方法对比
并排查看您选择的方法；存在差异的行会高亮显示。
| 傅里叶自回归积分滑动平均模型× | 自回归积分滑动平均模型 (ARIMA)× | |
|---|---|---|
| 领域 | 计量经济学 | 计量经济学 |
| 方法族 | Regression model | Regression model |
| 起源年份≠ | 2004-2012 | 1970 |
| 提出者≠ | Becker, Enders, and Hurn; further extended by Enders and Lee | George Box and Gwilym Jenkins |
| 类型≠ | Time series model | Time series forecasting model |
| 开创性文献≠ | 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 ↗ |
| 别名 | Fourier ARIMA, ARIMA with Fourier terms, trigonometric ARIMA, Fourier-flexible ARIMA | ARIMA, Box-Jenkins model, integrated ARMA, ARIMA(p,d,q) |
| 相关≠ | 2 | 6 |
| 摘要≠ | 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. |
| ScholarGate数据集 ↗ |
|
|