Vertaile menetelmiä

	Kausiluonteinen ARIMA (SARIMA)	Holt-Wintersin kolminkertainen eksponentiaalinen tasoitus	Prophet – Hajotettava aikasarjaennustemalli
Tieteenala	Ekonometria	Ekonometria	Ekonometria
Menetelmäperhe	Regression model	Regression model	Regression model
Syntyvuosi	2015	1960	2018
Kehittäjä	Box & Jenkins (seasonal extension of ARIMA)	Charles C. Holt and Peter R. Winters	Taylor & Letham (Facebook/Meta)
Tyyppi	Seasonal time-series model	Exponential smoothing forecasting model	Decomposable (structural) time series model
Alkuperäislähde	Box, G.E.P., Jenkins, G.M., Reinsel, G.C. & Ljung, G.M. (2015). Time Series Analysis: Forecasting and Control (5th ed.). Wiley. ISBN: 978-1118675021	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.
Rinnakkaisnimet	seasonal ARIMA, Box-Jenkins seasonal model, SARIMA — Mevsimsel ARIMA	triple exponential smoothing, Winters' method, Holt-Winters seasonal method, Holt-Winters Üçlü Üstel Düzleştirme	Prophet, Facebook Prophet, Meta Prophet, forecasting at scale
Liittyvät	5	4	5
Tiivistelmä	SARIMA is a seasonal extension of the Box-Jenkins ARIMA model that adds seasonal differencing and seasonal autoregressive and moving-average terms. Developed within the Box, Jenkins, Reinsel and Ljung framework (5th edition, 2015), it forecasts series whose pattern repeats on a yearly, monthly, or weekly period.	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.