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<h1>Source code for pyFTS.models.ensemble.ensemble</h1><div class="highlight"><pre>
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<span></span><span class="sd">"""</span>
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<span class="sd">EnsembleFTS wraps several FTS methods to ensemble their forecasts, providing point,</span>
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<span class="sd">interval and probabilistic forecasting.</span>
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<span class="sd">Silva, P. C. L et al. Probabilistic Forecasting with Seasonal Ensemble Fuzzy Time-Series</span>
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<span class="sd">XIII Brazilian Congress on Computational Intelligence, 2017. Rio de Janeiro, Brazil.</span>
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<span class="sd">"""</span>
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<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
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<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
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<span class="kn">from</span> <span class="nn">pyFTS.common</span> <span class="k">import</span> <span class="n">SortedCollection</span><span class="p">,</span> <span class="n">fts</span><span class="p">,</span> <span class="n">tree</span>
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<span class="kn">from</span> <span class="nn">pyFTS.models</span> <span class="k">import</span> <span class="n">chen</span><span class="p">,</span> <span class="n">cheng</span><span class="p">,</span> <span class="n">hofts</span><span class="p">,</span> <span class="n">hwang</span><span class="p">,</span> <span class="n">ismailefendi</span><span class="p">,</span> <span class="n">sadaei</span><span class="p">,</span> <span class="n">song</span><span class="p">,</span> <span class="n">yu</span>
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<span class="kn">from</span> <span class="nn">pyFTS.probabilistic</span> <span class="k">import</span> <span class="n">ProbabilityDistribution</span>
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<span class="kn">import</span> <span class="nn">scipy.stats</span> <span class="k">as</span> <span class="nn">st</span>
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<div class="viewcode-block" id="sampler"><a class="viewcode-back" href="../../../../pyFTS.models.ensemble.html#pyFTS.models.ensemble.ensemble.sampler">[docs]</a><span class="k">def</span> <span class="nf">sampler</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">quantiles</span><span class="p">):</span>
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<span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
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<span class="k">for</span> <span class="n">qt</span> <span class="ow">in</span> <span class="n">quantiles</span><span class="p">:</span>
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<span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">nanpercentile</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">q</span><span class="o">=</span><span class="n">qt</span> <span class="o">*</span> <span class="mi">100</span><span class="p">))</span>
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<span class="k">return</span> <span class="n">ret</span></div>
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<div class="viewcode-block" id="EnsembleFTS"><a class="viewcode-back" href="../../../../pyFTS.models.ensemble.html#pyFTS.models.ensemble.ensemble.EnsembleFTS">[docs]</a><span class="k">class</span> <span class="nc">EnsembleFTS</span><span class="p">(</span><span class="n">fts</span><span class="o">.</span><span class="n">FTS</span><span class="p">):</span>
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<span class="sd">"""</span>
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<span class="sd"> Ensemble FTS</span>
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<span class="sd"> """</span>
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<span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
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<span class="nb">super</span><span class="p">(</span><span class="n">EnsembleFTS</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">shortname</span> <span class="o">=</span> <span class="s2">"EnsembleFTS"</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="s2">"Ensemble FTS"</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span> <span class="o">=</span> <span class="p">{}</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">is_wrapper</span> <span class="o">=</span> <span class="kc">True</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">has_point_forecasting</span> <span class="o">=</span> <span class="kc">True</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">has_interval_forecasting</span> <span class="o">=</span> <span class="kc">True</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">has_probability_forecasting</span> <span class="o">=</span> <span class="kc">True</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">is_high_order</span> <span class="o">=</span> <span class="kc">True</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">models</span> <span class="o">=</span> <span class="p">[]</span>
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<span class="sd">"""A list of FTS models, the ensemble components"""</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">parameters</span> <span class="o">=</span> <span class="p">[]</span>
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<span class="sd">"""A list with the parameters for each component model"""</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">alpha</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">"alpha"</span><span class="p">,</span> <span class="mf">0.05</span><span class="p">)</span>
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<span class="sd">"""The quantiles """</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">point_method</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'point_method'</span><span class="p">,</span> <span class="s1">'mean'</span><span class="p">)</span>
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<span class="sd">"""The method used to mix the several model's forecasts into a unique point forecast. Options: mean, median, quantile, exponential"""</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">interval_method</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'interval_method'</span><span class="p">,</span> <span class="s1">'quantile'</span><span class="p">)</span>
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<span class="sd">"""The method used to mix the several model's forecasts into a interval forecast. Options: quantile, extremum, normal"""</span>
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<div class="viewcode-block" id="EnsembleFTS.append_model"><a class="viewcode-back" href="../../../../pyFTS.models.ensemble.html#pyFTS.models.ensemble.ensemble.EnsembleFTS.append_model">[docs]</a> <span class="k">def</span> <span class="nf">append_model</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">model</span><span class="p">):</span>
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<span class="sd">"""</span>
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<span class="sd"> Append a new trained model to the ensemble</span>
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<span class="sd"> :param model: FTS model</span>
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<span class="sd"> """</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">models</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">model</span><span class="p">)</span>
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<span class="k">if</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="p">:</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">order</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">order</span>
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<span class="k">if</span> <span class="n">model</span><span class="o">.</span><span class="n">is_multivariate</span><span class="p">:</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">is_multivariate</span> <span class="o">=</span> <span class="kc">True</span>
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<span class="k">if</span> <span class="n">model</span><span class="o">.</span><span class="n">has_seasonality</span><span class="p">:</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">has_seasonality</span> <span class="o">=</span> <span class="kc">True</span>
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<span class="k">if</span> <span class="n">model</span><span class="o">.</span><span class="n">original_min</span> <span class="o"><</span> <span class="bp">self</span><span class="o">.</span><span class="n">original_min</span><span class="p">:</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">original_min</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">original_min</span>
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<span class="k">elif</span> <span class="n">model</span><span class="o">.</span><span class="n">original_max</span> <span class="o">></span> <span class="bp">self</span><span class="o">.</span><span class="n">original_max</span><span class="p">:</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">original_max</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">original_max</span></div>
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<div class="viewcode-block" id="EnsembleFTS.train"><a class="viewcode-back" href="../../../../pyFTS.models.ensemble.html#pyFTS.models.ensemble.ensemble.EnsembleFTS.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>
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<span class="k">pass</span></div>
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<div class="viewcode-block" id="EnsembleFTS.get_models_forecasts"><a class="viewcode-back" href="../../../../pyFTS.models.ensemble.html#pyFTS.models.ensemble.ensemble.EnsembleFTS.get_models_forecasts">[docs]</a> <span class="k">def</span> <span class="nf">get_models_forecasts</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span><span class="n">data</span><span class="p">):</span>
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<span class="n">tmp</span> <span class="o">=</span> <span class="p">[]</span>
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<span class="k">for</span> <span class="n">model</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">models</span><span class="p">:</span>
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<span class="k">if</span> <span class="n">model</span><span class="o">.</span><span class="n">is_multivariate</span> <span class="ow">or</span> <span class="n">model</span><span class="o">.</span><span class="n">has_seasonality</span><span class="p">:</span>
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<span class="n">forecast</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">data</span><span class="p">)</span>
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<span class="k">else</span><span class="p">:</span>
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<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">)</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">indexer</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
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<span class="n">data</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">indexer</span><span class="o">.</span><span class="n">get_data</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
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<span class="n">sample</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="o">-</span><span class="n">model</span><span class="o">.</span><span class="n">order</span><span class="p">:]</span>
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<span class="n">forecast</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">sample</span><span class="p">)</span>
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<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">forecast</span><span class="p">,</span> <span class="p">(</span><span class="nb">list</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">))</span> <span class="ow">and</span> <span class="nb">len</span><span class="p">(</span><span class="n">forecast</span><span class="p">)</span> <span class="o">></span> <span class="mi">0</span><span class="p">:</span>
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<span class="n">forecast</span> <span class="o">=</span> <span class="n">forecast</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
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|
<span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">forecast</span><span class="p">,</span> <span class="p">(</span><span class="nb">list</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">))</span> <span class="ow">and</span> <span class="nb">len</span><span class="p">(</span><span class="n">forecast</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
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<span class="n">forecast</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
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<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">forecast</span><span class="p">,</span> <span class="nb">list</span><span class="p">):</span>
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<span class="n">tmp</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">forecast</span><span class="p">)</span>
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<span class="k">else</span><span class="p">:</span>
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<span class="n">tmp</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">forecast</span><span class="p">)</span>
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<span class="k">return</span> <span class="n">tmp</span></div>
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<div class="viewcode-block" id="EnsembleFTS.get_point"><a class="viewcode-back" href="../../../../pyFTS.models.ensemble.html#pyFTS.models.ensemble.ensemble.EnsembleFTS.get_point">[docs]</a> <span class="k">def</span> <span class="nf">get_point</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span><span class="n">forecasts</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
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<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">point_method</span> <span class="o">==</span> <span class="s1">'mean'</span><span class="p">:</span>
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<span class="n">ret</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nanmean</span><span class="p">(</span><span class="n">forecasts</span><span class="p">)</span>
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<span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">point_method</span> <span class="o">==</span> <span class="s1">'median'</span><span class="p">:</span>
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<span class="n">ret</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nanpercentile</span><span class="p">(</span><span class="n">forecasts</span><span class="p">,</span> <span class="mi">50</span><span class="p">)</span>
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<span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">point_method</span> <span class="o">==</span> <span class="s1">'quantile'</span><span class="p">:</span>
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<span class="n">alpha</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">"alpha"</span><span class="p">,</span><span class="mf">0.05</span><span class="p">)</span>
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<span class="n">ret</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">percentile</span><span class="p">(</span><span class="n">forecasts</span><span class="p">,</span> <span class="n">alpha</span><span class="o">*</span><span class="mi">100</span><span class="p">)</span>
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<span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">point_method</span> <span class="o">==</span> <span class="s1">'exponential'</span><span class="p">:</span>
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|
<span class="n">l</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">models</span><span class="p">)</span>
|
|
<span class="k">if</span> <span class="n">l</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
|
|
<span class="k">return</span> <span class="n">forecasts</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
|
|
<span class="n">w</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="p">(</span><span class="n">l</span> <span class="o">-</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="nb">range</span><span class="p">(</span><span class="n">l</span><span class="p">)])</span>
|
|
<span class="n">w</span> <span class="o">=</span> <span class="n">w</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">(</span><span class="n">w</span><span class="p">)</span>
|
|
<span class="n">ret</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">([</span><span class="n">w</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">*</span> <span class="n">forecasts</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="nb">range</span><span class="p">(</span><span class="n">l</span><span class="p">)])</span>
|
|
|
|
<span class="k">return</span> <span class="n">ret</span></div>
|
|
|
|
<div class="viewcode-block" id="EnsembleFTS.get_interval"><a class="viewcode-back" href="../../../../pyFTS.models.ensemble.html#pyFTS.models.ensemble.ensemble.EnsembleFTS.get_interval">[docs]</a> <span class="k">def</span> <span class="nf">get_interval</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">forecasts</span><span class="p">):</span>
|
|
<span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
|
|
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">interval_method</span> <span class="o">==</span> <span class="s1">'extremum'</span><span class="p">:</span>
|
|
<span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">([</span><span class="nb">min</span><span class="p">(</span><span class="n">forecasts</span><span class="p">),</span> <span class="nb">max</span><span class="p">(</span><span class="n">forecasts</span><span class="p">)])</span>
|
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<span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">interval_method</span> <span class="o">==</span> <span class="s1">'quantile'</span><span class="p">:</span>
|
|
<span class="n">qt_lo</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nanpercentile</span><span class="p">(</span><span class="n">forecasts</span><span class="p">,</span> <span class="n">q</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">alpha</span> <span class="o">*</span> <span class="mi">100</span><span class="p">)</span>
|
|
<span class="n">qt_up</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nanpercentile</span><span class="p">(</span><span class="n">forecasts</span><span class="p">,</span> <span class="n">q</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">alpha</span><span class="p">)</span> <span class="o">*</span> <span class="mi">100</span><span class="p">)</span>
|
|
<span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">([</span><span class="n">qt_lo</span><span class="p">,</span> <span class="n">qt_up</span><span class="p">])</span>
|
|
<span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">interval_method</span> <span class="o">==</span> <span class="s1">'normal'</span><span class="p">:</span>
|
|
<span class="n">mu</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nanmean</span><span class="p">(</span><span class="n">forecasts</span><span class="p">)</span>
|
|
<span class="n">sigma</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">nanvar</span><span class="p">(</span><span class="n">forecasts</span><span class="p">))</span>
|
|
<span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">mu</span> <span class="o">+</span> <span class="n">st</span><span class="o">.</span><span class="n">norm</span><span class="o">.</span><span class="n">ppf</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">alpha</span><span class="p">)</span> <span class="o">*</span> <span class="n">sigma</span><span class="p">)</span>
|
|
<span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">mu</span> <span class="o">+</span> <span class="n">st</span><span class="o">.</span><span class="n">norm</span><span class="o">.</span><span class="n">ppf</span><span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">alpha</span><span class="p">)</span> <span class="o">*</span> <span class="n">sigma</span><span class="p">)</span>
|
|
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<span class="k">return</span> <span class="n">ret</span></div>
|
|
|
|
<div class="viewcode-block" id="EnsembleFTS.get_distribution_interquantile"><a class="viewcode-back" href="../../../../pyFTS.models.ensemble.html#pyFTS.models.ensemble.ensemble.EnsembleFTS.get_distribution_interquantile">[docs]</a> <span class="k">def</span> <span class="nf">get_distribution_interquantile</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span><span class="n">forecasts</span><span class="p">,</span> <span class="n">alpha</span><span class="p">):</span>
|
|
<span class="n">size</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">forecasts</span><span class="p">)</span>
|
|
<span class="n">qt_lower</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">ceil</span><span class="p">(</span><span class="n">size</span> <span class="o">*</span> <span class="n">alpha</span><span class="p">))</span> <span class="o">-</span> <span class="mi">1</span>
|
|
<span class="n">qt_upper</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">ceil</span><span class="p">(</span><span class="n">size</span> <span class="o">*</span> <span class="p">(</span><span class="mi">1</span><span class="o">-</span> <span class="n">alpha</span><span class="p">)))</span> <span class="o">-</span> <span class="mi">1</span>
|
|
|
|
<span class="n">ret</span> <span class="o">=</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">forecasts</span><span class="p">)[</span><span class="n">qt_lower</span> <span class="p">:</span> <span class="n">qt_upper</span><span class="p">]</span>
|
|
|
|
<span class="k">return</span> <span class="n">ret</span></div>
|
|
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|
<div class="viewcode-block" id="EnsembleFTS.forecast"><a class="viewcode-back" href="../../../../pyFTS.models.ensemble.html#pyFTS.models.ensemble.ensemble.EnsembleFTS.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">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
|
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|
<span class="k">if</span> <span class="s2">"method"</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="p">:</span>
|
|
<span class="bp">self</span><span class="o">.</span><span class="n">point_method</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'method'</span><span class="p">,</span><span class="s1">'mean'</span><span class="p">)</span>
|
|
|
|
<span class="n">l</span> <span class="o">=</span> <span class="nb">len</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">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">,</span> <span class="n">l</span><span class="o">+</span><span class="mi">1</span><span class="p">):</span>
|
|
<span class="n">sample</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="n">k</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span> <span class="p">:</span> <span class="n">k</span><span class="p">]</span>
|
|
<span class="n">tmp</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_models_forecasts</span><span class="p">(</span><span class="n">sample</span><span class="p">)</span>
|
|
<span class="n">point</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_point</span><span class="p">(</span><span class="n">tmp</span><span class="p">)</span>
|
|
<span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">point</span><span class="p">)</span>
|
|
|
|
<span class="k">return</span> <span class="n">ret</span></div>
|
|
|
|
<div class="viewcode-block" id="EnsembleFTS.forecast_interval"><a class="viewcode-back" href="../../../../pyFTS.models.ensemble.html#pyFTS.models.ensemble.ensemble.EnsembleFTS.forecast_interval">[docs]</a> <span class="k">def</span> <span class="nf">forecast_interval</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="k">if</span> <span class="s2">"method"</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="p">:</span>
|
|
<span class="bp">self</span><span class="o">.</span><span class="n">interval_method</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'method'</span><span class="p">,</span><span class="s1">'quantile'</span><span class="p">)</span>
|
|
|
|
<span class="k">if</span> <span class="s1">'alpha'</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="p">:</span>
|
|
<span class="bp">self</span><span class="o">.</span><span class="n">alpha</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'alpha'</span><span class="p">,</span><span class="mf">0.05</span><span class="p">)</span>
|
|
|
|
<span class="n">l</span> <span class="o">=</span> <span class="nb">len</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">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">,</span> <span class="n">l</span><span class="o">+</span><span class="mi">1</span><span class="p">):</span>
|
|
<span class="n">sample</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="n">k</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span> <span class="p">:</span> <span class="n">k</span><span class="p">]</span>
|
|
<span class="n">tmp</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_models_forecasts</span><span class="p">(</span><span class="n">sample</span><span class="p">)</span>
|
|
<span class="n">interval</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_interval</span><span class="p">(</span><span class="n">tmp</span><span class="p">)</span>
|
|
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">interval</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
|
|
<span class="n">interval</span> <span class="o">=</span> <span class="n">interval</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
|
|
<span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">interval</span><span class="p">)</span>
|
|
|
|
<span class="k">return</span> <span class="n">ret</span></div>
|
|
|
|
<div class="viewcode-block" id="EnsembleFTS.forecast_ahead_interval"><a class="viewcode-back" href="../../../../pyFTS.models.ensemble.html#pyFTS.models.ensemble.ensemble.EnsembleFTS.forecast_ahead_interval">[docs]</a> <span class="k">def</span> <span class="nf">forecast_ahead_interval</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="k">if</span> <span class="s1">'method'</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="p">:</span>
|
|
<span class="bp">self</span><span class="o">.</span><span class="n">interval_method</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'method'</span><span class="p">,</span><span class="s1">'quantile'</span><span class="p">)</span>
|
|
|
|
<span class="k">if</span> <span class="s1">'alpha'</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="p">:</span>
|
|
<span class="bp">self</span><span class="o">.</span><span class="n">alpha</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'alpha'</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">alpha</span><span class="p">)</span>
|
|
|
|
<span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
|
|
|
|
<span class="n">samples</span> <span class="o">=</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="p">[</span><span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">:]]</span>
|
|
|
|
<span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">,</span> <span class="n">steps</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">):</span>
|
|
<span class="n">forecasts</span> <span class="o">=</span> <span class="p">[]</span>
|
|
<span class="n">lags</span> <span class="o">=</span> <span class="p">{}</span>
|
|
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">):</span> <span class="n">lags</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">samples</span><span class="p">[</span><span class="n">k</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span> <span class="o">+</span> <span class="n">i</span><span class="p">]</span>
|
|
|
|
<span class="c1"># Build the tree with all possible paths</span>
|
|
|
|
<span class="n">root</span> <span class="o">=</span> <span class="n">tree</span><span class="o">.</span><span class="n">FLRGTreeNode</span><span class="p">(</span><span class="kc">None</span><span class="p">)</span>
|
|
|
|
<span class="n">tree</span><span class="o">.</span><span class="n">build_tree_without_order</span><span class="p">(</span><span class="n">root</span><span class="p">,</span> <span class="n">lags</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
|
|
|
|
<span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">root</span><span class="o">.</span><span class="n">paths</span><span class="p">():</span>
|
|
<span class="n">path</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">reversed</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="nb">filter</span><span class="p">(</span><span class="kc">None</span><span class="o">.</span><span class="fm">__ne__</span><span class="p">,</span> <span class="n">p</span><span class="p">))))</span>
|
|
|
|
<span class="n">forecasts</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">get_models_forecasts</span><span class="p">(</span><span class="n">path</span><span class="p">))</span>
|
|
|
|
<span class="n">samples</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">sampler</span><span class="p">(</span><span class="n">forecasts</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mf">0.1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mf">0.2</span><span class="p">)))</span>
|
|
<span class="n">interval</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_interval</span><span class="p">(</span><span class="n">forecasts</span><span class="p">)</span>
|
|
|
|
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">interval</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
|
|
<span class="n">interval</span> <span class="o">=</span> <span class="n">interval</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
|
|
|
|
<span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">interval</span><span class="p">)</span>
|
|
|
|
<span class="k">return</span> <span class="n">ret</span></div>
|
|
|
|
<div class="viewcode-block" id="EnsembleFTS.forecast_distribution"><a class="viewcode-back" href="../../../../pyFTS.models.ensemble.html#pyFTS.models.ensemble.ensemble.EnsembleFTS.forecast_distribution">[docs]</a> <span class="k">def</span> <span class="nf">forecast_distribution</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">ret</span> <span class="o">=</span> <span class="p">[]</span>
|
|
|
|
<span class="n">smooth</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">"smooth"</span><span class="p">,</span> <span class="s2">"KDE"</span><span class="p">)</span>
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<span class="n">alpha</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">"alpha"</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
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<span class="n">uod</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_UoD</span><span class="p">()</span>
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<span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">)):</span>
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<span class="n">sample</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="n">k</span><span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span> <span class="p">:</span> <span class="n">k</span><span class="p">]</span>
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<span class="n">forecasts</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_models_forecasts</span><span class="p">(</span><span class="n">sample</span><span class="p">)</span>
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<span class="k">if</span> <span class="n">alpha</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
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<span class="n">forecasts</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ravel</span><span class="p">(</span><span class="n">forecasts</span><span class="p">)</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
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<span class="k">else</span><span class="p">:</span>
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<span class="n">forecasts</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_distribution_interquantile</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">ravel</span><span class="p">(</span><span class="n">forecasts</span><span class="p">)</span><span class="o">.</span><span class="n">tolist</span><span class="p">(),</span> <span class="n">alpha</span><span class="p">)</span>
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<span class="n">dist</span> <span class="o">=</span> <span class="n">ProbabilityDistribution</span><span class="o">.</span><span class="n">ProbabilityDistribution</span><span class="p">(</span><span class="n">smooth</span><span class="p">,</span> <span class="n">uod</span><span class="o">=</span><span class="n">uod</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="n">forecasts</span><span class="p">,</span>
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<span class="n">name</span><span class="o">=</span><span class="s2">""</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
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<span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">dist</span><span class="p">)</span>
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<span class="k">return</span> <span class="n">ret</span></div>
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<div class="viewcode-block" id="EnsembleFTS.forecast_ahead_distribution"><a class="viewcode-back" href="../../../../pyFTS.models.ensemble.html#pyFTS.models.ensemble.ensemble.EnsembleFTS.forecast_ahead_distribution">[docs]</a> <span class="k">def</span> <span class="nf">forecast_ahead_distribution</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>
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|
<span class="k">if</span> <span class="s1">'method'</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="p">:</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">point_method</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'method'</span><span class="p">,</span><span class="s1">'mean'</span><span class="p">)</span>
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<span class="n">smooth</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">"smooth"</span><span class="p">,</span> <span class="s2">"KDE"</span><span class="p">)</span>
|
|
<span class="n">alpha</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">"alpha"</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
|
|
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<span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
|
|
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<span class="n">start</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'start'</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">)</span>
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|
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<span class="n">uod</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_UoD</span><span class="p">()</span>
|
|
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<span class="n">sample</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="n">start</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">:</span> <span class="n">start</span><span class="p">]</span>
|
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<span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">,</span> <span class="n">steps</span><span class="o">+</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">):</span>
|
|
<span class="n">forecasts</span> <span class="o">=</span> <span class="p">[]</span>
|
|
<span class="n">lags</span> <span class="o">=</span> <span class="p">{}</span>
|
|
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">):</span> <span class="n">lags</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">sample</span><span class="p">[</span><span class="n">k</span><span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">]</span>
|
|
|
|
<span class="c1"># Build the tree with all possible paths</span>
|
|
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|
<span class="n">root</span> <span class="o">=</span> <span class="n">tree</span><span class="o">.</span><span class="n">FLRGTreeNode</span><span class="p">(</span><span class="kc">None</span><span class="p">)</span>
|
|
|
|
<span class="n">tree</span><span class="o">.</span><span class="n">build_tree_without_order</span><span class="p">(</span><span class="n">root</span><span class="p">,</span> <span class="n">lags</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
|
|
|
|
<span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">root</span><span class="o">.</span><span class="n">paths</span><span class="p">():</span>
|
|
<span class="n">path</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">reversed</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="nb">filter</span><span class="p">(</span><span class="kc">None</span><span class="o">.</span><span class="fm">__ne__</span><span class="p">,</span> <span class="n">p</span><span class="p">))))</span>
|
|
|
|
<span class="n">forecasts</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">get_models_forecasts</span><span class="p">(</span><span class="n">path</span><span class="p">))</span>
|
|
|
|
<span class="n">sample</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">sampler</span><span class="p">(</span><span class="n">forecasts</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mf">0.1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">)))</span>
|
|
|
|
<span class="k">if</span> <span class="n">alpha</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
|
|
<span class="n">forecasts</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ravel</span><span class="p">(</span><span class="n">forecasts</span><span class="p">)</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
|
|
<span class="k">else</span><span class="p">:</span>
|
|
<span class="n">forecasts</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_distribution_interquantile</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">ravel</span><span class="p">(</span><span class="n">forecasts</span><span class="p">)</span><span class="o">.</span><span class="n">tolist</span><span class="p">(),</span> <span class="n">alpha</span><span class="p">)</span>
|
|
|
|
<span class="n">dist</span> <span class="o">=</span> <span class="n">ProbabilityDistribution</span><span class="o">.</span><span class="n">ProbabilityDistribution</span><span class="p">(</span><span class="n">smooth</span><span class="p">,</span> <span class="n">uod</span><span class="o">=</span><span class="n">uod</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="n">forecasts</span><span class="p">,</span>
|
|
<span class="n">name</span><span class="o">=</span><span class="s2">""</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
|
|
|
|
<span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">dist</span><span class="p">)</span>
|
|
|
|
<span class="k">return</span> <span class="n">ret</span></div></div>
|
|
|
|
|
|
<div class="viewcode-block" id="AllMethodEnsembleFTS"><a class="viewcode-back" href="../../../../pyFTS.models.ensemble.html#pyFTS.models.ensemble.ensemble.AllMethodEnsembleFTS">[docs]</a><span class="k">class</span> <span class="nc">AllMethodEnsembleFTS</span><span class="p">(</span><span class="n">EnsembleFTS</span><span class="p">):</span>
|
|
<span class="sd">"""</span>
|
|
<span class="sd"> Creates an EnsembleFTS with all point forecast methods, sharing the same partitioner</span>
|
|
<span class="sd"> """</span>
|
|
<span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
|
<span class="nb">super</span><span class="p">(</span><span class="n">AllMethodEnsembleFTS</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
|
|
<span class="bp">self</span><span class="o">.</span><span class="n">min_order</span> <span class="o">=</span> <span class="mi">3</span>
|
|
<span class="bp">self</span><span class="o">.</span><span class="n">shortname</span> <span class="o">=</span><span class="s2">"Ensemble FTS"</span>
|
|
|
|
<div class="viewcode-block" id="AllMethodEnsembleFTS.set_transformations"><a class="viewcode-back" href="../../../../pyFTS.models.ensemble.html#pyFTS.models.ensemble.ensemble.AllMethodEnsembleFTS.set_transformations">[docs]</a> <span class="k">def</span> <span class="nf">set_transformations</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">model</span><span class="p">):</span>
|
|
<span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">transformations</span><span class="p">:</span>
|
|
<span class="n">model</span><span class="o">.</span><span class="n">append_transformation</span><span class="p">(</span><span class="n">t</span><span class="p">)</span></div>
|
|
|
|
<div class="viewcode-block" id="AllMethodEnsembleFTS.train"><a class="viewcode-back" href="../../../../pyFTS.models.ensemble.html#pyFTS.models.ensemble.ensemble.AllMethodEnsembleFTS.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="n">fo_methods</span> <span class="o">=</span> <span class="p">[</span><span class="n">song</span><span class="o">.</span><span class="n">ConventionalFTS</span><span class="p">,</span> <span class="n">chen</span><span class="o">.</span><span class="n">ConventionalFTS</span><span class="p">,</span> <span class="n">yu</span><span class="o">.</span><span class="n">WeightedFTS</span><span class="p">,</span> <span class="n">cheng</span><span class="o">.</span><span class="n">TrendWeightedFTS</span><span class="p">,</span>
|
|
<span class="n">sadaei</span><span class="o">.</span><span class="n">ExponentialyWeightedFTS</span><span class="p">,</span> <span class="n">ismailefendi</span><span class="o">.</span><span class="n">ImprovedWeightedFTS</span><span class="p">]</span>
|
|
|
|
<span class="n">ho_methods</span> <span class="o">=</span> <span class="p">[</span><span class="n">hofts</span><span class="o">.</span><span class="n">HighOrderFTS</span><span class="p">,</span> <span class="n">hwang</span><span class="o">.</span><span class="n">HighOrderFTS</span><span class="p">]</span>
|
|
|
|
<span class="k">for</span> <span class="n">method</span> <span class="ow">in</span> <span class="n">fo_methods</span><span class="p">:</span>
|
|
<span class="n">model</span> <span class="o">=</span> <span class="n">method</span><span class="p">(</span><span class="n">partitioner</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="p">)</span>
|
|
<span class="bp">self</span><span class="o">.</span><span class="n">set_transformations</span><span class="p">(</span><span class="n">model</span><span class="p">)</span>
|
|
<span class="n">model</span><span class="o">.</span><span class="n">fit</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="bp">self</span><span class="o">.</span><span class="n">append_model</span><span class="p">(</span><span class="n">model</span><span class="p">)</span>
|
|
|
|
<span class="k">for</span> <span class="n">method</span> <span class="ow">in</span> <span class="n">ho_methods</span><span class="p">:</span>
|
|
<span class="k">for</span> <span class="n">o</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="o">+</span><span class="mi">1</span><span class="p">):</span>
|
|
<span class="n">model</span> <span class="o">=</span> <span class="n">method</span><span class="p">(</span><span class="n">partitioner</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="p">)</span>
|
|
<span class="k">if</span> <span class="n">model</span><span class="o">.</span><span class="n">min_order</span> <span class="o">>=</span> <span class="n">o</span><span class="p">:</span>
|
|
<span class="n">model</span><span class="o">.</span><span class="n">order</span> <span class="o">=</span> <span class="n">o</span>
|
|
<span class="bp">self</span><span class="o">.</span><span class="n">set_transformations</span><span class="p">(</span><span class="n">model</span><span class="p">)</span>
|
|
<span class="n">model</span><span class="o">.</span><span class="n">fit</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="bp">self</span><span class="o">.</span><span class="n">append_model</span><span class="p">(</span><span class="n">model</span><span class="p">)</span></div></div>
|
|
|
|
|
|
|
|
|
|
</pre></div>
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</div>
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