方法对比
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| ARIMA（自回归积分滑动平均）模型× | 结构时间序列模型（基本结构模型）× | |
|---|---|---|
| 领域 | 计量经济学 | 计量经济学 |
| 方法族 | Regression model | Regression model |
| 起源年份≠ | 2015 | 1990 |
| 提出者≠ | Box & Jenkins (Box-Jenkins methodology) | Andrew C. Harvey |
| 类型≠ | Univariate time-series model | State-space (unobserved components) time series model |
| 开创性文献≠ | Box, G. E. P., Jenkins, G. M., Reinsel, G. C. & Ljung, G. M. (2015). Time Series Analysis: Forecasting and Control (5th ed.). Wiley. ISBN: 978-1118675021 | Harvey, A. C. (1990). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press. ISBN: 978-0521405737 |
| 别名≠ | Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli | BSM, basic structural model, unobserved components model, Yapısal Zaman Serisi Modeli (BSM) |
| 相关≠ | 5 | 4 |
| 摘要≠ | ARIMA is a univariate time-series forecasting model that combines autoregressive, integrated (differencing), and moving-average components to predict a single continuous series from its own past. It is the centrepiece of the Box-Jenkins methodology set out in Box, Jenkins, Reinsel & Ljung's Time Series Analysis (5th ed., 2015). | The Structural Time Series Model, in its Basic Structural Model (BSM) form, is Andrew Harvey's state-space approach that decomposes a series into separate stochastic trend, seasonal, cyclical, and irregular components. Developed in Harvey's 1990 treatment, it is prized for interpretability and component decomposition where ARIMA only delivers a black-box fit. |
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