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。
| SARIMA模型× | 自回归移动平均模型 (ARMA)× | |
|---|---|---|
| 领域 | 计量经济学 | 计量经济学 |
| 方法族 | Regression model | Regression model |
| 起源年份≠ | 1970 (first edition); 1976 (revised) | 1970 |
| 提出者≠ | Box, Jenkins, and Reinsel | George E. P. Box and Gwilym M. Jenkins |
| 类型≠ | Seasonal time series model | Time series model |
| 开创性文献≠ | Box, G. E. P., Jenkins, G. M., & Reinsel, G. C. (1976). Time Series Analysis: Forecasting and Control (revised ed.). Holden-Day. ISBN: 978-0130607744 | Box, G. E. P., & Jenkins, G. M. (1970). Time Series Analysis: Forecasting and Control. Holden-Day. link ↗ |
| 别名 | SARIMA, seasonal ARIMA, Box-Jenkins seasonal model, ARIMA with seasonal component | ARMA, Box-Jenkins model, autoregressive moving average, AR(p)MA(q) |
| 相关 | 5 | 5 |
| 摘要≠ | SARIMA extends ARIMA by adding seasonal autoregressive and moving-average operators to capture repeating patterns at fixed intervals — such as monthly, quarterly, or annual cycles. Denoted SARIMA(p,d,q)(P,D,Q)s, it is the standard workhorse for univariate seasonal time series forecasting in econometrics, economics, and official statistics. | 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. |
| ScholarGate数据集 ↗ |
|
|