方法对比

	SARIMAX	状态空间模型（卡尔曼滤波器）
领域	计量经济学	计量经济学
方法族	Regression model	Regression model
起源年份	2015	1990
提出者	Box & Jenkins (ARIMA framework); SARIMAX extension with exogenous regressors	Harvey; Durbin & Koopman (state space treatment); Kalman filter
类型	Seasonal time-series regression model	State space time series model
开创性文献	Hyndman, R. J. & Athanasopoulos, G. (2021). Forecasting: Principles and Practice (3rd ed.). OTexts.	Harvey, A. C. (1990). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press.
别名	seasonal ARIMA with exogenous variables, SARIMA with regressors, ARIMAX, SARIMAX — Dışsal Değişkenli Mevsimsel ARIMA	state space, Kalman filter, unobserved components model, Durum Uzayı Modeli (State Space / Kalman Filter)
相关	4	4
摘要	SARIMAX extends the seasonal ARIMA (Box-Jenkins) model by adding exogenous explanatory variables, so it can capture the effect of holidays, economic indicators, or policy variables on a time series. It combines non-seasonal and seasonal autoregressive and moving-average dynamics with external regressors, and is estimated by maximum likelihood in state-space form.	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.