction intervals</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt id="pyFTS.benchmarks.BSTS.ARIMA.inference">
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<code class="sig-name descname">inference</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">steps</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/BSTS.html#ARIMA.inference"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.BSTS.ARIMA.inference" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
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<dl class="py method">
<dt id="pyFTS.benchmarks.BSTS.ARIMA.train">
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<code class="sig-name descname">train</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/BSTS.html#ARIMA.train"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.BSTS.ARIMA.train" title="Permalink to this definition"></a></dt>
<dd><p>Method specific parameter fitting</p>
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<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>data</strong> training time series data</p></li>
<li><p><strong>kwargs</strong> Method specific parameters</p></li>
</ul>
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</dd>
</dl>
</dd></dl>
</dd></dl>
</div>
</div>
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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>
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<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>
<li><a class="reference internal" href="#pyfts-benchmarks-gaussianproc-module">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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