Bugfix on MVFTS.forecast_ahead
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@ -67,7 +67,9 @@ class ClusteredMVFTS(mvfts.MVFTS):
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ndata = self.check_data(ndata)
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return self.model.forecast(ndata, fuzzyfied=self.pre_fuzzyfy, **kwargs)
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pre_fuzz = kwargs.get('pre_fuzzyfy', self.pre_fuzzyfy)
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return self.model.forecast(ndata, fuzzyfied=pre_fuzz, **kwargs)
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def forecast_multivariate(self, data, **kwargs):
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@ -170,11 +170,11 @@ class MVFTS(fts.FTS):
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ndata = self.apply_transformations(data)
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start = kwargs.get('start_at', self.order)
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start = kwargs.get('start_at', self.max_lag)
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ret = []
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for k in np.arange(0, steps):
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ix = ndata.index[start-self.max_lag+k:k+start]
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for k in np.arange(start, start+steps):
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ix = ndata.index[k-self.max_lag:k]
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sample = ndata.loc[ix]
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tmp = self.forecast(sample, **kwargs)
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@ -200,9 +200,12 @@ class MVFTS(fts.FTS):
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new_data_point[self.target_variable.data_label] = tmp
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print(k)
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print(new_data_point)
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ndata = ndata.append(new_data_point, ignore_index=True)
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return ret[-steps]
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return ret[-steps:]
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def forecast_interval(self, data, **kwargs):
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ret = []
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@ -63,9 +63,11 @@ vavg = variable.Variable("Radiation", data_label="glo_avg", alias='rad',
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fs = grid.GridCluster(explanatory_variables=[vmonth, vhour, vavg], target_variable=vavg)
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model = cmvfts.ClusteredMVFTS(explanatory_variables=[vmonth, vhour, vavg], target_variable=vavg, partitioner=fs,
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order=2, knn=1)
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#model = wmvfts.WeightedMVFTS(explanatory_variables=[vmonth, vhour, vavg], target_variable=vavg)
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model.fit(train_mv)
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forecasts = model.predict(test_mv.iloc[:100])
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generator = lambda x : x + pd.to_timedelta(1, unit='h')
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forecasts = model.predict(test_mv.iloc[:3], steps_ahead=48, generators={'data': generator} )
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print(forecasts)
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