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	Prophet	ETS: Wykładnicze wygładzanie z uwzględnieniem błędu, trendu i sezonowości	Potrójne wygładzanie wykładnicze Holta-Wintersa
Dziedzina	Ekonometria	Ekonometria	Ekonometria
Rodzina	Regression model	Regression model	Regression model
Rok powstania	2018	2008	1960
Twórca	Taylor & Letham (Facebook/Meta)	Hyndman, Koehler, Ord & Snyder (state space framework)	Charles C. Holt and Peter R. Winters
Typ	Decomposable (structural) time series model	Exponential smoothing state space model	Exponential smoothing forecasting model
Źródło pierwotne	Taylor, S. J. & Letham, B. (2018). Forecasting at Scale. The American Statistician, 72(1), 37-45.	Hyndman, R. J., Koehler, A. B., Ord, J. K. & Snyder, R. D. (2008). Forecasting with Exponential Smoothing: The State Space Approach. Springer.	Winters, P. R. (1960). Forecasting Sales by Exponentially Weighted Moving Averages. Management Science, 6(3), 324-342.
Inne nazwy	Prophet, Facebook Prophet, Meta Prophet, forecasting at scale	exponential smoothing state space model, innovations state space model, Holt-Winters family, ETS — Hata/Trend/Mevsimsellik Üstel Düzleştirme	triple exponential smoothing, Winters' method, Holt-Winters seasonal method, Holt-Winters Üçlü Üstel Düzleştirme
Pokrewne	5	5	4
Podsumowanie	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.	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.	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.