Comparer des méthodes

	Lissage exponentiel simple et double (SES / Holt)	Modèle structurel de séries temporelles (Modèle structurel de base)
Domaine	Économétrie	Économétrie
Famille	Regression model	Regression model
Année d'origine	1957	1990
Auteur d'origine	Robert G. Brown (SES); Charles C. Holt (linear trend)	Andrew C. Harvey
Type	Exponential smoothing forecasting model	State-space (unobserved components) time series model
Source fondatrice	Brown, R. G. (1959). Statistical Forecasting for Inventory Control. McGraw-Hill.	Harvey, A. C. (1990). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press. ISBN: 978-0521405737
Alias	SES, Holt's linear trend method, exponential smoothing forecasting, Basit ve Çift Üstel Düzleştirme (SES / Holt)	BSM, basic structural model, unobserved components model, Yapısal Zaman Serisi Modeli (BSM)
Apparentées	3	4
Résumé	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.	The Structural Time Series Model, in its Basic Structural Model (BSM) form, is Andrew Harvey's state-space approach that decomposes a series into separate stochastic trend, seasonal, cyclical, and irregular components. Developed in Harvey's 1990 treatment, it is prized for interpretability and component decomposition where ARIMA only delivers a black-box fit.