方法对比
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| SARIMAX× | 霍尔特-温特斯三指数平滑法× | 状态空间模型(卡尔曼滤波器)× | |
|---|---|---|---|
| 领域 | 计量经济学 | 计量经济学 | 计量经济学 |
| 方法族 | Regression model | Regression model | Regression model |
| 起源年份≠ | 2015 | 1960 | 1990 |
| 提出者≠ | Box & Jenkins (ARIMA framework); SARIMAX extension with exogenous regressors | Charles C. Holt and Peter R. Winters | Harvey; Durbin & Koopman (state space treatment); Kalman filter |
| 类型≠ | Seasonal time-series regression model | Exponential smoothing forecasting model | State space time series model |
| 开创性文献≠ | Hyndman, R. J. & Athanasopoulos, G. (2021). Forecasting: Principles and Practice (3rd ed.). OTexts. link ↗ | Winters, P. R. (1960). Forecasting Sales by Exponentially Weighted Moving Averages. Management Science, 6(3), 324-342. DOI ↗ | Harvey, A. C. (1990). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press. DOI ↗ |
| 别名 | seasonal ARIMA with exogenous variables, SARIMA with regressors, ARIMAX, SARIMAX — Dışsal Değişkenli Mevsimsel ARIMA | triple exponential smoothing, Winters' method, Holt-Winters seasonal method, Holt-Winters Üçlü Üstel Düzleştirme | state space, Kalman filter, unobserved components model, Durum Uzayı Modeli (State Space / Kalman Filter) |
| 相关 | 4 | 4 | 4 |
| 摘要≠ | SARIMAX extends the seasonal ARIMA (Box-Jenkins) model by adding exogenous explanatory variables, so it can capture the effect of holidays, economic indicators, or policy variables on a time series. It combines non-seasonal and seasonal autoregressive and moving-average dynamics with external regressors, and is estimated by maximum likelihood in state-space form. | Holt-Winters triple exponential smoothing is a forecasting model that extends Holt's double smoothing by adding a seasonal component, introduced by Peter Winters in 1960 building on Charles Holt's work. It tracks three evolving quantities — level, trend, and season — and combines them to forecast a continuous time series. | A state space model is a general time series framework that describes a series through unobserved (latent) state variables linked by a measurement equation and a transition equation, with the states estimated in real time by the Kalman filter. Developed in the state space tradition of Harvey (1990) and Durbin & Koopman (2012), it nests ARIMA and exponential smoothing as special cases. |
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