Updating documentation
This commit is contained in:
Petrônio Cândido
parent
25d69a72ad
commit
9a66da2d5a
BIN
docs/build/doctrees/environment.pickle
vendored
BIN
docs/build/doctrees/environment.pickle
vendored
Binary file not shown.
BIN
docs/build/doctrees/pyFTS.common.doctree
vendored
BIN
docs/build/doctrees/pyFTS.common.doctree
vendored
Binary file not shown.
Binary file not shown.
29
docs/build/html/_modules/pyFTS/common/fts.html
vendored
29
docs/build/html/_modules/pyFTS/common/fts.html
vendored
@ -162,7 +162,7 @@
|
||||
|
||||
<span class="sd"> :param data: time series with minimal length to the order of the model</span>
|
||||
|
||||
<span class="sd"> :keyword type: the forecasting type, one of these values: point(default), interval or distribution.</span>
|
||||
<span class="sd"> :keyword type: the forecasting type, one of these values: point(default), interval, distribution or multivariate.</span>
|
||||
<span class="sd"> :keyword steps_ahead: The forecasting horizon, i. e., the number of steps ahead to forecast</span>
|
||||
<span class="sd"> :keyword start: in the multi step forecasting, the index of the data where to start forecasting</span>
|
||||
<span class="sd"> :keyword distributed: boolean, indicate if the forecasting procedure will be distributed in a dispy cluster</span>
|
||||
@ -204,6 +204,8 @@
|
||||
<span class="n">ret</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">forecast_interval</span><span class="p">(</span><span class="n">ndata</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
|
||||
<span class="k">elif</span> <span class="nb">type</span> <span class="o">==</span> <span class="s1">'distribution'</span><span class="p">:</span>
|
||||
<span class="n">ret</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">forecast_distribution</span><span class="p">(</span><span class="n">ndata</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
|
||||
<span class="k">elif</span> <span class="nb">type</span> <span class="o">==</span> <span class="s1">'multivariate'</span><span class="p">:</span>
|
||||
<span class="n">ret</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">forecast_multivariate</span><span class="p">(</span><span class="n">ndata</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
|
||||
<span class="k">elif</span> <span class="n">steps_ahead</span> <span class="o">></span> <span class="mi">1</span><span class="p">:</span>
|
||||
<span class="k">if</span> <span class="nb">type</span> <span class="o">==</span> <span class="s1">'point'</span><span class="p">:</span>
|
||||
<span class="n">ret</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">forecast_ahead</span><span class="p">(</span><span class="n">ndata</span><span class="p">,</span> <span class="n">steps_ahead</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
|
||||
@ -211,8 +213,10 @@
|
||||
<span class="n">ret</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">forecast_ahead_interval</span><span class="p">(</span><span class="n">ndata</span><span class="p">,</span> <span class="n">steps_ahead</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
|
||||
<span class="k">elif</span> <span class="nb">type</span> <span class="o">==</span> <span class="s1">'distribution'</span><span class="p">:</span>
|
||||
<span class="n">ret</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">forecast_ahead_distribution</span><span class="p">(</span><span class="n">ndata</span><span class="p">,</span> <span class="n">steps_ahead</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
|
||||
<span class="k">elif</span> <span class="nb">type</span> <span class="o">==</span> <span class="s1">'multivariate'</span><span class="p">:</span>
|
||||
<span class="n">ret</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">forecast_ahead_multivariate</span><span class="p">(</span><span class="n">ndata</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="ow">not</span> <span class="p">[</span><span class="s1">'point'</span><span class="p">,</span> <span class="s1">'interval'</span><span class="p">,</span> <span class="s1">'distribution'</span><span class="p">]</span><span class="o">.</span><span class="fm">__contains__</span><span class="p">(</span><span class="nb">type</span><span class="p">):</span>
|
||||
<span class="k">if</span> <span class="ow">not</span> <span class="p">[</span><span class="s1">'point'</span><span class="p">,</span> <span class="s1">'interval'</span><span class="p">,</span> <span class="s1">'distribution'</span><span class="p">,</span> <span class="s1">'multivariate'</span><span class="p">]</span><span class="o">.</span><span class="fm">__contains__</span><span class="p">(</span><span class="nb">type</span><span class="p">):</span>
|
||||
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">'The argument </span><span class="se">\'</span><span class="s1">type</span><span class="se">\'</span><span class="s1"> has an unknown value.'</span><span class="p">)</span>
|
||||
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
@ -258,6 +262,16 @@
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="s1">'This model do not perform one step ahead distribution forecasts!'</span><span class="p">)</span></div>
|
||||
|
||||
<div class="viewcode-block" id="FTS.forecast_multivariate"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.fts.FTS.forecast_multivariate">[docs]</a> <span class="k">def</span> <span class="nf">forecast_multivariate</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Multivariate forecast one step ahead</span>
|
||||
|
||||
<span class="sd"> :param data: Pandas dataframe with one column for each variable and with the minimal length equal to the max_lag of the model</span>
|
||||
<span class="sd"> :param kwargs: model specific parameters</span>
|
||||
<span class="sd"> :return: a Pandas Dataframe object representing the forecasted values for each variable</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="s1">'This model do not perform one step ahead multivariate forecasts!'</span><span class="p">)</span></div>
|
||||
|
||||
<div class="viewcode-block" id="FTS.forecast_ahead"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.fts.FTS.forecast_ahead">[docs]</a> <span class="k">def</span> <span class="nf">forecast_ahead</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">steps</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Point forecast n steps ahead</span>
|
||||
@ -307,6 +321,17 @@
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="s1">'This model do not perform multi step ahead distribution forecasts!'</span><span class="p">)</span></div>
|
||||
|
||||
<div class="viewcode-block" id="FTS.forecast_ahead_multivariate"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.fts.FTS.forecast_ahead_multivariate">[docs]</a> <span class="k">def</span> <span class="nf">forecast_ahead_multivariate</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">steps</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Multivariate forecast n step ahead</span>
|
||||
|
||||
<span class="sd"> :param data: Pandas dataframe with one column for each variable and with the minimal length equal to the max_lag of the model</span>
|
||||
<span class="sd"> :param steps: the number of steps ahead to forecast</span>
|
||||
<span class="sd"> :param kwargs: model specific parameters</span>
|
||||
<span class="sd"> :return: a Pandas Dataframe object representing the forecasted values for each variable</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="s1">'This model do not perform one step ahead multivariate forecasts!'</span><span class="p">)</span></div>
|
||||
|
||||
<div class="viewcode-block" id="FTS.train"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.fts.FTS.train">[docs]</a> <span class="k">def</span> <span class="nf">train</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Method specific parameter fitting</span>
|
||||
|
||||
@ -74,6 +74,7 @@
|
||||
<h1>Source code for pyFTS.models.multivariate.cmvfts</h1><div class="highlight"><pre>
|
||||
<span></span>
|
||||
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
|
||||
<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
|
||||
<span class="kn">from</span> <span class="nn">pyFTS.common</span> <span class="k">import</span> <span class="n">FuzzySet</span><span class="p">,</span> <span class="n">FLR</span><span class="p">,</span> <span class="n">fts</span><span class="p">,</span> <span class="n">flrg</span>
|
||||
<span class="kn">from</span> <span class="nn">pyFTS.models</span> <span class="k">import</span> <span class="n">hofts</span>
|
||||
<span class="kn">from</span> <span class="nn">pyFTS.models.multivariate</span> <span class="k">import</span> <span class="n">mvfts</span><span class="p">,</span> <span class="n">grid</span><span class="p">,</span> <span class="n">common</span>
|
||||
@ -129,24 +130,38 @@
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">is_high_order</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">order</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span>
|
||||
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">pre_fuzzyfy</span><span class="p">:</span>
|
||||
<span class="n">ndata</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">fuzzyfy</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">ndata</span> <span class="o">=</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">format_data</span><span class="p">(</span><span class="n">k</span><span class="p">)</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">data</span><span class="o">.</span><span class="n">to_dict</span><span class="p">(</span><span class="s1">'records'</span><span class="p">)]</span>
|
||||
<span class="n">ndata</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">check_data</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">train</span><span class="p">(</span><span class="n">ndata</span><span class="p">,</span> <span class="n">fuzzyfied</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">pre_fuzzyfy</span><span class="p">)</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">cluster</span><span class="o">.</span><span class="n">prune</span><span class="p">()</span></div>
|
||||
|
||||
<div class="viewcode-block" id="ClusteredMVFTS.check_data"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.cmvfts.ClusteredMVFTS.check_data">[docs]</a> <span class="k">def</span> <span class="nf">check_data</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">):</span>
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">pre_fuzzyfy</span><span class="p">:</span>
|
||||
<span class="n">ndata</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">fuzzyfy</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">ndata</span> <span class="o">=</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">format_data</span><span class="p">(</span><span class="n">k</span><span class="p">)</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">data</span><span class="o">.</span><span class="n">to_dict</span><span class="p">(</span><span class="s1">'records'</span><span class="p">)]</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">ndata</span></div>
|
||||
|
||||
<div class="viewcode-block" id="ClusteredMVFTS.forecast"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.cmvfts.ClusteredMVFTS.forecast">[docs]</a> <span class="k">def</span> <span class="nf">forecast</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ndata</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">pre_fuzzyfy</span><span class="p">:</span>
|
||||
<span class="n">ndata</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">fuzzyfy</span><span class="p">(</span><span class="n">ndata</span><span class="p">)</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">ndata</span> <span class="o">=</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">format_data</span><span class="p">(</span><span class="n">k</span><span class="p">)</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">ndata</span><span class="o">.</span><span class="n">to_dict</span><span class="p">(</span><span class="s1">'records'</span><span class="p">)]</span>
|
||||
<span class="n">ndata</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">check_data</span><span class="p">(</span><span class="n">ndata</span><span class="p">)</span>
|
||||
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">forecast</span><span class="p">(</span><span class="n">ndata</span><span class="p">,</span> <span class="n">fuzzyfied</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">pre_fuzzyfy</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>
|
||||
|
||||
<div class="viewcode-block" id="ClusteredMVFTS.forecast_multivariate"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.cmvfts.ClusteredMVFTS.forecast_multivariate">[docs]</a> <span class="k">def</span> <span class="nf">forecast_multivariate</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
|
||||
<span class="n">ndata</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">check_data</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
|
||||
|
||||
<span class="n">ret</span> <span class="o">=</span> <span class="p">{}</span>
|
||||
<span class="k">for</span> <span class="n">var</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">explanatory_variables</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">cluster</span><span class="o">.</span><span class="n">change_target_variable</span><span class="p">(</span><span class="n">var</span><span class="p">)</span>
|
||||
<span class="n">ret</span><span class="p">[</span><span class="n">var</span><span class="o">.</span><span class="n">name</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">forecast</span><span class="p">(</span><span class="n">ndata</span><span class="p">,</span> <span class="n">fuzzyfied</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">pre_fuzzyfy</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
|
||||
|
||||
<span class="n">columns</span> <span class="o">=</span> <span class="n">ret</span><span class="o">.</span><span class="n">keys</span><span class="p">()</span>
|
||||
<span class="k">return</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">ret</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="n">columns</span><span class="p">)</span></div>
|
||||
|
||||
<span class="k">def</span> <span class="nf">__str__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="sd">"""String representation of the model"""</span>
|
||||
<span class="k">return</span> <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="p">)</span>
|
||||
|
||||
14
docs/build/html/genindex.html
vendored
14
docs/build/html/genindex.html
vendored
@ -322,6 +322,8 @@
|
||||
<li><a href="pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.common.check_bounds_index">(in module pyFTS.models.nonstationary.common)</a>
|
||||
</li>
|
||||
</ul></li>
|
||||
<li><a href="pyFTS.models.multivariate.html#pyFTS.models.multivariate.cmvfts.ClusteredMVFTS.check_data">check_data() (pyFTS.models.multivariate.cmvfts.ClusteredMVFTS method)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.Util.check_ignore_list">check_ignore_list() (in module pyFTS.benchmarks.Util)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.Util.check_replace_list">check_replace_list() (in module pyFTS.benchmarks.Util)</a>
|
||||
@ -345,11 +347,11 @@
|
||||
<li><a href="pyFTS.models.multivariate.html#pyFTS.models.multivariate.cmvfts.ClusteredMVFTS.cluster_params">cluster_params (pyFTS.models.multivariate.cmvfts.ClusteredMVFTS attribute)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.models.multivariate.html#pyFTS.models.multivariate.cmvfts.ClusteredMVFTS">ClusteredMVFTS (class in pyFTS.models.multivariate.cmvfts)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.partitioners.html#pyFTS.partitioners.CMeans.CMeansPartitioner">CMeansPartitioner (class in pyFTS.partitioners.CMeans)</a>
|
||||
</li>
|
||||
</ul></td>
|
||||
<td style="width: 33%; vertical-align: top;"><ul>
|
||||
<li><a href="pyFTS.partitioners.html#pyFTS.partitioners.CMeans.CMeansPartitioner">CMeansPartitioner (class in pyFTS.partitioners.CMeans)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.ResidualAnalysis.compare_residuals">compare_residuals() (in module pyFTS.benchmarks.ResidualAnalysis)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.benchmarks.compareModelsPlot">compareModelsPlot() (in module pyFTS.benchmarks.benchmarks)</a>
|
||||
@ -614,6 +616,8 @@
|
||||
<li><a href="pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFTS.forecast_ahead_interval">(pyFTS.models.pwfts.ProbabilisticWeightedFTS method)</a>
|
||||
</li>
|
||||
</ul></li>
|
||||
<li><a href="pyFTS.common.html#pyFTS.common.fts.FTS.forecast_ahead_multivariate">forecast_ahead_multivariate() (pyFTS.common.fts.FTS method)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.arima.ARIMA.forecast_distribution">forecast_distribution() (pyFTS.benchmarks.arima.ARIMA method)</a>
|
||||
|
||||
<ul>
|
||||
@ -648,6 +652,12 @@
|
||||
<li><a href="pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.nsfts.NonStationaryFTS.forecast_interval">(pyFTS.models.nonstationary.nsfts.NonStationaryFTS method)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFTS.forecast_interval">(pyFTS.models.pwfts.ProbabilisticWeightedFTS method)</a>
|
||||
</li>
|
||||
</ul></li>
|
||||
<li><a href="pyFTS.common.html#pyFTS.common.fts.FTS.forecast_multivariate">forecast_multivariate() (pyFTS.common.fts.FTS method)</a>
|
||||
|
||||
<ul>
|
||||
<li><a href="pyFTS.models.multivariate.html#pyFTS.models.multivariate.cmvfts.ClusteredMVFTS.forecast_multivariate">(pyFTS.models.multivariate.cmvfts.ClusteredMVFTS method)</a>
|
||||
</li>
|
||||
</ul></li>
|
||||
<li><a href="pyFTS.models.multivariate.html#pyFTS.models.multivariate.mvfts.MVFTS.format_data">format_data() (pyFTS.models.multivariate.mvfts.MVFTS method)</a>
|
||||
|
||||
BIN
docs/build/html/objects.inv
vendored
BIN
docs/build/html/objects.inv
vendored
Binary file not shown.
45
docs/build/html/pyFTS.common.html
vendored
45
docs/build/html/pyFTS.common.html
vendored
@ -1667,6 +1667,28 @@ when the LHS pattern is identified on time t.</p>
|
||||
</table>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="method">
|
||||
<dt id="pyFTS.common.fts.FTS.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="reference internal" href="_modules/pyFTS/common/fts.html#FTS.forecast_ahead_multivariate"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.common.fts.FTS.forecast_ahead_multivariate" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Multivariate forecast n 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> – Pandas dataframe with one column for each variable and with the minimal length equal to the max_lag of the model</li>
|
||||
<li><strong>steps</strong> – the number of steps ahead to forecast</li>
|
||||
<li><strong>kwargs</strong> – model specific parameters</li>
|
||||
</ul>
|
||||
</td>
|
||||
</tr>
|
||||
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">a Pandas Dataframe object representing the forecasted values for each variable</p>
|
||||
</td>
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="method">
|
||||
<dt id="pyFTS.common.fts.FTS.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="reference internal" href="_modules/pyFTS/common/fts.html#FTS.forecast_distribution"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.common.fts.FTS.forecast_distribution" title="Permalink to this definition">¶</a></dt>
|
||||
@ -1709,6 +1731,27 @@ when the LHS pattern is identified on time t.</p>
|
||||
</table>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="method">
|
||||
<dt id="pyFTS.common.fts.FTS.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="reference internal" href="_modules/pyFTS/common/fts.html#FTS.forecast_multivariate"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.common.fts.FTS.forecast_multivariate" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Multivariate 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> – Pandas dataframe with one column for each variable and 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 Pandas Dataframe object representing the forecasted values for each variable</p>
|
||||
</td>
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="method">
|
||||
<dt id="pyFTS.common.fts.FTS.fuzzy">
|
||||
<code class="descname">fuzzy</code><span class="sig-paren">(</span><em>data</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/common/fts.html#FTS.fuzzy"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.common.fts.FTS.fuzzy" title="Permalink to this definition">¶</a></dt>
|
||||
@ -1846,7 +1889,7 @@ needed to forecast a single step ahead</p>
|
||||
<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 with minimal length to the order of the model</li>
|
||||
<li><strong>type</strong> – the forecasting type, one of these values: point(default), interval or distribution.</li>
|
||||
<li><strong>type</strong> – the forecasting type, one of these values: point(default), interval, distribution or multivariate.</li>
|
||||
<li><strong>steps_ahead</strong> – The forecasting horizon, i. e., the number of steps ahead to forecast</li>
|
||||
<li><strong>start</strong> – in the multi step forecasting, the index of the data where to start forecasting</li>
|
||||
<li><strong>distributed</strong> – boolean, indicate if the forecasting procedure will be distributed in a dispy cluster</li>
|
||||
|
||||
26
docs/build/html/pyFTS.models.multivariate.html
vendored
26
docs/build/html/pyFTS.models.multivariate.html
vendored
@ -487,6 +487,11 @@ transformations and partitioners.</p>
|
||||
<em class="property">class </em><code class="descclassname">pyFTS.models.multivariate.cmvfts.</code><code class="descname">ClusteredMVFTS</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/multivariate/cmvfts.html#ClusteredMVFTS"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.multivariate.cmvfts.ClusteredMVFTS" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Bases: <a class="reference internal" href="#pyFTS.models.multivariate.mvfts.MVFTS" title="pyFTS.models.multivariate.mvfts.MVFTS"><code class="xref py py-class docutils literal notranslate"><span class="pre">pyFTS.models.multivariate.mvfts.MVFTS</span></code></a></p>
|
||||
<p>Meta model for multivariate, high order, clustered multivariate FTS</p>
|
||||
<dl class="method">
|
||||
<dt id="pyFTS.models.multivariate.cmvfts.ClusteredMVFTS.check_data">
|
||||
<code class="descname">check_data</code><span class="sig-paren">(</span><em>data</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/multivariate/cmvfts.html#ClusteredMVFTS.check_data"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.multivariate.cmvfts.ClusteredMVFTS.check_data" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="attribute">
|
||||
<dt id="pyFTS.models.multivariate.cmvfts.ClusteredMVFTS.cluster">
|
||||
<code class="descname">cluster</code><em class="property"> = None</em><a class="headerlink" href="#pyFTS.models.multivariate.cmvfts.ClusteredMVFTS.cluster" title="Permalink to this definition">¶</a></dt>
|
||||
@ -526,6 +531,27 @@ transformations and partitioners.</p>
|
||||
</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="reference internal" href="_modules/pyFTS/models/multivariate/cmvfts.html#ClusteredMVFTS.forecast_multivariate"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.multivariate.cmvfts.ClusteredMVFTS.forecast_multivariate" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Multivariate 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> – Pandas dataframe with one column for each variable and 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 Pandas Dataframe object representing the forecasted values for each variable</p>
|
||||
</td>
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="attribute">
|
||||
<dt id="pyFTS.models.multivariate.cmvfts.ClusteredMVFTS.fts_method">
|
||||
<code class="descname">fts_method</code><em class="property"> = None</em><a class="headerlink" href="#pyFTS.models.multivariate.cmvfts.ClusteredMVFTS.fts_method" title="Permalink to this definition">¶</a></dt>
|
||||
|
||||
2
docs/build/html/searchindex.js
vendored
2
docs/build/html/searchindex.js
vendored
File diff suppressed because one or more lines are too long
Loading…
Reference in New Issue
Block a user