def test_FittedParamExtractor(param_names):
forecaster = ExponentialSmoothing()
t = FittedParamExtractor(forecaster=forecaster, param_names=param_names)
Xt = t.fit_transform(X_train)
assert Xt.shape == (X_train.shape[0],
len(t._check_param_names(param_names)))
# check specific value
forecaster.fit(X_train.iloc[47, 0])
fitted_param = forecaster.get_fitted_params()[param_names]
assert Xt.iloc[47, 0] == fitted_param
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()
forecaster = ARIMA(
order=(1, 1, 0), seasonal_order=(0, 1, 0, 12), suppress_warnings=True