Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Потрійне експоненційне згладжування Хольта-Вінтерса× | Модель простір-стан (фільтр Калмана)× | |
|---|---|---|
| Галузь | Економетрика | Економетрика |
| Родина | Regression model | Regression model |
| Рік появи≠ | 1960 | 1990 |
| Автор методу≠ | Charles C. Holt and Peter R. Winters | Harvey; Durbin & Koopman (state space treatment); Kalman filter |
| Тип≠ | Exponential smoothing forecasting model | State space time series model |
| Основоположне джерело≠ | 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 ↗ |
| Інші назви | 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) |
| Пов'язані | 4 | 4 |
| Підсумок≠ | 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. |
| ScholarGateНабір даних ↗ |
|
|