手法を比較

	Prophet	ETS: 誤差、トレンド、季節指数平滑法
分野	計量経済学	計量経済学
系統	Regression model	Regression model
提唱年	2018	2008
提唱者	Taylor & Letham (Facebook/Meta)	Hyndman, Koehler, Ord & Snyder (state space framework)
種類	Decomposable (structural) time series model	Exponential smoothing state space model
原典	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.
別名	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
関連	5	5
概要	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.