Improvements on EnsembleFTS.forecast_distribution_ahead
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@ -276,13 +276,15 @@ class EnsembleFTS(fts.FTS):
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'''
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lags = []
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for i in np.arange(0, self.order):
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lags.append(sample[k - self.order])
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lags.append(sample[i - self.order])
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print(k, lags)
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# Trace the possible paths
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for path in product(*lags):
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forecasts.extend(self.get_models_forecasts(path))
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sample.append(sampler(forecasts, np.arange(0.1, 1, 0.1)))
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sample.append(sampler(forecasts, np.arange(0.05, .99, 0.1)))
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if alpha is None:
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forecasts = np.ravel(forecasts).tolist()
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@ -29,7 +29,7 @@ for k in [15, 25, 35]:
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model.append_model(tmp)
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forecasts = model.predict(data, type='distribution', smooth='histogram', steps_ahead=10)
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forecasts = model.predict(data, type='distribution', smooth='histogram', steps_ahead=5)
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from pyFTS.benchmarks import benchmarks as bchmk
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