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| Фурие-АРМА модел× | Модел ARIMA (Авторегресионен интегриран плъзгащ се среден)× | |
|---|---|---|
| Област | Иконометрия | Иконометрия |
| Семейство | Regression model | Regression model |
| Година на възникване≠ | 2004–2006 | 1970 |
| Създател≠ | Becker, Enders, and Hurn | George Box and Gwilym Jenkins |
| Тип≠ | Time series model with smooth structural change | Time series forecasting model |
| Основополагащ източник≠ | Becker, R., Enders, W., & Hurn, S. (2006). A general test for time dependence in parameters. Journal of Applied Econometrics, 21(7), 1005–1028. link ↗ | Box, G. E. P., & Jenkins, G. M. (1970). Time Series Analysis: Forecasting and Control. Holden-Day. link ↗ |
| Други названия | Fourier ARMA, ARMA with Fourier terms, trigonometric ARMA, smooth structural change ARMA | ARIMA, Box-Jenkins model, integrated ARMA, ARIMA(p,d,q) |
| Свързани≠ | 5 | 6 |
| Резюме≠ | 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 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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