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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>
<dl class="py function">
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<dt class="sig sig-object py" id="pyFTS.benchmarks.benchmarks.common_process_interval_jobs">
<span class="sig-prename descclassname"><span class="pre">pyFTS.benchmarks.benchmarks.</span></span><span class="sig-name descname"><span class="pre">common_process_interval_jobs</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">conn</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">job</span></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"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.common_process_interval_jobs" title="Permalink to this definition"></a></dt>
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<dd></dd></dl>
<dl class="py function">
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<dt class="sig sig-object py" id="pyFTS.benchmarks.benchmarks.common_process_point_jobs">
<span class="sig-prename descclassname"><span class="pre">pyFTS.benchmarks.benchmarks.</span></span><span class="sig-name descname"><span class="pre">common_process_point_jobs</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">conn</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">job</span></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"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.common_process_point_jobs" title="Permalink to this definition"></a></dt>
2021-01-13 01:04:42 +04:00
<dd></dd></dl>
<dl class="py function">
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<dt class="sig sig-object py" id="pyFTS.benchmarks.benchmarks.common_process_probabilistic_jobs">
<span class="sig-prename descclassname"><span class="pre">pyFTS.benchmarks.benchmarks.</span></span><span class="sig-name descname"><span class="pre">common_process_probabilistic_jobs</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">conn</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">job</span></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"><span class="pre">[source]</span></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>
<dl class="py function">
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<dt class="sig sig-object py" id="pyFTS.benchmarks.benchmarks.common_process_time_jobs">
<span class="sig-prename descclassname"><span class="pre">pyFTS.benchmarks.benchmarks.</span></span><span class="sig-name descname"><span class="pre">common_process_time_jobs</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">conn</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">job</span></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"><span class="pre">[source]</span></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>
<dl class="py function">
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<dt class="sig sig-object py" id="pyFTS.benchmarks.benchmarks.compareModelsPlot">
<span class="sig-prename descclassname"><span class="pre">pyFTS.benchmarks.benchmarks.</span></span><span class="sig-name descname"><span class="pre">compareModelsPlot</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">original</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">models_fo</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">models_ho</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/benchmarks.html#compareModelsPlot"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.compareModelsPlot" title="Permalink to this definition"></a></dt>
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<dd></dd></dl>
<dl class="py function">
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<dt class="sig sig-object py" id="pyFTS.benchmarks.benchmarks.compareModelsTable">
<span class="sig-prename descclassname"><span class="pre">pyFTS.benchmarks.benchmarks.</span></span><span class="sig-name descname"><span class="pre">compareModelsTable</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">original</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">models_fo</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">models_ho</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/benchmarks.html#compareModelsTable"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.compareModelsTable" title="Permalink to this definition"></a></dt>
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<dd></dd></dl>
<dl class="py function">
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<dt class="sig sig-object py" id="pyFTS.benchmarks.benchmarks.distributed_model_train_test_time">
<span class="sig-prename descclassname"><span class="pre">pyFTS.benchmarks.benchmarks.</span></span><span class="sig-name descname"><span class="pre">distributed_model_train_test_time</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">models</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">windowsize</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">train</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.8</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></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"><span class="pre">[source]</span></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>
<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 class="sig sig-object py" id="pyFTS.benchmarks.benchmarks.get_benchmark_interval_methods">
<span class="sig-prename descclassname"><span class="pre">pyFTS.benchmarks.benchmarks.</span></span><span class="sig-name descname"><span class="pre">get_benchmark_interval_methods</span></span><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"><span class="pre">[source]</span></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>
<dl class="py function">
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<dt class="sig sig-object py" id="pyFTS.benchmarks.benchmarks.get_benchmark_point_methods">
<span class="sig-prename descclassname"><span class="pre">pyFTS.benchmarks.benchmarks.</span></span><span class="sig-name descname"><span class="pre">get_benchmark_point_methods</span></span><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"><span class="pre">[source]</span></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>
<dl class="py function">
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<dt class="sig sig-object py" id="pyFTS.benchmarks.benchmarks.get_benchmark_probabilistic_methods">
<span class="sig-prename descclassname"><span class="pre">pyFTS.benchmarks.benchmarks.</span></span><span class="sig-name descname"><span class="pre">get_benchmark_probabilistic_methods</span></span><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"><span class="pre">[source]</span></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>
<dl class="py function">
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<dt class="sig sig-object py" id="pyFTS.benchmarks.benchmarks.get_interval_methods">
<span class="sig-prename descclassname"><span class="pre">pyFTS.benchmarks.benchmarks.</span></span><span class="sig-name descname"><span class="pre">get_interval_methods</span></span><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"><span class="pre">[source]</span></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>
<dl class="py function">
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<dt class="sig sig-object py" id="pyFTS.benchmarks.benchmarks.get_point_methods">
<span class="sig-prename descclassname"><span class="pre">pyFTS.benchmarks.benchmarks.</span></span><span class="sig-name descname"><span class="pre">get_point_methods</span></span><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"><span class="pre">[source]</span></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>
<dl class="py function">
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<dt class="sig sig-object py" id="pyFTS.benchmarks.benchmarks.get_point_multivariate_methods">
<span class="sig-prename descclassname"><span class="pre">pyFTS.benchmarks.benchmarks.</span></span><span class="sig-name descname"><span class="pre">get_point_multivariate_methods</span></span><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"><span class="pre">[source]</span></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>
<dl class="py function">
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<dt class="sig sig-object py" id="pyFTS.benchmarks.benchmarks.get_probabilistic_methods">
<span class="sig-prename descclassname"><span class="pre">pyFTS.benchmarks.benchmarks.</span></span><span class="sig-name descname"><span class="pre">get_probabilistic_methods</span></span><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"><span class="pre">[source]</span></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>
<dl class="py function">
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<dt class="sig sig-object py" id="pyFTS.benchmarks.benchmarks.multivariate_sliding_window_benchmarks2">
<span class="sig-prename descclassname"><span class="pre">pyFTS.benchmarks.benchmarks.</span></span><span class="sig-name descname"><span class="pre">multivariate_sliding_window_benchmarks2</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">windowsize</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">train</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.8</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></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"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.multivariate_sliding_window_benchmarks2" title="Permalink to this definition"></a></dt>
2021-01-13 01:04:42 +04:00
<dd></dd></dl>
<dl class="py function">
2022-04-10 21:32:24 +04:00
<dt class="sig sig-object py" id="pyFTS.benchmarks.benchmarks.mv_run_interval2">
<span class="sig-prename descclassname"><span class="pre">pyFTS.benchmarks.benchmarks.</span></span><span class="sig-name descname"><span class="pre">mv_run_interval2</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mfts</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">train_data</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">test_data</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">window_key</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/benchmarks.html#mv_run_interval2"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.mv_run_interval2" title="Permalink to this definition"></a></dt>
2021-01-13 01:04:42 +04:00
<dd></dd></dl>
<dl class="py function">
2022-04-10 21:32:24 +04:00
<dt class="sig sig-object py" id="pyFTS.benchmarks.benchmarks.mv_run_point2">
<span class="sig-prename descclassname"><span class="pre">pyFTS.benchmarks.benchmarks.</span></span><span class="sig-name descname"><span class="pre">mv_run_point2</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mfts</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">train_data</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">test_data</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">window_key</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/benchmarks.html#mv_run_point2"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.mv_run_point2" title="Permalink to this definition"></a></dt>
2021-01-13 01:04:42 +04:00
<dd></dd></dl>
<dl class="py function">
2022-04-10 21:32:24 +04:00
<dt class="sig sig-object py" id="pyFTS.benchmarks.benchmarks.mv_run_probabilistic2">
<span class="sig-prename descclassname"><span class="pre">pyFTS.benchmarks.benchmarks.</span></span><span class="sig-name descname"><span class="pre">mv_run_probabilistic2</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mfts</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">train_data</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">test_data</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">window_key</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/benchmarks.html#mv_run_probabilistic2"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.mv_run_probabilistic2" title="Permalink to this definition"></a></dt>
2021-01-13 01:04:42 +04:00
<dd></dd></dl>
<dl class="py function">
2022-04-10 21:32:24 +04:00
<dt class="sig sig-object py" id="pyFTS.benchmarks.benchmarks.pftsExploreOrderAndPartitions">
<span class="sig-prename descclassname"><span class="pre">pyFTS.benchmarks.benchmarks.</span></span><span class="sig-name descname"><span class="pre">pftsExploreOrderAndPartitions</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">save</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">file</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/benchmarks.html#pftsExploreOrderAndPartitions"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.pftsExploreOrderAndPartitions" title="Permalink to this definition"></a></dt>
2021-01-13 01:04:42 +04:00
<dd></dd></dl>
<dl class="py function">
2022-04-10 21:32:24 +04:00
<dt class="sig sig-object py" id="pyFTS.benchmarks.benchmarks.plotCompared">
<span class="sig-prename descclassname"><span class="pre">pyFTS.benchmarks.benchmarks.</span></span><span class="sig-name descname"><span class="pre">plotCompared</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">original</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">forecasts</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">labels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">title</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/benchmarks.html#plotCompared"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.plotCompared" title="Permalink to this definition"></a></dt>
2021-01-13 01:04:42 +04:00
<dd></dd></dl>
<dl class="py function">
2022-04-10 21:32:24 +04:00
<dt class="sig sig-object py" id="pyFTS.benchmarks.benchmarks.plot_compared_series">
<span class="sig-prename descclassname"><span class="pre">pyFTS.benchmarks.benchmarks.</span></span><span class="sig-name descname"><span class="pre">plot_compared_series</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">original</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">models</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">colors</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">typeonlegend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">save</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">file</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tam</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">[20,</span> <span class="pre">5]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">points</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">intervals</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">linewidth</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.5</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/benchmarks.html#plot_compared_series"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.plot_compared_series" title="Permalink to this definition"></a></dt>
2021-01-13 01:04:42 +04:00
<dd><p>Plot the forecasts of several one step ahead models, by point or by interval</p>
<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">
2022-04-10 21:32:24 +04:00
<dt class="sig sig-object py" id="pyFTS.benchmarks.benchmarks.plot_point">
<span class="sig-prename descclassname"><span class="pre">pyFTS.benchmarks.benchmarks.</span></span><span class="sig-name descname"><span class="pre">plot_point</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">axis</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">points</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">order</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">label</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">color</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'red'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ls</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'-'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">linewidth</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/benchmarks.html#plot_point"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.plot_point" title="Permalink to this definition"></a></dt>
2021-01-13 01:04:42 +04:00
<dd></dd></dl>
<dl class="py function">
2022-04-10 21:32:24 +04:00
<dt class="sig sig-object py" id="pyFTS.benchmarks.benchmarks.print_distribution_statistics">
<span class="sig-prename descclassname"><span class="pre">pyFTS.benchmarks.benchmarks.</span></span><span class="sig-name descname"><span class="pre">print_distribution_statistics</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">original</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">models</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">steps</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">resolution</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/benchmarks.html#print_distribution_statistics"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.print_distribution_statistics" title="Permalink to this definition"></a></dt>
2021-01-13 01:04:42 +04:00
<dd><p>Run probabilistic benchmarks on given models and data and print the results</p>
<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">
2022-04-10 21:32:24 +04:00
<dt class="sig sig-object py" id="pyFTS.benchmarks.benchmarks.print_interval_statistics">
<span class="sig-prename descclassname"><span class="pre">pyFTS.benchmarks.benchmarks.</span></span><span class="sig-name descname"><span class="pre">print_interval_statistics</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">original</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">models</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/benchmarks.html#print_interval_statistics"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.print_interval_statistics" title="Permalink to this definition"></a></dt>
2021-01-13 01:04:42 +04:00
<dd><p>Run interval benchmarks on given models and data and print the results</p>
<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">
2022-04-10 21:32:24 +04:00
<dt class="sig sig-object py" id="pyFTS.benchmarks.benchmarks.print_point_statistics">
<span class="sig-prename descclassname"><span class="pre">pyFTS.benchmarks.benchmarks.</span></span><span class="sig-name descname"><span class="pre">print_point_statistics</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">models</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">externalmodels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">externalforecasts</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">indexers</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/benchmarks.html#print_point_statistics"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.print_point_statistics" title="Permalink to this definition"></a></dt>
2021-01-13 01:04:42 +04:00
<dd><p>Run point benchmarks on given models and data and print the results</p>
<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">
2022-04-10 21:32:24 +04:00
<dt class="sig sig-object py" id="pyFTS.benchmarks.benchmarks.process_interval_jobs">
<span class="sig-prename descclassname"><span class="pre">pyFTS.benchmarks.benchmarks.</span></span><span class="sig-name descname"><span class="pre">process_interval_jobs</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tag</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">job</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">conn</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/benchmarks.html#process_interval_jobs"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.process_interval_jobs" title="Permalink to this definition"></a></dt>
2021-01-13 01:04:42 +04:00
<dd><p>Extract information from an dictionary with interval benchmark results and save it on a database</p>
<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">
2022-04-10 21:32:24 +04:00
<dt class="sig sig-object py" id="pyFTS.benchmarks.benchmarks.process_interval_jobs2">
<span class="sig-prename descclassname"><span class="pre">pyFTS.benchmarks.benchmarks.</span></span><span class="sig-name descname"><span class="pre">process_interval_jobs2</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tag</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">job</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">conn</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/benchmarks.html#process_interval_jobs2"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.process_interval_jobs2" title="Permalink to this definition"></a></dt>
2021-01-13 01:04:42 +04:00
<dd></dd></dl>
<dl class="py function">
2022-04-10 21:32:24 +04:00
<dt class="sig sig-object py" id="pyFTS.benchmarks.benchmarks.process_point_jobs">
<span class="sig-prename descclassname"><span class="pre">pyFTS.benchmarks.benchmarks.</span></span><span class="sig-name descname"><span class="pre">process_point_jobs</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tag</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">job</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">conn</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/benchmarks.html#process_point_jobs"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.process_point_jobs" title="Permalink to this definition"></a></dt>
2021-01-13 01:04:42 +04:00
<dd><p>Extract information from a dictionary with point benchmark results and save it on a database</p>
<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">
2022-04-10 21:32:24 +04:00
<dt class="sig sig-object py" id="pyFTS.benchmarks.benchmarks.process_point_jobs2">
<span class="sig-prename descclassname"><span class="pre">pyFTS.benchmarks.benchmarks.</span></span><span class="sig-name descname"><span class="pre">process_point_jobs2</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tag</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">job</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">conn</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/benchmarks.html#process_point_jobs2"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.process_point_jobs2" title="Permalink to this definition"></a></dt>
2021-01-13 01:04:42 +04:00
<dd><p>Extract information from a dictionary with point benchmark results and save it on a database</p>
<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">
2022-04-10 21:32:24 +04:00
<dt class="sig sig-object py" id="pyFTS.benchmarks.benchmarks.process_probabilistic_jobs">
<span class="sig-prename descclassname"><span class="pre">pyFTS.benchmarks.benchmarks.</span></span><span class="sig-name descname"><span class="pre">process_probabilistic_jobs</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tag</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">job</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">conn</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/benchmarks.html#process_probabilistic_jobs"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.process_probabilistic_jobs" title="Permalink to this definition"></a></dt>
2021-01-13 01:04:42 +04:00
<dd><p>Extract information from an dictionary with probabilistic benchmark results and save it on a database</p>
<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">
2022-04-10 21:32:24 +04:00
<dt class="sig sig-object py" id="pyFTS.benchmarks.benchmarks.process_probabilistic_jobs2">
<span class="sig-prename descclassname"><span class="pre">pyFTS.benchmarks.benchmarks.</span></span><span class="sig-name descname"><span class="pre">process_probabilistic_jobs2</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tag</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">job</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">conn</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/benchmarks.html#process_probabilistic_jobs2"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.process_probabilistic_jobs2" title="Permalink to this definition"></a></dt>
2021-01-13 01:04:42 +04:00
<dd><p>Extract information from an dictionary with probabilistic benchmark results and save it on a database</p>
<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">
2022-04-10 21:32:24 +04:00
<dt class="sig sig-object py" id="pyFTS.benchmarks.benchmarks.run_interval">
<span class="sig-prename descclassname"><span class="pre">pyFTS.benchmarks.benchmarks.</span></span><span class="sig-name descname"><span class="pre">run_interval</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mfts</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">partitioner</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">train_data</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">test_data</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">window_key</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/benchmarks.html#run_interval"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.run_interval" title="Permalink to this definition"></a></dt>
2021-01-13 01:04:42 +04:00
<dd><p>Run the interval forecasting benchmarks</p>
<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">
2022-04-10 21:32:24 +04:00
<dt class="sig sig-object py" id="pyFTS.benchmarks.benchmarks.run_interval2">
<span class="sig-prename descclassname"><span class="pre">pyFTS.benchmarks.benchmarks.</span></span><span class="sig-name descname"><span class="pre">run_interval2</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fts_method</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">order</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">partitioner_method</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">partitions</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transformation</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">train_data</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">test_data</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">window_key</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/benchmarks.html#run_interval2"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.run_interval2" title="Permalink to this definition"></a></dt>
2021-01-13 01:04:42 +04:00
<dd></dd></dl>
<dl class="py function">
2022-04-10 21:32:24 +04:00
<dt class="sig sig-object py" id="pyFTS.benchmarks.benchmarks.run_point">
<span class="sig-prename descclassname"><span class="pre">pyFTS.benchmarks.benchmarks.</span></span><span class="sig-name descname"><span class="pre">run_point</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mfts</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">partitioner</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">train_data</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">test_data</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">window_key</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/benchmarks.html#run_point"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.run_point" title="Permalink to this definition"></a></dt>
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<dd><p>Run the point forecasting benchmarks</p>
<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">
2022-04-10 21:32:24 +04:00
<dt class="sig sig-object py" id="pyFTS.benchmarks.benchmarks.run_point2">
<span class="sig-prename descclassname"><span class="pre">pyFTS.benchmarks.benchmarks.</span></span><span class="sig-name descname"><span class="pre">run_point2</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fts_method</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">order</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">partitioner_method</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">partitions</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transformation</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">train_data</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">test_data</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">window_key</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/benchmarks.html#run_point2"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.run_point2" title="Permalink to this definition"></a></dt>
2021-01-13 01:04:42 +04:00
<dd></dd></dl>
<dl class="py function">
2022-04-10 21:32:24 +04:00
<dt class="sig sig-object py" id="pyFTS.benchmarks.benchmarks.run_probabilistic">
<span class="sig-prename descclassname"><span class="pre">pyFTS.benchmarks.benchmarks.</span></span><span class="sig-name descname"><span class="pre">run_probabilistic</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mfts</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">partitioner</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">train_data</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">test_data</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">window_key</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/benchmarks.html#run_probabilistic"><span class="viewcode-link"><span class="pre">[source]</span></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>
<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">
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<dt class="sig sig-object py" id="pyFTS.benchmarks.benchmarks.run_probabilistic2">
<span class="sig-prename descclassname"><span class="pre">pyFTS.benchmarks.benchmarks.</span></span><span class="sig-name descname"><span class="pre">run_probabilistic2</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fts_method</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">order</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">partitioner_method</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">partitions</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transformation</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">train_data</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">test_data</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">window_key</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/benchmarks.html#run_probabilistic2"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.run_probabilistic2" title="Permalink to this definition"></a></dt>
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<dd></dd></dl>
<dl class="py function">
2022-04-10 21:32:24 +04:00
<dt class="sig sig-object py" id="pyFTS.benchmarks.benchmarks.simpleSearch_RMSE">
<span class="sig-prename descclassname"><span class="pre">pyFTS.benchmarks.benchmarks.</span></span><span class="sig-name descname"><span class="pre">simpleSearch_RMSE</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">train,</span> <span class="pre">test,</span> <span class="pre">model,</span> <span class="pre">partitions,</span> <span class="pre">orders,</span> <span class="pre">save=False,</span> <span class="pre">file=None,</span> <span class="pre">tam=[10,</span> <span class="pre">15],</span> <span class="pre">plotforecasts=False,</span> <span class="pre">elev=30,</span> <span class="pre">azim=144,</span> <span class="pre">intervals=False,</span> <span class="pre">parameters=None,</span> <span class="pre">partitioner=&lt;class</span> <span class="pre">'pyFTS.partitioners.Grid.GridPartitioner'&gt;,</span> <span class="pre">transformation=None,</span> <span class="pre">indexer=None</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/benchmarks.html#simpleSearch_RMSE"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.simpleSearch_RMSE" title="Permalink to this definition"></a></dt>
2021-01-13 01:04:42 +04:00
<dd></dd></dl>
<dl class="py function">
2022-04-10 21:32:24 +04:00
<dt class="sig sig-object py" id="pyFTS.benchmarks.benchmarks.sliding_window_benchmarks">
<span class="sig-prename descclassname"><span class="pre">pyFTS.benchmarks.benchmarks.</span></span><span class="sig-name descname"><span class="pre">sliding_window_benchmarks</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">windowsize</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">train</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.8</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/benchmarks.html#sliding_window_benchmarks"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.sliding_window_benchmarks" title="Permalink to this definition"></a></dt>
2021-01-13 01:04:42 +04:00
<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>
<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">
2022-04-10 21:32:24 +04:00
<dt class="sig sig-object py" id="pyFTS.benchmarks.benchmarks.sliding_window_benchmarks2">
<span class="sig-prename descclassname"><span class="pre">pyFTS.benchmarks.benchmarks.</span></span><span class="sig-name descname"><span class="pre">sliding_window_benchmarks2</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">windowsize</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">train</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.8</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/benchmarks.html#sliding_window_benchmarks2"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.sliding_window_benchmarks2" title="Permalink to this definition"></a></dt>
2021-01-13 01:04:42 +04:00
<dd></dd></dl>
<dl class="py function">
2022-04-10 21:32:24 +04:00
<dt class="sig sig-object py" id="pyFTS.benchmarks.benchmarks.train_test_time">
<span class="sig-prename descclassname"><span class="pre">pyFTS.benchmarks.benchmarks.</span></span><span class="sig-name descname"><span class="pre">train_test_time</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">windowsize</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">train</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.8</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/benchmarks.html#train_test_time"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.train_test_time" title="Permalink to this definition"></a></dt>
2021-01-13 01:04:42 +04:00
<dd></dd></dl>
2018-08-30 23:04:52 +04:00
</div>
2021-01-13 01:04:42 +04:00
<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>
<dl class="py function">
2022-04-10 21:32:24 +04:00
<dt class="sig sig-object py" id="pyFTS.benchmarks.Measures.TheilsInequality">
<span class="sig-prename descclassname"><span class="pre">pyFTS.benchmarks.Measures.</span></span><span class="sig-name descname"><span class="pre">TheilsInequality</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">targets</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">forecasts</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Measures.html#TheilsInequality"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.benchmarks.Measures.TheilsInequality" title="Permalink to this definition"></a></dt>
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<dd><p>Theils Inequality Coefficient</p>
<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 class="sig sig-object py" id="pyFTS.benchmarks.Measures.UStatistic">
<span class="sig-prename descclassname"><span class="pre">pyFTS.benchmarks.Measures.</span></span><span class="sig-name descname"><span class="pre">UStatistic</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">targets</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">forecasts</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Measures.html#UStatistic"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.benchmarks.Measures.UStatistic" title="Permalink to this definition"></a></dt>
2021-01-13 01:04:42 +04:00
<dd><p>Theils U Statistic</p>
<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">
2022-04-10 21:32:24 +04:00
<dt class="sig sig-object py" id="pyFTS.benchmarks.Measures.acf">
<span class="sig-prename descclassname"><span class="pre">pyFTS.benchmarks.Measures.</span></span><span class="sig-name descname"><span class="pre">acf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">k</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Measures.html#acf"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.benchmarks.Measures.acf" title="Permalink to this definition"></a></dt>
2021-01-13 01:04:42 +04:00
<dd><p>Autocorrelation function estimative</p>
<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">
2022-04-10 21:32:24 +04:00
<dt class="sig sig-object py" id="pyFTS.benchmarks.Measures.brier_score">
<span class="sig-prename descclassname"><span class="pre">pyFTS.benchmarks.Measures.</span></span><span class="sig-name descname"><span class="pre">brier_score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">targets</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">densities</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Measures.html#brier_score"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.benchmarks.Measures.brier_score" title="Permalink to this definition"></a></dt>
2021-01-13 01:04:42 +04:00
<dd><p>Brier Score for probabilistic forecasts.
Brier (1950). “Verification of Forecasts Expressed in Terms of Probability”. Monthly Weather Review. 78: 13.</p>
<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">
2022-04-10 21:32:24 +04:00
<dt class="sig sig-object py" id="pyFTS.benchmarks.Measures.coverage">
<span class="sig-prename descclassname"><span class="pre">pyFTS.benchmarks.Measures.</span></span><span class="sig-name descname"><span class="pre">coverage</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">targets</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">forecasts</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Measures.html#coverage"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.benchmarks.Measures.coverage" title="Permalink to this definition"></a></dt>
2021-01-13 01:04:42 +04:00
<dd><p>Percent of target values that fall inside forecasted interval</p>
</dd></dl>
<dl class="py function">
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<dt class="sig sig-object py" id="pyFTS.benchmarks.Measures.crps">
<span class="sig-prename descclassname"><span class="pre">pyFTS.benchmarks.Measures.</span></span><span class="sig-name descname"><span class="pre">crps</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">targets</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">densities</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Measures.html#crps"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.benchmarks.Measures.crps" title="Permalink to this definition"></a></dt>
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<dd><p>Continuous Ranked Probability Score</p>
<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 class="sig sig-object py" id="pyFTS.benchmarks.Measures.get_distribution_ahead_statistics">
<span class="sig-prename descclassname"><span class="pre">pyFTS.benchmarks.Measures.</span></span><span class="sig-name descname"><span class="pre">get_distribution_ahead_statistics</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">distributions</span></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"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.benchmarks.Measures.get_distribution_ahead_statistics" title="Permalink to this definition"></a></dt>
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<dd><p>Get CRPS statistic and time for a forecasting model</p>
<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 class="sig sig-object py" id="pyFTS.benchmarks.Measures.get_distribution_statistics">
<span class="sig-prename descclassname"><span class="pre">pyFTS.benchmarks.Measures.</span></span><span class="sig-name descname"><span class="pre">get_distribution_statistics</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Measures.html#get_distribution_statistics"><span class="viewcode-link"><span class="pre">[source]</span></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>
<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 class="sig sig-object py" id="pyFTS.benchmarks.Measures.get_interval_ahead_statistics">
<span class="sig-prename descclassname"><span class="pre">pyFTS.benchmarks.Measures.</span></span><span class="sig-name descname"><span class="pre">get_interval_ahead_statistics</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">intervals</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></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"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.benchmarks.Measures.get_interval_ahead_statistics" title="Permalink to this definition"></a></dt>
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<dd><p>Condensate all measures for point interval forecasters</p>
<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>
</dd>
</dl>
</dd></dl>
<dl class="py function">
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<dt class="sig sig-object py" id="pyFTS.benchmarks.Measures.get_interval_statistics">
<span class="sig-prename descclassname"><span class="pre">pyFTS.benchmarks.Measures.</span></span><span class="sig-name descname"><span class="pre">get_interval_statistics</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Measures.html#get_interval_statistics"><span class="viewcode-link"><span class="pre">[source]</span></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>
<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>
</dd>
</dl>
</dd></dl>
<dl class="py function">
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<dt class="sig sig-object py" id="pyFTS.benchmarks.Measures.get_point_ahead_statistics">
<span class="sig-prename descclassname"><span class="pre">pyFTS.benchmarks.Measures.</span></span><span class="sig-name descname"><span class="pre">get_point_ahead_statistics</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">forecasts</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></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"><span class="pre">[source]</span></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>
<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 class="sig sig-object py" id="pyFTS.benchmarks.Measures.get_point_statistics">
<span class="sig-prename descclassname"><span class="pre">pyFTS.benchmarks.Measures.</span></span><span class="sig-name descname"><span class="pre">get_point_statistics</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Measures.html#get_point_statistics"><span class="viewcode-link"><span class="pre">[source]</span></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>
<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 class="sig sig-object py" id="pyFTS.benchmarks.Measures.logarithm_score">
<span class="sig-prename descclassname"><span class="pre">pyFTS.benchmarks.Measures.</span></span><span class="sig-name descname"><span class="pre">logarithm_score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">targets</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">densities</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Measures.html#logarithm_score"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.benchmarks.Measures.logarithm_score" title="Permalink to this definition"></a></dt>
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<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>
<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 class="sig sig-object py" id="pyFTS.benchmarks.Measures.mape">
<span class="sig-prename descclassname"><span class="pre">pyFTS.benchmarks.Measures.</span></span><span class="sig-name descname"><span class="pre">mape</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">targets</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">forecasts</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Measures.html#mape"><span class="viewcode-link"><span class="pre">[source]</span></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>
<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 class="sig sig-object py" id="pyFTS.benchmarks.Measures.mape_interval">
<span class="sig-prename descclassname"><span class="pre">pyFTS.benchmarks.Measures.</span></span><span class="sig-name descname"><span class="pre">mape_interval</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">targets</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">forecasts</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Measures.html#mape_interval"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.benchmarks.Measures.mape_interval" title="Permalink to this definition"></a></dt>
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<dd></dd></dl>
<dl class="py function">
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<dt class="sig sig-object py" id="pyFTS.benchmarks.Measures.nmrse">
<span class="sig-prename descclassname"><span class="pre">pyFTS.benchmarks.Measures.</span></span><span class="sig-name descname"><span class="pre">nmrse</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">targets</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">forecasts</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">order</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">offset</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Measures.html#nmrse"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.benchmarks.Measures.nmrse" title="Permalink to this definition"></a></dt>
<dd><p>Normalized Root Mean Squared Error</p>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="pyFTS.benchmarks.Measures.pinball">
<span class="sig-prename descclassname"><span class="pre">pyFTS.benchmarks.Measures.</span></span><span class="sig-name descname"><span class="pre">pinball</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tau</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">forecast</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Measures.html#pinball"><span class="viewcode-link"><span class="pre">[source]</span></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>
<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 class="sig sig-object py" id="pyFTS.benchmarks.Measures.pinball_mean">
<span class="sig-prename descclassname"><span class="pre">pyFTS.benchmarks.Measures.</span></span><span class="sig-name descname"><span class="pre">pinball_mean</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tau</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">targets</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">forecasts</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Measures.html#pinball_mean"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.benchmarks.Measures.pinball_mean" title="Permalink to this definition"></a></dt>
2021-01-13 01:04:42 +04:00
<dd><p>Mean pinball loss value of the forecast for a given tau-quantile of the targets</p>
<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 class="sig sig-object py" id="pyFTS.benchmarks.Measures.resolution">
<span class="sig-prename descclassname"><span class="pre">pyFTS.benchmarks.Measures.</span></span><span class="sig-name descname"><span class="pre">resolution</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">forecasts</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Measures.html#resolution"><span class="viewcode-link"><span class="pre">[source]</span></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>
<dl class="py function">
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<dt class="sig sig-object py" id="pyFTS.benchmarks.Measures.rmse">
<span class="sig-prename descclassname"><span class="pre">pyFTS.benchmarks.Measures.</span></span><span class="sig-name descname"><span class="pre">rmse</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">targets</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">forecasts</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">order</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">offset</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Measures.html#rmse"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.benchmarks.Measures.rmse" title="Permalink to this definition"></a></dt>
2021-01-13 01:04:42 +04:00
<dd><p>Root Mean Squared Error</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
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<li><p><strong>targets</strong> array of targets</p></li>
<li><p><strong>forecasts</strong> array of forecasts</p></li>
<li><p><strong>order</strong> model order</p></li>
<li><p><strong>offset</strong> forecast offset related to target.</p></li>
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</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 class="sig sig-object py" id="pyFTS.benchmarks.Measures.rmse_interval">
<span class="sig-prename descclassname"><span class="pre">pyFTS.benchmarks.Measures.</span></span><span class="sig-name descname"><span class="pre">rmse_interval</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">targets</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">forecasts</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Measures.html#rmse_interval"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.benchmarks.Measures.rmse_interval" title="Permalink to this definition"></a></dt>
2021-01-13 01:04:42 +04:00
<dd><p>Root Mean Squared Error</p>
<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 class="sig sig-object py" id="pyFTS.benchmarks.Measures.sharpness">
<span class="sig-prename descclassname"><span class="pre">pyFTS.benchmarks.Measures.</span></span><span class="sig-name descname"><span class="pre">sharpness</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">forecasts</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Measures.html#sharpness"><span class="viewcode-link"><span class="pre">[source]</span></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>
<dl class="py function">
2022-04-10 21:32:24 +04:00
<dt class="sig sig-object py" id="pyFTS.benchmarks.Measures.smape">
<span class="sig-prename descclassname"><span class="pre">pyFTS.benchmarks.Measures.</span></span><span class="sig-name descname"><span class="pre">smape</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">targets</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">forecasts</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Measures.html#smape"><span class="viewcode-link"><span class="pre">[source]</span></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>
<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 class="sig sig-object py" id="pyFTS.benchmarks.Measures.winkler_mean">
<span class="sig-prename descclassname"><span class="pre">pyFTS.benchmarks.Measures.</span></span><span class="sig-name descname"><span class="pre">winkler_mean</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tau</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">targets</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">forecasts</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Measures.html#winkler_mean"><span class="viewcode-link"><span class="pre">[source]</span></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>
<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 class="sig sig-object py" id="pyFTS.benchmarks.Measures.winkler_score">
<span class="sig-prename descclassname"><span class="pre">pyFTS.benchmarks.Measures.</span></span><span class="sig-name descname"><span class="pre">winkler_score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tau</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">forecast</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Measures.html#winkler_score"><span class="viewcode-link"><span class="pre">[source]</span></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">
<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>
</ol>
</li>
</ol>
<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>
</dd></dl>
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</div>
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<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>
<dl class="py function">
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<dt class="sig sig-object py" id="pyFTS.benchmarks.ResidualAnalysis.compare_residuals">
<span class="sig-prename descclassname"><span class="pre">pyFTS.benchmarks.ResidualAnalysis.</span></span><span class="sig-name descname"><span class="pre">compare_residuals</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">models</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.05</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/ResidualAnalysis.html#compare_residuals"><span class="viewcode-link"><span class="pre">[source]</span></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>
<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 class="sig sig-object py" id="pyFTS.benchmarks.ResidualAnalysis.ljung_box_test">
<span class="sig-prename descclassname"><span class="pre">pyFTS.benchmarks.ResidualAnalysis.</span></span><span class="sig-name descname"><span class="pre">ljung_box_test</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">residuals</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lags</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">[1,</span> <span class="pre">2,</span> <span class="pre">3]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/ResidualAnalysis.html#ljung_box_test"><span class="viewcode-link"><span class="pre">[source]</span></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>
<dl class="py function">
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<dt class="sig sig-object py" id="pyFTS.benchmarks.ResidualAnalysis.plot_residuals_by_model">
<span class="sig-prename descclassname"><span class="pre">pyFTS.benchmarks.ResidualAnalysis.</span></span><span class="sig-name descname"><span class="pre">plot_residuals_by_model</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">targets</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">models</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tam</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">[8,</span> <span class="pre">8]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">save</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">file</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></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"><span class="pre">[source]</span></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>
<dl class="py function">
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<dt class="sig sig-object py" id="pyFTS.benchmarks.ResidualAnalysis.residuals">
<span class="sig-prename descclassname"><span class="pre">pyFTS.benchmarks.ResidualAnalysis.</span></span><span class="sig-name descname"><span class="pre">residuals</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">targets</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">forecasts</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">order</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/ResidualAnalysis.html#residuals"><span class="viewcode-link"><span class="pre">[source]</span></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>
<dl class="py function">
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<dt class="sig sig-object py" id="pyFTS.benchmarks.ResidualAnalysis.single_plot_residuals">
<span class="sig-prename descclassname"><span class="pre">pyFTS.benchmarks.ResidualAnalysis.</span></span><span class="sig-name descname"><span class="pre">single_plot_residuals</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">res</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">order</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tam</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">[10,</span> <span class="pre">7]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">save</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">file</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/ResidualAnalysis.html#single_plot_residuals"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.benchmarks.ResidualAnalysis.single_plot_residuals" title="Permalink to this definition"></a></dt>
2021-01-13 01:04:42 +04:00
<dd></dd></dl>
2018-08-30 23:04:52 +04:00
</div>
2021-01-13 01:04:42 +04:00
<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>
<dl class="py function">
2022-04-10 21:32:24 +04:00
<dt class="sig sig-object py" id="pyFTS.benchmarks.Tests.BoxLjungStatistic">
<span class="sig-prename descclassname"><span class="pre">pyFTS.benchmarks.Tests.</span></span><span class="sig-name descname"><span class="pre">BoxLjungStatistic</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">h</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Tests.html#BoxLjungStatistic"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.benchmarks.Tests.BoxLjungStatistic" title="Permalink to this definition"></a></dt>
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<dd><p>Q Statistic for LjungBox test</p>
<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">
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<dt class="sig sig-object py" id="pyFTS.benchmarks.Tests.BoxPierceStatistic">
<span class="sig-prename descclassname"><span class="pre">pyFTS.benchmarks.Tests.</span></span><span class="sig-name descname"><span class="pre">BoxPierceStatistic</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">h</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Tests.html#BoxPierceStatistic"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.benchmarks.Tests.BoxPierceStatistic" title="Permalink to this definition"></a></dt>
2021-01-13 01:04:42 +04:00
<dd><p>Q Statistic for Box-Pierce test</p>
<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">
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<dt class="sig sig-object py" id="pyFTS.benchmarks.Tests.format_experiment_table">
<span class="sig-prename descclassname"><span class="pre">pyFTS.benchmarks.Tests.</span></span><span class="sig-name descname"><span class="pre">format_experiment_table</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">df</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">exclude</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">[]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">replace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">{}</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">csv</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">std</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Tests.html#format_experiment_table"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.benchmarks.Tests.format_experiment_table" title="Permalink to this definition"></a></dt>
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<dd></dd></dl>
<dl class="py function">
2022-04-10 21:32:24 +04:00
<dt class="sig sig-object py" id="pyFTS.benchmarks.Tests.post_hoc_tests">
<span class="sig-prename descclassname"><span class="pre">pyFTS.benchmarks.Tests.</span></span><span class="sig-name descname"><span class="pre">post_hoc_tests</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">post_hoc</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">control_method</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'finner'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Tests.html#post_hoc_tests"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.benchmarks.Tests.post_hoc_tests" title="Permalink to this definition"></a></dt>
2021-01-13 01:04:42 +04:00
<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>
<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">
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<dt class="sig sig-object py" id="pyFTS.benchmarks.Tests.test_mean_equality">
<span class="sig-prename descclassname"><span class="pre">pyFTS.benchmarks.Tests.</span></span><span class="sig-name descname"><span class="pre">test_mean_equality</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tests</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'friedman'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Tests.html#test_mean_equality"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.benchmarks.Tests.test_mean_equality" title="Permalink to this definition"></a></dt>
2021-01-13 01:04:42 +04:00
<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>
<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>
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<div class="section" id="module-pyFTS.benchmarks.Util">
<span id="pyfts-benchmarks-util-module"></span><h2>pyFTS.benchmarks.Util module<a class="headerlink" href="#module-pyFTS.benchmarks.Util" title="Permalink to this headline"></a></h2>
<p>Facilities for pyFTS Benchmark module</p>
<dl class="py function">
2022-04-10 21:32:24 +04:00
<dt class="sig sig-object py" id="pyFTS.benchmarks.Util.analytic_tabular_dataframe">
<span class="sig-prename descclassname"><span class="pre">pyFTS.benchmarks.Util.</span></span><span class="sig-name descname"><span class="pre">analytic_tabular_dataframe</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataframe</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Util.html#analytic_tabular_dataframe"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.benchmarks.Util.analytic_tabular_dataframe" title="Permalink to this definition"></a></dt>
2021-01-13 01:04:42 +04:00
<dd></dd></dl>
<dl class="py function">
2022-04-10 21:32:24 +04:00
<dt class="sig sig-object py" id="pyFTS.benchmarks.Util.analytical_data_columns">
<span class="sig-prename descclassname"><span class="pre">pyFTS.benchmarks.Util.</span></span><span class="sig-name descname"><span class="pre">analytical_data_columns</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">experiments</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Util.html#analytical_data_columns"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.benchmarks.Util.analytical_data_columns" title="Permalink to this definition"></a></dt>
2021-01-13 01:04:42 +04:00
<dd></dd></dl>
<dl class="py function">
2022-04-10 21:32:24 +04:00
<dt class="sig sig-object py" id="pyFTS.benchmarks.Util.base_dataframe_columns">
<span class="sig-prename descclassname"><span class="pre">pyFTS.benchmarks.Util.</span></span><span class="sig-name descname"><span class="pre">base_dataframe_columns</span></span><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"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.benchmarks.Util.base_dataframe_columns" title="Permalink to this definition"></a></dt>
2021-01-13 01:04:42 +04:00
<dd></dd></dl>
<dl class="py function">
2022-04-10 21:32:24 +04:00
<dt class="sig sig-object py" id="pyFTS.benchmarks.Util.cast_dataframe_to_synthetic">
<span class="sig-prename descclassname"><span class="pre">pyFTS.benchmarks.Util.</span></span><span class="sig-name descname"><span class="pre">cast_dataframe_to_synthetic</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">infile</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">outfile</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">experiments</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">type</span></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"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.benchmarks.Util.cast_dataframe_to_synthetic" title="Permalink to this definition"></a></dt>
2021-01-13 01:04:42 +04:00
<dd></dd></dl>
<dl class="py function">
2022-04-10 21:32:24 +04:00
<dt class="sig sig-object py" id="pyFTS.benchmarks.Util.cast_dataframe_to_synthetic_interval">
<span class="sig-prename descclassname"><span class="pre">pyFTS.benchmarks.Util.</span></span><span class="sig-name descname"><span class="pre">cast_dataframe_to_synthetic_interval</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">df</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data_columns</span></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"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.benchmarks.Util.cast_dataframe_to_synthetic_interval" title="Permalink to this definition"></a></dt>
2021-01-13 01:04:42 +04:00
<dd></dd></dl>
<dl class="py function">
2022-04-10 21:32:24 +04:00
<dt class="sig sig-object py" id="pyFTS.benchmarks.Util.cast_dataframe_to_synthetic_point">
<span class="sig-prename descclassname"><span class="pre">pyFTS.benchmarks.Util.</span></span><span class="sig-name descname"><span class="pre">cast_dataframe_to_synthetic_point</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">df</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data_columns</span></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"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.benchmarks.Util.cast_dataframe_to_synthetic_point" title="Permalink to this definition"></a></dt>
2021-01-13 01:04:42 +04:00
<dd></dd></dl>
<dl class="py function">
2022-04-10 21:32:24 +04:00
<dt class="sig sig-object py" id="pyFTS.benchmarks.Util.cast_dataframe_to_synthetic_probabilistic">
<span class="sig-prename descclassname"><span class="pre">pyFTS.benchmarks.Util.</span></span><span class="sig-name descname"><span class="pre">cast_dataframe_to_synthetic_probabilistic</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">df</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data_columns</span></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"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.benchmarks.Util.cast_dataframe_to_synthetic_probabilistic" title="Permalink to this definition"></a></dt>
2021-01-13 01:04:42 +04:00
<dd></dd></dl>
<dl class="py function">
2022-04-10 21:32:24 +04:00
<dt class="sig sig-object py" id="pyFTS.benchmarks.Util.check_ignore_list">
<span class="sig-prename descclassname"><span class="pre">pyFTS.benchmarks.Util.</span></span><span class="sig-name descname"><span class="pre">check_ignore_list</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">b</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ignore</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Util.html#check_ignore_list"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.benchmarks.Util.check_ignore_list" title="Permalink to this definition"></a></dt>
2021-01-13 01:04:42 +04:00
<dd></dd></dl>
<dl class="py function">
2022-04-10 21:32:24 +04:00
<dt class="sig sig-object py" id="pyFTS.benchmarks.Util.check_replace_list">
<span class="sig-prename descclassname"><span class="pre">pyFTS.benchmarks.Util.</span></span><span class="sig-name descname"><span class="pre">check_replace_list</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">m</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">replace</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Util.html#check_replace_list"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.benchmarks.Util.check_replace_list" title="Permalink to this definition"></a></dt>
2021-01-13 01:04:42 +04:00
<dd></dd></dl>
<dl class="py function">
2022-04-10 21:32:24 +04:00
<dt class="sig sig-object py" id="pyFTS.benchmarks.Util.create_benchmark_tables">
<span class="sig-prename descclassname"><span class="pre">pyFTS.benchmarks.Util.</span></span><span class="sig-name descname"><span class="pre">create_benchmark_tables</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">conn</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Util.html#create_benchmark_tables"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.benchmarks.Util.create_benchmark_tables" title="Permalink to this definition"></a></dt>
2021-01-13 01:04:42 +04:00
<dd><p>Create a sqlite3 table designed to store benchmark results.</p>
<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>
<dl class="py function">
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<dt class="sig sig-object py" id="pyFTS.benchmarks.Util.extract_measure">
<span class="sig-prename descclassname"><span class="pre">pyFTS.benchmarks.Util.</span></span><span class="sig-name descname"><span class="pre">extract_measure</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataframe</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">measure</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data_columns</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Util.html#extract_measure"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.benchmarks.Util.extract_measure" title="Permalink to this definition"></a></dt>
2021-01-13 01:04:42 +04:00
<dd></dd></dl>
<dl class="py function">
2022-04-10 21:32:24 +04:00
<dt class="sig sig-object py" id="pyFTS.benchmarks.Util.find_best">
<span class="sig-prename descclassname"><span class="pre">pyFTS.benchmarks.Util.</span></span><span class="sig-name descname"><span class="pre">find_best</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataframe</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">criteria</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ascending</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Util.html#find_best"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.benchmarks.Util.find_best" title="Permalink to this definition"></a></dt>
2021-01-13 01:04:42 +04:00
<dd></dd></dl>
<dl class="py function">
2022-04-10 21:32:24 +04:00
<dt class="sig sig-object py" id="pyFTS.benchmarks.Util.get_dataframe_from_bd">
<span class="sig-prename descclassname"><span class="pre">pyFTS.benchmarks.Util.</span></span><span class="sig-name descname"><span class="pre">get_dataframe_from_bd</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">file</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">filter</span></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"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.benchmarks.Util.get_dataframe_from_bd" title="Permalink to this definition"></a></dt>
2021-01-13 01:04:42 +04:00
<dd><p>Query the sqlite benchmark database and return a pandas dataframe with the results</p>
<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">
2022-04-10 21:32:24 +04:00
<dt class="sig sig-object py" id="pyFTS.benchmarks.Util.insert_benchmark">
<span class="sig-prename descclassname"><span class="pre">pyFTS.benchmarks.Util.</span></span><span class="sig-name descname"><span class="pre">insert_benchmark</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">conn</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Util.html#insert_benchmark"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.benchmarks.Util.insert_benchmark" title="Permalink to this definition"></a></dt>
2021-01-13 01:04:42 +04:00
<dd><p>Insert benchmark data on database</p>
<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>
<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>
<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">
2022-04-10 21:32:24 +04:00
<dt class="sig sig-object py" id="pyFTS.benchmarks.Util.interval_dataframe_analytic_columns">
<span class="sig-prename descclassname"><span class="pre">pyFTS.benchmarks.Util.</span></span><span class="sig-name descname"><span class="pre">interval_dataframe_analytic_columns</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">experiments</span></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"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.benchmarks.Util.interval_dataframe_analytic_columns" title="Permalink to this definition"></a></dt>
2021-01-13 01:04:42 +04:00
<dd></dd></dl>
<dl class="py function">
2022-04-10 21:32:24 +04:00
<dt class="sig sig-object py" id="pyFTS.benchmarks.Util.interval_dataframe_synthetic_columns">
<span class="sig-prename descclassname"><span class="pre">pyFTS.benchmarks.Util.</span></span><span class="sig-name descname"><span class="pre">interval_dataframe_synthetic_columns</span></span><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"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.benchmarks.Util.interval_dataframe_synthetic_columns" title="Permalink to this definition"></a></dt>
2021-01-13 01:04:42 +04:00
<dd></dd></dl>
<dl class="py function">
2022-04-10 21:32:24 +04:00
<dt class="sig sig-object py" id="pyFTS.benchmarks.Util.open_benchmark_db">
<span class="sig-prename descclassname"><span class="pre">pyFTS.benchmarks.Util.</span></span><span class="sig-name descname"><span class="pre">open_benchmark_db</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Util.html#open_benchmark_db"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.benchmarks.Util.open_benchmark_db" title="Permalink to this definition"></a></dt>
2021-01-13 01:04:42 +04:00
<dd><p>Open a connection with a Sqlite database designed to store benchmark results.</p>
<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">
2022-04-10 21:32:24 +04:00
<dt class="sig sig-object py" id="pyFTS.benchmarks.Util.plot_dataframe_interval">
<span class="sig-prename descclassname"><span class="pre">pyFTS.benchmarks.Util.</span></span><span class="sig-name descname"><span class="pre">plot_dataframe_interval</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">file_synthetic</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">file_analytic</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">experiments</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tam</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">save</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">file</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sort_columns</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">['COVAVG',</span> <span class="pre">'SHARPAVG',</span> <span class="pre">'COVSTD',</span> <span class="pre">'SHARPSTD']</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sort_ascend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">[True,</span> <span class="pre">False,</span> <span class="pre">True,</span> <span class="pre">True]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">save_best</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ignore</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">replace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Util.html#plot_dataframe_interval"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.benchmarks.Util.plot_dataframe_interval" title="Permalink to this definition"></a></dt>
2021-01-13 01:04:42 +04:00
<dd></dd></dl>
<dl class="py function">
2022-04-10 21:32:24 +04:00
<dt class="sig sig-object py" id="pyFTS.benchmarks.Util.plot_dataframe_interval_pinball">
<span class="sig-prename descclassname"><span class="pre">pyFTS.benchmarks.Util.</span></span><span class="sig-name descname"><span class="pre">plot_dataframe_interval_pinball</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">file_synthetic</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">file_analytic</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">experiments</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tam</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">save</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">file</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sort_columns</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">['COVAVG',</span> <span class="pre">'SHARPAVG',</span> <span class="pre">'COVSTD',</span> <span class="pre">'SHARPSTD']</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sort_ascend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">[True,</span> <span class="pre">False,</span> <span class="pre">True,</span> <span class="pre">True]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">save_best</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ignore</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">replace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></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"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.benchmarks.Util.plot_dataframe_interval_pinball" title="Permalink to this definition"></a></dt>
2021-01-13 01:04:42 +04:00
<dd></dd></dl>
<dl class="py function">
2022-04-10 21:32:24 +04:00
<dt class="sig sig-object py" id="pyFTS.benchmarks.Util.plot_dataframe_point">
<span class="sig-prename descclassname"><span class="pre">pyFTS.benchmarks.Util.</span></span><span class="sig-name descname"><span class="pre">plot_dataframe_point</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">file_synthetic</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">file_analytic</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">experiments</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tam</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">save</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">file</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sort_columns</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">['UAVG',</span> <span class="pre">'RMSEAVG',</span> <span class="pre">'USTD',</span> <span class="pre">'RMSESTD']</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sort_ascend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">[1,</span> <span class="pre">1,</span> <span class="pre">1,</span> <span class="pre">1]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">save_best</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ignore</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">replace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Util.html#plot_dataframe_point"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.benchmarks.Util.plot_dataframe_point" title="Permalink to this definition"></a></dt>
2021-01-13 01:04:42 +04:00
<dd></dd></dl>
<dl class="py function">
2022-04-10 21:32:24 +04:00
<dt class="sig sig-object py" id="pyFTS.benchmarks.Util.plot_dataframe_probabilistic">
<span class="sig-prename descclassname"><span class="pre">pyFTS.benchmarks.Util.</span></span><span class="sig-name descname"><span class="pre">plot_dataframe_probabilistic</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">file_synthetic</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">file_analytic</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">experiments</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tam</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">save</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">file</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sort_columns</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">['CRPS1AVG',</span> <span class="pre">'CRPS2AVG',</span> <span class="pre">'CRPS1STD',</span> <span class="pre">'CRPS2STD']</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sort_ascend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">[True,</span> <span class="pre">True,</span> <span class="pre">True,</span> <span class="pre">True]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">save_best</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ignore</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">replace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Util.html#plot_dataframe_probabilistic"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.benchmarks.Util.plot_dataframe_probabilistic" title="Permalink to this definition"></a></dt>
2021-01-13 01:04:42 +04:00
<dd></dd></dl>
<dl class="py function">
2022-04-10 21:32:24 +04:00
<dt class="sig sig-object py" id="pyFTS.benchmarks.Util.point_dataframe_analytic_columns">
<span class="sig-prename descclassname"><span class="pre">pyFTS.benchmarks.Util.</span></span><span class="sig-name descname"><span class="pre">point_dataframe_analytic_columns</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">experiments</span></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"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.benchmarks.Util.point_dataframe_analytic_columns" title="Permalink to this definition"></a></dt>
2021-01-13 01:04:42 +04:00
<dd></dd></dl>
<dl class="py function">
2022-04-10 21:32:24 +04:00
<dt class="sig sig-object py" id="pyFTS.benchmarks.Util.point_dataframe_synthetic_columns">
<span class="sig-prename descclassname"><span class="pre">pyFTS.benchmarks.Util.</span></span><span class="sig-name descname"><span class="pre">point_dataframe_synthetic_columns</span></span><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"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.benchmarks.Util.point_dataframe_synthetic_columns" title="Permalink to this definition"></a></dt>
2021-01-13 01:04:42 +04:00
<dd></dd></dl>
<dl class="py function">
2022-04-10 21:32:24 +04:00
<dt class="sig sig-object py" id="pyFTS.benchmarks.Util.probabilistic_dataframe_analytic_columns">
<span class="sig-prename descclassname"><span class="pre">pyFTS.benchmarks.Util.</span></span><span class="sig-name descname"><span class="pre">probabilistic_dataframe_analytic_columns</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">experiments</span></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"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.benchmarks.Util.probabilistic_dataframe_analytic_columns" title="Permalink to this definition"></a></dt>
2021-01-13 01:04:42 +04:00
<dd></dd></dl>
<dl class="py function">
2022-04-10 21:32:24 +04:00
<dt class="sig sig-object py" id="pyFTS.benchmarks.Util.probabilistic_dataframe_synthetic_columns">
<span class="sig-prename descclassname"><span class="pre">pyFTS.benchmarks.Util.</span></span><span class="sig-name descname"><span class="pre">probabilistic_dataframe_synthetic_columns</span></span><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"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.benchmarks.Util.probabilistic_dataframe_synthetic_columns" title="Permalink to this definition"></a></dt>
2021-01-13 01:04:42 +04:00
<dd></dd></dl>
<dl class="py function">
2022-04-10 21:32:24 +04:00
<dt class="sig sig-object py" id="pyFTS.benchmarks.Util.process_common_data">
<span class="sig-prename descclassname"><span class="pre">pyFTS.benchmarks.Util.</span></span><span class="sig-name descname"><span class="pre">process_common_data</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tag</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">type</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">job</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Util.html#process_common_data"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.benchmarks.Util.process_common_data" title="Permalink to this definition"></a></dt>
2021-01-13 01:04:42 +04:00
<dd><p>Wraps benchmark information on a tuple for sqlite database</p>
<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">
2022-04-10 21:32:24 +04:00
<dt class="sig sig-object py" id="pyFTS.benchmarks.Util.process_common_data2">
<span class="sig-prename descclassname"><span class="pre">pyFTS.benchmarks.Util.</span></span><span class="sig-name descname"><span class="pre">process_common_data2</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tag</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">type</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">job</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Util.html#process_common_data2"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.benchmarks.Util.process_common_data2" title="Permalink to this definition"></a></dt>
2021-01-13 01:04:42 +04:00
<dd><p>Wraps benchmark information on a tuple for sqlite database</p>
<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">
2022-04-10 21:32:24 +04:00
<dt class="sig sig-object py" id="pyFTS.benchmarks.Util.save_dataframe_interval">
<span class="sig-prename descclassname"><span class="pre">pyFTS.benchmarks.Util.</span></span><span class="sig-name descname"><span class="pre">save_dataframe_interval</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">coverage</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">experiments</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">file</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">objs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">resolution</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">save</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sharpness</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">synthetic</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">times</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">q05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">q25</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">q75</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">q95</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">steps</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">method</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Util.html#save_dataframe_interval"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.benchmarks.Util.save_dataframe_interval" title="Permalink to this definition"></a></dt>
2021-01-13 01:04:42 +04:00
<dd></dd></dl>
<dl class="py function">
2022-04-10 21:32:24 +04:00
<dt class="sig sig-object py" id="pyFTS.benchmarks.Util.save_dataframe_point">
<span class="sig-prename descclassname"><span class="pre">pyFTS.benchmarks.Util.</span></span><span class="sig-name descname"><span class="pre">save_dataframe_point</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">experiments</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">file</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">objs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rmse</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">save</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">synthetic</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">smape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">times</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">u</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">steps</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">method</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Util.html#save_dataframe_point"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.benchmarks.Util.save_dataframe_point" title="Permalink to this definition"></a></dt>
2021-01-13 01:04:42 +04:00
<dd><p>Create a dataframe to store the benchmark results</p>
<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">
2022-04-10 21:32:24 +04:00
<dt class="sig sig-object py" id="pyFTS.benchmarks.Util.save_dataframe_probabilistic">
<span class="sig-prename descclassname"><span class="pre">pyFTS.benchmarks.Util.</span></span><span class="sig-name descname"><span class="pre">save_dataframe_probabilistic</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">experiments</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">file</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">objs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">crps</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">times</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">save</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">synthetic</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">steps</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">method</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Util.html#save_dataframe_probabilistic"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.benchmarks.Util.save_dataframe_probabilistic" title="Permalink to this definition"></a></dt>
2021-01-13 01:04:42 +04:00
<dd><p>Save benchmark results for m-step ahead probabilistic forecasters
:param experiments:
:param file:
:param objs:
:param crps_interval:
:param crps_distr:
:param times:
:param times2:
:param save:
:param synthetic:
:return:</p>
</dd></dl>
<dl class="py function">
2022-04-10 21:32:24 +04:00
<dt class="sig sig-object py" id="pyFTS.benchmarks.Util.scale">
<span class="sig-prename descclassname"><span class="pre">pyFTS.benchmarks.Util.</span></span><span class="sig-name descname"><span class="pre">scale</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Util.html#scale"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.benchmarks.Util.scale" title="Permalink to this definition"></a></dt>
2021-01-13 01:04:42 +04:00
<dd></dd></dl>
<dl class="py function">
2022-04-10 21:32:24 +04:00
<dt class="sig sig-object py" id="pyFTS.benchmarks.Util.scale_params">
<span class="sig-prename descclassname"><span class="pre">pyFTS.benchmarks.Util.</span></span><span class="sig-name descname"><span class="pre">scale_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Util.html#scale_params"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.benchmarks.Util.scale_params" title="Permalink to this definition"></a></dt>
2021-01-13 01:04:42 +04:00
<dd></dd></dl>
<dl class="py function">
2022-04-10 21:32:24 +04:00
<dt class="sig sig-object py" id="pyFTS.benchmarks.Util.simple_synthetic_dataframe">
<span class="sig-prename descclassname"><span class="pre">pyFTS.benchmarks.Util.</span></span><span class="sig-name descname"><span class="pre">simple_synthetic_dataframe</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">file</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tag</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">measure</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sql</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Util.html#simple_synthetic_dataframe"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.benchmarks.Util.simple_synthetic_dataframe" title="Permalink to this definition"></a></dt>
2021-01-13 01:04:42 +04:00
<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>
<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">
2022-04-10 21:32:24 +04:00
<dt class="sig sig-object py" id="pyFTS.benchmarks.Util.stats">
<span class="sig-prename descclassname"><span class="pre">pyFTS.benchmarks.Util.</span></span><span class="sig-name descname"><span class="pre">stats</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">measure</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Util.html#stats"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.benchmarks.Util.stats" title="Permalink to this definition"></a></dt>
2021-01-13 01:04:42 +04:00
<dd></dd></dl>
<dl class="py function">
2022-04-10 21:32:24 +04:00
<dt class="sig sig-object py" id="pyFTS.benchmarks.Util.tabular_dataframe_columns">
<span class="sig-prename descclassname"><span class="pre">pyFTS.benchmarks.Util.</span></span><span class="sig-name descname"><span class="pre">tabular_dataframe_columns</span></span><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"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.benchmarks.Util.tabular_dataframe_columns" title="Permalink to this definition"></a></dt>
2021-01-13 01:04:42 +04:00
<dd></dd></dl>
<dl class="py function">
2022-04-10 21:32:24 +04:00
<dt class="sig sig-object py" id="pyFTS.benchmarks.Util.unified_scaled_interval">
<span class="sig-prename descclassname"><span class="pre">pyFTS.benchmarks.Util.</span></span><span class="sig-name descname"><span class="pre">unified_scaled_interval</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">experiments</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tam</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">save</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">file</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sort_columns</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">['COVAVG',</span> <span class="pre">'SHARPAVG',</span> <span class="pre">'COVSTD',</span> <span class="pre">'SHARPSTD']</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sort_ascend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">[True,</span> <span class="pre">False,</span> <span class="pre">True,</span> <span class="pre">True]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">save_best</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ignore</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">replace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Util.html#unified_scaled_interval"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.benchmarks.Util.unified_scaled_interval" title="Permalink to this definition"></a></dt>
2021-01-13 01:04:42 +04:00
<dd></dd></dl>
<dl class="py function">
2022-04-10 21:32:24 +04:00
<dt class="sig sig-object py" id="pyFTS.benchmarks.Util.unified_scaled_interval_pinball">
<span class="sig-prename descclassname"><span class="pre">pyFTS.benchmarks.Util.</span></span><span class="sig-name descname"><span class="pre">unified_scaled_interval_pinball</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">experiments</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tam</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">save</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">file</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sort_columns</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">['COVAVG',</span> <span class="pre">'SHARPAVG',</span> <span class="pre">'COVSTD',</span> <span class="pre">'SHARPSTD']</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sort_ascend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">[True,</span> <span class="pre">False,</span> <span class="pre">True,</span> <span class="pre">True]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">save_best</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ignore</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">replace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></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"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.benchmarks.Util.unified_scaled_interval_pinball" title="Permalink to this definition"></a></dt>
2021-01-13 01:04:42 +04:00
<dd></dd></dl>
<dl class="py function">
2022-04-10 21:32:24 +04:00
<dt class="sig sig-object py" id="pyFTS.benchmarks.Util.unified_scaled_point">
<span class="sig-prename descclassname"><span class="pre">pyFTS.benchmarks.Util.</span></span><span class="sig-name descname"><span class="pre">unified_scaled_point</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">experiments</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tam</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">save</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">file</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sort_columns</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">['UAVG',</span> <span class="pre">'RMSEAVG',</span> <span class="pre">'USTD',</span> <span class="pre">'RMSESTD']</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sort_ascend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">[1,</span> <span class="pre">1,</span> <span class="pre">1,</span> <span class="pre">1]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">save_best</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ignore</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">replace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Util.html#unified_scaled_point"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.benchmarks.Util.unified_scaled_point" title="Permalink to this definition"></a></dt>
2021-01-13 01:04:42 +04:00
<dd></dd></dl>
<dl class="py function">
2022-04-10 21:32:24 +04:00
<dt class="sig sig-object py" id="pyFTS.benchmarks.Util.unified_scaled_probabilistic">
<span class="sig-prename descclassname"><span class="pre">pyFTS.benchmarks.Util.</span></span><span class="sig-name descname"><span class="pre">unified_scaled_probabilistic</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">experiments</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tam</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">save</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">file</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sort_columns</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">['CRPSAVG',</span> <span class="pre">'CRPSSTD']</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sort_ascend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">[True,</span> <span class="pre">True]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">save_best</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ignore</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">replace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/Util.html#unified_scaled_probabilistic"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.benchmarks.Util.unified_scaled_probabilistic" title="Permalink to this definition"></a></dt>
2021-01-13 01:04:42 +04:00
<dd></dd></dl>
2018-08-30 23:04:52 +04:00
</div>
2021-01-13 01:04:42 +04:00
<div class="section" id="module-pyFTS.benchmarks.arima">
<span id="pyfts-benchmarks-arima-module"></span><h2>pyFTS.benchmarks.arima module<a class="headerlink" href="#module-pyFTS.benchmarks.arima" title="Permalink to this headline"></a></h2>
<dl class="py class">
2022-04-10 21:32:24 +04:00
<dt class="sig sig-object py" id="pyFTS.benchmarks.arima.ARIMA">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">pyFTS.benchmarks.arima.</span></span><span class="sig-name descname"><span class="pre">ARIMA</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/arima.html#ARIMA"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.benchmarks.arima.ARIMA" title="Permalink to this definition"></a></dt>
2021-01-13 01:04:42 +04:00
<dd><p>Bases: <a class="reference internal" href="pyFTS.common.html#pyFTS.common.fts.FTS" title="pyFTS.common.fts.FTS"><code class="xref py py-class docutils literal notranslate"><span class="pre">pyFTS.common.fts.FTS</span></code></a></p>
<p>Façade for statsmodels.tsa.arima_model</p>
<dl class="py method">
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<dt class="sig sig-object py" id="pyFTS.benchmarks.arima.ARIMA.ar">
<span class="sig-name descname"><span class="pre">ar</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/arima.html#ARIMA.ar"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.benchmarks.arima.ARIMA.ar" title="Permalink to this definition"></a></dt>
2021-01-13 01:04:42 +04:00
<dd></dd></dl>
<dl class="py method">
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<dt class="sig sig-object py" id="pyFTS.benchmarks.arima.ARIMA.forecast">
<span class="sig-name descname"><span class="pre">forecast</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ndata</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/arima.html#ARIMA.forecast"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.benchmarks.arima.ARIMA.forecast" title="Permalink to this definition"></a></dt>
2021-01-13 01:04:42 +04:00
<dd><p>Point forecast one step ahead</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>data</strong> time series data with the minimal length equal to the max_lag of the model</p></li>
<li><p><strong>kwargs</strong> model specific parameters</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>a list with the forecasted values</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
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<dt class="sig sig-object py" id="pyFTS.benchmarks.arima.ARIMA.forecast_ahead_distribution">
<span class="sig-name descname"><span class="pre">forecast_ahead_distribution</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">steps</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></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"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.benchmarks.arima.ARIMA.forecast_ahead_distribution" title="Permalink to this definition"></a></dt>
2021-01-13 01:04:42 +04:00
<dd><p>Probabilistic forecast from 1 to H steps ahead, where H is given by the steps parameter</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>data</strong> time series data with the minimal length equal to the max_lag of the model</p></li>
<li><p><strong>steps</strong> the number of steps ahead to forecast</p></li>
<li><p><strong>start_at</strong> in the multi step forecasting, the index of the data where to start forecasting (default: 0)</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>a list with the forecasted Probability Distributions</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
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<dt class="sig sig-object py" id="pyFTS.benchmarks.arima.ARIMA.forecast_ahead_interval">
<span class="sig-name descname"><span class="pre">forecast_ahead_interval</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ndata</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">steps</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></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"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.benchmarks.arima.ARIMA.forecast_ahead_interval" title="Permalink to this definition"></a></dt>
2021-01-13 01:04:42 +04:00
<dd><p>Interval forecast from 1 to H steps ahead, where H is given by the steps parameter</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>data</strong> time series data with the minimal length equal to the max_lag of the model</p></li>
<li><p><strong>steps</strong> the number of steps ahead to forecast</p></li>
<li><p><strong>start_at</strong> in the multi step forecasting, the index of the data where to start forecasting (default: 0)</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>a list with the forecasted intervals</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
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<dt class="sig sig-object py" id="pyFTS.benchmarks.arima.ARIMA.forecast_distribution">
<span class="sig-name descname"><span class="pre">forecast_distribution</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/arima.html#ARIMA.forecast_distribution"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.benchmarks.arima.ARIMA.forecast_distribution" title="Permalink to this definition"></a></dt>
2021-01-13 01:04:42 +04:00
<dd><p>Probabilistic forecast one step ahead</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>data</strong> time series data with the minimal length equal to the max_lag of the model</p></li>
<li><p><strong>kwargs</strong> model specific parameters</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>a list with probabilistic.ProbabilityDistribution objects representing the forecasted Probability Distributions</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
2022-04-10 21:32:24 +04:00
<dt class="sig sig-object py" id="pyFTS.benchmarks.arima.ARIMA.forecast_interval">
<span class="sig-name descname"><span class="pre">forecast_interval</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/arima.html#ARIMA.forecast_interval"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.benchmarks.arima.ARIMA.forecast_interval" title="Permalink to this definition"></a></dt>
2021-01-13 01:04:42 +04:00
<dd><p>Interval forecast one step ahead</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>data</strong> time series data with the minimal length equal to the max_lag of the model</p></li>
<li><p><strong>kwargs</strong> model specific parameters</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>a list with the prediction intervals</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
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<dt class="sig sig-object py" id="pyFTS.benchmarks.arima.ARIMA.ma">
<span class="sig-name descname"><span class="pre">ma</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/arima.html#ARIMA.ma"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.benchmarks.arima.ARIMA.ma" title="Permalink to this definition"></a></dt>
2021-01-13 01:04:42 +04:00
<dd></dd></dl>
<dl class="py method">
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<dt class="sig sig-object py" id="pyFTS.benchmarks.arima.ARIMA.train">
<span class="sig-name descname"><span class="pre">train</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/arima.html#ARIMA.train"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.benchmarks.arima.ARIMA.train" title="Permalink to this definition"></a></dt>
2021-01-13 01:04:42 +04:00
<dd><p>Method specific parameter fitting</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>data</strong> training time series data</p></li>
<li><p><strong>kwargs</strong> Method specific parameters</p></li>
</ul>
</dd>
</dl>
</dd></dl>
</dd></dl>
</div>
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<div class="section" id="module-pyFTS.benchmarks.knn">
<span id="pyfts-benchmarks-knn-module"></span><h2>pyFTS.benchmarks.knn module<a class="headerlink" href="#module-pyFTS.benchmarks.knn" title="Permalink to this headline"></a></h2>
<dl class="py class">
2022-04-10 21:32:24 +04:00
<dt class="sig sig-object py" id="pyFTS.benchmarks.knn.KNearestNeighbors">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">pyFTS.benchmarks.knn.</span></span><span class="sig-name descname"><span class="pre">KNearestNeighbors</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/knn.html#KNearestNeighbors"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.benchmarks.knn.KNearestNeighbors" title="Permalink to this definition"></a></dt>
2021-01-13 01:04:42 +04:00
<dd><p>Bases: <a class="reference internal" href="pyFTS.common.html#pyFTS.common.fts.FTS" title="pyFTS.common.fts.FTS"><code class="xref py py-class docutils literal notranslate"><span class="pre">pyFTS.common.fts.FTS</span></code></a></p>
<p>A façade for sklearn.neighbors</p>
<dl class="py method">
2022-04-10 21:32:24 +04:00
<dt class="sig sig-object py" id="pyFTS.benchmarks.knn.KNearestNeighbors.forecast">
<span class="sig-name descname"><span class="pre">forecast</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/knn.html#KNearestNeighbors.forecast"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.benchmarks.knn.KNearestNeighbors.forecast" title="Permalink to this definition"></a></dt>
2021-01-13 01:04:42 +04:00
<dd><p>Point forecast one step ahead</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>data</strong> time series data with the minimal length equal to the max_lag of the model</p></li>
<li><p><strong>kwargs</strong> model specific parameters</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>a list with the forecasted values</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
2022-04-10 21:32:24 +04:00
<dt class="sig sig-object py" id="pyFTS.benchmarks.knn.KNearestNeighbors.forecast_ahead_distribution">
<span class="sig-name descname"><span class="pre">forecast_ahead_distribution</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">steps</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></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"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.benchmarks.knn.KNearestNeighbors.forecast_ahead_distribution" title="Permalink to this definition"></a></dt>
2021-01-13 01:04:42 +04:00
<dd><p>Probabilistic forecast from 1 to H steps ahead, where H is given by the steps parameter</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>data</strong> time series data with the minimal length equal to the max_lag of the model</p></li>
<li><p><strong>steps</strong> the number of steps ahead to forecast</p></li>
<li><p><strong>start_at</strong> in the multi step forecasting, the index of the data where to start forecasting (default: 0)</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>a list with the forecasted Probability Distributions</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
2022-04-10 21:32:24 +04:00
<dt class="sig sig-object py" id="pyFTS.benchmarks.knn.KNearestNeighbors.forecast_ahead_interval">
<span class="sig-name descname"><span class="pre">forecast_ahead_interval</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">steps</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></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"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.benchmarks.knn.KNearestNeighbors.forecast_ahead_interval" title="Permalink to this definition"></a></dt>
2021-01-13 01:04:42 +04:00
<dd><p>Interval forecast from 1 to H steps ahead, where H is given by the steps parameter</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>data</strong> time series data with the minimal length equal to the max_lag of the model</p></li>
<li><p><strong>steps</strong> the number of steps ahead to forecast</p></li>
<li><p><strong>start_at</strong> in the multi step forecasting, the index of the data where to start forecasting (default: 0)</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>a list with the forecasted intervals</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
2022-04-10 21:32:24 +04:00
<dt class="sig sig-object py" id="pyFTS.benchmarks.knn.KNearestNeighbors.forecast_distribution">
<span class="sig-name descname"><span class="pre">forecast_distribution</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/knn.html#KNearestNeighbors.forecast_distribution"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.benchmarks.knn.KNearestNeighbors.forecast_distribution" title="Permalink to this definition"></a></dt>
2021-01-13 01:04:42 +04:00
<dd><p>Probabilistic forecast one step ahead</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>data</strong> time series data with the minimal length equal to the max_lag of the model</p></li>
<li><p><strong>kwargs</strong> model specific parameters</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>a list with probabilistic.ProbabilityDistribution objects representing the forecasted Probability Distributions</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
2022-04-10 21:32:24 +04:00
<dt class="sig sig-object py" id="pyFTS.benchmarks.knn.KNearestNeighbors.forecast_interval">
<span class="sig-name descname"><span class="pre">forecast_interval</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/knn.html#KNearestNeighbors.forecast_interval"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.benchmarks.knn.KNearestNeighbors.forecast_interval" title="Permalink to this definition"></a></dt>
2021-01-13 01:04:42 +04:00
<dd><p>Interval forecast one step ahead</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>data</strong> time series data with the minimal length equal to the max_lag of the model</p></li>
<li><p><strong>kwargs</strong> model specific parameters</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>a list with the prediction intervals</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
2022-04-10 21:32:24 +04:00
<dt class="sig sig-object py" id="pyFTS.benchmarks.knn.KNearestNeighbors.knn">
<span class="sig-name descname"><span class="pre">knn</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/knn.html#KNearestNeighbors.knn"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.benchmarks.knn.KNearestNeighbors.knn" title="Permalink to this definition"></a></dt>
2021-01-13 01:04:42 +04:00
<dd></dd></dl>
<dl class="py method">
2022-04-10 21:32:24 +04:00
<dt class="sig sig-object py" id="pyFTS.benchmarks.knn.KNearestNeighbors.train">
<span class="sig-name descname"><span class="pre">train</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/knn.html#KNearestNeighbors.train"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.benchmarks.knn.KNearestNeighbors.train" title="Permalink to this definition"></a></dt>
2021-01-13 01:04:42 +04:00
<dd><p>Method specific parameter fitting</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>data</strong> training time series data</p></li>
<li><p><strong>kwargs</strong> Method specific parameters</p></li>
</ul>
</dd>
</dl>
</dd></dl>
</dd></dl>
</div>
2021-01-13 01:04:42 +04:00
<div class="section" id="module-pyFTS.benchmarks.naive">
<span id="pyfts-benchmarks-naive-module"></span><h2>pyFTS.benchmarks.naive module<a class="headerlink" href="#module-pyFTS.benchmarks.naive" title="Permalink to this headline"></a></h2>
<dl class="py class">
2022-04-10 21:32:24 +04:00
<dt class="sig sig-object py" id="pyFTS.benchmarks.naive.Naive">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">pyFTS.benchmarks.naive.</span></span><span class="sig-name descname"><span class="pre">Naive</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/naive.html#Naive"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.benchmarks.naive.Naive" title="Permalink to this definition"></a></dt>
2021-01-13 01:04:42 +04:00
<dd><p>Bases: <a class="reference internal" href="pyFTS.common.html#pyFTS.common.fts.FTS" title="pyFTS.common.fts.FTS"><code class="xref py py-class docutils literal notranslate"><span class="pre">pyFTS.common.fts.FTS</span></code></a></p>
<p>Naïve Forecasting method</p>
<dl class="py method">
2022-04-10 21:32:24 +04:00
<dt class="sig sig-object py" id="pyFTS.benchmarks.naive.Naive.forecast">
<span class="sig-name descname"><span class="pre">forecast</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/naive.html#Naive.forecast"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.benchmarks.naive.Naive.forecast" title="Permalink to this definition"></a></dt>
2021-01-13 01:04:42 +04:00
<dd><p>Point forecast one step ahead</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>data</strong> time series data with the minimal length equal to the max_lag of the model</p></li>
<li><p><strong>kwargs</strong> model specific parameters</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>a list with the forecasted values</p>
</dd>
</dl>
</dd></dl>
</dd></dl>
</div>
2021-01-13 01:04:42 +04:00
<div class="section" id="module-pyFTS.benchmarks.quantreg">
<span id="pyfts-benchmarks-quantreg-module"></span><h2>pyFTS.benchmarks.quantreg module<a class="headerlink" href="#module-pyFTS.benchmarks.quantreg" title="Permalink to this headline"></a></h2>
<dl class="py class">
2022-04-10 21:32:24 +04:00
<dt class="sig sig-object py" id="pyFTS.benchmarks.quantreg.QuantileRegression">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">pyFTS.benchmarks.quantreg.</span></span><span class="sig-name descname"><span class="pre">QuantileRegression</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/quantreg.html#QuantileRegression"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.benchmarks.quantreg.QuantileRegression" title="Permalink to this definition"></a></dt>
2021-01-13 01:04:42 +04:00
<dd><p>Bases: <a class="reference internal" href="pyFTS.common.html#pyFTS.common.fts.FTS" title="pyFTS.common.fts.FTS"><code class="xref py py-class docutils literal notranslate"><span class="pre">pyFTS.common.fts.FTS</span></code></a></p>
<p>Façade for statsmodels.regression.quantile_regression</p>
<dl class="py method">
2022-04-10 21:32:24 +04:00
<dt class="sig sig-object py" id="pyFTS.benchmarks.quantreg.QuantileRegression.forecast">
<span class="sig-name descname"><span class="pre">forecast</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ndata</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/quantreg.html#QuantileRegression.forecast"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.benchmarks.quantreg.QuantileRegression.forecast" title="Permalink to this definition"></a></dt>
2021-01-13 01:04:42 +04:00
<dd><p>Point forecast one step ahead</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>data</strong> time series data with the minimal length equal to the max_lag of the model</p></li>
<li><p><strong>kwargs</strong> model specific parameters</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>a list with the forecasted values</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
2022-04-10 21:32:24 +04:00
<dt class="sig sig-object py" id="pyFTS.benchmarks.quantreg.QuantileRegression.forecast_ahead_distribution">
<span class="sig-name descname"><span class="pre">forecast_ahead_distribution</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ndata</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">steps</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></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"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.benchmarks.quantreg.QuantileRegression.forecast_ahead_distribution" title="Permalink to this definition"></a></dt>
2021-01-13 01:04:42 +04:00
<dd><p>Probabilistic forecast from 1 to H steps ahead, where H is given by the steps parameter</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>data</strong> time series data with the minimal length equal to the max_lag of the model</p></li>
<li><p><strong>steps</strong> the number of steps ahead to forecast</p></li>
<li><p><strong>start_at</strong> in the multi step forecasting, the index of the data where to start forecasting (default: 0)</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>a list with the forecasted Probability Distributions</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
2022-04-10 21:32:24 +04:00
<dt class="sig sig-object py" id="pyFTS.benchmarks.quantreg.QuantileRegression.forecast_ahead_interval">
<span class="sig-name descname"><span class="pre">forecast_ahead_interval</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ndata</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">steps</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></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"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.benchmarks.quantreg.QuantileRegression.forecast_ahead_interval" title="Permalink to this definition"></a></dt>
2021-01-13 01:04:42 +04:00
<dd><p>Interval forecast from 1 to H steps ahead, where H is given by the steps parameter</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>data</strong> time series data with the minimal length equal to the max_lag of the model</p></li>
<li><p><strong>steps</strong> the number of steps ahead to forecast</p></li>
<li><p><strong>start_at</strong> in the multi step forecasting, the index of the data where to start forecasting (default: 0)</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>a list with the forecasted intervals</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
2022-04-10 21:32:24 +04:00
<dt class="sig sig-object py" id="pyFTS.benchmarks.quantreg.QuantileRegression.forecast_distribution">
<span class="sig-name descname"><span class="pre">forecast_distribution</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ndata</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/quantreg.html#QuantileRegression.forecast_distribution"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.benchmarks.quantreg.QuantileRegression.forecast_distribution" title="Permalink to this definition"></a></dt>
2021-01-13 01:04:42 +04:00
<dd><p>Probabilistic forecast one step ahead</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>data</strong> time series data with the minimal length equal to the max_lag of the model</p></li>
<li><p><strong>kwargs</strong> model specific parameters</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>a list with probabilistic.ProbabilityDistribution objects representing the forecasted Probability Distributions</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
2022-04-10 21:32:24 +04:00
<dt class="sig sig-object py" id="pyFTS.benchmarks.quantreg.QuantileRegression.forecast_interval">
<span class="sig-name descname"><span class="pre">forecast_interval</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ndata</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/quantreg.html#QuantileRegression.forecast_interval"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.benchmarks.quantreg.QuantileRegression.forecast_interval" title="Permalink to this definition"></a></dt>
2021-01-13 01:04:42 +04:00
<dd><p>Interval forecast one step ahead</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>data</strong> time series data with the minimal length equal to the max_lag of the model</p></li>
<li><p><strong>kwargs</strong> model specific parameters</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>a list with the prediction intervals</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
2022-04-10 21:32:24 +04:00
<dt class="sig sig-object py" id="pyFTS.benchmarks.quantreg.QuantileRegression.interval_to_interval">
<span class="sig-name descname"><span class="pre">interval_to_interval</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lo_params</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">up_params</span></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"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.benchmarks.quantreg.QuantileRegression.interval_to_interval" title="Permalink to this definition"></a></dt>
2021-01-13 01:04:42 +04:00
<dd></dd></dl>
<dl class="py method">
2022-04-10 21:32:24 +04:00
<dt class="sig sig-object py" id="pyFTS.benchmarks.quantreg.QuantileRegression.linearmodel">
<span class="sig-name descname"><span class="pre">linearmodel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/quantreg.html#QuantileRegression.linearmodel"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.benchmarks.quantreg.QuantileRegression.linearmodel" title="Permalink to this definition"></a></dt>
2021-01-13 01:04:42 +04:00
<dd></dd></dl>
<dl class="py method">
2022-04-10 21:32:24 +04:00
<dt class="sig sig-object py" id="pyFTS.benchmarks.quantreg.QuantileRegression.point_to_interval">
<span class="sig-name descname"><span class="pre">point_to_interval</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lo_params</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">up_params</span></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"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.benchmarks.quantreg.QuantileRegression.point_to_interval" title="Permalink to this definition"></a></dt>
2021-01-13 01:04:42 +04:00
<dd></dd></dl>
<dl class="py method">
2022-04-10 21:32:24 +04:00
<dt class="sig sig-object py" id="pyFTS.benchmarks.quantreg.QuantileRegression.train">
<span class="sig-name descname"><span class="pre">train</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/quantreg.html#QuantileRegression.train"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.benchmarks.quantreg.QuantileRegression.train" title="Permalink to this definition"></a></dt>
2021-01-13 01:04:42 +04:00
<dd><p>Method specific parameter fitting</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>data</strong> training time series data</p></li>
<li><p><strong>kwargs</strong> Method specific parameters</p></li>
</ul>
</dd>
</dl>
</dd></dl>
</dd></dl>
</div>
2020-08-19 00:06:41 +04:00
<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>
2021-01-13 01:04:42 +04:00
<div class="section" id="module-pyFTS.benchmarks.BSTS">
<span id="pyfts-benchmarks-bsts-module"></span><h2>pyFTS.benchmarks.BSTS module<a class="headerlink" href="#module-pyFTS.benchmarks.BSTS" title="Permalink to this headline"></a></h2>
<dl class="py class">
2022-04-10 21:32:24 +04:00
<dt class="sig sig-object py" id="pyFTS.benchmarks.BSTS.ARIMA">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">pyFTS.benchmarks.BSTS.</span></span><span class="sig-name descname"><span class="pre">ARIMA</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/BSTS.html#ARIMA"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.benchmarks.BSTS.ARIMA" title="Permalink to this definition"></a></dt>
2021-01-13 01:04:42 +04:00
<dd><p>Bases: <a class="reference internal" href="pyFTS.common.html#pyFTS.common.fts.FTS" title="pyFTS.common.fts.FTS"><code class="xref py py-class docutils literal notranslate"><span class="pre">pyFTS.common.fts.FTS</span></code></a></p>
<p>Façade for statsmodels.tsa.arima_model</p>
<dl class="py method">
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<dt class="sig sig-object py" id="pyFTS.benchmarks.BSTS.ARIMA.forecast">
<span class="sig-name descname"><span class="pre">forecast</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ndata</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/BSTS.html#ARIMA.forecast"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.benchmarks.BSTS.ARIMA.forecast" title="Permalink to this definition"></a></dt>
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<dd><p>Point forecast one step ahead</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>data</strong> time series data with the minimal length equal to the max_lag of the model</p></li>
<li><p><strong>kwargs</strong> model specific parameters</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>a list with the forecasted values</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
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<dt class="sig sig-object py" id="pyFTS.benchmarks.BSTS.ARIMA.forecast_ahead">
<span class="sig-name descname"><span class="pre">forecast_ahead</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">steps</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/BSTS.html#ARIMA.forecast_ahead"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.benchmarks.BSTS.ARIMA.forecast_ahead" title="Permalink to this definition"></a></dt>
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<dd><p>Point forecast from 1 to H steps ahead, where H is given by the steps parameter</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>data</strong> time series data with the minimal length equal to the max_lag of the model</p></li>
<li><p><strong>steps</strong> the number of steps ahead to forecast (default: 1)</p></li>
<li><p><strong>start_at</strong> in the multi step forecasting, the index of the data where to start forecasting (default: 0)</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>a list with the forecasted values</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
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<dt class="sig sig-object py" id="pyFTS.benchmarks.BSTS.ARIMA.forecast_ahead_distribution">
<span class="sig-name descname"><span class="pre">forecast_ahead_distribution</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">steps</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></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"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.benchmarks.BSTS.ARIMA.forecast_ahead_distribution" title="Permalink to this definition"></a></dt>
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<dd><p>Probabilistic forecast from 1 to H steps ahead, where H is given by the steps parameter</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>data</strong> time series data with the minimal length equal to the max_lag of the model</p></li>
<li><p><strong>steps</strong> the number of steps ahead to forecast</p></li>
<li><p><strong>start_at</strong> in the multi step forecasting, the index of the data where to start forecasting (default: 0)</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>a list with the forecasted Probability Distributions</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
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<dt class="sig sig-object py" id="pyFTS.benchmarks.BSTS.ARIMA.forecast_ahead_interval">
<span class="sig-name descname"><span class="pre">forecast_ahead_interval</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ndata</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">steps</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></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"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.benchmarks.BSTS.ARIMA.forecast_ahead_interval" title="Permalink to this definition"></a></dt>
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<dd><p>Interval forecast from 1 to H steps ahead, where H is given by the steps parameter</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>data</strong> time series data with the minimal length equal to the max_lag of the model</p></li>
<li><p><strong>steps</strong> the number of steps ahead to forecast</p></li>
<li><p><strong>start_at</strong> in the multi step forecasting, the index of the data where to start forecasting (default: 0)</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>a list with the forecasted intervals</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
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<dt class="sig sig-object py" id="pyFTS.benchmarks.BSTS.ARIMA.forecast_distribution">
<span class="sig-name descname"><span class="pre">forecast_distribution</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/BSTS.html#ARIMA.forecast_distribution"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.benchmarks.BSTS.ARIMA.forecast_distribution" title="Permalink to this definition"></a></dt>
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<dd><p>Probabilistic forecast one step ahead</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>data</strong> time series data with the minimal length equal to the max_lag of the model</p></li>
<li><p><strong>kwargs</strong> model specific parameters</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>a list with probabilistic.ProbabilityDistribution objects representing the forecasted Probability Distributions</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
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<dt class="sig sig-object py" id="pyFTS.benchmarks.BSTS.ARIMA.forecast_interval">
<span class="sig-name descname"><span class="pre">forecast_interval</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/BSTS.html#ARIMA.forecast_interval"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.benchmarks.BSTS.ARIMA.forecast_interval" title="Permalink to this definition"></a></dt>
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<dd><p>Interval forecast one step ahead</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>data</strong> time series data with the minimal length equal to the max_lag of the model</p></li>
<li><p><strong>kwargs</strong> model specific parameters</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>a list with the prediction intervals</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
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<dt class="sig sig-object py" id="pyFTS.benchmarks.BSTS.ARIMA.inference">
<span class="sig-name descname"><span class="pre">inference</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">steps</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/BSTS.html#ARIMA.inference"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.benchmarks.BSTS.ARIMA.inference" title="Permalink to this definition"></a></dt>
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<dd></dd></dl>
<dl class="py method">
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<dt class="sig sig-object py" id="pyFTS.benchmarks.BSTS.ARIMA.train">
<span class="sig-name descname"><span class="pre">train</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/BSTS.html#ARIMA.train"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.benchmarks.BSTS.ARIMA.train" title="Permalink to this definition"></a></dt>
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<dd><p>Method specific parameter fitting</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>data</strong> training time series data</p></li>
<li><p><strong>kwargs</strong> Method specific parameters</p></li>
</ul>
</dd>
</dl>
</dd></dl>
</dd></dl>
</div>
</div>
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<li><a class="reference internal" href="#">pyFTS.benchmarks package</a><ul>
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<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>
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<li><a class="reference internal" href="#pyfts-benchmarks-gaussianproc-module">pyFTS.benchmarks.gaussianproc module</a></li>
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<li><a class="reference internal" href="#module-pyFTS.benchmarks.BSTS">pyFTS.benchmarks.BSTS module</a></li>
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