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| Model ARIMA (Autoregresivni integrirani pokretni prosjek)× | Troslojko eksponencijalno izglađivanje Holt-Winters× | Model prostora stanja (Kalmanov filtar)× | |
|---|---|---|---|
| Područje | Ekonometrija | Ekonometrija | Ekonometrija |
| Obitelj | Regression model | Regression model | Regression model |
| Godina nastanka≠ | 2015 | 1960 | 1990 |
| Tvorac≠ | Box & Jenkins (Box-Jenkins methodology) | Charles C. Holt and Peter R. Winters | Harvey; Durbin & Koopman (state space treatment); Kalman filter |
| Vrsta≠ | Univariate time-series model | Exponential smoothing forecasting model | State space time series model |
| Temeljni izvor≠ | 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 | 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. DOI ↗ |
| Drugi nazivi≠ | Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli | triple exponential smoothing, Winters' method, Holt-Winters seasonal method, Holt-Winters Üçlü Üstel Düzleştirme | state space, Kalman filter, unobserved components model, Durum Uzayı Modeli (State Space / Kalman Filter) |
| Srodne≠ | 5 | 4 | 4 |
| Sažetak≠ | 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). | 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. | A state space model is a general time series framework that describes a series through unobserved (latent) state variables linked by a measurement equation and a transition equation, with the states estimated in real time by the Kalman filter. Developed in the state space tradition of Harvey (1990) and Durbin & Koopman (2012), it nests ARIMA and exponential smoothing as special cases. |
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