So sánh phương pháp

	Mô hình SARIMA Bayes	Mô hình không gian trạng thái (Bộ lọc Kalman)
Lĩnh vực	Kinh tế lượng	Kinh tế lượng
Họ	Regression model	Regression model
Năm ra đời	1970s–1990s	1990
Người khởi xướng	Box & Jenkins (classical SARIMA); Bayesian extensions developed through Zellner, Geweke, and later MCMC-era researchers	Harvey; Durbin & Koopman (state space treatment); Kalman filter
Loại	Bayesian time-series model	State space time series model
Công trình gốc	Box, G. E. P., Jenkins, G. M., Reinsel, G. C., & Ljung, G. M. (2015). Time Series Analysis: Forecasting and Control (5th ed.). Wiley. ISBN: 978-1118675021	Harvey, A. C. (1990). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press.
Tên gọi khác	Bayesian SARIMA, Bayesian seasonal ARIMA, BSARIMA, Bayesian seasonal time-series model	state space, Kalman filter, unobserved components model, Durum Uzayı Modeli (State Space / Kalman Filter)
Liên quan	4	4
Tóm tắt	The Bayesian SARIMA model combines the classical Box-Jenkins Seasonal ARIMA framework with Bayesian inference to handle seasonal time-series data. Rather than producing a single point estimate, it yields a full posterior distribution over model parameters, propagating parameter uncertainty directly into forecasts and enabling principled incorporation of prior knowledge.	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.