Сравнение методов

	ETS: Экспоненциальное сглаживание с учетом ошибки, тренда и сезонности	Простое и двойное экспоненциальное сглаживание (SES / Холт)
Область	Эконометрика	Эконометрика
Семейство	Regression model	Regression model
Год появления	2008	1957
Автор метода	Hyndman, Koehler, Ord & Snyder (state space framework)	Robert G. Brown (SES); Charles C. Holt (linear trend)
Тип	Exponential smoothing state space model	Exponential smoothing forecasting 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.	Brown, R. G. (1959). Statistical Forecasting for Inventory Control. McGraw-Hill.
Другие названия	exponential smoothing state space model, innovations state space model, Holt-Winters family, ETS — Hata/Trend/Mevsimsellik Üstel Düzleştirme	SES, Holt's linear trend method, exponential smoothing forecasting, Basit ve Çift Üstel Düzleştirme (SES / Holt)
Связанные	5	3
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.	Exponential smoothing is a family of basic time-series forecasting models in which each new observation updates a smoothed estimate by a weighting parameter. Simple exponential smoothing (SES), introduced by Robert G. Brown in 1959, forecasts series with a stable level, while Holt's double exponential smoothing, introduced by Charles C. Holt in 1957, adds a trend term using the parameters alpha and beta.