Compară metode
Examinează metodele selectate una lângă alta; rândurile care diferă sunt evidențiate.
| Model ARIMA Bayesian× | Model ARIMA (Autoregresiv Integrat Medie Mobilă)× | |
|---|---|---|
| Domeniu | Econometrie | Econometrie |
| Familie | Regression model | Regression model |
| Anul apariției≠ | 1970s (ARIMA); Bayesian extension prominent from 1990s | 1970 |
| Autorul original≠ | Pole, West & Harrison (Bayesian treatment); Box & Jenkins (ARIMA foundation) | George Box and Gwilym Jenkins |
| Tip≠ | Bayesian time series model | Time series forecasting model |
| Sursa seminală≠ | 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 ↗ |
| Denumiri alternative | Bayesian ARIMA, BARIMA, Bayesian Box-Jenkins model, Bayesian integrated time series model | ARIMA, Box-Jenkins model, integrated ARMA, ARIMA(p,d,q) |
| Înrudite | 6 | 6 |
| Rezumat≠ | 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. |
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