Bugfix in forecast_ahead
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07832878c9
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0b03fbfa57
@ -239,9 +239,9 @@ class FTS(object):
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start = kwargs.get('start_at',0)
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ret = []
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ret = data[:start+self.max_lag]
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for k in np.arange(start+self.max_lag, steps+start+self.max_lag):
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tmp = self.forecast(data[k-self.max_lag:k], **kwargs)
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tmp = self.forecast(ret[k-self.max_lag:k], **kwargs)
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if isinstance(tmp,(list, np.ndarray)):
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tmp = tmp[-1]
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@ -364,7 +364,7 @@ class FTS(object):
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if dump == 'time':
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print("[{0: %H:%M:%S}] Start training".format(datetime.datetime.now()))
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if num_batches is not None:
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if num_batches is not None and not self.is_wrapper:
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n = len(data)
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batch_size = int(n / num_batches)
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bcount = 1
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@ -29,12 +29,19 @@ from pyFTS.partitioners import Grid
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partitioner = Grid.GridPartitioner(data=train_data, npart=35)
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from pyFTS.models import pwfts
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from pyFTS.models import pwfts, hofts
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model = pwfts.ProbabilisticWeightedFTS(partitioner=partitioner, order=2)
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model.train(train_data)
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#model = pwfts.ProbabilisticWeightedFTS(partitioner=partitioner, order=2)
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#from pyFTS.models.incremental import TimeVariant
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print(model.predict(test_data[:100]))
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#model = TimeVariant.Retrainer(partitioner_method=Grid.GridPartitioner, partitioner_params={'npart': 35},
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# fts_method=pwfts.ProbabilisticWeightedFTS, fts_params={}, order=2 ,
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# batch_size=100, window_length=500)
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model = hofts.HighOrderFTS(partitioner=partitioner, order=2)
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model.fit(train_data)
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print(model.predict(test_data, steps_ahead=10))
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'''
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