Comparer des méthodes

	Modèle SARIMA à paramètres variant dans le temps (TVP-SARIMA)	Modèle d'espace d'états (Filtre de Kalman)
Domaine	Économétrie	Économétrie
Famille	Regression model	Regression model
Année d'origine	1990s	1990
Auteur d'origine	Harvey, A. C.; Durbin, J. & Koopman, S. J. (state-space framework)	Harvey; Durbin & Koopman (state space treatment); Kalman filter
Type	Time-varying state-space model	State space time series model
Source fondatrice	Harvey, A. C. (1990). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press. ISBN: 9780521321969	Harvey, A. C. (1990). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press.
Alias	TVP-SARIMA, time-varying SARIMA, state-space SARIMA, adaptive SARIMA	state space, Kalman filter, unobserved components model, Durum Uzayı Modeli (State Space / Kalman Filter)
Apparentées	4	4
Résumé	The Time-Varying Parameter SARIMA model extends the classical SARIMA framework by allowing autoregressive and moving-average coefficients to evolve over time. Cast as a state-space system and estimated with the Kalman filter, it captures both seasonal patterns and structural change within a single unified model.	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.