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Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| Modèle ARIMA de Fourier× | Modèle ARIMA (Modèle Autorégressif Intégré à Moyenne Mobile)× | |
|---|---|---|
| Domaine | Économétrie | Économétrie |
| Famille | Regression model | Regression model |
| Année d'origine≠ | 2004-2012 | 1970 |
| Auteur d'origine≠ | Becker, Enders, and Hurn; further extended by Enders and Lee | George Box and Gwilym Jenkins |
| Type≠ | Time series model | Time series forecasting model |
| Source fondatrice≠ | Enders, W., & Lee, J. (2012). The flexible Fourier form and Dickey-Fuller type unit root tests. Economics Letters, 117(1), 196-202. DOI ↗ | Box, G. E. P., & Jenkins, G. M. (1970). Time Series Analysis: Forecasting and Control. Holden-Day. link ↗ |
| Alias | Fourier ARIMA, ARIMA with Fourier terms, trigonometric ARIMA, Fourier-flexible ARIMA | ARIMA, Box-Jenkins model, integrated ARMA, ARIMA(p,d,q) |
| Apparentées≠ | 2 | 6 |
| Résumé≠ | The Fourier ARIMA model augments a standard ARIMA specification with trigonometric sine and cosine terms, allowing it to capture smooth, gradual structural change and flexible nonlinear seasonality without specifying the exact timing or number of breaks in advance. It is widely used in applied macroeconometrics and finance for series exhibiting slowly evolving dynamics. | 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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