Vertaile menetelmiä

	Holt-Wintersin kolminkertainen eksponentiaalinen tasoitus	Prophet – Hajotettava aikasarjaennustemalli	SARIMAX
Tieteenala	Ekonometria	Ekonometria	Ekonometria
Menetelmäperhe	Regression model	Regression model	Regression model
Syntyvuosi	1960	2018	2015
Kehittäjä	Charles C. Holt and Peter R. Winters	Taylor & Letham (Facebook/Meta)	Box & Jenkins (ARIMA framework); SARIMAX extension with exogenous regressors
Tyyppi	Exponential smoothing forecasting model	Decomposable (structural) time series model	Seasonal time-series regression model
Alkuperäislähde	Winters, P. R. (1960). Forecasting Sales by Exponentially Weighted Moving Averages. Management Science, 6(3), 324-342.	Taylor, S. J. & Letham, B. (2018). Forecasting at Scale. The American Statistician, 72(1), 37-45.	Hyndman, R. J. & Athanasopoulos, G. (2021). Forecasting: Principles and Practice (3rd ed.). OTexts.
Rinnakkaisnimet	triple exponential smoothing, Winters' method, Holt-Winters seasonal method, Holt-Winters Üçlü Üstel Düzleştirme	Prophet, Facebook Prophet, Meta Prophet, forecasting at scale	seasonal ARIMA with exogenous variables, SARIMA with regressors, ARIMAX, SARIMAX — Dışsal Değişkenli Mevsimsel ARIMA
Liittyvät	4	5	4
Tiivistelmä	Holt-Winters triple exponential smoothing is a forecasting model that extends Holt's double smoothing by adding a seasonal component, introduced by Peter Winters in 1960 building on Charles Holt's work. It tracks three evolving quantities — level, trend, and season — and combines them to forecast a continuous time series.	Prophet is a Bayesian structural time series model introduced by Taylor and Letham at Facebook/Meta in 2018. It forecasts a continuous series by decomposing it into separate, interpretable trend, seasonality, and holiday components, and is designed to be approachable for analysts working at scale.	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.