เปรียบเทียบวิธี

	ฟิลเตอร์คาลมาน	แบบจำลอง ARIMA (Autoregressive Integrated Moving Average)
สาขาวิชา	การเงิน	เศรษฐมิติ
ตระกูล	Regression model	Regression model
ปีกำเนิด	1989	2015
ผู้ริเริ่ม	Harvey (structural time series treatment); Kim & Nelson (state-space with regime switching)	Box & Jenkins (Box-Jenkins methodology)
ประเภท	Linear state-space model	Univariate time-series model
แหล่งต้นตำรับ	Harvey, A. C. (1989). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press. ISBN: 978-0521405737	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
ชื่อเรียกอื่น	state-space model, dynamic linear model, recursive Bayesian filter, Kalman Filtresi — Finansal Durum Uzayı Modeli	Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli
ที่เกี่ยวข้อง	5	5
สรุป	The Kalman filter is a recursive algorithm that estimates financial models with time-varying parameters, hidden factors, and noisy observations inside a dynamic state-space framework. The structural time series treatment was set out by Harvey (1989), with state-space and regime-switching extensions developed by Kim and Nelson (1999); it is widely applied to pairs trading, time-varying beta estimation, and yield-curve modelling.	ARIMA is a univariate time-series forecasting model that combines autoregressive, integrated (differencing), and moving-average components to predict a single continuous series from its own past. It is the centrepiece of the Box-Jenkins methodology set out in Box, Jenkins, Reinsel & Ljung's Time Series Analysis (5th ed., 2015).