เปรียบเทียบวิธี

	แบบจำลองฟูเรียร์ ARMA	แบบจำลอง ARIMA (Autoregressive Integrated Moving Average)
สาขาวิชา	เศรษฐมิติ	เศรษฐมิติ
ตระกูล	Regression model	Regression model
ปีกำเนิด	2004–2006	1970
ผู้ริเริ่ม	Becker, Enders, and Hurn	George Box and Gwilym Jenkins
ประเภท	Time series model with smooth structural change	Time series forecasting model
แหล่งต้นตำรับ	Becker, R., Enders, W., & Hurn, S. (2006). A general test for time dependence in parameters. Journal of Applied Econometrics, 21(7), 1005–1028.	Box, G. E. P., & Jenkins, G. M. (1970). Time Series Analysis: Forecasting and Control. Holden-Day.
ชื่อเรียกอื่น	Fourier ARMA, ARMA with Fourier terms, trigonometric ARMA, smooth structural change ARMA	ARIMA, Box-Jenkins model, integrated ARMA, ARIMA(p,d,q)
ที่เกี่ยวข้อง	5	6
สรุป	The Fourier ARMA model augments the classical Autoregressive Moving Average framework with low-frequency Fourier (sine and cosine) terms to capture smooth, gradual shifts in the mean or trend of a time series. Unlike dummy-variable approaches, it requires no prior knowledge of when structural change occurred, approximating change with flexible trigonometric functions.	The ARIMA(p,d,q) model is the standard workhorse for univariate time series forecasting. It combines autoregressive terms (past values), differencing to induce stationarity, and moving average terms (past shocks) into a unified linear framework. Developed by Box and Jenkins (1970), it remains one of the most widely applied models in econometrics and applied statistics.