Módszerek összehasonlítása

	Fourier mozgóátlag (Fourier MA) modell	ARIMA modell (Autoregressive Integrated Moving Average)
Tudományterület	Ökonometria	Ökonometria
Módszercsalád	Regression model	Regression model
Keletkezés éve	1990s–2000s	1970
Megalkotó	Harvey, A. C.; Hyndman, R. J.	George Box and Gwilym Jenkins
Típus	Time series model	Time series forecasting model
Alapmű	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.
Alternatív nevek	Fourier MA, Fourier-augmented moving average, trigonometric MA model, harmonic moving average model	ARIMA, Box-Jenkins model, integrated ARMA, ARIMA(p,d,q)
Kapcsolódó	2	6
Összefoglaló	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.