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| ARIMA-Modell (Autoregressive Integrated Moving Average)× | ETS: Fehler, Trend, Saisonale Exponentielle Glättung× | |
|---|---|---|
| Fachgebiet | Ökonometrie | Ökonometrie |
| Familie | Regression model | Regression model |
| Entstehungsjahr≠ | 2015 | 2008 |
| Urheber≠ | Box & Jenkins (Box-Jenkins methodology) | Hyndman, Koehler, Ord & Snyder (state space framework) |
| Typ≠ | Univariate time-series model | Exponential smoothing state space model |
| Wegweisende Quelle≠ | 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 ↗ |
| Aliasnamen≠ | Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli | exponential smoothing state space model, innovations state space model, Holt-Winters family, ETS — Hata/Trend/Mevsimsellik Üstel Düzleştirme |
| Verwandt | 5 | 5 |
| Zusammenfassung≠ | ARIMA is a univariate time-series forecasting model that combines autoregressive, integrated (differencing), and moving-average components to predict a single continuous series from its own past. It is the centrepiece of the Box-Jenkins methodology set out in Box, Jenkins, Reinsel & Ljung's Time Series Analysis (5th ed., 2015). | 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. |
| ScholarGateDatensatz ↗ |
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