Сравнение методов

	ETS: Экспоненциальное сглаживание с учетом ошибки, тренда и сезонности	SARIMAX
Область	Эконометрика	Эконометрика
Семейство	Regression model	Regression model
Год появления	2008	2015
Автор метода	Hyndman, Koehler, Ord & Snyder (state space framework)	Box & Jenkins (ARIMA framework); SARIMAX extension with exogenous regressors
Тип	Exponential smoothing state space model	Seasonal time-series regression model
Основополагающий источник	Hyndman, R. J., Koehler, A. B., Ord, J. K. & Snyder, R. D. (2008). Forecasting with Exponential Smoothing: The State Space Approach. Springer.	Hyndman, R. J. & Athanasopoulos, G. (2021). Forecasting: Principles and Practice (3rd ed.). OTexts.
Другие названия	exponential smoothing state space model, innovations state space model, Holt-Winters family, ETS — Hata/Trend/Mevsimsellik Üstel Düzleştirme	seasonal ARIMA with exogenous variables, SARIMA with regressors, ARIMAX, SARIMAX — Dışsal Değişkenli Mevsimsel ARIMA
Связанные	5	4
Сводка	ETS is a comprehensive exponential smoothing framework that automatically selects additive or multiplicative combinations of the error (E), trend (T) and seasonal (S) components of a time series. Formalised as an innovations state space model by Hyndman, Koehler, Ord and Snyder in 2008, it unifies and generalises the Holt-Winters family of forecasting methods.	SARIMAX extends the seasonal ARIMA (Box-Jenkins) model by adding exogenous explanatory variables, so it can capture the effect of holidays, economic indicators, or policy variables on a time series. It combines non-seasonal and seasonal autoregressive and moving-average dynamics with external regressors, and is estimated by maximum likelihood in state-space form.