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