เปรียบเทียบวิธี

	Seasonal ARIMA (SARIMA)	แบบจำลองปริภูมิสถานะ (ตัวกรองคาลมาน)
สาขาวิชา	เศรษฐมิติ	เศรษฐมิติ
ตระกูล	Regression model	Regression model
ปีกำเนิด	2015	1990
ผู้ริเริ่ม	Box & Jenkins (seasonal extension of ARIMA)	Harvey; Durbin & Koopman (state space treatment); Kalman filter
ประเภท	Seasonal time-series model	State space time series model
แหล่งต้นตำรับ	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.
ชื่อเรียกอื่น	seasonal ARIMA, Box-Jenkins seasonal model, SARIMA — Mevsimsel ARIMA	state space, Kalman filter, unobserved components model, Durum Uzayı Modeli (State Space / Kalman Filter)
ที่เกี่ยวข้อง	5	4
สรุป	SARIMA is a seasonal extension of the Box-Jenkins ARIMA model that adds seasonal differencing and seasonal autoregressive and moving-average terms. Developed within the Box, Jenkins, Reinsel and Ljung framework (5th edition, 2015), it forecasts series whose pattern repeats on a yearly, monthly, or weekly period.	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.