<spanid="pyfts-benchmarks-benchmarks-module"></span><h2>pyFTS.benchmarks.benchmarks module<aclass="headerlink"href="#module-pyFTS.benchmarks.benchmarks"title="Permalink to this headline">¶</a></h2>
<codeclass="descclassname">pyFTS.benchmarks.benchmarks.</code><codeclass="descname">SelecaoSimples_MenorRMSE</code><spanclass="sig-paren">(</span><em>original</em>, <em>parameters</em>, <em>modelo</em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/pyFTS/benchmarks/benchmarks.html#SelecaoSimples_MenorRMSE"><spanclass="viewcode-link">[source]</span></a><aclass="headerlink"href="#pyFTS.benchmarks.benchmarks.SelecaoSimples_MenorRMSE"title="Permalink to this definition">¶</a></dt>
<codeclass="descclassname">pyFTS.benchmarks.benchmarks.</code><codeclass="descname">compareModelsPlot</code><spanclass="sig-paren">(</span><em>original</em>, <em>models_fo</em>, <em>models_ho</em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/pyFTS/benchmarks/benchmarks.html#compareModelsPlot"><spanclass="viewcode-link">[source]</span></a><aclass="headerlink"href="#pyFTS.benchmarks.benchmarks.compareModelsPlot"title="Permalink to this definition">¶</a></dt>
<codeclass="descclassname">pyFTS.benchmarks.benchmarks.</code><codeclass="descname">compareModelsTable</code><spanclass="sig-paren">(</span><em>original</em>, <em>models_fo</em>, <em>models_ho</em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/pyFTS/benchmarks/benchmarks.html#compareModelsTable"><spanclass="viewcode-link">[source]</span></a><aclass="headerlink"href="#pyFTS.benchmarks.benchmarks.compareModelsTable"title="Permalink to this definition">¶</a></dt>
<codeclass="descclassname">pyFTS.benchmarks.benchmarks.</code><codeclass="descname">get_benchmark_interval_methods</code><spanclass="sig-paren">(</span><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/pyFTS/benchmarks/benchmarks.html#get_benchmark_interval_methods"><spanclass="viewcode-link">[source]</span></a><aclass="headerlink"href="#pyFTS.benchmarks.benchmarks.get_benchmark_interval_methods"title="Permalink to this definition">¶</a></dt>
<dd><p>Return all non FTS methods for point_to_interval forecasting</p>
<codeclass="descclassname">pyFTS.benchmarks.benchmarks.</code><codeclass="descname">get_benchmark_point_methods</code><spanclass="sig-paren">(</span><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/pyFTS/benchmarks/benchmarks.html#get_benchmark_point_methods"><spanclass="viewcode-link">[source]</span></a><aclass="headerlink"href="#pyFTS.benchmarks.benchmarks.get_benchmark_point_methods"title="Permalink to this definition">¶</a></dt>
<dd><p>Return all non FTS methods for point forecasting</p>
<codeclass="descclassname">pyFTS.benchmarks.benchmarks.</code><codeclass="descname">get_benchmark_probabilistic_methods</code><spanclass="sig-paren">(</span><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/pyFTS/benchmarks/benchmarks.html#get_benchmark_probabilistic_methods"><spanclass="viewcode-link">[source]</span></a><aclass="headerlink"href="#pyFTS.benchmarks.benchmarks.get_benchmark_probabilistic_methods"title="Permalink to this definition">¶</a></dt>
<dd><p>Return all FTS methods for probabilistic forecasting</p>
<codeclass="descclassname">pyFTS.benchmarks.benchmarks.</code><codeclass="descname">get_interval_methods</code><spanclass="sig-paren">(</span><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/pyFTS/benchmarks/benchmarks.html#get_interval_methods"><spanclass="viewcode-link">[source]</span></a><aclass="headerlink"href="#pyFTS.benchmarks.benchmarks.get_interval_methods"title="Permalink to this definition">¶</a></dt>
<dd><p>Return all FTS methods for point_to_interval forecasting</p>
<codeclass="descclassname">pyFTS.benchmarks.benchmarks.</code><codeclass="descname">get_point_methods</code><spanclass="sig-paren">(</span><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/pyFTS/benchmarks/benchmarks.html#get_point_methods"><spanclass="viewcode-link">[source]</span></a><aclass="headerlink"href="#pyFTS.benchmarks.benchmarks.get_point_methods"title="Permalink to this definition">¶</a></dt>
<dd><p>Return all FTS methods for point forecasting</p>
<codeclass="descclassname">pyFTS.benchmarks.benchmarks.</code><codeclass="descname">get_probabilistic_methods</code><spanclass="sig-paren">(</span><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/pyFTS/benchmarks/benchmarks.html#get_probabilistic_methods"><spanclass="viewcode-link">[source]</span></a><aclass="headerlink"href="#pyFTS.benchmarks.benchmarks.get_probabilistic_methods"title="Permalink to this definition">¶</a></dt>
<dd><p>Return all FTS methods for probabilistic forecasting</p>
<codeclass="descclassname">pyFTS.benchmarks.benchmarks.</code><codeclass="descname">pftsExploreOrderAndPartitions</code><spanclass="sig-paren">(</span><em>data</em>, <em>save=False</em>, <em>file=None</em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/pyFTS/benchmarks/benchmarks.html#pftsExploreOrderAndPartitions"><spanclass="viewcode-link">[source]</span></a><aclass="headerlink"href="#pyFTS.benchmarks.benchmarks.pftsExploreOrderAndPartitions"title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>
<dlclass="function">
<dtid="pyFTS.benchmarks.benchmarks.plotCompared">
<codeclass="descclassname">pyFTS.benchmarks.benchmarks.</code><codeclass="descname">plotCompared</code><spanclass="sig-paren">(</span><em>original</em>, <em>forecasts</em>, <em>labels</em>, <em>title</em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/pyFTS/benchmarks/benchmarks.html#plotCompared"><spanclass="viewcode-link">[source]</span></a><aclass="headerlink"href="#pyFTS.benchmarks.benchmarks.plotCompared"title="Permalink to this definition">¶</a></dt>
<codeclass="descclassname">pyFTS.benchmarks.benchmarks.</code><codeclass="descname">plot_distribution</code><spanclass="sig-paren">(</span><em>ax</em>, <em>cmap</em>, <em>probabilitydist</em>, <em>fig</em>, <em>time_from</em>, <em>reference_data=None</em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/pyFTS/benchmarks/benchmarks.html#plot_distribution"><spanclass="viewcode-link">[source]</span></a><aclass="headerlink"href="#pyFTS.benchmarks.benchmarks.plot_distribution"title="Permalink to this definition">¶</a></dt>
<codeclass="descclassname">pyFTS.benchmarks.benchmarks.</code><codeclass="descname">plot_probability_distributions</code><spanclass="sig-paren">(</span><em>pmfs, lcolors, tam=[15, 7]</em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/pyFTS/benchmarks/benchmarks.html#plot_probability_distributions"><spanclass="viewcode-link">[source]</span></a><aclass="headerlink"href="#pyFTS.benchmarks.benchmarks.plot_probability_distributions"title="Permalink to this definition">¶</a></dt>
<codeclass="descclassname">pyFTS.benchmarks.benchmarks.</code><codeclass="descname">print_distribution_statistics</code><spanclass="sig-paren">(</span><em>original</em>, <em>models</em>, <em>steps</em>, <em>resolution</em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/pyFTS/benchmarks/benchmarks.html#print_distribution_statistics"><spanclass="viewcode-link">[source]</span></a><aclass="headerlink"href="#pyFTS.benchmarks.benchmarks.print_distribution_statistics"title="Permalink to this definition">¶</a></dt>
<dd><p>Run probabilistic benchmarks on given models and data and print the results</p>
<codeclass="descclassname">pyFTS.benchmarks.benchmarks.</code><codeclass="descname">print_interval_statistics</code><spanclass="sig-paren">(</span><em>original</em>, <em>models</em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/pyFTS/benchmarks/benchmarks.html#print_interval_statistics"><spanclass="viewcode-link">[source]</span></a><aclass="headerlink"href="#pyFTS.benchmarks.benchmarks.print_interval_statistics"title="Permalink to this definition">¶</a></dt>
<dd><p>Run interval benchmarks on given models and data and print the results</p>
<codeclass="descclassname">pyFTS.benchmarks.benchmarks.</code><codeclass="descname">print_point_statistics</code><spanclass="sig-paren">(</span><em>data</em>, <em>models</em>, <em>externalmodels=None</em>, <em>externalforecasts=None</em>, <em>indexers=None</em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/pyFTS/benchmarks/benchmarks.html#print_point_statistics"><spanclass="viewcode-link">[source]</span></a><aclass="headerlink"href="#pyFTS.benchmarks.benchmarks.print_point_statistics"title="Permalink to this definition">¶</a></dt>
<dd><p>Run point benchmarks on given models and data and print the results</p>
<codeclass="descclassname">pyFTS.benchmarks.benchmarks.</code><codeclass="descname">process_interval_jobs</code><spanclass="sig-paren">(</span><em>dataset</em>, <em>tag</em>, <em>job</em>, <em>conn</em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/pyFTS/benchmarks/benchmarks.html#process_interval_jobs"><spanclass="viewcode-link">[source]</span></a><aclass="headerlink"href="#pyFTS.benchmarks.benchmarks.process_interval_jobs"title="Permalink to this definition">¶</a></dt>
<dd><p>Extract information from an dictionary with interval benchmark results and save it on a database</p>
<codeclass="descclassname">pyFTS.benchmarks.benchmarks.</code><codeclass="descname">process_point_jobs</code><spanclass="sig-paren">(</span><em>dataset</em>, <em>tag</em>, <em>job</em>, <em>conn</em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/pyFTS/benchmarks/benchmarks.html#process_point_jobs"><spanclass="viewcode-link">[source]</span></a><aclass="headerlink"href="#pyFTS.benchmarks.benchmarks.process_point_jobs"title="Permalink to this definition">¶</a></dt>
<dd><p>Extract information from a dictionary with point benchmark results and save it on a database</p>
<codeclass="descclassname">pyFTS.benchmarks.benchmarks.</code><codeclass="descname">process_probabilistic_jobs</code><spanclass="sig-paren">(</span><em>dataset</em>, <em>tag</em>, <em>job</em>, <em>conn</em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/pyFTS/benchmarks/benchmarks.html#process_probabilistic_jobs"><spanclass="viewcode-link">[source]</span></a><aclass="headerlink"href="#pyFTS.benchmarks.benchmarks.process_probabilistic_jobs"title="Permalink to this definition">¶</a></dt>
<dd><p>Extract information from an dictionary with probabilistic benchmark results and save it on a database</p>
<codeclass="descclassname">pyFTS.benchmarks.benchmarks.</code><codeclass="descname">run_interval</code><spanclass="sig-paren">(</span><em>mfts</em>, <em>partitioner</em>, <em>train_data</em>, <em>test_data</em>, <em>window_key=None</em>, <em>**kwargs</em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/pyFTS/benchmarks/benchmarks.html#run_interval"><spanclass="viewcode-link">[source]</span></a><aclass="headerlink"href="#pyFTS.benchmarks.benchmarks.run_interval"title="Permalink to this definition">¶</a></dt>
<dd><p>Run the interval forecasting benchmarks</p>
<codeclass="descclassname">pyFTS.benchmarks.benchmarks.</code><codeclass="descname">run_point</code><spanclass="sig-paren">(</span><em>mfts</em>, <em>partitioner</em>, <em>train_data</em>, <em>test_data</em>, <em>window_key=None</em>, <em>**kwargs</em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/pyFTS/benchmarks/benchmarks.html#run_point"><spanclass="viewcode-link">[source]</span></a><aclass="headerlink"href="#pyFTS.benchmarks.benchmarks.run_point"title="Permalink to this definition">¶</a></dt>
<codeclass="descclassname">pyFTS.benchmarks.benchmarks.</code><codeclass="descname">run_probabilistic</code><spanclass="sig-paren">(</span><em>mfts</em>, <em>partitioner</em>, <em>train_data</em>, <em>test_data</em>, <em>window_key=None</em>, <em>**kwargs</em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/pyFTS/benchmarks/benchmarks.html#run_probabilistic"><spanclass="viewcode-link">[source]</span></a><aclass="headerlink"href="#pyFTS.benchmarks.benchmarks.run_probabilistic"title="Permalink to this definition">¶</a></dt>
<dd><p>Run the probabilistic forecasting benchmarks</p>
<codeclass="descclassname">pyFTS.benchmarks.benchmarks.</code><codeclass="descname">sliding_window_benchmarks</code><spanclass="sig-paren">(</span><em>data</em>, <em>windowsize</em>, <em>train=0.8</em>, <em>**kwargs</em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/pyFTS/benchmarks/benchmarks.html#sliding_window_benchmarks"><spanclass="viewcode-link">[source]</span></a><aclass="headerlink"href="#pyFTS.benchmarks.benchmarks.sliding_window_benchmarks"title="Permalink to this definition">¶</a></dt>
<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>
<li><strong>benchmark_methods</strong>– a list with Non FTS models to benchmark. The default is None.</li>
<li><strong>benchmark_methods_parameters</strong>– a list with Non FTS models parameters. The default is None.</li>
<li><strong>benchmark_models</strong>– A boolean value indicating if external FTS methods will be used on benchmark. The default is False.</li>
<li><strong>build_methods</strong>– A boolean value indicating if the default FTS methods will be used on benchmark. The default is True.</li>
<li><strong>dataset</strong>– the dataset name to identify the current set of benchmarks results on database.</li>
<li><strong>distributed</strong>– A boolean value indicating if the forecasting procedure will be distributed in a dispy cluster. . The default is False</li>
<li><strong>file</strong>– file path to save the results. The default is benchmarks.db.</li>
<li><strong>inc</strong>– a float on interval [0,1] indicating the percentage of the windowsize to move the window</li>
<li><strong>methods</strong>– a list with FTS class names. The default depends on the forecasting type and contains the list of all FTS methods.</li>
<li><strong>models</strong>– a list with prebuilt FTS objects. The default is None.</li>
<li><strong>nodes</strong>– a list with the dispy cluster nodes addresses. The default is [127.0.0.1].</li>
<li><strong>orders</strong>– a list with orders of the models (for high order models). The default is [1,2,3].</li>
<li><strong>partitions</strong>– a list with the numbers of partitions on the Universe of Discourse. The default is [10].</li>
<li><strong>partitioners_models</strong>– a list with prebuilt Universe of Discourse partitioners objects. The default is None.</li>
<li><strong>partitioners_methods</strong>– a list with Universe of Discourse partitioners class names. The default is [partitioners.Grid.GridPartitioner].</li>
<li><strong>progress</strong>– If true a progress bar will be displayed during the benchmarks. The default is False.</li>
<li><strong>start</strong>– in the multi step forecasting, the index of the data where to start forecasting. The default is 0.</li>
<li><strong>steps_ahead</strong>– a list with the forecasting horizons, i. e., the number of steps ahead to forecast. The default is 1.</li>
<li><strong>tag</strong>– a name to identify the current set of benchmarks results on database.</li>
<li><strong>type</strong>– the forecasting type, one of these values: point(default), interval or distribution. The default is point.</li>
<li><strong>transformations</strong>– a list with data transformations do apply . The default is [None].</li>
<spanid="pyfts-benchmarks-measures-module"></span><h2>pyFTS.benchmarks.Measures module<aclass="headerlink"href="#module-pyFTS.benchmarks.Measures"title="Permalink to this headline">¶</a></h2>
<codeclass="descclassname">pyFTS.benchmarks.Measures.</code><codeclass="descname">BoxLjungStatistic</code><spanclass="sig-paren">(</span><em>data</em>, <em>h</em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/pyFTS/benchmarks/Measures.html#BoxLjungStatistic"><spanclass="viewcode-link">[source]</span></a><aclass="headerlink"href="#pyFTS.benchmarks.Measures.BoxLjungStatistic"title="Permalink to this definition">¶</a></dt>
<codeclass="descclassname">pyFTS.benchmarks.Measures.</code><codeclass="descname">BoxPierceStatistic</code><spanclass="sig-paren">(</span><em>data</em>, <em>h</em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/pyFTS/benchmarks/Measures.html#BoxPierceStatistic"><spanclass="viewcode-link">[source]</span></a><aclass="headerlink"href="#pyFTS.benchmarks.Measures.BoxPierceStatistic"title="Permalink to this definition">¶</a></dt>
<codeclass="descclassname">pyFTS.benchmarks.Measures.</code><codeclass="descname">TheilsInequality</code><spanclass="sig-paren">(</span><em>targets</em>, <em>forecasts</em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/pyFTS/benchmarks/Measures.html#TheilsInequality"><spanclass="viewcode-link">[source]</span></a><aclass="headerlink"href="#pyFTS.benchmarks.Measures.TheilsInequality"title="Permalink to this definition">¶</a></dt>
<codeclass="descclassname">pyFTS.benchmarks.Measures.</code><codeclass="descname">UStatistic</code><spanclass="sig-paren">(</span><em>targets</em>, <em>forecasts</em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/pyFTS/benchmarks/Measures.html#UStatistic"><spanclass="viewcode-link">[source]</span></a><aclass="headerlink"href="#pyFTS.benchmarks.Measures.UStatistic"title="Permalink to this definition">¶</a></dt>
<codeclass="descclassname">pyFTS.benchmarks.Measures.</code><codeclass="descname">acf</code><spanclass="sig-paren">(</span><em>data</em>, <em>k</em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/pyFTS/benchmarks/Measures.html#acf"><spanclass="viewcode-link">[source]</span></a><aclass="headerlink"href="#pyFTS.benchmarks.Measures.acf"title="Permalink to this definition">¶</a></dt>
<codeclass="descclassname">pyFTS.benchmarks.Measures.</code><codeclass="descname">brier_score</code><spanclass="sig-paren">(</span><em>targets</em>, <em>densities</em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/pyFTS/benchmarks/Measures.html#brier_score"><spanclass="viewcode-link">[source]</span></a><aclass="headerlink"href="#pyFTS.benchmarks.Measures.brier_score"title="Permalink to this definition">¶</a></dt>
<dd><p>Brier (1950). “Verification of Forecasts Expressed in Terms of Probability”. Monthly Weather Review. 78: 1–3.</p>
<codeclass="descclassname">pyFTS.benchmarks.Measures.</code><codeclass="descname">coverage</code><spanclass="sig-paren">(</span><em>targets</em>, <em>forecasts</em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/pyFTS/benchmarks/Measures.html#coverage"><spanclass="viewcode-link">[source]</span></a><aclass="headerlink"href="#pyFTS.benchmarks.Measures.coverage"title="Permalink to this definition">¶</a></dt>
<dd><p>Percent of target values that fall inside forecasted interval</p>
</dd></dl>
<dlclass="function">
<dtid="pyFTS.benchmarks.Measures.crps">
<codeclass="descclassname">pyFTS.benchmarks.Measures.</code><codeclass="descname">crps</code><spanclass="sig-paren">(</span><em>targets</em>, <em>densities</em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/pyFTS/benchmarks/Measures.html#crps"><spanclass="viewcode-link">[source]</span></a><aclass="headerlink"href="#pyFTS.benchmarks.Measures.crps"title="Permalink to this definition">¶</a></dt>
<codeclass="descclassname">pyFTS.benchmarks.Measures.</code><codeclass="descname">get_distribution_statistics</code><spanclass="sig-paren">(</span><em>data</em>, <em>model</em>, <em>**kwargs</em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/pyFTS/benchmarks/Measures.html#get_distribution_statistics"><spanclass="viewcode-link">[source]</span></a><aclass="headerlink"href="#pyFTS.benchmarks.Measures.get_distribution_statistics"title="Permalink to this definition">¶</a></dt>
<dd><p>Get CRPS statistic and time for a forecasting model</p>
<codeclass="descclassname">pyFTS.benchmarks.Measures.</code><codeclass="descname">get_interval_statistics</code><spanclass="sig-paren">(</span><em>data</em>, <em>model</em>, <em>**kwargs</em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/pyFTS/benchmarks/Measures.html#get_interval_statistics"><spanclass="viewcode-link">[source]</span></a><aclass="headerlink"href="#pyFTS.benchmarks.Measures.get_interval_statistics"title="Permalink to this definition">¶</a></dt>
<dd><p>Condensate all measures for point interval forecasters</p>
<trclass="field-even field"><thclass="field-name">Returns:</th><tdclass="field-body"><pclass="first last">a list with the sharpness, resolution, coverage, .05 pinball mean,</p>
<codeclass="descclassname">pyFTS.benchmarks.Measures.</code><codeclass="descname">get_point_statistics</code><spanclass="sig-paren">(</span><em>data</em>, <em>model</em>, <em>**kwargs</em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/pyFTS/benchmarks/Measures.html#get_point_statistics"><spanclass="viewcode-link">[source]</span></a><aclass="headerlink"href="#pyFTS.benchmarks.Measures.get_point_statistics"title="Permalink to this definition">¶</a></dt>
<dd><p>Condensate all measures for point forecasters</p>
<li><strong>model</strong>– FTS model with point forecasting capability</li>
<li><strong>kwargs</strong>–</li>
</ul>
</td>
</tr>
<trclass="field-even field"><thclass="field-name">Returns:</th><tdclass="field-body"><pclass="first last">a list with the RMSE, SMAPE and U Statistic</p>
<codeclass="descclassname">pyFTS.benchmarks.Measures.</code><codeclass="descname">heavyside</code><spanclass="sig-paren">(</span><em>bin</em>, <em>target</em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/pyFTS/benchmarks/Measures.html#heavyside"><spanclass="viewcode-link">[source]</span></a><aclass="headerlink"href="#pyFTS.benchmarks.Measures.heavyside"title="Permalink to this definition">¶</a></dt>
<codeclass="descclassname">pyFTS.benchmarks.Measures.</code><codeclass="descname">heavyside_cdf</code><spanclass="sig-paren">(</span><em>bins</em>, <em>targets</em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/pyFTS/benchmarks/Measures.html#heavyside_cdf"><spanclass="viewcode-link">[source]</span></a><aclass="headerlink"href="#pyFTS.benchmarks.Measures.heavyside_cdf"title="Permalink to this definition">¶</a></dt>
<codeclass="descclassname">pyFTS.benchmarks.Measures.</code><codeclass="descname">mape</code><spanclass="sig-paren">(</span><em>targets</em>, <em>forecasts</em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/pyFTS/benchmarks/Measures.html#mape"><spanclass="viewcode-link">[source]</span></a><aclass="headerlink"href="#pyFTS.benchmarks.Measures.mape"title="Permalink to this definition">¶</a></dt>
<codeclass="descclassname">pyFTS.benchmarks.Measures.</code><codeclass="descname">mape_interval</code><spanclass="sig-paren">(</span><em>targets</em>, <em>forecasts</em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/pyFTS/benchmarks/Measures.html#mape_interval"><spanclass="viewcode-link">[source]</span></a><aclass="headerlink"href="#pyFTS.benchmarks.Measures.mape_interval"title="Permalink to this definition">¶</a></dt>
<codeclass="descclassname">pyFTS.benchmarks.Measures.</code><codeclass="descname">pinball</code><spanclass="sig-paren">(</span><em>tau</em>, <em>target</em>, <em>forecast</em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/pyFTS/benchmarks/Measures.html#pinball"><spanclass="viewcode-link">[source]</span></a><aclass="headerlink"href="#pyFTS.benchmarks.Measures.pinball"title="Permalink to this definition">¶</a></dt>
<dd><p>Pinball loss function. Measure the distance of forecast to the tau-quantile of the target</p>
<li><strong>tau</strong>– quantile value in the range (0,1)</li>
<li><strong>target</strong>–</li>
<li><strong>forecast</strong>–</li>
</ul>
</td>
</tr>
<trclass="field-even field"><thclass="field-name">Returns:</th><tdclass="field-body"><pclass="first last">float, distance of forecast to the tau-quantile of the target</p>
<codeclass="descclassname">pyFTS.benchmarks.Measures.</code><codeclass="descname">pinball_mean</code><spanclass="sig-paren">(</span><em>tau</em>, <em>targets</em>, <em>forecasts</em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/pyFTS/benchmarks/Measures.html#pinball_mean"><spanclass="viewcode-link">[source]</span></a><aclass="headerlink"href="#pyFTS.benchmarks.Measures.pinball_mean"title="Permalink to this definition">¶</a></dt>
<dd><p>Mean pinball loss value of the forecast for a given tau-quantile of the targets</p>
<li><strong>tau</strong>– quantile value in the range (0,1)</li>
<li><strong>targets</strong>– list of target values</li>
<li><strong>forecasts</strong>– list of prediction intervals</li>
</ul>
</td>
</tr>
<trclass="field-even field"><thclass="field-name">Returns:</th><tdclass="field-body"><pclass="first last">float, the pinball loss mean for tau quantile</p>
<codeclass="descclassname">pyFTS.benchmarks.Measures.</code><codeclass="descname">pmf_to_cdf</code><spanclass="sig-paren">(</span><em>density</em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/pyFTS/benchmarks/Measures.html#pmf_to_cdf"><spanclass="viewcode-link">[source]</span></a><aclass="headerlink"href="#pyFTS.benchmarks.Measures.pmf_to_cdf"title="Permalink to this definition">¶</a></dt>
<codeclass="descclassname">pyFTS.benchmarks.Measures.</code><codeclass="descname">resolution</code><spanclass="sig-paren">(</span><em>forecasts</em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/pyFTS/benchmarks/Measures.html#resolution"><spanclass="viewcode-link">[source]</span></a><aclass="headerlink"href="#pyFTS.benchmarks.Measures.resolution"title="Permalink to this definition">¶</a></dt>
<dd><p>Resolution - Standard deviation of the intervals</p>
<codeclass="descclassname">pyFTS.benchmarks.Measures.</code><codeclass="descname">rmse</code><spanclass="sig-paren">(</span><em>targets</em>, <em>forecasts</em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/pyFTS/benchmarks/Measures.html#rmse"><spanclass="viewcode-link">[source]</span></a><aclass="headerlink"href="#pyFTS.benchmarks.Measures.rmse"title="Permalink to this definition">¶</a></dt>
<codeclass="descclassname">pyFTS.benchmarks.Measures.</code><codeclass="descname">rmse_interval</code><spanclass="sig-paren">(</span><em>targets</em>, <em>forecasts</em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/pyFTS/benchmarks/Measures.html#rmse_interval"><spanclass="viewcode-link">[source]</span></a><aclass="headerlink"href="#pyFTS.benchmarks.Measures.rmse_interval"title="Permalink to this definition">¶</a></dt>
<codeclass="descclassname">pyFTS.benchmarks.Measures.</code><codeclass="descname">sharpness</code><spanclass="sig-paren">(</span><em>forecasts</em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/pyFTS/benchmarks/Measures.html#sharpness"><spanclass="viewcode-link">[source]</span></a><aclass="headerlink"href="#pyFTS.benchmarks.Measures.sharpness"title="Permalink to this definition">¶</a></dt>
<codeclass="descclassname">pyFTS.benchmarks.Measures.</code><codeclass="descname">smape</code><spanclass="sig-paren">(</span><em>targets</em>, <em>forecasts</em>, <em>type=2</em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/pyFTS/benchmarks/Measures.html#smape"><spanclass="viewcode-link">[source]</span></a><aclass="headerlink"href="#pyFTS.benchmarks.Measures.smape"title="Permalink to this definition">¶</a></dt>
<dd><p>Symmetric Mean Average Percentual Error</p>
<codeclass="descclassname">pyFTS.benchmarks.Measures.</code><codeclass="descname">winkler_mean</code><spanclass="sig-paren">(</span><em>tau</em>, <em>targets</em>, <em>forecasts</em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/pyFTS/benchmarks/Measures.html#winkler_mean"><spanclass="viewcode-link">[source]</span></a><aclass="headerlink"href="#pyFTS.benchmarks.Measures.winkler_mean"title="Permalink to this definition">¶</a></dt>
<dd><p>Mean Winkler score value of the forecast for a given tau-quantile of the targets</p>
<li><strong>tau</strong>– quantile value in the range (0,1)</li>
<li><strong>targets</strong>– list of target values</li>
<li><strong>forecasts</strong>– list of prediction intervals</li>
</ul>
</td>
</tr>
<trclass="field-even field"><thclass="field-name">Returns:</th><tdclass="field-body"><pclass="first last">float, the Winkler score mean for tau quantile</p>
<codeclass="descclassname">pyFTS.benchmarks.Measures.</code><codeclass="descname">winkler_score</code><spanclass="sig-paren">(</span><em>tau</em>, <em>target</em>, <em>forecast</em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/pyFTS/benchmarks/Measures.html#winkler_score"><spanclass="viewcode-link">[source]</span></a><aclass="headerlink"href="#pyFTS.benchmarks.Measures.winkler_score"title="Permalink to this definition">¶</a></dt>
<dd><olclass="upperalpha simple"start="18">
<li><olclass="first upperalpha"start="12">
<li>Winkler, A Decision-Theoretic Approach to Interval Estimation, J. Am. Stat. Assoc. 67 (337) (1972) 187–191. doi:10.2307/2284720.</li>
<spanid="pyfts-benchmarks-residualanalysis-module"></span><h2>pyFTS.benchmarks.ResidualAnalysis module<aclass="headerlink"href="#module-pyFTS.benchmarks.ResidualAnalysis"title="Permalink to this headline">¶</a></h2>
<codeclass="descclassname">pyFTS.benchmarks.ResidualAnalysis.</code><codeclass="descname">chi_squared</code><spanclass="sig-paren">(</span><em>q</em>, <em>h</em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/pyFTS/benchmarks/ResidualAnalysis.html#chi_squared"><spanclass="viewcode-link">[source]</span></a><aclass="headerlink"href="#pyFTS.benchmarks.ResidualAnalysis.chi_squared"title="Permalink to this definition">¶</a></dt>
<codeclass="descclassname">pyFTS.benchmarks.ResidualAnalysis.</code><codeclass="descname">compare_residuals</code><spanclass="sig-paren">(</span><em>data</em>, <em>models</em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/pyFTS/benchmarks/ResidualAnalysis.html#compare_residuals"><spanclass="viewcode-link">[source]</span></a><aclass="headerlink"href="#pyFTS.benchmarks.ResidualAnalysis.compare_residuals"title="Permalink to this definition">¶</a></dt>
<dd><p>Compare residual’s statistics of several models</p>
<trclass="field-even field"><thclass="field-name">Returns:</th><tdclass="field-body"><pclass="first last">a Pandas dataframe with the Box-Ljung statistic for each model</p>
<codeclass="descclassname">pyFTS.benchmarks.ResidualAnalysis.</code><codeclass="descname">plotResiduals</code><spanclass="sig-paren">(</span><em>targets, models, tam=[8, 8], save=False, file=None</em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/pyFTS/benchmarks/ResidualAnalysis.html#plotResiduals"><spanclass="viewcode-link">[source]</span></a><aclass="headerlink"href="#pyFTS.benchmarks.ResidualAnalysis.plotResiduals"title="Permalink to this definition">¶</a></dt>
<codeclass="descclassname">pyFTS.benchmarks.ResidualAnalysis.</code><codeclass="descname">plot_residuals</code><spanclass="sig-paren">(</span><em>targets, models, tam=[8, 8], save=False, file=None</em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/pyFTS/benchmarks/ResidualAnalysis.html#plot_residuals"><spanclass="viewcode-link">[source]</span></a><aclass="headerlink"href="#pyFTS.benchmarks.ResidualAnalysis.plot_residuals"title="Permalink to this definition">¶</a></dt>
<codeclass="descclassname">pyFTS.benchmarks.ResidualAnalysis.</code><codeclass="descname">residuals</code><spanclass="sig-paren">(</span><em>targets</em>, <em>forecasts</em>, <em>order=1</em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/pyFTS/benchmarks/ResidualAnalysis.html#residuals"><spanclass="viewcode-link">[source]</span></a><aclass="headerlink"href="#pyFTS.benchmarks.ResidualAnalysis.residuals"title="Permalink to this definition">¶</a></dt>
<spanid="pyfts-benchmarks-util-module"></span><h2>pyFTS.benchmarks.Util module<aclass="headerlink"href="#module-pyFTS.benchmarks.Util"title="Permalink to this headline">¶</a></h2>
<codeclass="descclassname">pyFTS.benchmarks.Util.</code><codeclass="descname">analytic_tabular_dataframe</code><spanclass="sig-paren">(</span><em>dataframe</em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/pyFTS/benchmarks/Util.html#analytic_tabular_dataframe"><spanclass="viewcode-link">[source]</span></a><aclass="headerlink"href="#pyFTS.benchmarks.Util.analytic_tabular_dataframe"title="Permalink to this definition">¶</a></dt>
<codeclass="descclassname">pyFTS.benchmarks.Util.</code><codeclass="descname">analytical_data_columns</code><spanclass="sig-paren">(</span><em>experiments</em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/pyFTS/benchmarks/Util.html#analytical_data_columns"><spanclass="viewcode-link">[source]</span></a><aclass="headerlink"href="#pyFTS.benchmarks.Util.analytical_data_columns"title="Permalink to this definition">¶</a></dt>
<codeclass="descclassname">pyFTS.benchmarks.Util.</code><codeclass="descname">base_dataframe_columns</code><spanclass="sig-paren">(</span><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/pyFTS/benchmarks/Util.html#base_dataframe_columns"><spanclass="viewcode-link">[source]</span></a><aclass="headerlink"href="#pyFTS.benchmarks.Util.base_dataframe_columns"title="Permalink to this definition">¶</a></dt>
<codeclass="descclassname">pyFTS.benchmarks.Util.</code><codeclass="descname">cast_dataframe_to_synthetic</code><spanclass="sig-paren">(</span><em>infile</em>, <em>outfile</em>, <em>experiments</em>, <em>type</em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/pyFTS/benchmarks/Util.html#cast_dataframe_to_synthetic"><spanclass="viewcode-link">[source]</span></a><aclass="headerlink"href="#pyFTS.benchmarks.Util.cast_dataframe_to_synthetic"title="Permalink to this definition">¶</a></dt>
<codeclass="descclassname">pyFTS.benchmarks.Util.</code><codeclass="descname">cast_dataframe_to_synthetic_interval</code><spanclass="sig-paren">(</span><em>df</em>, <em>data_columns</em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/pyFTS/benchmarks/Util.html#cast_dataframe_to_synthetic_interval"><spanclass="viewcode-link">[source]</span></a><aclass="headerlink"href="#pyFTS.benchmarks.Util.cast_dataframe_to_synthetic_interval"title="Permalink to this definition">¶</a></dt>
<codeclass="descclassname">pyFTS.benchmarks.Util.</code><codeclass="descname">cast_dataframe_to_synthetic_point</code><spanclass="sig-paren">(</span><em>df</em>, <em>data_columns</em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/pyFTS/benchmarks/Util.html#cast_dataframe_to_synthetic_point"><spanclass="viewcode-link">[source]</span></a><aclass="headerlink"href="#pyFTS.benchmarks.Util.cast_dataframe_to_synthetic_point"title="Permalink to this definition">¶</a></dt>
<codeclass="descclassname">pyFTS.benchmarks.Util.</code><codeclass="descname">cast_dataframe_to_synthetic_probabilistic</code><spanclass="sig-paren">(</span><em>df</em>, <em>data_columns</em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/pyFTS/benchmarks/Util.html#cast_dataframe_to_synthetic_probabilistic"><spanclass="viewcode-link">[source]</span></a><aclass="headerlink"href="#pyFTS.benchmarks.Util.cast_dataframe_to_synthetic_probabilistic"title="Permalink to this definition">¶</a></dt>
<codeclass="descclassname">pyFTS.benchmarks.Util.</code><codeclass="descname">check_ignore_list</code><spanclass="sig-paren">(</span><em>b</em>, <em>ignore</em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/pyFTS/benchmarks/Util.html#check_ignore_list"><spanclass="viewcode-link">[source]</span></a><aclass="headerlink"href="#pyFTS.benchmarks.Util.check_ignore_list"title="Permalink to this definition">¶</a></dt>
<codeclass="descclassname">pyFTS.benchmarks.Util.</code><codeclass="descname">check_replace_list</code><spanclass="sig-paren">(</span><em>m</em>, <em>replace</em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/pyFTS/benchmarks/Util.html#check_replace_list"><spanclass="viewcode-link">[source]</span></a><aclass="headerlink"href="#pyFTS.benchmarks.Util.check_replace_list"title="Permalink to this definition">¶</a></dt>
<codeclass="descclassname">pyFTS.benchmarks.Util.</code><codeclass="descname">create_benchmark_tables</code><spanclass="sig-paren">(</span><em>conn</em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/pyFTS/benchmarks/Util.html#create_benchmark_tables"><spanclass="viewcode-link">[source]</span></a><aclass="headerlink"href="#pyFTS.benchmarks.Util.create_benchmark_tables"title="Permalink to this definition">¶</a></dt>
<dd><p>Create a sqlite3 table designed to store benchmark results.</p>
<codeclass="descclassname">pyFTS.benchmarks.Util.</code><codeclass="descname">extract_measure</code><spanclass="sig-paren">(</span><em>dataframe</em>, <em>measure</em>, <em>data_columns</em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/pyFTS/benchmarks/Util.html#extract_measure"><spanclass="viewcode-link">[source]</span></a><aclass="headerlink"href="#pyFTS.benchmarks.Util.extract_measure"title="Permalink to this definition">¶</a></dt>
<codeclass="descclassname">pyFTS.benchmarks.Util.</code><codeclass="descname">find_best</code><spanclass="sig-paren">(</span><em>dataframe</em>, <em>criteria</em>, <em>ascending</em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/pyFTS/benchmarks/Util.html#find_best"><spanclass="viewcode-link">[source]</span></a><aclass="headerlink"href="#pyFTS.benchmarks.Util.find_best"title="Permalink to this definition">¶</a></dt>
<codeclass="descclassname">pyFTS.benchmarks.Util.</code><codeclass="descname">get_dataframe_from_bd</code><spanclass="sig-paren">(</span><em>file</em>, <em>filter</em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/pyFTS/benchmarks/Util.html#get_dataframe_from_bd"><spanclass="viewcode-link">[source]</span></a><aclass="headerlink"href="#pyFTS.benchmarks.Util.get_dataframe_from_bd"title="Permalink to this definition">¶</a></dt>
<dd><p>Query the sqlite benchmark database and return a pandas dataframe with the results</p>
<codeclass="descclassname">pyFTS.benchmarks.Util.</code><codeclass="descname">insert_benchmark</code><spanclass="sig-paren">(</span><em>data</em>, <em>conn</em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/pyFTS/benchmarks/Util.html#insert_benchmark"><spanclass="viewcode-link">[source]</span></a><aclass="headerlink"href="#pyFTS.benchmarks.Util.insert_benchmark"title="Permalink to this definition">¶</a></dt>
<trclass="field-odd field"><thclass="field-name">Parameters:</th><tdclass="field-body"><strong>data</strong>– a tuple with the benchmark data with format:</td>
<codeclass="descclassname">pyFTS.benchmarks.Util.</code><codeclass="descname">interval_dataframe_analytic_columns</code><spanclass="sig-paren">(</span><em>experiments</em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/pyFTS/benchmarks/Util.html#interval_dataframe_analytic_columns"><spanclass="viewcode-link">[source]</span></a><aclass="headerlink"href="#pyFTS.benchmarks.Util.interval_dataframe_analytic_columns"title="Permalink to this definition">¶</a></dt>
<codeclass="descclassname">pyFTS.benchmarks.Util.</code><codeclass="descname">interval_dataframe_synthetic_columns</code><spanclass="sig-paren">(</span><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/pyFTS/benchmarks/Util.html#interval_dataframe_synthetic_columns"><spanclass="viewcode-link">[source]</span></a><aclass="headerlink"href="#pyFTS.benchmarks.Util.interval_dataframe_synthetic_columns"title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>
<dlclass="function">
<dtid="pyFTS.benchmarks.Util.open_benchmark_db">
<codeclass="descclassname">pyFTS.benchmarks.Util.</code><codeclass="descname">open_benchmark_db</code><spanclass="sig-paren">(</span><em>name</em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/pyFTS/benchmarks/Util.html#open_benchmark_db"><spanclass="viewcode-link">[source]</span></a><aclass="headerlink"href="#pyFTS.benchmarks.Util.open_benchmark_db"title="Permalink to this definition">¶</a></dt>
<dd><p>Open a connection with a Sqlite database designed to store benchmark results.</p>
<codeclass="descclassname">pyFTS.benchmarks.Util.</code><codeclass="descname">point_dataframe_analytic_columns</code><spanclass="sig-paren">(</span><em>experiments</em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/pyFTS/benchmarks/Util.html#point_dataframe_analytic_columns"><spanclass="viewcode-link">[source]</span></a><aclass="headerlink"href="#pyFTS.benchmarks.Util.point_dataframe_analytic_columns"title="Permalink to this definition">¶</a></dt>
<codeclass="descclassname">pyFTS.benchmarks.Util.</code><codeclass="descname">point_dataframe_synthetic_columns</code><spanclass="sig-paren">(</span><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/pyFTS/benchmarks/Util.html#point_dataframe_synthetic_columns"><spanclass="viewcode-link">[source]</span></a><aclass="headerlink"href="#pyFTS.benchmarks.Util.point_dataframe_synthetic_columns"title="Permalink to this definition">¶</a></dt>
<codeclass="descclassname">pyFTS.benchmarks.Util.</code><codeclass="descname">probabilistic_dataframe_analytic_columns</code><spanclass="sig-paren">(</span><em>experiments</em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/pyFTS/benchmarks/Util.html#probabilistic_dataframe_analytic_columns"><spanclass="viewcode-link">[source]</span></a><aclass="headerlink"href="#pyFTS.benchmarks.Util.probabilistic_dataframe_analytic_columns"title="Permalink to this definition">¶</a></dt>
<codeclass="descclassname">pyFTS.benchmarks.Util.</code><codeclass="descname">probabilistic_dataframe_synthetic_columns</code><spanclass="sig-paren">(</span><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/pyFTS/benchmarks/Util.html#probabilistic_dataframe_synthetic_columns"><spanclass="viewcode-link">[source]</span></a><aclass="headerlink"href="#pyFTS.benchmarks.Util.probabilistic_dataframe_synthetic_columns"title="Permalink to this definition">¶</a></dt>
<codeclass="descclassname">pyFTS.benchmarks.Util.</code><codeclass="descname">process_common_data</code><spanclass="sig-paren">(</span><em>dataset</em>, <em>tag</em>, <em>type</em>, <em>job</em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/pyFTS/benchmarks/Util.html#process_common_data"><spanclass="viewcode-link">[source]</span></a><aclass="headerlink"href="#pyFTS.benchmarks.Util.process_common_data"title="Permalink to this definition">¶</a></dt>
<dd><p>Wraps benchmark information on a tuple for sqlite database</p>
<codeclass="descclassname">pyFTS.benchmarks.Util.</code><codeclass="descname">save_dataframe_probabilistic</code><spanclass="sig-paren">(</span><em>experiments</em>, <em>file</em>, <em>objs</em>, <em>crps</em>, <em>times</em>, <em>save</em>, <em>synthetic</em>, <em>steps</em>, <em>method</em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/pyFTS/benchmarks/Util.html#save_dataframe_probabilistic"><spanclass="viewcode-link">[source]</span></a><aclass="headerlink"href="#pyFTS.benchmarks.Util.save_dataframe_probabilistic"title="Permalink to this definition">¶</a></dt>
<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>
<dlclass="function">
<dtid="pyFTS.benchmarks.Util.scale">
<codeclass="descclassname">pyFTS.benchmarks.Util.</code><codeclass="descname">scale</code><spanclass="sig-paren">(</span><em>data</em>, <em>params</em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/pyFTS/benchmarks/Util.html#scale"><spanclass="viewcode-link">[source]</span></a><aclass="headerlink"href="#pyFTS.benchmarks.Util.scale"title="Permalink to this definition">¶</a></dt>
<codeclass="descclassname">pyFTS.benchmarks.Util.</code><codeclass="descname">scale_params</code><spanclass="sig-paren">(</span><em>data</em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/pyFTS/benchmarks/Util.html#scale_params"><spanclass="viewcode-link">[source]</span></a><aclass="headerlink"href="#pyFTS.benchmarks.Util.scale_params"title="Permalink to this definition">¶</a></dt>
<codeclass="descclassname">pyFTS.benchmarks.Util.</code><codeclass="descname">stats</code><spanclass="sig-paren">(</span><em>measure</em>, <em>data</em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/pyFTS/benchmarks/Util.html#stats"><spanclass="viewcode-link">[source]</span></a><aclass="headerlink"href="#pyFTS.benchmarks.Util.stats"title="Permalink to this definition">¶</a></dt>
<codeclass="descclassname">pyFTS.benchmarks.Util.</code><codeclass="descname">tabular_dataframe_columns</code><spanclass="sig-paren">(</span><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/pyFTS/benchmarks/Util.html#tabular_dataframe_columns"><spanclass="viewcode-link">[source]</span></a><aclass="headerlink"href="#pyFTS.benchmarks.Util.tabular_dataframe_columns"title="Permalink to this definition">¶</a></dt>
<codeclass="descclassname">pyFTS.benchmarks.Util.</code><codeclass="descname">unified_scaled_probabilistic</code><spanclass="sig-paren">(</span><em>experiments, tam, save=False, file=None, sort_columns=['CRPSAVG', 'CRPSSTD'], sort_ascend=[True, True], save_best=False, ignore=None, replace=None</em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/pyFTS/benchmarks/Util.html#unified_scaled_probabilistic"><spanclass="viewcode-link">[source]</span></a><aclass="headerlink"href="#pyFTS.benchmarks.Util.unified_scaled_probabilistic"title="Permalink to this definition">¶</a></dt>
<spanid="pyfts-benchmarks-arima-module"></span><h2>pyFTS.benchmarks.arima module<aclass="headerlink"href="#module-pyFTS.benchmarks.arima"title="Permalink to this headline">¶</a></h2>
<dlclass="class">
<dtid="pyFTS.benchmarks.arima.ARIMA">
<emclass="property">class </em><codeclass="descclassname">pyFTS.benchmarks.arima.</code><codeclass="descname">ARIMA</code><spanclass="sig-paren">(</span><em>**kwargs</em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/pyFTS/benchmarks/arima.html#ARIMA"><spanclass="viewcode-link">[source]</span></a><aclass="headerlink"href="#pyFTS.benchmarks.arima.ARIMA"title="Permalink to this definition">¶</a></dt>
<codeclass="descname">ar</code><spanclass="sig-paren">(</span><em>data</em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/pyFTS/benchmarks/arima.html#ARIMA.ar"><spanclass="viewcode-link">[source]</span></a><aclass="headerlink"href="#pyFTS.benchmarks.arima.ARIMA.ar"title="Permalink to this definition">¶</a></dt>
<codeclass="descname">forecast</code><spanclass="sig-paren">(</span><em>ndata</em>, <em>**kwargs</em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/pyFTS/benchmarks/arima.html#ARIMA.forecast"><spanclass="viewcode-link">[source]</span></a><aclass="headerlink"href="#pyFTS.benchmarks.arima.ARIMA.forecast"title="Permalink to this definition">¶</a></dt>
<codeclass="descname">forecast_ahead_distribution</code><spanclass="sig-paren">(</span><em>data</em>, <em>steps</em>, <em>**kwargs</em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/pyFTS/benchmarks/arima.html#ARIMA.forecast_ahead_distribution"><spanclass="viewcode-link">[source]</span></a><aclass="headerlink"href="#pyFTS.benchmarks.arima.ARIMA.forecast_ahead_distribution"title="Permalink to this definition">¶</a></dt>
<li><strong>data</strong>– time series data with the minimal length equal to the max_lag of the model</li>
<li><strong>steps</strong>– the number of steps ahead to forecast</li>
<li><strong>kwargs</strong>– model specific parameters</li>
</ul>
</td>
</tr>
<trclass="field-even field"><thclass="field-name">Returns:</th><tdclass="field-body"><pclass="first last">a list with the forecasted Probability Distributions</p>
<codeclass="descname">forecast_ahead_interval</code><spanclass="sig-paren">(</span><em>ndata</em>, <em>steps</em>, <em>**kwargs</em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/pyFTS/benchmarks/arima.html#ARIMA.forecast_ahead_interval"><spanclass="viewcode-link">[source]</span></a><aclass="headerlink"href="#pyFTS.benchmarks.arima.ARIMA.forecast_ahead_interval"title="Permalink to this definition">¶</a></dt>
<codeclass="descname">forecast_distribution</code><spanclass="sig-paren">(</span><em>data</em>, <em>**kwargs</em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/pyFTS/benchmarks/arima.html#ARIMA.forecast_distribution"><spanclass="viewcode-link">[source]</span></a><aclass="headerlink"href="#pyFTS.benchmarks.arima.ARIMA.forecast_distribution"title="Permalink to this definition">¶</a></dt>
<trclass="field-even field"><thclass="field-name">Returns:</th><tdclass="field-body"><pclass="first last">a list with the forecasted Probability Distributions</p>
<codeclass="descname">forecast_interval</code><spanclass="sig-paren">(</span><em>data</em>, <em>**kwargs</em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/pyFTS/benchmarks/arima.html#ARIMA.forecast_interval"><spanclass="viewcode-link">[source]</span></a><aclass="headerlink"href="#pyFTS.benchmarks.arima.ARIMA.forecast_interval"title="Permalink to this definition">¶</a></dt>
<codeclass="descname">ma</code><spanclass="sig-paren">(</span><em>data</em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/pyFTS/benchmarks/arima.html#ARIMA.ma"><spanclass="viewcode-link">[source]</span></a><aclass="headerlink"href="#pyFTS.benchmarks.arima.ARIMA.ma"title="Permalink to this definition">¶</a></dt>
<codeclass="descname">train</code><spanclass="sig-paren">(</span><em>data</em>, <em>**kwargs</em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/pyFTS/benchmarks/arima.html#ARIMA.train"><spanclass="viewcode-link">[source]</span></a><aclass="headerlink"href="#pyFTS.benchmarks.arima.ARIMA.train"title="Permalink to this definition">¶</a></dt>
<spanid="pyfts-benchmarks-knn-module"></span><h2>pyFTS.benchmarks.knn module<aclass="headerlink"href="#module-pyFTS.benchmarks.knn"title="Permalink to this headline">¶</a></h2>
<dlclass="class">
<dtid="pyFTS.benchmarks.knn.KNearestNeighbors">
<emclass="property">class </em><codeclass="descclassname">pyFTS.benchmarks.knn.</code><codeclass="descname">KNearestNeighbors</code><spanclass="sig-paren">(</span><em>**kwargs</em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/pyFTS/benchmarks/knn.html#KNearestNeighbors"><spanclass="viewcode-link">[source]</span></a><aclass="headerlink"href="#pyFTS.benchmarks.knn.KNearestNeighbors"title="Permalink to this definition">¶</a></dt>
<codeclass="descname">forecast_distribution</code><spanclass="sig-paren">(</span><em>data</em>, <em>**kwargs</em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/pyFTS/benchmarks/knn.html#KNearestNeighbors.forecast_distribution"><spanclass="viewcode-link">[source]</span></a><aclass="headerlink"href="#pyFTS.benchmarks.knn.KNearestNeighbors.forecast_distribution"title="Permalink to this definition">¶</a></dt>
<li><strong>data</strong>– time series data with the minimal length equal to the max_lag of the model</li>
<li><strong>kwargs</strong>– model specific parameters</li>
</ul>
</td>
</tr>
<trclass="field-even field"><thclass="field-name">Returns:</th><tdclass="field-body"><pclass="first last">a list with the forecasted Probability Distributions</p>
<codeclass="descname">knn</code><spanclass="sig-paren">(</span><em>sample</em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/pyFTS/benchmarks/knn.html#KNearestNeighbors.knn"><spanclass="viewcode-link">[source]</span></a><aclass="headerlink"href="#pyFTS.benchmarks.knn.KNearestNeighbors.knn"title="Permalink to this definition">¶</a></dt>
<codeclass="descname">train</code><spanclass="sig-paren">(</span><em>data</em>, <em>**kwargs</em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/pyFTS/benchmarks/knn.html#KNearestNeighbors.train"><spanclass="viewcode-link">[source]</span></a><aclass="headerlink"href="#pyFTS.benchmarks.knn.KNearestNeighbors.train"title="Permalink to this definition">¶</a></dt>
<spanid="pyfts-benchmarks-naive-module"></span><h2>pyFTS.benchmarks.naive module<aclass="headerlink"href="#module-pyFTS.benchmarks.naive"title="Permalink to this headline">¶</a></h2>
<dlclass="class">
<dtid="pyFTS.benchmarks.naive.Naive">
<emclass="property">class </em><codeclass="descclassname">pyFTS.benchmarks.naive.</code><codeclass="descname">Naive</code><spanclass="sig-paren">(</span><em>**kwargs</em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/pyFTS/benchmarks/naive.html#Naive"><spanclass="viewcode-link">[source]</span></a><aclass="headerlink"href="#pyFTS.benchmarks.naive.Naive"title="Permalink to this definition">¶</a></dt>
<codeclass="descname">forecast</code><spanclass="sig-paren">(</span><em>data</em>, <em>**kwargs</em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/pyFTS/benchmarks/naive.html#Naive.forecast"><spanclass="viewcode-link">[source]</span></a><aclass="headerlink"href="#pyFTS.benchmarks.naive.Naive.forecast"title="Permalink to this definition">¶</a></dt>
<spanid="pyfts-benchmarks-quantreg-module"></span><h2>pyFTS.benchmarks.quantreg module<aclass="headerlink"href="#module-pyFTS.benchmarks.quantreg"title="Permalink to this headline">¶</a></h2>
<emclass="property">class </em><codeclass="descclassname">pyFTS.benchmarks.quantreg.</code><codeclass="descname">QuantileRegression</code><spanclass="sig-paren">(</span><em>**kwargs</em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/pyFTS/benchmarks/quantreg.html#QuantileRegression"><spanclass="viewcode-link">[source]</span></a><aclass="headerlink"href="#pyFTS.benchmarks.quantreg.QuantileRegression"title="Permalink to this definition">¶</a></dt>
<codeclass="descname">forecast</code><spanclass="sig-paren">(</span><em>ndata</em>, <em>**kwargs</em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/pyFTS/benchmarks/quantreg.html#QuantileRegression.forecast"><spanclass="viewcode-link">[source]</span></a><aclass="headerlink"href="#pyFTS.benchmarks.quantreg.QuantileRegression.forecast"title="Permalink to this definition">¶</a></dt>
<codeclass="descname">forecast_ahead_distribution</code><spanclass="sig-paren">(</span><em>ndata</em>, <em>steps</em>, <em>**kwargs</em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/pyFTS/benchmarks/quantreg.html#QuantileRegression.forecast_ahead_distribution"><spanclass="viewcode-link">[source]</span></a><aclass="headerlink"href="#pyFTS.benchmarks.quantreg.QuantileRegression.forecast_ahead_distribution"title="Permalink to this definition">¶</a></dt>
<li><strong>data</strong>– time series data with the minimal length equal to the max_lag of the model</li>
<li><strong>steps</strong>– the number of steps ahead to forecast</li>
<li><strong>kwargs</strong>– model specific parameters</li>
</ul>
</td>
</tr>
<trclass="field-even field"><thclass="field-name">Returns:</th><tdclass="field-body"><pclass="first last">a list with the forecasted Probability Distributions</p>
<codeclass="descname">forecast_ahead_interval</code><spanclass="sig-paren">(</span><em>ndata</em>, <em>steps</em>, <em>**kwargs</em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/pyFTS/benchmarks/quantreg.html#QuantileRegression.forecast_ahead_interval"><spanclass="viewcode-link">[source]</span></a><aclass="headerlink"href="#pyFTS.benchmarks.quantreg.QuantileRegression.forecast_ahead_interval"title="Permalink to this definition">¶</a></dt>
<codeclass="descname">forecast_distribution</code><spanclass="sig-paren">(</span><em>ndata</em>, <em>**kwargs</em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/pyFTS/benchmarks/quantreg.html#QuantileRegression.forecast_distribution"><spanclass="viewcode-link">[source]</span></a><aclass="headerlink"href="#pyFTS.benchmarks.quantreg.QuantileRegression.forecast_distribution"title="Permalink to this definition">¶</a></dt>
<li><strong>data</strong>– time series data with the minimal length equal to the max_lag of the model</li>
<li><strong>kwargs</strong>– model specific parameters</li>
</ul>
</td>
</tr>
<trclass="field-even field"><thclass="field-name">Returns:</th><tdclass="field-body"><pclass="first last">a list with the forecasted Probability Distributions</p>
<codeclass="descname">forecast_interval</code><spanclass="sig-paren">(</span><em>ndata</em>, <em>**kwargs</em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/pyFTS/benchmarks/quantreg.html#QuantileRegression.forecast_interval"><spanclass="viewcode-link">[source]</span></a><aclass="headerlink"href="#pyFTS.benchmarks.quantreg.QuantileRegression.forecast_interval"title="Permalink to this definition">¶</a></dt>
<codeclass="descname">interval_to_interval</code><spanclass="sig-paren">(</span><em>data</em>, <em>lo_params</em>, <em>up_params</em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/pyFTS/benchmarks/quantreg.html#QuantileRegression.interval_to_interval"><spanclass="viewcode-link">[source]</span></a><aclass="headerlink"href="#pyFTS.benchmarks.quantreg.QuantileRegression.interval_to_interval"title="Permalink to this definition">¶</a></dt>
<codeclass="descname">linearmodel</code><spanclass="sig-paren">(</span><em>data</em>, <em>params</em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/pyFTS/benchmarks/quantreg.html#QuantileRegression.linearmodel"><spanclass="viewcode-link">[source]</span></a><aclass="headerlink"href="#pyFTS.benchmarks.quantreg.QuantileRegression.linearmodel"title="Permalink to this definition">¶</a></dt>
<codeclass="descname">point_to_interval</code><spanclass="sig-paren">(</span><em>data</em>, <em>lo_params</em>, <em>up_params</em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/pyFTS/benchmarks/quantreg.html#QuantileRegression.point_to_interval"><spanclass="viewcode-link">[source]</span></a><aclass="headerlink"href="#pyFTS.benchmarks.quantreg.QuantileRegression.point_to_interval"title="Permalink to this definition">¶</a></dt>
<codeclass="descname">train</code><spanclass="sig-paren">(</span><em>data</em>, <em>**kwargs</em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/pyFTS/benchmarks/quantreg.html#QuantileRegression.train"><spanclass="viewcode-link">[source]</span></a><aclass="headerlink"href="#pyFTS.benchmarks.quantreg.QuantileRegression.train"title="Permalink to this definition">¶</a></dt>
