Methoden vergelijken

	Fourier Moving Average (Fourier MA) Model	ARIMA model
Vakgebied	Econometrie	Econometrie
Familie	Regression model	Regression model
Jaar van ontstaan	1990s–2000s	1970
Grondlegger	Harvey, A. C.; Hyndman, R. J.	George Box and Gwilym Jenkins
Type	Time series model	Time series forecasting model
Oorspronkelijke bron	Hyndman, R. J., & Athanasopoulos, G. (2021). Forecasting: Principles and Practice (3rd ed.). OTexts.	Box, G. E. P., & Jenkins, G. M. (1970). Time Series Analysis: Forecasting and Control. Holden-Day.
Aliassen	Fourier MA, Fourier-augmented moving average, trigonometric MA model, harmonic moving average model	ARIMA, Box-Jenkins model, integrated ARMA, ARIMA(p,d,q)
Verwant	2	6
Samenvatting	The Fourier MA model combines a Moving Average (MA) error structure with Fourier series terms — sine and cosine pairs — to capture complex or high-frequency seasonal patterns in time series data. It is particularly useful when the seasonal period is long or irregular, making classical seasonal ARIMA parameterisation infeasible.	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.