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| ETS: 誤差、トレンド、季節指数平滑法× | 状態空間モデル（カルマンフィルタ）× | |
|---|---|---|
| 分野 | 計量経済学 | 計量経済学 |
| 系統 | Regression model | Regression model |
| 提唱年≠ | 2008 | 1990 |
| 提唱者≠ | Hyndman, Koehler, Ord & Snyder (state space framework) | Harvey; Durbin & Koopman (state space treatment); Kalman filter |
| 種類≠ | Exponential smoothing state space model | State space time series model |
| 原典≠ | Hyndman, R. J., Koehler, A. B., Ord, J. K. & Snyder, R. D. (2008). Forecasting with Exponential Smoothing: The State Space Approach. Springer. DOI ↗ | Harvey, A. C. (1990). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press. DOI ↗ |
| 別名 | exponential smoothing state space model, innovations state space model, Holt-Winters family, ETS — Hata/Trend/Mevsimsellik Üstel Düzleştirme | state space, Kalman filter, unobserved components model, Durum Uzayı Modeli (State Space / Kalman Filter) |
| 関連≠ | 5 | 4 |
| 概要≠ | ETS is a comprehensive exponential smoothing framework that automatically selects additive or multiplicative combinations of the error (E), trend (T) and seasonal (S) components of a time series. Formalised as an innovations state space model by Hyndman, Koehler, Ord and Snyder in 2008, it unifies and generalises the Holt-Winters family of forecasting methods. | 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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