Bugfix in ensemble.Ensemble
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@ -64,6 +64,10 @@ class EnsembleFTS(fts.FTS):
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if model.has_seasonality:
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self.has_seasonality = True
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if model.original_min < self.original_min:
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self.original_min = model.original_min
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elif model.original_max > self.original_max:
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self.original_max = model.original_max
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def train(self, data, **kwargs):
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pass
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@ -12,6 +12,34 @@ from pyFTS.models.incremental import IncrementalEnsemble, TimeVariant
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from pyFTS.data import AirPassengers, artificial
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from pyFTS.models.ensemble import ensemble
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from pyFTS.models import hofts
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from pyFTS.data import TAIEX
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data = TAIEX.get_data()
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model = ensemble.EnsembleFTS()
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for k in [15, 25, 35]:
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for order in [1, 2]:
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fs = Grid.GridPartitioner(data=data, npart=k)
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tmp = hofts.WeightedHighOrderFTS(partitioner=fs)
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tmp.fit(data)
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model.append_model(tmp)
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forecasts = model.predict(data, type='interval', method='quantile', alpha=.05)
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from pyFTS.benchmarks import benchmarks as bchmk
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#f, ax = plt.subplots(1, 1, figsize=[20, 5])
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#ax.plot(data)
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#bchmk.plot_interval(ax, forecasts, 3, "")
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print(forecasts)
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'''
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mu_local = 5
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sigma_local = 0.25
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mu_drift = 10
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@ -33,7 +61,7 @@ model2 = IncrementalEnsemble.IncrementalEnsembleFTS(partitioner_method=Grid.Grid
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forecasts = model2.predict(signal)
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print(forecasts)
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'''
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'''
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passengers = np.array(passengers["Passengers"])
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