Sammenlign metoder

	Holt-Winters tredobbelt eksponentiel udjævning	Model for tilstandsrum (Kalmanfilter)
Fagområde	Økonometri	Økonometri
Familie	Regression model	Regression model
Oprindelsesår	1960	1990
Ophavsperson	Charles C. Holt and Peter R. Winters	Harvey; Durbin & Koopman (state space treatment); Kalman filter
Type	Exponential smoothing forecasting model	State space time series model
Oprindelig kilde	Winters, P. R. (1960). Forecasting Sales by Exponentially Weighted Moving Averages. Management Science, 6(3), 324-342.	Harvey, A. C. (1990). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press.
Aliasser	triple exponential smoothing, Winters' method, Holt-Winters seasonal method, Holt-Winters Üçlü Üstel Düzleştirme	state space, Kalman filter, unobserved components model, Durum Uzayı Modeli (State Space / Kalman Filter)
Relaterede	4	4
Resumé	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.	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.