def test_set_params():
params = {"trend": "additive"}
f = ExponentialSmoothing(**params)
f.fit(y_train, fh=1)
expected = f.predict()
f = ExponentialSmoothing()
f.set_params(**params)
f.fit(y_train, fh=1)
y_pred = f.predict()
assert_array_equal(y_pred, expected)
def test_exponential_smoothing():
"""Test bug in 1876.
https://github.com/alan-turing-institute/sktime/issues/1876#issue-1103752402.
"""
y = load_airline()
# Change index to 10 min interval
freq = "10Min"
time_range = pd.date_range(
pd.to_datetime("2019-01-01 00:00"),
pd.to_datetime("2019-01-01 23:55"),
freq=freq,
)
# Period Index does not work
y.index = time_range.to_period()
forecaster = ExponentialSmoothing(trend="add",
seasonal="multiplicative",
sp=12)
forecaster.fit(y, fh=[1, 2, 3, 4, 5, 6])
y_pred = forecaster.predict()
pd.testing.assert_index_equal(
y_pred.index, pd.period_range("2019-01-02 00:00", periods=6,
freq=freq))
def main():
df = datasets.load_airline(
) #Univariate, monthly records from 1949 to 60 (144 records)
y_train, y_test = temporal_train_test_split(
df, test_size=36) #36 months for testing
forecaster = NaiveForecaster(
strategy='seasonal_last', sp=12
) #model strategy: last, mean, seasonal_last. sp=12months (yearly season)
forecaster.fit(y_train) #fit
fh = np.arange(1,
len(y_test) +
1) #forecast horizon: array with the same lenght of y_test
y_pred = forecaster.predict(fh) #pred
forecaster2 = AutoARIMA(sp=12, suppress_warnings=True, trace=1)
forecaster2.fit(y_train)
y_pred2 = forecaster2.predict(fh)
forecaster3 = ExponentialSmoothing(trend='add',
damped='True',
seasonal='multiplicative',
sp=12)
forecaster3.fit(y_train)
y_pred3 = forecaster3.predict(fh)
forecaster4 = ThetaForecaster(sp=12)
forecaster4.fit(y_train)
y_pred4 = forecaster4.predict(fh)
forecaster5 = EnsembleForecaster([
('NaiveForecaster', NaiveForecaster(strategy='seasonal_last', sp=12)),
('AutoARIMA', AutoARIMA(sp=12, suppress_warnings=True)),
('Exp Smoothing',
ExponentialSmoothing(trend='add',
damped='True',
seasonal='multiplicative',
sp=12)), ('Theta', ThetaForecaster(sp=12))
])
forecaster5.fit(y_train)
y_pred5 = forecaster5.predict(fh)
plot_ys(y_train,
y_test,
y_pred,
y_pred2,
y_pred3,
y_pred4,
y_pred5,
labels=[
'Train', 'Test', 'Naive Forecaster', 'AutoARIMA',
'Exp Smoothing', 'Theta', 'Ensemble'
])
plt.xlabel('Months')
plt.ylabel('Number of flights')
plt.title(
'Time series of the number of international flights in function of time'
)
plt.show()
print('SMAPE Error for NaiveForecaster is:',
100 * round(smape_loss(y_test, y_pred), 3), '%')
print('SMAPE Error for AutoARIMA is:',
100 * round(smape_loss(y_test, y_pred2), 3), '%')
print('SMAPE Error for Exp Smoothing is:',
100 * round(smape_loss(y_test, y_pred3), 3), '%')
print('SMAPE Error for Theta is:',
100 * round(smape_loss(y_test, y_pred4), 3), '%')
print('SMAPE Error for Ensemble is:',
100 * round(smape_loss(y_test, y_pred5), 3), '%')
y_train, y_test = temporal_train_test_split(y, test_size = 24) plot_series(y_train, y_test) fh = ForecastingHorizon(y_test.index, is_relative=False) fh ets_frcstr = ExponentialSmoothing(trend='additive', seasonal='additive', sp=12) ets_frcstr.fit(y_train) y_pred = ets_frcstr.predict(fh) plot_series(y_train, y_test, y_pred, labels=['Обучающая', 'т', 'п']) ets_frcstr.get_fitted_params() ets_frcstr.get_params() smape_loss(y_test, y_pred) auto_ets_frr = AutoETS() auto_ets_frr.fit(y_pred) auto_ets_frr.summary() arima_frr = AutoARIMA() arima_frr = ARIMA()