مقایسهٔ روش‌ها

	مدل آریما فوریه	مدل ARMA (میانگین متحرک خودرگرسیو)
حوزه	اقتصادسنجی	اقتصادسنجی
خانواده	Regression model	Regression model
سال پیدایش	2004–2006	1970
پدیدآور	Becker, Enders, and Hurn	George E. P. Box and Gwilym M. Jenkins
نوع	Time series model with smooth structural change	Time series model
منبع بنیادین	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.
نام‌های دیگر	Fourier ARMA, ARMA with Fourier terms, trigonometric ARMA, smooth structural change ARMA	ARMA, Box-Jenkins model, autoregressive moving average, AR(p)MA(q)
مرتبط	5	5
خلاصه	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 ARMA(p,q) model describes a stationary time series as a combination of two components: an autoregressive part that regresses the current value on its own past p values, and a moving average part that accounts for past q error terms. It is the foundational framework of the Box-Jenkins methodology for univariate time series modelling and short-run forecasting.