Documentation update
This commit is contained in:
Petrônio Cândido
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@ -335,8 +335,6 @@
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<li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.Util.check_ignore_list">check_ignore_list() (in module pyFTS.benchmarks.Util)</a>
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</li>
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<li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.Util.check_replace_list">check_replace_list() (in module pyFTS.benchmarks.Util)</a>
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</li>
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<li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.ResidualAnalysis.chi_squared">chi_squared() (in module pyFTS.benchmarks.ResidualAnalysis)</a>
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</li>
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<li><a href="pyFTS.common.html#pyFTS.common.SortedCollection.SortedCollection.clear">clear() (pyFTS.common.SortedCollection.SortedCollection method)</a>
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</li>
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@ -357,6 +355,8 @@
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<li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.benchmarks.common_process_point_jobs">common_process_point_jobs() (in module pyFTS.benchmarks.benchmarks)</a>
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</li>
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<li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.benchmarks.common_process_probabilistic_jobs">common_process_probabilistic_jobs() (in module pyFTS.benchmarks.benchmarks)</a>
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</li>
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<li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.benchmarks.common_process_time_jobs">common_process_time_jobs() (in module pyFTS.benchmarks.benchmarks)</a>
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</li>
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</ul></td>
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<td style="width: 33%; vertical-align: top;"><ul>
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@ -432,6 +432,12 @@
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</li>
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<li><a href="pyFTS.models.seasonal.html#pyFTS.models.seasonal.common.DateTime.day_of_year">day_of_year (pyFTS.models.seasonal.common.DateTime attribute)</a>
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</li>
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<li><a href="pyFTS.models.multivariate.html#pyFTS.models.multivariate.grid.GridCluster.defuzzyfy">defuzzyfy() (pyFTS.models.multivariate.grid.GridCluster method)</a>
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<ul>
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<li><a href="pyFTS.partitioners.html#pyFTS.partitioners.partitioner.Partitioner.defuzzyfy">(pyFTS.partitioners.partitioner.Partitioner method)</a>
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</li>
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</ul></li>
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</ul></td>
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<td style="width: 33%; vertical-align: top;"><ul>
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<li><a href="pyFTS.probabilistic.html#pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution.density">density() (pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution method)</a>
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@ -468,10 +474,10 @@
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</li>
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<li><a href="pyFTS.common.html#pyFTS.common.Util.enumerate2">enumerate2() (in module pyFTS.common.Util)</a>
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</li>
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</ul></td>
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<td style="width: 33%; vertical-align: top;"><ul>
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<li><a href="pyFTS.probabilistic.html#pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution.expected_value">expected_value() (pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution method)</a>
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</li>
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</ul></td>
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<td style="width: 33%; vertical-align: top;"><ul>
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<li><a href="pyFTS.partitioners.html#pyFTS.partitioners.parallel_util.explore_partitioners">explore_partitioners() (in module pyFTS.partitioners.parallel_util)</a>
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<ul>
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@ -486,6 +492,12 @@
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</li>
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<li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.Util.extract_measure">extract_measure() (in module pyFTS.benchmarks.Util)</a>
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</li>
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<li><a href="pyFTS.models.seasonal.html#pyFTS.models.seasonal.partitioner.TimeGridPartitioner.extractor">extractor() (pyFTS.models.seasonal.partitioner.TimeGridPartitioner method)</a>
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<ul>
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<li><a href="pyFTS.partitioners.html#pyFTS.partitioners.partitioner.Partitioner.extractor">(pyFTS.partitioners.partitioner.Partitioner method)</a>
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</li>
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</ul></li>
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</ul></td>
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</tr></table>
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@ -525,6 +537,8 @@
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</li>
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</ul></li>
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<li><a href="pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFTS.flrg_lhs_conditional_probability">flrg_lhs_conditional_probability() (pyFTS.models.pwfts.ProbabilisticWeightedFTS method)</a>
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</li>
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<li><a href="pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFTS.flrg_lhs_conditional_probability_fuzzyfied">flrg_lhs_conditional_probability_fuzzyfied() (pyFTS.models.pwfts.ProbabilisticWeightedFTS method)</a>
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</li>
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<li><a href="pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFTS.flrg_lhs_unconditional_probability">flrg_lhs_unconditional_probability() (pyFTS.models.pwfts.ProbabilisticWeightedFTS method)</a>
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</li>
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@ -588,10 +602,10 @@
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<li><a href="pyFTS.models.html#pyFTS.models.yu.WeightedFTS.forecast">(pyFTS.models.yu.WeightedFTS method)</a>
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</li>
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</ul></li>
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<li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.gaussianproc.GPR.forecast_ahead">forecast_ahead() (pyFTS.benchmarks.gaussianproc.GPR method)</a>
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<li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.BSTS.ARIMA.forecast_ahead">forecast_ahead() (pyFTS.benchmarks.BSTS.ARIMA method)</a>
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<ul>
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<li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.knn.KNearestNeighbors.forecast_ahead">(pyFTS.benchmarks.knn.KNearestNeighbors method)</a>
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<li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.gaussianproc.GPR.forecast_ahead">(pyFTS.benchmarks.gaussianproc.GPR method)</a>
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</li>
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<li><a href="pyFTS.common.html#pyFTS.common.fts.FTS.forecast_ahead">(pyFTS.common.fts.FTS method)</a>
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</li>
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@ -618,6 +632,8 @@
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<li><a href="pyFTS.common.html#pyFTS.common.fts.FTS.forecast_ahead_distribution">(pyFTS.common.fts.FTS method)</a>
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</li>
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<li><a href="pyFTS.models.ensemble.html#pyFTS.models.ensemble.ensemble.EnsembleFTS.forecast_ahead_distribution">(pyFTS.models.ensemble.ensemble.EnsembleFTS method)</a>
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</li>
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<li><a href="pyFTS.models.multivariate.html#pyFTS.models.multivariate.cmvfts.ClusteredMVFTS.forecast_ahead_distribution">(pyFTS.models.multivariate.cmvfts.ClusteredMVFTS method)</a>
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</li>
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<li><a href="pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFTS.forecast_ahead_distribution">(pyFTS.models.pwfts.ProbabilisticWeightedFTS method)</a>
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</li>
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@ -640,6 +656,8 @@
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<li><a href="pyFTS.models.html#pyFTS.models.ifts.IntervalFTS.forecast_ahead_interval">(pyFTS.models.ifts.IntervalFTS method)</a>
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</li>
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<li><a href="pyFTS.models.html#pyFTS.models.ifts.WeightedIntervalFTS.forecast_ahead_interval">(pyFTS.models.ifts.WeightedIntervalFTS method)</a>
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</li>
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<li><a href="pyFTS.models.multivariate.html#pyFTS.models.multivariate.cmvfts.ClusteredMVFTS.forecast_ahead_interval">(pyFTS.models.multivariate.cmvfts.ClusteredMVFTS method)</a>
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</li>
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<li><a href="pyFTS.models.multivariate.html#pyFTS.models.multivariate.mvfts.MVFTS.forecast_ahead_interval">(pyFTS.models.multivariate.mvfts.MVFTS method)</a>
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</li>
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@ -670,6 +688,8 @@
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<li><a href="pyFTS.models.ensemble.html#pyFTS.models.ensemble.ensemble.EnsembleFTS.forecast_distribution">(pyFTS.models.ensemble.ensemble.EnsembleFTS method)</a>
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</li>
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<li><a href="pyFTS.models.ensemble.html#pyFTS.models.ensemble.multiseasonal.SeasonalEnsembleFTS.forecast_distribution">(pyFTS.models.ensemble.multiseasonal.SeasonalEnsembleFTS method)</a>
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</li>
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<li><a href="pyFTS.models.multivariate.html#pyFTS.models.multivariate.cmvfts.ClusteredMVFTS.forecast_distribution">(pyFTS.models.multivariate.cmvfts.ClusteredMVFTS method)</a>
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</li>
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<li><a href="pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFTS.forecast_distribution">(pyFTS.models.pwfts.ProbabilisticWeightedFTS method)</a>
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</li>
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@ -692,6 +712,8 @@
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<li><a href="pyFTS.models.html#pyFTS.models.ifts.IntervalFTS.forecast_interval">(pyFTS.models.ifts.IntervalFTS method)</a>
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</li>
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<li><a href="pyFTS.models.html#pyFTS.models.ifts.WeightedIntervalFTS.forecast_interval">(pyFTS.models.ifts.WeightedIntervalFTS method)</a>
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</li>
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<li><a href="pyFTS.models.multivariate.html#pyFTS.models.multivariate.cmvfts.ClusteredMVFTS.forecast_interval">(pyFTS.models.multivariate.cmvfts.ClusteredMVFTS method)</a>
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</li>
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<li><a href="pyFTS.models.multivariate.html#pyFTS.models.multivariate.mvfts.MVFTS.forecast_interval">(pyFTS.models.multivariate.mvfts.MVFTS method)</a>
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</li>
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@ -806,8 +828,14 @@
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</ul></li>
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<li><a href="pyFTS.models.html#pyFTS.models.yu.WeightedFTS.generate_FLRG">generate_FLRG() (pyFTS.models.yu.WeightedFTS method)</a>
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</li>
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<li><a href="pyFTS.models.html#pyFTS.models.hofts.HighOrderFTS.generate_flrg_fuzzyfied">generate_flrg_fuzzyfied() (pyFTS.models.hofts.HighOrderFTS method)</a>
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<li><a href="pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFTS.generate_flrg2">generate_flrg2() (pyFTS.models.pwfts.ProbabilisticWeightedFTS method)</a>
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</li>
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<li><a href="pyFTS.models.html#pyFTS.models.hofts.HighOrderFTS.generate_flrg_fuzzyfied">generate_flrg_fuzzyfied() (pyFTS.models.hofts.HighOrderFTS method)</a>
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<ul>
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<li><a href="pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFTS.generate_flrg_fuzzyfied">(pyFTS.models.pwfts.ProbabilisticWeightedFTS method)</a>
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</li>
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</ul></li>
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<li><a href="pyFTS.models.multivariate.html#pyFTS.models.multivariate.mvfts.MVFTS.generate_flrs">generate_flrs() (pyFTS.models.multivariate.mvfts.MVFTS method)</a>
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</li>
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<li><a href="pyFTS.data.html#pyFTS.data.artificial.generate_gaussian_linear">generate_gaussian_linear() (in module pyFTS.data.artificial)</a>
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@ -1061,6 +1089,8 @@
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<li><a href="pyFTS.distributed.html#pyFTS.distributed.spark.get_partitioner">get_partitioner() (in module pyFTS.distributed.spark)</a>
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</li>
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<li><a href="pyFTS.models.ensemble.html#pyFTS.models.ensemble.ensemble.EnsembleFTS.get_point">get_point() (pyFTS.models.ensemble.ensemble.EnsembleFTS method)</a>
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</li>
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<li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.Measures.get_point_ahead_statistics">get_point_ahead_statistics() (in module pyFTS.benchmarks.Measures)</a>
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</li>
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<li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.benchmarks.get_point_methods">get_point_methods() (in module pyFTS.benchmarks.benchmarks)</a>
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</li>
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@ -1098,6 +1128,8 @@
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<li><a href="pyFTS.models.html#pyFTS.models.ifts.WeightedIntervalFTS.get_sequence_membership">(pyFTS.models.ifts.WeightedIntervalFTS method)</a>
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</li>
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</ul></li>
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<li><a href="pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFTS.get_sets_from_both_fuzzyfication">get_sets_from_both_fuzzyfication() (pyFTS.models.pwfts.ProbabilisticWeightedFTS method)</a>
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</li>
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<li><a href="pyFTS.common.html#pyFTS.common.fts.FTS.get_UoD">get_UoD() (pyFTS.common.fts.FTS method)</a>
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<ul>
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@ -1267,6 +1299,8 @@
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<li><a href="pyFTS.common.html#pyFTS.common.fts.FTS.len_total">len_total() (pyFTS.common.fts.FTS method)</a>
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</li>
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<li><a href="pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFLRG.lhs_conditional_probability">lhs_conditional_probability() (pyFTS.models.pwfts.ProbabilisticWeightedFLRG method)</a>
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</li>
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<li><a href="pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFLRG.lhs_conditional_probability_fuzzyfied">lhs_conditional_probability_fuzzyfied() (pyFTS.models.pwfts.ProbabilisticWeightedFLRG method)</a>
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</li>
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<li><a href="pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.perturbation.linear">linear() (in module pyFTS.models.nonstationary.perturbation)</a>
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</li>
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@ -1275,6 +1309,8 @@
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</ul></td>
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<td style="width: 33%; vertical-align: top;"><ul>
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<li><a href="pyFTS.models.seasonal.html#pyFTS.models.seasonal.SeasonalIndexer.LinearSeasonalIndexer">LinearSeasonalIndexer (class in pyFTS.models.seasonal.SeasonalIndexer)</a>
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</li>
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<li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.ResidualAnalysis.ljung_box_test">ljung_box_test() (in module pyFTS.benchmarks.ResidualAnalysis)</a>
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</li>
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<li><a href="pyFTS.common.html#pyFTS.common.Util.load_env">load_env() (in module pyFTS.common.Util)</a>
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</li>
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@ -1446,7 +1482,7 @@
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</li>
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<li><a href="pyFTS.common.html#pyFTS.common.Util.plot_probability_distributions">plot_probability_distributions() (in module pyFTS.common.Util)</a>
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</li>
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<li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.ResidualAnalysis.plot_residuals">plot_residuals() (in module pyFTS.benchmarks.ResidualAnalysis)</a>
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<li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.ResidualAnalysis.plot_residuals_by_model">plot_residuals_by_model() (in module pyFTS.benchmarks.ResidualAnalysis)</a>
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</li>
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<li><a href="pyFTS.common.html#pyFTS.common.Util.plot_rules">plot_rules() (in module pyFTS.common.Util)</a>
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</li>
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@ -1461,8 +1497,6 @@
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<li><a href="pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.util.plot_sets_conditional">plot_sets_conditional() (in module pyFTS.models.nonstationary.util)</a>
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</li>
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<li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.benchmarks.plotCompared">plotCompared() (in module pyFTS.benchmarks.benchmarks)</a>
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</li>
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<li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.ResidualAnalysis.plotResiduals">plotResiduals() (in module pyFTS.benchmarks.ResidualAnalysis)</a>
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</li>
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<li><a href="pyFTS.partitioners.html#pyFTS.partitioners.Entropy.PMF">PMF() (in module pyFTS.partitioners.Entropy)</a>
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</li>
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@ -1529,6 +1563,8 @@
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</li>
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</ul></li>
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<li><a href="pyFTS.probabilistic.html#pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution.pseudologlikelihood">pseudologlikelihood() (pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution method)</a>
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</li>
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<li><a href="pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFTS.pwflrg_lhs_memberhip_fuzzyfied">pwflrg_lhs_memberhip_fuzzyfied() (pyFTS.models.pwfts.ProbabilisticWeightedFTS method)</a>
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</li>
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<li><a href="pyFTS.html#module-pyFTS">pyFTS (module)</a>
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</li>
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@ -2025,6 +2061,8 @@
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</ul></td>
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<td style="width: 33%; vertical-align: top;"><ul>
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<li><a href="pyFTS.models.ensemble.html#pyFTS.models.ensemble.multiseasonal.train_individual_model">train_individual_model() (in module pyFTS.models.ensemble.multiseasonal)</a>
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</li>
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<li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.benchmarks.train_test_time">train_test_time() (in module pyFTS.benchmarks.benchmarks)</a>
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</li>
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<li><a href="pyFTS.common.html#pyFTS.common.Composite.FuzzySet.transform">transform() (pyFTS.common.Composite.FuzzySet method)</a>
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<code class="descclassname">pyFTS.benchmarks.benchmarks.</code><code class="descname">common_process_probabilistic_jobs</code><span class="sig-paren">(</span><em>conn</em>, <em>data</em>, <em>job</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.common_process_probabilistic_jobs" title="Permalink to this definition">¶</a></dt>
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<dd></dd></dl>
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<dl class="function">
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<dt id="pyFTS.benchmarks.benchmarks.common_process_time_jobs">
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<code class="descclassname">pyFTS.benchmarks.benchmarks.</code><code class="descname">common_process_time_jobs</code><span class="sig-paren">(</span><em>conn</em>, <em>data</em>, <em>job</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.common_process_time_jobs" title="Permalink to this definition">¶</a></dt>
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<dd></dd></dl>
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<dl class="function">
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<dt id="pyFTS.benchmarks.benchmarks.compareModelsPlot">
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<code class="descclassname">pyFTS.benchmarks.benchmarks.</code><code class="descname">compareModelsPlot</code><span class="sig-paren">(</span><em>original</em>, <em>models_fo</em>, <em>models_ho</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.compareModelsPlot" title="Permalink to this definition">¶</a></dt>
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@ -581,6 +586,11 @@ informing the list of dispy nodes on ‘nodes’ parameter.</p>
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<code class="descclassname">pyFTS.benchmarks.benchmarks.</code><code class="descname">sliding_window_benchmarks2</code><span class="sig-paren">(</span><em>data</em>, <em>windowsize</em>, <em>train=0.8</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.sliding_window_benchmarks2" title="Permalink to this definition">¶</a></dt>
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<dd></dd></dl>
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<dl class="function">
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<dt id="pyFTS.benchmarks.benchmarks.train_test_time">
|
||||
<code class="descclassname">pyFTS.benchmarks.benchmarks.</code><code class="descname">train_test_time</code><span class="sig-paren">(</span><em>data</em>, <em>windowsize</em>, <em>train=0.8</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.train_test_time" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
</div>
|
||||
<div class="section" id="module-pyFTS.benchmarks.Measures">
|
||||
<span id="pyfts-benchmarks-measures-module"></span><h2>pyFTS.benchmarks.Measures module<a class="headerlink" href="#module-pyFTS.benchmarks.Measures" title="Permalink to this headline">¶</a></h2>
|
||||
@ -751,7 +761,7 @@ Brier (1950). “Verification of Forecasts Expressed in Terms of Probability”.
|
||||
<tbody valign="top">
|
||||
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
|
||||
<li><strong>data</strong> – test data</li>
|
||||
<li><strong>model</strong> – FTS model with interval forecasting capability</li>
|
||||
<li><strong>intervals</strong> – predicted intervals for each datapoint</li>
|
||||
<li><strong>kwargs</strong> – </li>
|
||||
</ul>
|
||||
</td>
|
||||
@ -787,6 +797,28 @@ Brier (1950). “Verification of Forecasts Expressed in Terms of Probability”.
|
||||
</table>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="function">
|
||||
<dt id="pyFTS.benchmarks.Measures.get_point_ahead_statistics">
|
||||
<code class="descclassname">pyFTS.benchmarks.Measures.</code><code class="descname">get_point_ahead_statistics</code><span class="sig-paren">(</span><em>data</em>, <em>forecasts</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.Measures.get_point_ahead_statistics" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Condensate all measures for point forecasters</p>
|
||||
<table class="docutils field-list" frame="void" rules="none">
|
||||
<col class="field-name" />
|
||||
<col class="field-body" />
|
||||
<tbody valign="top">
|
||||
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
|
||||
<li><strong>data</strong> – test data</li>
|
||||
<li><strong>model</strong> – FTS model with point forecasting capability</li>
|
||||
<li><strong>kwargs</strong> – </li>
|
||||
</ul>
|
||||
</td>
|
||||
</tr>
|
||||
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">a list with the RMSE, SMAPE and U Statistic</p>
|
||||
</td>
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="function">
|
||||
<dt id="pyFTS.benchmarks.Measures.get_point_statistics">
|
||||
<code class="descclassname">pyFTS.benchmarks.Measures.</code><code class="descname">get_point_statistics</code><span class="sig-paren">(</span><em>data</em>, <em>model</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.Measures.get_point_statistics" title="Permalink to this definition">¶</a></dt>
|
||||
@ -1030,30 +1062,9 @@ Good IJ (1952). “Rational Decisions.”Journal of the Royal Statistical Socie
|
||||
<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="function">
|
||||
<dt id="pyFTS.benchmarks.ResidualAnalysis.chi_squared">
|
||||
<code class="descclassname">pyFTS.benchmarks.ResidualAnalysis.</code><code class="descname">chi_squared</code><span class="sig-paren">(</span><em>q</em>, <em>h</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.ResidualAnalysis.chi_squared" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Chi-Squared value</p>
|
||||
<table class="docutils field-list" frame="void" rules="none">
|
||||
<col class="field-name" />
|
||||
<col class="field-body" />
|
||||
<tbody valign="top">
|
||||
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
|
||||
<li><strong>q</strong> – </li>
|
||||
<li><strong>h</strong> – </li>
|
||||
</ul>
|
||||
</td>
|
||||
</tr>
|
||||
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last"></p>
|
||||
</td>
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="function">
|
||||
<dt id="pyFTS.benchmarks.ResidualAnalysis.compare_residuals">
|
||||
<code class="descclassname">pyFTS.benchmarks.ResidualAnalysis.</code><code class="descname">compare_residuals</code><span class="sig-paren">(</span><em>data</em>, <em>models</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.ResidualAnalysis.compare_residuals" title="Permalink to this definition">¶</a></dt>
|
||||
<code class="descclassname">pyFTS.benchmarks.ResidualAnalysis.</code><code class="descname">compare_residuals</code><span class="sig-paren">(</span><em>data</em>, <em>models</em>, <em>alpha=0.05</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.ResidualAnalysis.compare_residuals" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Compare residual’s statistics of several models</p>
|
||||
<table class="docutils field-list" frame="void" rules="none">
|
||||
<col class="field-name" />
|
||||
@ -1073,32 +1084,13 @@ Good IJ (1952). “Rational Decisions.”Journal of the Royal Statistical Socie
|
||||
</dd></dl>
|
||||
|
||||
<dl class="function">
|
||||
<dt id="pyFTS.benchmarks.ResidualAnalysis.plotResiduals">
|
||||
<code class="descclassname">pyFTS.benchmarks.ResidualAnalysis.</code><code class="descname">plotResiduals</code><span class="sig-paren">(</span><em>targets, models, tam=[8, 8], save=False, file=None</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.ResidualAnalysis.plotResiduals" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Plot residuals and statistics</p>
|
||||
<table class="docutils field-list" frame="void" rules="none">
|
||||
<col class="field-name" />
|
||||
<col class="field-body" />
|
||||
<tbody valign="top">
|
||||
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
|
||||
<li><strong>targets</strong> – </li>
|
||||
<li><strong>models</strong> – </li>
|
||||
<li><strong>tam</strong> – </li>
|
||||
<li><strong>save</strong> – </li>
|
||||
<li><strong>file</strong> – </li>
|
||||
</ul>
|
||||
</td>
|
||||
</tr>
|
||||
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last"></p>
|
||||
</td>
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
</dd></dl>
|
||||
<dt id="pyFTS.benchmarks.ResidualAnalysis.ljung_box_test">
|
||||
<code class="descclassname">pyFTS.benchmarks.ResidualAnalysis.</code><code class="descname">ljung_box_test</code><span class="sig-paren">(</span><em>residuals, lags=[1, 2, 3], alpha=0.5</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.ResidualAnalysis.ljung_box_test" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="function">
|
||||
<dt id="pyFTS.benchmarks.ResidualAnalysis.plot_residuals">
|
||||
<code class="descclassname">pyFTS.benchmarks.ResidualAnalysis.</code><code class="descname">plot_residuals</code><span class="sig-paren">(</span><em>targets, models, tam=[8, 8], save=False, file=None</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.ResidualAnalysis.plot_residuals" title="Permalink to this definition">¶</a></dt>
|
||||
<dt id="pyFTS.benchmarks.ResidualAnalysis.plot_residuals_by_model">
|
||||
<code class="descclassname">pyFTS.benchmarks.ResidualAnalysis.</code><code class="descname">plot_residuals_by_model</code><span class="sig-paren">(</span><em>targets, models, tam=[8, 8], save=False, file=None</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.ResidualAnalysis.plot_residuals_by_model" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="function">
|
||||
@ -1109,7 +1101,7 @@ Good IJ (1952). “Rational Decisions.”Journal of the Royal Statistical Socie
|
||||
|
||||
<dl class="function">
|
||||
<dt id="pyFTS.benchmarks.ResidualAnalysis.single_plot_residuals">
|
||||
<code class="descclassname">pyFTS.benchmarks.ResidualAnalysis.</code><code class="descname">single_plot_residuals</code><span class="sig-paren">(</span><em>targets, forecasts, order, tam=[8, 8], save=False, file=None</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.ResidualAnalysis.single_plot_residuals" title="Permalink to this definition">¶</a></dt>
|
||||
<code class="descclassname">pyFTS.benchmarks.ResidualAnalysis.</code><code class="descname">single_plot_residuals</code><span class="sig-paren">(</span><em>res, order, tam=[10, 7], save=False, file=None</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.ResidualAnalysis.single_plot_residuals" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
</div>
|
||||
@ -1741,28 +1733,6 @@ of the metric ‘measure’ with the same ‘tag’, returning a Pandas DataFram
|
||||
</table>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="method">
|
||||
<dt id="pyFTS.benchmarks.knn.KNearestNeighbors.forecast_ahead">
|
||||
<code class="descname">forecast_ahead</code><span class="sig-paren">(</span><em>data</em>, <em>steps</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.knn.KNearestNeighbors.forecast_ahead" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Point forecast n steps ahead</p>
|
||||
<table class="docutils field-list" frame="void" rules="none">
|
||||
<col class="field-name" />
|
||||
<col class="field-body" />
|
||||
<tbody valign="top">
|
||||
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
|
||||
<li><strong>data</strong> – time series data with the minimal length equal to the max_lag of the model</li>
|
||||
<li><strong>steps</strong> – the number of steps ahead to forecast (default: 1)</li>
|
||||
<li><strong>start_at</strong> – in the multi step forecasting, the index of the data where to start forecasting (default: 0)</li>
|
||||
</ul>
|
||||
</td>
|
||||
</tr>
|
||||
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">a list with the forecasted values</p>
|
||||
</td>
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="method">
|
||||
<dt id="pyFTS.benchmarks.knn.KNearestNeighbors.forecast_ahead_distribution">
|
||||
<code class="descname">forecast_ahead_distribution</code><span class="sig-paren">(</span><em>data</em>, <em>steps</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.knn.KNearestNeighbors.forecast_ahead_distribution" title="Permalink to this definition">¶</a></dt>
|
||||
@ -2241,6 +2211,28 @@ of the metric ‘measure’ with the same ‘tag’, returning a Pandas DataFram
|
||||
</table>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="method">
|
||||
<dt id="pyFTS.benchmarks.BSTS.ARIMA.forecast_ahead">
|
||||
<code class="descname">forecast_ahead</code><span class="sig-paren">(</span><em>data</em>, <em>steps</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.BSTS.ARIMA.forecast_ahead" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Point forecast n steps ahead</p>
|
||||
<table class="docutils field-list" frame="void" rules="none">
|
||||
<col class="field-name" />
|
||||
<col class="field-body" />
|
||||
<tbody valign="top">
|
||||
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
|
||||
<li><strong>data</strong> – time series data with the minimal length equal to the max_lag of the model</li>
|
||||
<li><strong>steps</strong> – the number of steps ahead to forecast (default: 1)</li>
|
||||
<li><strong>start_at</strong> – in the multi step forecasting, the index of the data where to start forecasting (default: 0)</li>
|
||||
</ul>
|
||||
</td>
|
||||
</tr>
|
||||
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">a list with the forecasted values</p>
|
||||
</td>
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="method">
|
||||
<dt id="pyFTS.benchmarks.BSTS.ARIMA.forecast_ahead_distribution">
|
||||
<code class="descname">forecast_ahead_distribution</code><span class="sig-paren">(</span><em>data</em>, <em>steps</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.BSTS.ARIMA.forecast_ahead_distribution" title="Permalink to this definition">¶</a></dt>
|
||||
|
||||
34
docs/build/html/pyFTS.models.html
vendored
34
docs/build/html/pyFTS.models.html
vendored
@ -941,6 +941,11 @@ US Dollar to Ringgit Malaysia,” Int. J. Comput. Intell. Appl., vol. 12, no. 1,
|
||||
<code class="descname">lhs_conditional_probability</code><span class="sig-paren">(</span><em>x</em>, <em>sets</em>, <em>norm</em>, <em>uod</em>, <em>nbins</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.models.pwfts.ProbabilisticWeightedFLRG.lhs_conditional_probability" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="method">
|
||||
<dt id="pyFTS.models.pwfts.ProbabilisticWeightedFLRG.lhs_conditional_probability_fuzzyfied">
|
||||
<code class="descname">lhs_conditional_probability_fuzzyfied</code><span class="sig-paren">(</span><em>lhs_mv</em>, <em>sets</em>, <em>norm</em>, <em>uod</em>, <em>nbins</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.models.pwfts.ProbabilisticWeightedFLRG.lhs_conditional_probability_fuzzyfied" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="method">
|
||||
<dt id="pyFTS.models.pwfts.ProbabilisticWeightedFLRG.partition_function">
|
||||
<code class="descname">partition_function</code><span class="sig-paren">(</span><em>sets</em>, <em>uod</em>, <em>nbins=100</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.models.pwfts.ProbabilisticWeightedFLRG.partition_function" title="Permalink to this definition">¶</a></dt>
|
||||
@ -973,6 +978,11 @@ US Dollar to Ringgit Malaysia,” Int. J. Comput. Intell. Appl., vol. 12, no. 1,
|
||||
<code class="descname">flrg_lhs_conditional_probability</code><span class="sig-paren">(</span><em>x</em>, <em>flrg</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.models.pwfts.ProbabilisticWeightedFTS.flrg_lhs_conditional_probability" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="method">
|
||||
<dt id="pyFTS.models.pwfts.ProbabilisticWeightedFTS.flrg_lhs_conditional_probability_fuzzyfied">
|
||||
<code class="descname">flrg_lhs_conditional_probability_fuzzyfied</code><span class="sig-paren">(</span><em>x</em>, <em>flrg</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.models.pwfts.ProbabilisticWeightedFTS.flrg_lhs_conditional_probability_fuzzyfied" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="method">
|
||||
<dt id="pyFTS.models.pwfts.ProbabilisticWeightedFTS.flrg_lhs_unconditional_probability">
|
||||
<code class="descname">flrg_lhs_unconditional_probability</code><span class="sig-paren">(</span><em>flrg</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.models.pwfts.ProbabilisticWeightedFTS.flrg_lhs_unconditional_probability" title="Permalink to this definition">¶</a></dt>
|
||||
@ -1117,6 +1127,16 @@ US Dollar to Ringgit Malaysia,” Int. J. Comput. Intell. Appl., vol. 12, no. 1,
|
||||
<code class="descname">generate_flrg</code><span class="sig-paren">(</span><em>data</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.models.pwfts.ProbabilisticWeightedFTS.generate_flrg" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="method">
|
||||
<dt id="pyFTS.models.pwfts.ProbabilisticWeightedFTS.generate_flrg2">
|
||||
<code class="descname">generate_flrg2</code><span class="sig-paren">(</span><em>data</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.models.pwfts.ProbabilisticWeightedFTS.generate_flrg2" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="method">
|
||||
<dt id="pyFTS.models.pwfts.ProbabilisticWeightedFTS.generate_flrg_fuzzyfied">
|
||||
<code class="descname">generate_flrg_fuzzyfied</code><span class="sig-paren">(</span><em>data</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.models.pwfts.ProbabilisticWeightedFTS.generate_flrg_fuzzyfied" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="method">
|
||||
<dt id="pyFTS.models.pwfts.ProbabilisticWeightedFTS.generate_lhs_flrg">
|
||||
<code class="descname">generate_lhs_flrg</code><span class="sig-paren">(</span><em>sample</em>, <em>explain=False</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.models.pwfts.ProbabilisticWeightedFTS.generate_lhs_flrg" title="Permalink to this definition">¶</a></dt>
|
||||
@ -1137,6 +1157,11 @@ US Dollar to Ringgit Malaysia,” Int. J. Comput. Intell. Appl., vol. 12, no. 1,
|
||||
<code class="descname">get_midpoint</code><span class="sig-paren">(</span><em>flrg</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.models.pwfts.ProbabilisticWeightedFTS.get_midpoint" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="method">
|
||||
<dt id="pyFTS.models.pwfts.ProbabilisticWeightedFTS.get_sets_from_both_fuzzyfication">
|
||||
<code class="descname">get_sets_from_both_fuzzyfication</code><span class="sig-paren">(</span><em>sample</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.models.pwfts.ProbabilisticWeightedFTS.get_sets_from_both_fuzzyfication" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="method">
|
||||
<dt id="pyFTS.models.pwfts.ProbabilisticWeightedFTS.get_upper">
|
||||
<code class="descname">get_upper</code><span class="sig-paren">(</span><em>flrg</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.models.pwfts.ProbabilisticWeightedFTS.get_upper" title="Permalink to this definition">¶</a></dt>
|
||||
@ -1144,12 +1169,12 @@ US Dollar to Ringgit Malaysia,” Int. J. Comput. Intell. Appl., vol. 12, no. 1,
|
||||
|
||||
<dl class="method">
|
||||
<dt id="pyFTS.models.pwfts.ProbabilisticWeightedFTS.interval_heuristic">
|
||||
<code class="descname">interval_heuristic</code><span class="sig-paren">(</span><em>sample</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.models.pwfts.ProbabilisticWeightedFTS.interval_heuristic" title="Permalink to this definition">¶</a></dt>
|
||||
<code class="descname">interval_heuristic</code><span class="sig-paren">(</span><em>sample</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.models.pwfts.ProbabilisticWeightedFTS.interval_heuristic" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="method">
|
||||
<dt id="pyFTS.models.pwfts.ProbabilisticWeightedFTS.interval_quantile">
|
||||
<code class="descname">interval_quantile</code><span class="sig-paren">(</span><em>ndata</em>, <em>alpha</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.models.pwfts.ProbabilisticWeightedFTS.interval_quantile" title="Permalink to this definition">¶</a></dt>
|
||||
<code class="descname">interval_quantile</code><span class="sig-paren">(</span><em>ndata</em>, <em>alpha</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.models.pwfts.ProbabilisticWeightedFTS.interval_quantile" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="method">
|
||||
@ -1162,6 +1187,11 @@ US Dollar to Ringgit Malaysia,” Int. J. Comput. Intell. Appl., vol. 12, no. 1,
|
||||
<code class="descname">point_heuristic</code><span class="sig-paren">(</span><em>sample</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.models.pwfts.ProbabilisticWeightedFTS.point_heuristic" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="method">
|
||||
<dt id="pyFTS.models.pwfts.ProbabilisticWeightedFTS.pwflrg_lhs_memberhip_fuzzyfied">
|
||||
<code class="descname">pwflrg_lhs_memberhip_fuzzyfied</code><span class="sig-paren">(</span><em>flrg</em>, <em>sample</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.models.pwfts.ProbabilisticWeightedFTS.pwflrg_lhs_memberhip_fuzzyfied" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="method">
|
||||
<dt id="pyFTS.models.pwfts.ProbabilisticWeightedFTS.train">
|
||||
<code class="descname">train</code><span class="sig-paren">(</span><em>data</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.models.pwfts.ProbabilisticWeightedFTS.train" title="Permalink to this definition">¶</a></dt>
|
||||
|
||||
91
docs/build/html/pyFTS.models.multivariate.html
vendored
91
docs/build/html/pyFTS.models.multivariate.html
vendored
@ -428,6 +428,11 @@ overlapped fuzzy sets.</p>
|
||||
</table>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="method">
|
||||
<dt id="pyFTS.models.multivariate.grid.GridCluster.defuzzyfy">
|
||||
<code class="descname">defuzzyfy</code><span class="sig-paren">(</span><em>values</em>, <em>mode='both'</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.models.multivariate.grid.GridCluster.defuzzyfy" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
</dd></dl>
|
||||
|
||||
<dl class="class">
|
||||
@ -779,6 +784,50 @@ multivariate fuzzy set base.</p>
|
||||
</table>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="method">
|
||||
<dt id="pyFTS.models.multivariate.cmvfts.ClusteredMVFTS.forecast_ahead_distribution">
|
||||
<code class="descname">forecast_ahead_distribution</code><span class="sig-paren">(</span><em>data</em>, <em>steps</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.models.multivariate.cmvfts.ClusteredMVFTS.forecast_ahead_distribution" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Probabilistic forecast n steps ahead</p>
|
||||
<table class="docutils field-list" frame="void" rules="none">
|
||||
<col class="field-name" />
|
||||
<col class="field-body" />
|
||||
<tbody valign="top">
|
||||
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
|
||||
<li><strong>data</strong> – time series data with the minimal length equal to the max_lag of the model</li>
|
||||
<li><strong>steps</strong> – the number of steps ahead to forecast</li>
|
||||
<li><strong>start_at</strong> – in the multi step forecasting, the index of the data where to start forecasting (default: 0)</li>
|
||||
</ul>
|
||||
</td>
|
||||
</tr>
|
||||
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">a list with the forecasted Probability Distributions</p>
|
||||
</td>
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="method">
|
||||
<dt id="pyFTS.models.multivariate.cmvfts.ClusteredMVFTS.forecast_ahead_interval">
|
||||
<code class="descname">forecast_ahead_interval</code><span class="sig-paren">(</span><em>data</em>, <em>steps</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.models.multivariate.cmvfts.ClusteredMVFTS.forecast_ahead_interval" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Interval forecast n steps ahead</p>
|
||||
<table class="docutils field-list" frame="void" rules="none">
|
||||
<col class="field-name" />
|
||||
<col class="field-body" />
|
||||
<tbody valign="top">
|
||||
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
|
||||
<li><strong>data</strong> – time series data with the minimal length equal to the max_lag of the model</li>
|
||||
<li><strong>steps</strong> – the number of steps ahead to forecast</li>
|
||||
<li><strong>start_at</strong> – in the multi step forecasting, the index of the data where to start forecasting (default: 0)</li>
|
||||
</ul>
|
||||
</td>
|
||||
</tr>
|
||||
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">a list with the forecasted intervals</p>
|
||||
</td>
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="method">
|
||||
<dt id="pyFTS.models.multivariate.cmvfts.ClusteredMVFTS.forecast_ahead_multivariate">
|
||||
<code class="descname">forecast_ahead_multivariate</code><span class="sig-paren">(</span><em>data</em>, <em>steps</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.models.multivariate.cmvfts.ClusteredMVFTS.forecast_ahead_multivariate" title="Permalink to this definition">¶</a></dt>
|
||||
@ -801,6 +850,48 @@ multivariate fuzzy set base.</p>
|
||||
</table>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="method">
|
||||
<dt id="pyFTS.models.multivariate.cmvfts.ClusteredMVFTS.forecast_distribution">
|
||||
<code class="descname">forecast_distribution</code><span class="sig-paren">(</span><em>data</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.models.multivariate.cmvfts.ClusteredMVFTS.forecast_distribution" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Probabilistic forecast one step ahead</p>
|
||||
<table class="docutils field-list" frame="void" rules="none">
|
||||
<col class="field-name" />
|
||||
<col class="field-body" />
|
||||
<tbody valign="top">
|
||||
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
|
||||
<li><strong>data</strong> – time series data with the minimal length equal to the max_lag of the model</li>
|
||||
<li><strong>kwargs</strong> – model specific parameters</li>
|
||||
</ul>
|
||||
</td>
|
||||
</tr>
|
||||
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">a list with probabilistic.ProbabilityDistribution objects representing the forecasted Probability Distributions</p>
|
||||
</td>
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="method">
|
||||
<dt id="pyFTS.models.multivariate.cmvfts.ClusteredMVFTS.forecast_interval">
|
||||
<code class="descname">forecast_interval</code><span class="sig-paren">(</span><em>data</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.models.multivariate.cmvfts.ClusteredMVFTS.forecast_interval" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Interval forecast one step ahead</p>
|
||||
<table class="docutils field-list" frame="void" rules="none">
|
||||
<col class="field-name" />
|
||||
<col class="field-body" />
|
||||
<tbody valign="top">
|
||||
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
|
||||
<li><strong>data</strong> – time series data with the minimal length equal to the max_lag of the model</li>
|
||||
<li><strong>kwargs</strong> – model specific parameters</li>
|
||||
</ul>
|
||||
</td>
|
||||
</tr>
|
||||
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">a list with the prediction intervals</p>
|
||||
</td>
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="method">
|
||||
<dt id="pyFTS.models.multivariate.cmvfts.ClusteredMVFTS.forecast_multivariate">
|
||||
<code class="descname">forecast_multivariate</code><span class="sig-paren">(</span><em>data</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.models.multivariate.cmvfts.ClusteredMVFTS.forecast_multivariate" title="Permalink to this definition">¶</a></dt>
|
||||
|
||||
6
docs/build/html/pyFTS.models.seasonal.html
vendored
6
docs/build/html/pyFTS.models.seasonal.html
vendored
@ -600,6 +600,12 @@
|
||||
<code class="descname">build_index</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.models.seasonal.partitioner.TimeGridPartitioner.build_index" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="method">
|
||||
<dt id="pyFTS.models.seasonal.partitioner.TimeGridPartitioner.extractor">
|
||||
<code class="descname">extractor</code><span class="sig-paren">(</span><em>x</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.models.seasonal.partitioner.TimeGridPartitioner.extractor" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Extract a single primitive type from an structured instance</p>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="method">
|
||||
<dt id="pyFTS.models.seasonal.partitioner.TimeGridPartitioner.plot">
|
||||
<code class="descname">plot</code><span class="sig-paren">(</span><em>ax</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.models.seasonal.partitioner.TimeGridPartitioner.plot" title="Permalink to this definition">¶</a></dt>
|
||||
|
||||
11
docs/build/html/pyFTS.partitioners.html
vendored
11
docs/build/html/pyFTS.partitioners.html
vendored
@ -167,6 +167,17 @@ fuzzy set.</p>
|
||||
<p>the data is inside the UoD.</p>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="method">
|
||||
<dt id="pyFTS.partitioners.partitioner.Partitioner.defuzzyfy">
|
||||
<code class="descname">defuzzyfy</code><span class="sig-paren">(</span><em>values</em>, <em>mode='both'</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.partitioners.partitioner.Partitioner.defuzzyfy" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="method">
|
||||
<dt id="pyFTS.partitioners.partitioner.Partitioner.extractor">
|
||||
<code class="descname">extractor</code><span class="sig-paren">(</span><em>x</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.partitioners.partitioner.Partitioner.extractor" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Extract a single primitive type from an structured instance</p>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="method">
|
||||
<dt id="pyFTS.partitioners.partitioner.Partitioner.fuzzyfy">
|
||||
<code class="descname">fuzzyfy</code><span class="sig-paren">(</span><em>data</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.partitioners.partitioner.Partitioner.fuzzyfy" title="Permalink to this definition">¶</a></dt>
|
||||
|
||||
2
docs/build/html/searchindex.js
vendored
2
docs/build/html/searchindex.js
vendored
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Reference in New Issue
Block a user