Сравнение на методи
Прегледайте избраните методи един до друг; редовете с разлики са откроени.
| Просто и двойно експоненциално изглаждане (SES / Holt)× | Структурен модел на времеви редове (Основен структурен модел)× | |
|---|---|---|
| Област | Иконометрия | Иконометрия |
| Семейство | Regression model | Regression model |
| Година на възникване≠ | 1957 | 1990 |
| Създател≠ | Robert G. Brown (SES); Charles C. Holt (linear trend) | Andrew C. Harvey |
| Тип≠ | Exponential smoothing forecasting model | State-space (unobserved components) time series model |
| Основополагащ източник≠ | Brown, R. G. (1959). Statistical Forecasting for Inventory Control. McGraw-Hill. link ↗ | Harvey, A. C. (1990). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press. ISBN: 978-0521405737 |
| Други названия | SES, Holt's linear trend method, exponential smoothing forecasting, Basit ve Çift Üstel Düzleştirme (SES / Holt) | BSM, basic structural model, unobserved components model, Yapısal Zaman Serisi Modeli (BSM) |
| Свързани≠ | 3 | 4 |
| Резюме≠ | Exponential smoothing is a family of basic time-series forecasting models in which each new observation updates a smoothed estimate by a weighting parameter. Simple exponential smoothing (SES), introduced by Robert G. Brown in 1959, forecasts series with a stable level, while Holt's double exponential smoothing, introduced by Charles C. Holt in 1957, adds a trend term using the parameters alpha and beta. | 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. |
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