Сравнение на методи
Прегледайте избраните методи един до друг; редовете с разлики са откроени.
| Сезонен ARIMA (SARIMA)× | ETS: Грешка, Тренд, Сезонно експоненциално изглаждане× | Prophet× | |
|---|---|---|---|
| Област | Иконометрия | Иконометрия | Иконометрия |
| Семейство | Regression model | Regression model | Regression model |
| Година на възникване≠ | 2015 | 2008 | 2018 |
| Създател≠ | Box & Jenkins (seasonal extension of ARIMA) | Hyndman, Koehler, Ord & Snyder (state space framework) | Taylor & Letham (Facebook/Meta) |
| Тип≠ | Seasonal time-series model | Exponential smoothing state space model | Decomposable (structural) time series model |
| Основополагащ източник≠ | Box, G.E.P., Jenkins, G.M., Reinsel, G.C. & Ljung, G.M. (2015). Time Series Analysis: Forecasting and Control (5th ed.). Wiley. ISBN: 978-1118675021 | 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 ↗ |
| Други названия≠ | seasonal ARIMA, Box-Jenkins seasonal model, SARIMA — Mevsimsel ARIMA | 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 |
| Свързани | 5 | 5 | 5 |
| Резюме≠ | SARIMA is a seasonal extension of the Box-Jenkins ARIMA model that adds seasonal differencing and seasonal autoregressive and moving-average terms. Developed within the Box, Jenkins, Reinsel and Ljung framework (5th edition, 2015), it forecasts series whose pattern repeats on a yearly, monthly, or weekly period. | 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 от данни ↗ |
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