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| Fourier SARIMA-Modell× | ARIMA-Modell (Autoregressives integriertes gleitendes Durchschnittsmodell)× | |
|---|---|---|
| Fachgebiet | Ökonometrie | Ökonometrie |
| Familie | Regression model | Regression model |
| Entstehungsjahr≠ | 1994 | 1970 |
| Urheber≠ | Harvey & Scott (1994); Hyndman & Athanasopoulos (popularization) | George Box and Gwilym Jenkins |
| Typ≠ | Seasonal time series model with trigonometric regressors | Time series forecasting model |
| Wegweisende Quelle≠ | Harvey, A., & Scott, A. (1994). Seasonality in dynamic regression models. The Economic Journal, 104(427), 1324-1345. link ↗ | Box, G. E. P., & Jenkins, G. M. (1970). Time Series Analysis: Forecasting and Control. Holden-Day. link ↗ |
| Aliasnamen | Fourier SARIMA, SARIMA with Fourier terms, Fourier-SARIMA, trigonometric SARIMA | ARIMA, Box-Jenkins model, integrated ARMA, ARIMA(p,d,q) |
| Verwandt | 6 | 6 |
| Zusammenfassung≠ | The Fourier SARIMA model extends the classical Seasonal ARIMA framework by incorporating trigonometric (Fourier) terms as deterministic regressors. This allows the model to approximate smooth, complex, or multiple-frequency seasonal patterns without requiring a full seasonal ARIMA structure for every frequency, making it particularly useful for high-frequency data or series with non-integer or evolving seasonality. | 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. |
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