Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Тройное экспоненциальное сглаживание Хольта-Винтерса× | Структурная модель временных рядов (базовая структурная модель)× | |
|---|---|---|
| Область | Эконометрика | Эконометрика |
| Семейство | Regression model | Regression model |
| Год появления≠ | 1960 | 1990 |
| Автор метода≠ | Charles C. Holt and Peter R. Winters | Andrew C. Harvey |
| Тип≠ | Exponential smoothing forecasting model | State-space (unobserved components) time series model |
| Основополагающий источник≠ | 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. ISBN: 978-0521405737 |
| Другие названия | triple exponential smoothing, Winters' method, Holt-Winters seasonal method, Holt-Winters Üçlü Üstel Düzleştirme | BSM, basic structural model, unobserved components model, Yapısal Zaman Serisi Modeli (BSM) |
| Связанные | 4 | 4 |
| Сводка≠ | 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. | 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. |
| ScholarGateНабор данных ↗ |
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