Documentation update

docs/build/html/_modules/index.html

@@ -161,6 +161,7 @@
 <li><a href="pyFTS/partitioners/Huarng.html">pyFTS.partitioners.Huarng</a></li>
 <li><a href="pyFTS/partitioners/Simple.html">pyFTS.partitioners.Simple</a></li>
 <li><a href="pyFTS/partitioners/Singleton.html">pyFTS.partitioners.Singleton</a></li>
+<li><a href="pyFTS/partitioners/SubClust.html">pyFTS.partitioners.SubClust</a></li>
 <li><a href="pyFTS/partitioners/Util.html">pyFTS.partitioners.Util</a></li>
 <li><a href="pyFTS/partitioners/parallel_util.html">pyFTS.partitioners.parallel_util</a></li>
 <li><a href="pyFTS/partitioners/partitioner.html">pyFTS.partitioners.partitioner</a></li>

docs/build/html/genindex.html

@@ -166,6 +166,8 @@
         <li><a href="pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.honsfts.HighOrderNonStationaryFLRG.append_rhs">(pyFTS.models.nonstationary.honsfts.HighOrderNonStationaryFLRG method)</a>
 </li>
         <li><a href="pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.nsfts.ConventionalNonStationaryFLRG.append_rhs">(pyFTS.models.nonstationary.nsfts.ConventionalNonStationaryFLRG method)</a>
+</li>
+        <li><a href="pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.nsfts.WeightedNonStationaryFLRG.append_rhs">(pyFTS.models.nonstationary.nsfts.WeightedNonStationaryFLRG method)</a>
 </li>
         <li><a href="pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFLRG.append_rhs">(pyFTS.models.pwfts.ProbabilisticWeightedFLRG method)</a>
 </li>
@@ -280,6 +282,8 @@
         <li><a href="pyFTS.partitioners.html#pyFTS.partitioners.Huarng.HuarngPartitioner.build">(pyFTS.partitioners.Huarng.HuarngPartitioner method)</a>
 </li>
         <li><a href="pyFTS.partitioners.html#pyFTS.partitioners.Singleton.SingletonPartitioner.build">(pyFTS.partitioners.Singleton.SingletonPartitioner method)</a>
+</li>
+        <li><a href="pyFTS.partitioners.html#pyFTS.partitioners.SubClust.SubClustPartitioner.build">(pyFTS.partitioners.SubClust.SubClustPartitioner method)</a>
 </li>
         <li><a href="pyFTS.partitioners.html#pyFTS.partitioners.partitioner.Partitioner.build">(pyFTS.partitioners.partitioner.Partitioner method)</a>
 </li>
@@ -358,10 +362,10 @@
 </li>
       <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.benchmarks.common_process_time_jobs">common_process_time_jobs() (in module pyFTS.benchmarks.benchmarks)</a>
 </li>
-  </ul></td>
-  <td style="width: 33%; vertical-align: top;"><ul>
       <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.ResidualAnalysis.compare_residuals">compare_residuals() (in module pyFTS.benchmarks.ResidualAnalysis)</a>
 </li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
       <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.benchmarks.compareModelsPlot">compareModelsPlot() (in module pyFTS.benchmarks.benchmarks)</a>
 </li>
       <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.benchmarks.compareModelsTable">compareModelsTable() (in module pyFTS.benchmarks.benchmarks)</a>
@@ -374,6 +378,8 @@
 
       <ul>
         <li><a href="pyFTS.models.html#pyFTS.models.hwang.HighOrderFTS.configure_lags">(pyFTS.models.hwang.HighOrderFTS method)</a>
+</li>
+        <li><a href="pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.honsfts.HighOrderNonStationaryFTS.configure_lags">(pyFTS.models.nonstationary.honsfts.HighOrderNonStationaryFTS method)</a>
 </li>
       </ul></li>
       <li><a href="pyFTS.models.seasonal.html#pyFTS.models.seasonal.cmsfts.ContextualMultiSeasonalFTS">ContextualMultiSeasonalFTS (class in pyFTS.models.seasonal.cmsfts)</a>
@@ -608,6 +614,10 @@
         <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.gaussianproc.GPR.forecast_ahead">(pyFTS.benchmarks.gaussianproc.GPR method)</a>
 </li>
         <li><a href="pyFTS.common.html#pyFTS.common.fts.FTS.forecast_ahead">(pyFTS.common.fts.FTS method)</a>
+</li>
+        <li><a href="pyFTS.models.incremental.html#pyFTS.models.incremental.IncrementalEnsemble.IncrementalEnsembleFTS.forecast_ahead">(pyFTS.models.incremental.IncrementalEnsemble.IncrementalEnsembleFTS method)</a>
+</li>
+        <li><a href="pyFTS.models.incremental.html#pyFTS.models.incremental.TimeVariant.Retrainer.forecast_ahead">(pyFTS.models.incremental.TimeVariant.Retrainer method)</a>
 </li>
         <li><a href="pyFTS.models.multivariate.html#pyFTS.models.multivariate.mvfts.MVFTS.forecast_ahead">(pyFTS.models.multivariate.mvfts.MVFTS method)</a>
 </li>
@@ -638,6 +648,8 @@
         <li><a href="pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFTS.forecast_ahead_distribution">(pyFTS.models.pwfts.ProbabilisticWeightedFTS method)</a>
 </li>
       </ul></li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
       <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.arima.ARIMA.forecast_ahead_interval">forecast_ahead_interval() (pyFTS.benchmarks.arima.ARIMA method)</a>
 
       <ul>
@@ -662,8 +674,6 @@
         <li><a href="pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFTS.forecast_ahead_interval">(pyFTS.models.pwfts.ProbabilisticWeightedFTS method)</a>
 </li>
       </ul></li>
-  </ul></td>
-  <td style="width: 33%; vertical-align: top;"><ul>
       <li><a href="pyFTS.common.html#pyFTS.common.fts.FTS.forecast_ahead_multivariate">forecast_ahead_multivariate() (pyFTS.common.fts.FTS method)</a>
 
       <ul>
@@ -718,8 +728,6 @@
         <li><a href="pyFTS.models.multivariate.html#pyFTS.models.multivariate.mvfts.MVFTS.forecast_interval">(pyFTS.models.multivariate.mvfts.MVFTS method)</a>
 </li>
         <li><a href="pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.cvfts.ConditionalVarianceFTS.forecast_interval">(pyFTS.models.nonstationary.cvfts.ConditionalVarianceFTS method)</a>
-</li>
-        <li><a href="pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.honsfts.HighOrderNonStationaryFTS.forecast_interval">(pyFTS.models.nonstationary.honsfts.HighOrderNonStationaryFTS method)</a>
 </li>
         <li><a href="pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.nsfts.NonStationaryFTS.forecast_interval">(pyFTS.models.nonstationary.nsfts.NonStationaryFTS method)</a>
 </li>
@@ -816,6 +824,8 @@
         <li><a href="pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.honsfts.HighOrderNonStationaryFTS.generate_flrg">(pyFTS.models.nonstationary.honsfts.HighOrderNonStationaryFTS method)</a>
 </li>
         <li><a href="pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.nsfts.NonStationaryFTS.generate_flrg">(pyFTS.models.nonstationary.nsfts.NonStationaryFTS method)</a>
+</li>
+        <li><a href="pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.nsfts.WeightedNonStationaryFTS.generate_flrg">(pyFTS.models.nonstationary.nsfts.WeightedNonStationaryFTS method)</a>
 </li>
         <li><a href="pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFTS.generate_flrg">(pyFTS.models.pwfts.ProbabilisticWeightedFTS method)</a>
 </li>
@@ -980,12 +990,12 @@
 </li>
       <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.Measures.get_distribution_ahead_statistics">get_distribution_ahead_statistics() (in module pyFTS.benchmarks.Measures)</a>
 </li>
-  </ul></td>
-  <td style="width: 33%; vertical-align: top;"><ul>
       <li><a href="pyFTS.models.ensemble.html#pyFTS.models.ensemble.ensemble.EnsembleFTS.get_distribution_interquantile">get_distribution_interquantile() (pyFTS.models.ensemble.ensemble.EnsembleFTS method)</a>
 </li>
       <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.Measures.get_distribution_statistics">get_distribution_statistics() (in module pyFTS.benchmarks.Measures)</a>
 </li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
       <li><a href="pyFTS.common.html#pyFTS.common.FuzzySet.get_fuzzysets">get_fuzzysets() (in module pyFTS.common.FuzzySet)</a>
 </li>
       <li><a href="pyFTS.models.seasonal.html#pyFTS.models.seasonal.SeasonalIndexer.DateTimeSeasonalIndexer.get_index">get_index() (pyFTS.models.seasonal.SeasonalIndexer.DateTimeSeasonalIndexer method)</a>
@@ -1020,6 +1030,8 @@
         <li><a href="pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.flrg.NonStationaryFLRG.get_key">(pyFTS.models.nonstationary.flrg.NonStationaryFLRG method)</a>
 </li>
         <li><a href="pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.nsfts.ConventionalNonStationaryFLRG.get_key">(pyFTS.models.nonstationary.nsfts.ConventionalNonStationaryFLRG method)</a>
+</li>
+        <li><a href="pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.nsfts.WeightedNonStationaryFLRG.get_key">(pyFTS.models.nonstationary.nsfts.WeightedNonStationaryFLRG method)</a>
 </li>
         <li><a href="pyFTS.models.seasonal.html#pyFTS.models.seasonal.sfts.SeasonalFLRG.get_key">(pyFTS.models.seasonal.sfts.SeasonalFLRG method)</a>
 </li>
@@ -1040,6 +1052,8 @@
         <li><a href="pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.common.FuzzySet.get_lower">(pyFTS.models.nonstationary.common.FuzzySet method)</a>
 </li>
         <li><a href="pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.flrg.NonStationaryFLRG.get_lower">(pyFTS.models.nonstationary.flrg.NonStationaryFLRG method)</a>
+</li>
+        <li><a href="pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.honsfts.HighOrderNonStationaryFLRG.get_lower">(pyFTS.models.nonstationary.honsfts.HighOrderNonStationaryFLRG method)</a>
 </li>
         <li><a href="pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFLRG.get_lower">(pyFTS.models.pwfts.ProbabilisticWeightedFLRG method)</a>
 </li>
@@ -1070,6 +1084,10 @@
         <li><a href="pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.common.FuzzySet.get_midpoint">(pyFTS.models.nonstationary.common.FuzzySet method)</a>
 </li>
         <li><a href="pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.flrg.NonStationaryFLRG.get_midpoint">(pyFTS.models.nonstationary.flrg.NonStationaryFLRG method)</a>
+</li>
+        <li><a href="pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.honsfts.HighOrderNonStationaryFLRG.get_midpoint">(pyFTS.models.nonstationary.honsfts.HighOrderNonStationaryFLRG method)</a>
+</li>
+        <li><a href="pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.nsfts.WeightedNonStationaryFLRG.get_midpoint">(pyFTS.models.nonstationary.nsfts.WeightedNonStationaryFLRG method)</a>
 </li>
         <li><a href="pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFLRG.get_midpoint">(pyFTS.models.pwfts.ProbabilisticWeightedFLRG method)</a>
 </li>
@@ -1154,6 +1172,8 @@
         <li><a href="pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.common.FuzzySet.get_upper">(pyFTS.models.nonstationary.common.FuzzySet method)</a>
 </li>
         <li><a href="pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.flrg.NonStationaryFLRG.get_upper">(pyFTS.models.nonstationary.flrg.NonStationaryFLRG method)</a>
+</li>
+        <li><a href="pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.honsfts.HighOrderNonStationaryFLRG.get_upper">(pyFTS.models.nonstationary.honsfts.HighOrderNonStationaryFLRG method)</a>
 </li>
         <li><a href="pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFLRG.get_upper">(pyFTS.models.pwfts.ProbabilisticWeightedFLRG method)</a>
 </li>
@@ -1216,6 +1236,8 @@
 <h2 id="I">I</h2>
 <table style="width: 100%" class="indextable genindextable"><tr>
   <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="pyFTS.partitioners.html#pyFTS.partitioners.SubClust.imax">imax() (in module pyFTS.partitioners.SubClust)</a>
+</li>
       <li><a href="pyFTS.models.html#pyFTS.models.ismailefendi.ImprovedWeightedFLRG">ImprovedWeightedFLRG (class in pyFTS.models.ismailefendi)</a>
 </li>
       <li><a href="pyFTS.models.html#pyFTS.models.ismailefendi.ImprovedWeightedFTS">ImprovedWeightedFTS (class in pyFTS.models.ismailefendi)</a>
@@ -1239,11 +1261,11 @@
       <li><a href="pyFTS.common.html#pyFTS.common.SortedCollection.SortedCollection.insert">insert() (pyFTS.common.SortedCollection.SortedCollection method)</a>
 </li>
       <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.Util.insert_benchmark">insert_benchmark() (in module pyFTS.benchmarks.Util)</a>
-</li>
-      <li><a href="pyFTS.hyperparam.html#pyFTS.hyperparam.Util.insert_hyperparam">insert_hyperparam() (in module pyFTS.hyperparam.Util)</a>
 </li>
   </ul></td>
   <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="pyFTS.hyperparam.html#pyFTS.hyperparam.Util.insert_hyperparam">insert_hyperparam() (in module pyFTS.hyperparam.Util)</a>
+</li>
       <li><a href="pyFTS.common.html#pyFTS.common.SortedCollection.SortedCollection.insert_right">insert_right() (pyFTS.common.SortedCollection.SortedCollection method)</a>
 </li>
       <li><a href="pyFTS.common.html#pyFTS.common.SortedCollection.SortedCollection.inside">inside() (pyFTS.common.SortedCollection.SortedCollection method)</a>
@@ -1398,10 +1420,18 @@
 <h2 id="O">O</h2>
 <table style="width: 100%" class="indextable genindextable"><tr>
   <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.Util.open_benchmark_db">open_benchmark_db() (in module pyFTS.benchmarks.Util)</a>
+      <li><a href="pyFTS.common.html#pyFTS.common.fts.FTS.offset">offset() (pyFTS.common.fts.FTS method)</a>
+
+      <ul>
+        <li><a href="pyFTS.models.incremental.html#pyFTS.models.incremental.IncrementalEnsemble.IncrementalEnsembleFTS.offset">(pyFTS.models.incremental.IncrementalEnsemble.IncrementalEnsembleFTS method)</a>
 </li>
+        <li><a href="pyFTS.models.incremental.html#pyFTS.models.incremental.TimeVariant.Retrainer.offset">(pyFTS.models.incremental.TimeVariant.Retrainer method)</a>
+</li>
+      </ul></li>
   </ul></td>
   <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.Util.open_benchmark_db">open_benchmark_db() (in module pyFTS.benchmarks.Util)</a>
+</li>
       <li><a href="pyFTS.hyperparam.html#pyFTS.hyperparam.Util.open_hyperparam_db">open_hyperparam_db() (in module pyFTS.hyperparam.Util)</a>
 </li>
       <li><a href="pyFTS.models.html#pyFTS.models.song.ConventionalFTS.operation_matrix">operation_matrix() (pyFTS.models.song.ConventionalFTS method)</a>
@@ -1791,6 +1821,8 @@
       <li><a href="pyFTS.partitioners.html#module-pyFTS.partitioners.Simple">pyFTS.partitioners.Simple (module)</a>
 </li>
       <li><a href="pyFTS.partitioners.html#module-pyFTS.partitioners.Singleton">pyFTS.partitioners.Singleton (module)</a>
+</li>
+      <li><a href="pyFTS.partitioners.html#module-pyFTS.partitioners.SubClust">pyFTS.partitioners.SubClust (module)</a>
 </li>
       <li><a href="pyFTS.partitioners.html#module-pyFTS.partitioners.Util">pyFTS.partitioners.Util (module)</a>
 </li>
@@ -1932,10 +1964,10 @@
 </li>
       <li><a href="pyFTS.models.ensemble.html#pyFTS.models.ensemble.ensemble.AllMethodEnsembleFTS.set_transformations">set_transformations() (pyFTS.models.ensemble.ensemble.AllMethodEnsembleFTS method)</a>
 </li>
-  </ul></td>
-  <td style="width: 33%; vertical-align: top;"><ul>
       <li><a href="pyFTS.distributed.html#pyFTS.distributed.spark.share_parameters">share_parameters() (in module pyFTS.distributed.spark)</a>
 </li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
       <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.Measures.sharpness">sharpness() (in module pyFTS.benchmarks.Measures)</a>
 </li>
       <li><a href="pyFTS.common.html#pyFTS.common.Util.show_and_save_image">show_and_save_image() (in module pyFTS.common.Util)</a>
@@ -1993,6 +2025,10 @@
       <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.Util.stats">stats() (in module pyFTS.benchmarks.Util)</a>
 </li>
       <li><a href="pyFTS.models.seasonal.html#pyFTS.models.seasonal.common.strip_datepart">strip_datepart() (in module pyFTS.models.seasonal.common)</a>
+</li>
+      <li><a href="pyFTS.partitioners.html#pyFTS.partitioners.SubClust.subclust">subclust() (in module pyFTS.partitioners.SubClust)</a>
+</li>
+      <li><a href="pyFTS.partitioners.html#pyFTS.partitioners.SubClust.SubClustPartitioner">SubClustPartitioner (class in pyFTS.partitioners.SubClust)</a>
 </li>
   </ul></td>
 </tr></table>
@@ -2054,6 +2090,8 @@
         <li><a href="pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.honsfts.HighOrderNonStationaryFTS.train">(pyFTS.models.nonstationary.honsfts.HighOrderNonStationaryFTS method)</a>
 </li>
         <li><a href="pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.nsfts.NonStationaryFTS.train">(pyFTS.models.nonstationary.nsfts.NonStationaryFTS method)</a>
+</li>
+        <li><a href="pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.nsfts.WeightedNonStationaryFTS.train">(pyFTS.models.nonstationary.nsfts.WeightedNonStationaryFTS method)</a>
 </li>
         <li><a href="pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFTS.train">(pyFTS.models.pwfts.ProbabilisticWeightedFTS method)</a>
 </li>
@@ -2155,6 +2193,10 @@
       <li><a href="pyFTS.models.html#pyFTS.models.ifts.WeightedIntervalFTS">WeightedIntervalFTS (class in pyFTS.models.ifts)</a>
 </li>
       <li><a href="pyFTS.models.multivariate.html#pyFTS.models.multivariate.wmvfts.WeightedMVFTS">WeightedMVFTS (class in pyFTS.models.multivariate.wmvfts)</a>
+</li>
+      <li><a href="pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.nsfts.WeightedNonStationaryFLRG">WeightedNonStationaryFLRG (class in pyFTS.models.nonstationary.nsfts)</a>
+</li>
+      <li><a href="pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.nsfts.WeightedNonStationaryFTS">WeightedNonStationaryFTS (class in pyFTS.models.nonstationary.nsfts)</a>
 </li>
       <li><a href="pyFTS.models.html#pyFTS.models.cheng.TrendWeightedFLRG.weights">weights() (pyFTS.models.cheng.TrendWeightedFLRG method)</a>
 
@@ -2164,6 +2206,10 @@
         <li><a href="pyFTS.models.html#pyFTS.models.ismailefendi.ImprovedWeightedFLRG.weights">(pyFTS.models.ismailefendi.ImprovedWeightedFLRG method)</a>
 </li>
         <li><a href="pyFTS.models.multivariate.html#pyFTS.models.multivariate.wmvfts.WeightedFLRG.weights">(pyFTS.models.multivariate.wmvfts.WeightedFLRG method)</a>
+</li>
+        <li><a href="pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.honsfts.HighOrderNonStationaryFLRG.weights">(pyFTS.models.nonstationary.honsfts.HighOrderNonStationaryFLRG method)</a>
+</li>
+        <li><a href="pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.nsfts.WeightedNonStationaryFLRG.weights">(pyFTS.models.nonstationary.nsfts.WeightedNonStationaryFLRG method)</a>
 </li>
         <li><a href="pyFTS.models.html#pyFTS.models.sadaei.ExponentialyWeightedFLRG.weights">(pyFTS.models.sadaei.ExponentialyWeightedFLRG method)</a>
 </li>

docs/build/html/modules.html

@@ -205,7 +205,7 @@
 <li class="toctree-l4"><a class="reference internal" href="pyFTS.partitioners.html#module-pyFTS.partitioners.Huarng">pyFTS.partitioners.Huarng module</a></li>
 <li class="toctree-l4"><a class="reference internal" href="pyFTS.partitioners.html#module-pyFTS.partitioners.Singleton">pyFTS.partitioners.Singleton module</a></li>
 <li class="toctree-l4"><a class="reference internal" href="pyFTS.partitioners.html#module-pyFTS.partitioners.Simple">pyFTS.partitioners.Simple module</a></li>
-<li class="toctree-l4"><a class="reference internal" href="pyFTS.partitioners.html#pyfts-partitioners-subclust-module">pyFTS.partitioners.SubClust module</a></li>
+<li class="toctree-l4"><a class="reference internal" href="pyFTS.partitioners.html#module-pyFTS.partitioners.SubClust">pyFTS.partitioners.SubClust module</a></li>
 <li class="toctree-l4"><a class="reference internal" href="pyFTS.partitioners.html#module-pyFTS.partitioners.Util">pyFTS.partitioners.Util module</a></li>
 <li class="toctree-l4"><a class="reference internal" href="pyFTS.partitioners.html#module-pyFTS.partitioners.parallel_util">pyFTS.partitioners.parallel_util module</a></li>
 </ul>

docs/build/html/py-modindex.html

@@ -615,6 +615,11 @@
        <td>&#160;&#160;&#160;
        <a href="pyFTS.partitioners.html#module-pyFTS.partitioners.Singleton"><code class="xref">pyFTS.partitioners.Singleton</code></a></td><td>
        <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="pyFTS.partitioners.html#module-pyFTS.partitioners.SubClust"><code class="xref">pyFTS.partitioners.SubClust</code></a></td><td>
+       <em></em></td></tr>
      <tr class="cg-1">
        <td></td>
        <td>&#160;&#160;&#160;

docs/build/html/pyFTS.common.html

@@ -1622,7 +1622,7 @@ when the LHS pattern is identified on time t.</p>
 <col class="field-name" />
 <col class="field-body" />
 <tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>model</strong> – </td>
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>model</strong> – a model to clone the parameters</td>
 </tr>
 </tbody>
 </table>
@@ -1843,7 +1843,17 @@ when the LHS pattern is identified on time t.</p>
 <dl class="method">
 <dt id="pyFTS.common.fts.FTS.get_UoD">
 <code class="descname">get_UoD</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.common.fts.FTS.get_UoD" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
+<dd><p>Returns the interval of the known bounds of the universe of discourse (UoD), i. e.,
+the known minimum and maximum values of the time series.</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">Returns:</th><td class="field-body">A set with the lower and the upper bounds of the UoD</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
 
 <dl class="method">
 <dt id="pyFTS.common.fts.FTS.len_total">
@@ -1875,6 +1885,22 @@ when the LHS pattern is identified on time t.</p>
 </table>
 </dd></dl>
 
+<dl class="method">
+<dt id="pyFTS.common.fts.FTS.offset">
+<code class="descname">offset</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.common.fts.FTS.offset" title="Permalink to this definition">¶</a></dt>
+<dd><p>Returns the number of lags to skip in the input test data in order to synchronize it with
+the forecasted values given by the predict function. This is necessary due to the order of the
+model, among other parameters.</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">Returns:</th><td class="field-body">An integer with the number of lags to skip</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+
 <dl class="method">
 <dt id="pyFTS.common.fts.FTS.predict">
 <code class="descname">predict</code><span class="sig-paren">(</span><em>data</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.common.fts.FTS.predict" title="Permalink to this definition">¶</a></dt>

docs/build/html/pyFTS.html

@@ -265,7 +265,7 @@
 <li class="toctree-l2"><a class="reference internal" href="pyFTS.partitioners.html#module-pyFTS.partitioners.Huarng">pyFTS.partitioners.Huarng module</a></li>
 <li class="toctree-l2"><a class="reference internal" href="pyFTS.partitioners.html#module-pyFTS.partitioners.Singleton">pyFTS.partitioners.Singleton module</a></li>
 <li class="toctree-l2"><a class="reference internal" href="pyFTS.partitioners.html#module-pyFTS.partitioners.Simple">pyFTS.partitioners.Simple module</a></li>
-<li class="toctree-l2"><a class="reference internal" href="pyFTS.partitioners.html#pyfts-partitioners-subclust-module">pyFTS.partitioners.SubClust module</a></li>
+<li class="toctree-l2"><a class="reference internal" href="pyFTS.partitioners.html#module-pyFTS.partitioners.SubClust">pyFTS.partitioners.SubClust module</a></li>
 <li class="toctree-l2"><a class="reference internal" href="pyFTS.partitioners.html#module-pyFTS.partitioners.Util">pyFTS.partitioners.Util module</a></li>
 <li class="toctree-l2"><a class="reference internal" href="pyFTS.partitioners.html#module-pyFTS.partitioners.parallel_util">pyFTS.partitioners.parallel_util module</a></li>
 </ul>

docs/build/html/pyFTS.models.ensemble.html

@@ -275,7 +275,17 @@ XIII Brazilian Congress on Computational Intelligence, 2017. Rio de Janeiro, Bra
 <dl class="method">
 <dt id="pyFTS.models.ensemble.ensemble.EnsembleFTS.get_UoD">
 <code class="descname">get_UoD</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.models.ensemble.ensemble.EnsembleFTS.get_UoD" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
+<dd><p>Returns the interval of the known bounds of the universe of discourse (UoD), i. e.,
+the known minimum and maximum values of the time series.</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">Returns:</th><td class="field-body">A set with the lower and the upper bounds of the UoD</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
 
 <dl class="method">
 <dt id="pyFTS.models.ensemble.ensemble.EnsembleFTS.get_distribution_interquantile">

docs/build/html/pyFTS.models.incremental.html

@@ -116,12 +116,15 @@
 </div>
 <div class="section" id="module-pyFTS.models.incremental.TimeVariant">
 <span id="pyfts-models-incremental-timevariant-module"></span><h2>pyFTS.models.incremental.TimeVariant module<a class="headerlink" href="#module-pyFTS.models.incremental.TimeVariant" title="Permalink to this headline">¶</a></h2>
-<p>Meta model that wraps another FTS method and continously retrain it using a data window with the most recent data</p>
+<p>Meta model that wraps another FTS method and continously retrain it using a data window with
+the most recent data</p>
 <dl class="class">
 <dt id="pyFTS.models.incremental.TimeVariant.Retrainer">
 <em class="property">class </em><code class="descclassname">pyFTS.models.incremental.TimeVariant.</code><code class="descname">Retrainer</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.models.incremental.TimeVariant.Retrainer" title="Permalink to this definition">¶</a></dt>
 <dd><p>Bases: <a class="reference internal" href="pyFTS.common.html#pyFTS.common.fts.FTS" title="pyFTS.common.fts.FTS"><code class="xref py py-class docutils literal notranslate"><span class="pre">pyFTS.common.fts.FTS</span></code></a></p>
-<p>Meta model for incremental/online learning</p>
+<p>Meta model for incremental/online learning that retrain its internal model after
+data windows controlled by the parameter ‘batch_size’, using as the training data a
+window of recent lags, whose size is controlled by the parameter ‘window_length’.</p>
 <dl class="method">
 <dt id="pyFTS.models.incremental.TimeVariant.Retrainer.forecast">
 <code class="descname">forecast</code><span class="sig-paren">(</span><em>data</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.models.incremental.TimeVariant.Retrainer.forecast" title="Permalink to this definition">¶</a></dt>
@@ -143,6 +146,44 @@
 </table>
 </dd></dl>
 
+<dl class="method">
+<dt id="pyFTS.models.incremental.TimeVariant.Retrainer.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.models.incremental.TimeVariant.Retrainer.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.models.incremental.TimeVariant.Retrainer.offset">
+<code class="descname">offset</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.models.incremental.TimeVariant.Retrainer.offset" title="Permalink to this definition">¶</a></dt>
+<dd><p>Returns the number of lags to skip in the input test data in order to synchronize it with
+the forecasted values given by the predict function. This is necessary due to the order of the
+model, among other parameters.</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">Returns:</th><td class="field-body">An integer with the number of lags to skip</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+
 <dl class="method">
 <dt id="pyFTS.models.incremental.TimeVariant.Retrainer.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.incremental.TimeVariant.Retrainer.train" title="Permalink to this definition">¶</a></dt>
@@ -193,6 +234,44 @@
 </table>
 </dd></dl>
 
+<dl class="method">
+<dt id="pyFTS.models.incremental.IncrementalEnsemble.IncrementalEnsembleFTS.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.models.incremental.IncrementalEnsemble.IncrementalEnsembleFTS.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.models.incremental.IncrementalEnsemble.IncrementalEnsembleFTS.offset">
+<code class="descname">offset</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.models.incremental.IncrementalEnsemble.IncrementalEnsembleFTS.offset" title="Permalink to this definition">¶</a></dt>
+<dd><p>Returns the number of lags to skip in the input test data in order to synchronize it with
+the forecasted values given by the predict function. This is necessary due to the order of the
+model, among other parameters.</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">Returns:</th><td class="field-body">An integer with the number of lags to skip</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+
 <dl class="method">
 <dt id="pyFTS.models.incremental.IncrementalEnsemble.IncrementalEnsembleFTS.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.incremental.IncrementalEnsemble.IncrementalEnsembleFTS.train" title="Permalink to this definition">¶</a></dt>

docs/build/html/pyFTS.models.multivariate.html

@@ -530,7 +530,7 @@ multivariate fuzzy set base.</p>
 <col class="field-name" />
 <col class="field-body" />
 <tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>model</strong> – </td>
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>model</strong> – a model to clone the parameters</td>
 </tr>
 </tbody>
 </table>

docs/build/html/pyFTS.models.nonstationary.html

@@ -426,16 +426,74 @@ IEEE Transactions on Fuzzy Systems, v. 16, n. 4, p. 1072-1086, 2008.</p>
 
 <dl class="method">
 <dt id="pyFTS.models.nonstationary.honsfts.HighOrderNonStationaryFLRG.append_rhs">
-<code class="descname">append_rhs</code><span class="sig-paren">(</span><em>c</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.models.nonstationary.honsfts.HighOrderNonStationaryFLRG.append_rhs" title="Permalink to this definition">¶</a></dt>
+<code class="descname">append_rhs</code><span class="sig-paren">(</span><em>fset</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.models.nonstationary.honsfts.HighOrderNonStationaryFLRG.append_rhs" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="method">
+<dt id="pyFTS.models.nonstationary.honsfts.HighOrderNonStationaryFLRG.get_lower">
+<code class="descname">get_lower</code><span class="sig-paren">(</span><em>sets</em>, <em>perturb</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.models.nonstationary.honsfts.HighOrderNonStationaryFLRG.get_lower" title="Permalink to this definition">¶</a></dt>
+<dd><p>Returns the lower bound value for the RHS fuzzy sets</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name" />
+<col class="field-body" />
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>sets</strong> – fuzzy sets</td>
+</tr>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body">lower bound value</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+
+<dl class="method">
+<dt id="pyFTS.models.nonstationary.honsfts.HighOrderNonStationaryFLRG.get_midpoint">
+<code class="descname">get_midpoint</code><span class="sig-paren">(</span><em>sets</em>, <em>perturb</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.models.nonstationary.honsfts.HighOrderNonStationaryFLRG.get_midpoint" title="Permalink to this definition">¶</a></dt>
+<dd><p>Returns the midpoint value for the RHS fuzzy sets</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name" />
+<col class="field-body" />
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>sets</strong> – fuzzy sets</td>
+</tr>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body">the midpoint value</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+
+<dl class="method">
+<dt id="pyFTS.models.nonstationary.honsfts.HighOrderNonStationaryFLRG.get_upper">
+<code class="descname">get_upper</code><span class="sig-paren">(</span><em>sets</em>, <em>perturb</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.models.nonstationary.honsfts.HighOrderNonStationaryFLRG.get_upper" title="Permalink to this definition">¶</a></dt>
+<dd><p>Returns the upper bound value for the RHS fuzzy sets</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name" />
+<col class="field-body" />
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>sets</strong> – fuzzy sets</td>
+</tr>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body">upper bound value</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+
+<dl class="method">
+<dt id="pyFTS.models.nonstationary.honsfts.HighOrderNonStationaryFLRG.weights">
+<code class="descname">weights</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.models.nonstationary.honsfts.HighOrderNonStationaryFLRG.weights" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 </dd></dl>
 
 <dl class="class">
 <dt id="pyFTS.models.nonstationary.honsfts.HighOrderNonStationaryFTS">
-<em class="property">class </em><code class="descclassname">pyFTS.models.nonstationary.honsfts.</code><code class="descname">HighOrderNonStationaryFTS</code><span class="sig-paren">(</span><em>name</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.models.nonstationary.honsfts.HighOrderNonStationaryFTS" title="Permalink to this definition">¶</a></dt>
+<em class="property">class </em><code class="descclassname">pyFTS.models.nonstationary.honsfts.</code><code class="descname">HighOrderNonStationaryFTS</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.models.nonstationary.honsfts.HighOrderNonStationaryFTS" title="Permalink to this definition">¶</a></dt>
-<dd><p>Bases: <a class="reference internal" href="pyFTS.models.html#pyFTS.models.hofts.HighOrderFTS" title="pyFTS.models.hofts.HighOrderFTS"><code class="xref py py-class docutils literal notranslate"><span class="pre">pyFTS.models.hofts.HighOrderFTS</span></code></a></p>
+<dd><p>Bases: <a class="reference internal" href="#pyFTS.models.nonstationary.nsfts.NonStationaryFTS" title="pyFTS.models.nonstationary.nsfts.NonStationaryFTS"><code class="xref py py-class docutils literal notranslate"><span class="pre">pyFTS.models.nonstationary.nsfts.NonStationaryFTS</span></code></a></p>
 <p>NonStationaryFTS Fuzzy Time Series</p>
+<dl class="method">
+<dt id="pyFTS.models.nonstationary.honsfts.HighOrderNonStationaryFTS.configure_lags">
+<code class="descname">configure_lags</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.models.nonstationary.honsfts.HighOrderNonStationaryFTS.configure_lags" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
 <dl class="method">
 <dt id="pyFTS.models.nonstationary.honsfts.HighOrderNonStationaryFTS.forecast">
 <code class="descname">forecast</code><span class="sig-paren">(</span><em>ndata</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.models.nonstationary.honsfts.HighOrderNonStationaryFTS.forecast" title="Permalink to this definition">¶</a></dt>
@@ -457,27 +515,6 @@ IEEE Transactions on Fuzzy Systems, v. 16, n. 4, p. 1072-1086, 2008.</p>
 </table>
 </dd></dl>
 
-<dl class="method">
-<dt id="pyFTS.models.nonstationary.honsfts.HighOrderNonStationaryFTS.forecast_interval">
-<code class="descname">forecast_interval</code><span class="sig-paren">(</span><em>ndata</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.models.nonstationary.honsfts.HighOrderNonStationaryFTS.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.nonstationary.honsfts.HighOrderNonStationaryFTS.generate_flrg">
 <code class="descname">generate_flrg</code><span class="sig-paren">(</span><em>data</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.models.nonstationary.honsfts.HighOrderNonStationaryFTS.generate_flrg" title="Permalink to this definition">¶</a></dt>
@@ -601,6 +638,75 @@ IEEE Transactions on Fuzzy Systems, v. 16, n. 4, p. 1072-1086, 2008.</p>
 
 </dd></dl>
 
+<dl class="class">
+<dt id="pyFTS.models.nonstationary.nsfts.WeightedNonStationaryFLRG">
+<em class="property">class </em><code class="descclassname">pyFTS.models.nonstationary.nsfts.</code><code class="descname">WeightedNonStationaryFLRG</code><span class="sig-paren">(</span><em>LHS</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.models.nonstationary.nsfts.WeightedNonStationaryFLRG" title="Permalink to this definition">¶</a></dt>
+<dd><p>Bases: <a class="reference internal" href="#pyFTS.models.nonstationary.flrg.NonStationaryFLRG" title="pyFTS.models.nonstationary.flrg.NonStationaryFLRG"><code class="xref py py-class docutils literal notranslate"><span class="pre">pyFTS.models.nonstationary.flrg.NonStationaryFLRG</span></code></a></p>
+<p>First Order NonStationary Fuzzy Logical Relationship Group</p>
+<dl class="method">
+<dt id="pyFTS.models.nonstationary.nsfts.WeightedNonStationaryFLRG.append_rhs">
+<code class="descname">append_rhs</code><span class="sig-paren">(</span><em>c</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.models.nonstationary.nsfts.WeightedNonStationaryFLRG.append_rhs" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="method">
+<dt id="pyFTS.models.nonstationary.nsfts.WeightedNonStationaryFLRG.get_key">
+<code class="descname">get_key</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.models.nonstationary.nsfts.WeightedNonStationaryFLRG.get_key" title="Permalink to this definition">¶</a></dt>
+<dd><p>Returns a unique identifier for this FLRG</p>
+</dd></dl>
+
+<dl class="method">
+<dt id="pyFTS.models.nonstationary.nsfts.WeightedNonStationaryFLRG.get_midpoint">
+<code class="descname">get_midpoint</code><span class="sig-paren">(</span><em>sets</em>, <em>perturb</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.models.nonstationary.nsfts.WeightedNonStationaryFLRG.get_midpoint" title="Permalink to this definition">¶</a></dt>
+<dd><p>Returns the midpoint value for the RHS fuzzy sets</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name" />
+<col class="field-body" />
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>sets</strong> – fuzzy sets</td>
+</tr>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body">the midpoint value</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+
+<dl class="method">
+<dt id="pyFTS.models.nonstationary.nsfts.WeightedNonStationaryFLRG.weights">
+<code class="descname">weights</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.models.nonstationary.nsfts.WeightedNonStationaryFLRG.weights" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<dl class="class">
+<dt id="pyFTS.models.nonstationary.nsfts.WeightedNonStationaryFTS">
+<em class="property">class </em><code class="descclassname">pyFTS.models.nonstationary.nsfts.</code><code class="descname">WeightedNonStationaryFTS</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.models.nonstationary.nsfts.WeightedNonStationaryFTS" title="Permalink to this definition">¶</a></dt>
+<dd><p>Bases: <a class="reference internal" href="#pyFTS.models.nonstationary.nsfts.NonStationaryFTS" title="pyFTS.models.nonstationary.nsfts.NonStationaryFTS"><code class="xref py py-class docutils literal notranslate"><span class="pre">pyFTS.models.nonstationary.nsfts.NonStationaryFTS</span></code></a></p>
+<p>Weighted NonStationaryFTS Fuzzy Time Series</p>
+<dl class="method">
+<dt id="pyFTS.models.nonstationary.nsfts.WeightedNonStationaryFTS.generate_flrg">
+<code class="descname">generate_flrg</code><span class="sig-paren">(</span><em>flrs</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.models.nonstationary.nsfts.WeightedNonStationaryFTS.generate_flrg" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="method">
+<dt id="pyFTS.models.nonstationary.nsfts.WeightedNonStationaryFTS.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.nonstationary.nsfts.WeightedNonStationaryFTS.train" title="Permalink to this definition">¶</a></dt>
+<dd><p>Method specific parameter fitting</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name" />
+<col class="field-body" />
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
+<li><strong>data</strong> – training time series data</li>
+<li><strong>kwargs</strong> – Method specific parameters</li>
+</ul>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+
+</dd></dl>
+
 </div>
 <div class="section" id="module-pyFTS.models.nonstationary.partitioners">
 <span id="pyfts-models-nonstationary-partitioners-module"></span><h2>pyFTS.models.nonstationary.partitioners module<a class="headerlink" href="#module-pyFTS.models.nonstationary.partitioners" title="Permalink to this headline">¶</a></h2>

docs/build/html/pyFTS.partitioners.html

@@ -73,7 +73,7 @@
 <li><a class="reference internal" href="#module-pyFTS.partitioners.Huarng">pyFTS.partitioners.Huarng module</a></li>
 <li><a class="reference internal" href="#module-pyFTS.partitioners.Singleton">pyFTS.partitioners.Singleton module</a></li>
 <li><a class="reference internal" href="#module-pyFTS.partitioners.Simple">pyFTS.partitioners.Simple module</a></li>
-<li><a class="reference internal" href="#pyfts-partitioners-subclust-module">pyFTS.partitioners.SubClust module</a></li>
+<li><a class="reference internal" href="#module-pyFTS.partitioners.SubClust">pyFTS.partitioners.SubClust module</a></li>
 <li><a class="reference internal" href="#module-pyFTS.partitioners.Util">pyFTS.partitioners.Util module</a></li>
 <li><a class="reference internal" href="#module-pyFTS.partitioners.parallel_util">pyFTS.partitioners.parallel_util module</a></li>
 </ul>
@@ -556,8 +556,42 @@ Fuzzy Sets Syst., vol. 123, no. 3, pp. 387–394, Nov. 2001.</p>
 </dd></dl>
 
 </div>
-<div class="section" id="pyfts-partitioners-subclust-module">
+<div class="section" id="module-pyFTS.partitioners.SubClust">
-<h2>pyFTS.partitioners.SubClust module<a class="headerlink" href="#pyfts-partitioners-subclust-module" title="Permalink to this headline">¶</a></h2>
+<span id="pyfts-partitioners-subclust-module"></span><h2>pyFTS.partitioners.SubClust module<a class="headerlink" href="#module-pyFTS.partitioners.SubClust" title="Permalink to this headline">¶</a></h2>
+<p>Chiu, Stephen L. “Fuzzy model identification based on cluster estimation.” Journal of Intelligent &amp; fuzzy systems 2.3 (1994): 267-278.</p>
+<dl class="class">
+<dt id="pyFTS.partitioners.SubClust.SubClustPartitioner">
+<em class="property">class </em><code class="descclassname">pyFTS.partitioners.SubClust.</code><code class="descname">SubClustPartitioner</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.partitioners.SubClust.SubClustPartitioner" title="Permalink to this definition">¶</a></dt>
+<dd><p>Bases: <a class="reference internal" href="#pyFTS.partitioners.partitioner.Partitioner" title="pyFTS.partitioners.partitioner.Partitioner"><code class="xref py py-class docutils literal notranslate"><span class="pre">pyFTS.partitioners.partitioner.Partitioner</span></code></a></p>
+<p>Subtractive Clustering Partitioner</p>
+<dl class="method">
+<dt id="pyFTS.partitioners.SubClust.SubClustPartitioner.build">
+<code class="descname">build</code><span class="sig-paren">(</span><em>data</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.partitioners.SubClust.SubClustPartitioner.build" title="Permalink to this definition">¶</a></dt>
+<dd><p>Perform the partitioning of the Universe of Discourse</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name" />
+<col class="field-body" />
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>data</strong> – training data</td>
+</tr>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"></td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+
+</dd></dl>
+
+<dl class="function">
+<dt id="pyFTS.partitioners.SubClust.imax">
+<code class="descclassname">pyFTS.partitioners.SubClust.</code><code class="descname">imax</code><span class="sig-paren">(</span><em>vec</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.partitioners.SubClust.imax" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="function">
+<dt id="pyFTS.partitioners.SubClust.subclust">
+<code class="descclassname">pyFTS.partitioners.SubClust.</code><code class="descname">subclust</code><span class="sig-paren">(</span><em>data</em>, <em>ra</em>, <em>rb</em>, <em>eps_sup</em>, <em>eps_inf</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.partitioners.SubClust.subclust" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
 </div>
 <div class="section" id="module-pyFTS.partitioners.Util">
 <span id="pyfts-partitioners-util-module"></span><h2>pyFTS.partitioners.Util module<a class="headerlink" href="#module-pyFTS.partitioners.Util" title="Permalink to this headline">¶</a></h2>