方法对比
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| 自回归移动平均模型 (ARMA)× | 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 | 1970 | 1970 |
| 提出者≠ | George E. P. Box and Gwilym M. Jenkins | George Box and Gwilym Jenkins |
| 类型≠ | Time series model | Time series forecasting model |
| 开创性文献 | Box, G. E. P., & Jenkins, G. M. (1970). Time Series Analysis: Forecasting and Control. Holden-Day. link ↗ | Box, G. E. P., & Jenkins, G. M. (1970). Time Series Analysis: Forecasting and Control. Holden-Day. link ↗ |
| 别名 | ARMA, Box-Jenkins model, autoregressive moving average, AR(p)MA(q) | ARIMA, Box-Jenkins model, integrated ARMA, ARIMA(p,d,q) |
| 相关≠ | 5 | 6 |
| 摘要≠ | The ARMA(p,q) model describes a stationary time series as a combination of two components: an autoregressive part that regresses the current value on its own past p values, and a moving average part that accounts for past q error terms. It is the foundational framework of the Box-Jenkins methodology for univariate time series modelling and short-run forecasting. | 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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