def test_untrained_models(self):
model = NaiveDrift()
_ = NaiveEnsembleModel([model])
# trained models should raise error
model.fit(self.series1)
with self.assertRaises(ValueError):
NaiveEnsembleModel([model])
def test_fit_univar_ts_with_covariates_for_local_models(self):
naive = NaiveEnsembleModel(
[NaiveDrift(),
NaiveSeasonal(),
Theta(),
ExponentialSmoothing()])
with self.assertRaises(ValueError):
naive.fit(self.series1, self.series2)
def test_predict_ensemble_local_models(self):
naive = NaiveSeasonal(K=5)
theta = Theta()
naive_ensemble = NaiveEnsembleModel([naive, theta])
naive_ensemble.fit(self.series1 + self.series2)
forecast_naive_ensemble = naive_ensemble.predict(5)
naive.fit(self.series1 + self.series2)
theta.fit(self.series1 + self.series2)
forecast_mean = 0.5 * naive.predict(5) + 0.5 * theta.predict(5)
self.assertTrue(
np.array_equal(forecast_naive_ensemble.values(),
forecast_mean.values()))
def test_input_models_local_models(self):
with self.assertRaises(ValueError):
NaiveEnsembleModel([])
with self.assertRaises(ValueError):
NaiveEnsembleModel(
[NaiveDrift, NaiveSeasonal, Theta, ExponentialSmoothing])
with self.assertRaises(ValueError):
NaiveEnsembleModel(
[NaiveDrift(), NaiveSeasonal,
Theta(),
ExponentialSmoothing()])
NaiveEnsembleModel(
[NaiveDrift(),
NaiveSeasonal(),
Theta(),
ExponentialSmoothing()])
def test_call_predict_global_models_univariate_input_no_covariates(self):
naive_ensemble = NaiveEnsembleModel([
RNNModel(12, n_epochs=1),
TCNModel(10, 2, n_epochs=1),
NBEATSModel(10, 2, n_epochs=1),
])
with self.assertRaises(Exception):
naive_ensemble.predict(5)
naive_ensemble.fit(self.series1)
naive_ensemble.predict(5)
def test_call_predict_global_models_multivariate_input_no_covariates(self):
naive_ensemble = NaiveEnsembleModel([
RNNModel(12, n_epochs=1),
TCNModel(10, 2, n_epochs=1),
NBEATSModel(10, 2, n_epochs=1),
])
naive_ensemble.fit(self.seq1)
naive_ensemble.predict(n=5, series=self.seq1)
def test_call_predict_global_models_multivariate_input_with_covariates(
self):
naive_ensemble = NaiveEnsembleModel([
RNNModel(12, n_epochs=1),
TCNModel(10, 2, n_epochs=1),
NBEATSModel(10, 2, n_epochs=1),
])
naive_ensemble.fit(self.seq1, self.cov1)
predict_series = [s[:12] for s in self.seq1]
predict_covariates = [c[:14] for c in self.cov1]
naive_ensemble.predict(n=2,
series=predict_series,
past_covariates=predict_covariates)
def test_input_models_global_models(self):
NaiveEnsembleModel([RNNModel(12), TCNModel(10, 2), NBEATSModel(10, 2)])
def test_call_predict_local_models(self):
naive_ensemble = NaiveEnsembleModel([NaiveSeasonal(), Theta()])
with self.assertRaises(Exception):
naive_ensemble.predict(5)
naive_ensemble.fit(self.series1)
naive_ensemble.predict(5)
def test_input_models_mixed(self):
with self.assertRaises(ValueError):
NaiveEnsembleModel([NaiveDrift(), Theta(), RNNModel(12)])