手法を比較

	フーリエARMAモデル	自己回帰和分移動平均モデル (ARIMA Model)
分野	計量経済学	計量経済学
系統	Regression model	Regression model
提唱年	2004–2006	1970
提唱者	Becker, Enders, and Hurn	George Box and Gwilym Jenkins
種類	Time series model with smooth structural change	Time series forecasting model
原典	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.
別名	Fourier ARMA, ARMA with Fourier terms, trigonometric ARMA, smooth structural change ARMA	ARIMA, Box-Jenkins model, integrated ARMA, ARIMA(p,d,q)
関連	5	6
概要	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.