2018-11-01 18:11:20 +04:00
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2019-05-07 23:24:59 +04:00
< title > pyFTS.models.incremental package — pyFTS 1.6 documentation< / title >
2018-11-01 18:11:20 +04:00
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< li > < a class = "reference internal" href = "#module-pyFTS.models.incremental.TimeVariant" > pyFTS.models.incremental.TimeVariant module< / a > < / li >
< li > < a class = "reference internal" href = "#module-pyFTS.models.incremental.IncrementalEnsemble" > pyFTS.models.incremental.IncrementalEnsemble module< / a > < / li >
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< div class = "section" id = "pyfts-models-incremental-package" >
< h1 > pyFTS.models.incremental package< a class = "headerlink" href = "#pyfts-models-incremental-package" title = "Permalink to this headline" > ¶< / a > < / h1 >
< div class = "section" id = "module-pyFTS.models.incremental" >
< span id = "module-contents" > < / span > < h2 > Module contents< a class = "headerlink" href = "#module-pyFTS.models.incremental" title = "Permalink to this headline" > ¶< / a > < / h2 >
< p > FTS methods with incremental/online learning< / p >
< / div >
< div class = "section" id = "submodules" >
< h2 > Submodules< a class = "headerlink" href = "#submodules" title = "Permalink to this headline" > ¶< / a > < / h2 >
< / div >
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< div class = "section" id = "module-pyFTS.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 >
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< p > Meta model that wraps another FTS method and continously retrain it using a data window with
the most recent data< / p >
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< dl class = "class" >
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< dt id = "pyFTS.models.incremental.TimeVariant.Retrainer" >
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< 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 >
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< dd > < p > Bases: < a class = "reference internal" href = "pyFTS.common.html#pyFTS.common.fts.FTS" title = "pyFTS.common.fts.FTS" > < code class = "xref py py-class docutils literal notranslate" > < span class = "pre" > pyFTS.common.fts.FTS< / span > < / code > < / a > < / p >
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< p > Meta model for incremental/online learning that retrain its internal model after
data windows controlled by the parameter ‘ ’
window of recent lags, whose size is controlled by the parameter ‘ ’ < / p >
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< dl class = "method" >
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< dt id = "pyFTS.models.incremental.TimeVariant.Retrainer.forecast" >
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< 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 >
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< dd > < p > Point 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 > – < / 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 forecasted values< / p >
< / td >
< / tr >
< / tbody >
< / table >
< / dd > < / dl >
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< 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 > – < / li >
< li > < strong > steps< / strong > – < / li >
< li > < strong > start_at< / 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 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 >
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< dl class = "method" >
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< dt id = "pyFTS.models.incremental.TimeVariant.Retrainer.train" >
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< 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 >
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< 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 > – < / li >
< li > < strong > kwargs< / strong > – < / li >
< / ul >
< / td >
< / tr >
< / tbody >
< / table >
< / dd > < / dl >
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< / dd > < / dl >
< / div >
< div class = "section" id = "module-pyFTS.models.incremental.IncrementalEnsemble" >
< span id = "pyfts-models-incremental-incrementalensemble-module" > < / span > < h2 > pyFTS.models.incremental.IncrementalEnsemble module< a class = "headerlink" href = "#module-pyFTS.models.incremental.IncrementalEnsemble" title = "Permalink to this headline" > ¶< / a > < / h2 >
< p > Time Variant/Incremental Ensemble of FTS methods< / p >
< dl class = "class" >
< dt id = "pyFTS.models.incremental.IncrementalEnsemble.IncrementalEnsembleFTS" >
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< em class = "property" > class < / em > < code class = "descclassname" > pyFTS.models.incremental.IncrementalEnsemble.< / code > < code class = "descname" > IncrementalEnsembleFTS< / code > < span class = "sig-paren" > (< / span > < em > **kwargs< / em > < span class = "sig-paren" > )< / span > < a class = "headerlink" href = "#pyFTS.models.incremental.IncrementalEnsemble.IncrementalEnsembleFTS" title = "Permalink to this definition" > ¶< / a > < / dt >
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< dd > < p > Bases: < a class = "reference internal" href = "pyFTS.models.ensemble.html#pyFTS.models.ensemble.ensemble.EnsembleFTS" title = "pyFTS.models.ensemble.ensemble.EnsembleFTS" > < code class = "xref py py-class docutils literal notranslate" > < span class = "pre" > pyFTS.models.ensemble.ensemble.EnsembleFTS< / span > < / code > < / a > < / p >
< p > Time Variant/Incremental Ensemble of FTS methods< / p >
< dl class = "method" >
< dt id = "pyFTS.models.incremental.IncrementalEnsemble.IncrementalEnsembleFTS.forecast" >
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< 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.IncrementalEnsemble.IncrementalEnsembleFTS.forecast" title = "Permalink to this definition" > ¶< / a > < / dt >
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< dd > < p > Point 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 > – < / 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 forecasted values< / p >
< / td >
< / tr >
< / tbody >
< / table >
< / dd > < / dl >
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< 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 > – < / li >
< li > < strong > steps< / strong > – < / li >
< li > < strong > start_at< / 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 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 >
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< dl class = "method" >
< dt id = "pyFTS.models.incremental.IncrementalEnsemble.IncrementalEnsembleFTS.train" >
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< 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 >
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< 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 > – < / li >
< li > < strong > kwargs< / strong > – < / li >
< / ul >
< / td >
< / tr >
< / tbody >
< / table >
< / dd > < / dl >
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< / dd > < / dl >
< / div >
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< / div >
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