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Mixing univariate and multivariate models in EnsembleFTS

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Petrônio Cândido 2018-06-29 16:32:21 -03:00
parent 3580f0b4b3
commit c7ee8c3cfe
1 changed files with 13 additions and 1 deletions
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14
pyFTS/models/ensemble/ensemble.py
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@ -31,21 +31,33 @@ class EnsembleFTS(fts.FTS):
self.alpha = kwargs.get("alpha", 0.05) self.alpha = kwargs.get("alpha", 0.05)
self.point_method = kwargs.get('point_method', 'mean') self.point_method = kwargs.get('point_method', 'mean')
self.interval_method = kwargs.get('interval_method', 'quantile') self.interval_method = kwargs.get('interval_method', 'quantile')
self.order = 1
def append_model(self, model): def append_model(self, model):
self.models.append(model) self.models.append(model)
if model.order > self.order: if model.order > self.order:
self.order = model.order self.order = model.order
if model.is_multivariate:
self.is_multivariate = True
if model.has_seasonality:
self.has_seasonality = True
def train(self, data, **kwargs): def train(self, data, **kwargs):
pass pass
def get_models_forecasts(self,data): def get_models_forecasts(self,data):
tmp = [] tmp = []
for model in self.models: for model in self.models:
if self.is_multivariate or self.has_seasonality: if model.is_multivariate or model.has_seasonality:
forecast = model.forecast(data) forecast = model.forecast(data)
else: else:
if isinstance(data, pd.DataFrame) and self.indexer is not None:
data = self.indexer.get_data(data)
sample = data[-model.order:] sample = data[-model.order:]
forecast = model.forecast(sample) forecast = model.forecast(sample)
if isinstance(forecast, (list,np.ndarray)) and len(forecast) > 0: if isinstance(forecast, (list,np.ndarray)) and len(forecast) > 0: