Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| ETS: Экспоненциальное сглаживание с учетом ошибки, тренда и сезонности× | Пророк× | |
|---|---|---|
| Область | Эконометрика | Эконометрика |
| Семейство | Regression model | Regression model |
| Год появления≠ | 2008 | 2018 |
| Автор метода≠ | Hyndman, Koehler, Ord & Snyder (state space framework) | Taylor & Letham (Facebook/Meta) |
| Тип≠ | Exponential smoothing state space model | Decomposable (structural) time series model |
| Основополагающий источник≠ | Hyndman, R. J., Koehler, A. B., Ord, J. K. & Snyder, R. D. (2008). Forecasting with Exponential Smoothing: The State Space Approach. Springer. DOI ↗ | Taylor, S. J. & Letham, B. (2018). Forecasting at Scale. The American Statistician, 72(1), 37-45. DOI ↗ |
| Другие названия≠ | exponential smoothing state space model, innovations state space model, Holt-Winters family, ETS — Hata/Trend/Mevsimsellik Üstel Düzleştirme | Prophet, Facebook Prophet, Meta Prophet, forecasting at scale |
| Связанные | 5 | 5 |
| Сводка≠ | 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. | Prophet is a Bayesian structural time series model introduced by Taylor and Letham at Facebook/Meta in 2018. It forecasts a continuous series by decomposing it into separate, interpretable trend, seasonality, and holiday components, and is designed to be approachable for analysts working at scale. |
| ScholarGateНабор данных ↗ |
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