Porównaj metody
Przeglądaj wybrane metody obok siebie; wiersze, które się różnią, są wyróżnione.
| Model Bayesa ARIMA× | Model ARIMA (Autoregresyjny Zintegrowany Model Średniej Ruchomej)× | |
|---|---|---|
| Dziedzina | Ekonometria | Ekonometria |
| Rodzina | Regression model | Regression model |
| Rok powstania≠ | 1970s (ARIMA); Bayesian extension prominent from 1990s | 1970 |
| Twórca≠ | Pole, West & Harrison (Bayesian treatment); Box & Jenkins (ARIMA foundation) | George Box and Gwilym Jenkins |
| Typ≠ | Bayesian time series model | Time series forecasting model |
| Źródło pierwotne≠ | Pole, A., West, M., & Harrison, J. (1994). Applied Bayesian Forecasting and Time Series Analysis. Chapman & Hall. ISBN: 978-0412416903 | Box, G. E. P., & Jenkins, G. M. (1970). Time Series Analysis: Forecasting and Control. Holden-Day. link ↗ |
| Inne nazwy | Bayesian ARIMA, BARIMA, Bayesian Box-Jenkins model, Bayesian integrated time series model | ARIMA, Box-Jenkins model, integrated ARMA, ARIMA(p,d,q) |
| Pokrewne | 6 | 6 |
| Podsumowanie≠ | The Bayesian ARIMA model combines the classical Box-Jenkins ARIMA framework with Bayesian inference. Instead of obtaining single point estimates for autoregressive and moving average parameters, it places prior distributions over them and uses observed data to update beliefs into a full posterior distribution, enabling coherent uncertainty quantification and probabilistic forecasting. | The ARIMA(p,d,q) model is the standard workhorse for univariate time series forecasting. It combines autoregressive terms (past values), differencing to induce stationarity, and moving average terms (past shocks) into a unified linear framework. Developed by Box and Jenkins (1970), it remains one of the most widely applied models in econometrics and applied statistics. |
| ScholarGateZbiór danych ↗ |
|
|