Improvements on cmvfts and pwfts for multivariate forecasting
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@ -86,7 +86,7 @@ class ClusteredMVFTS(mvfts.MVFTS):
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self.target_variable = var
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self.cluster.change_target_variable(var)
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self.model.partitioner = self.cluster
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self.reset_calculated_values()
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self.model.reset_calculated_values()
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ret[var.name] = self.model.forecast(ndata, fuzzyfied=self.pre_fuzzyfy, **kwargs)
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@ -45,4 +45,6 @@ ax[0][1].scatter(forecasts['x'].values,forecasts['y'].values)
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ax[1][0].scatter(test['y'].values,test['x'].values)
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ax[1][0].scatter(forecasts['y'].values,forecasts['x'].values)
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ax[1][1].plot(test['y'].values)
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ax[1][1].plot(forecasts['y'].values)
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ax[1][1].plot(forecasts['y'].values)
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print(forecasts)
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