Salīdzināt metodes

	Prophet	ETS: Kļūda, tendence, sezonas eksponenciālā izlīdzināšana	Valsts telpas modelis (Kalmana filtrs)
Nozare	Ekonometrija	Ekonometrija	Ekonometrija
Saime	Regression model	Regression model	Regression model
Izcelsmes gads	2018	2008	1990
Autors	Taylor & Letham (Facebook/Meta)	Hyndman, Koehler, Ord & Snyder (state space framework)	Harvey; Durbin & Koopman (state space treatment); Kalman filter
Tips	Decomposable (structural) time series model	Exponential smoothing state space model	State space time series model
Pirmavots	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.	Harvey, A. C. (1990). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press.
Citi nosaukumi	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	state space, Kalman filter, unobserved components model, Durum Uzayı Modeli (State Space / Kalman Filter)
Saistītās	5	5	4
Kopsavilkums	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.	A state space model is a general time series framework that describes a series through unobserved (latent) state variables linked by a measurement equation and a transition equation, with the states estimated in real time by the Kalman filter. Developed in the state space tradition of Harvey (1990) and Durbin & Koopman (2012), it nests ARIMA and exponential smoothing as special cases.