Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Bayesian ARIMA Model× | Модель ARIMA (авторегрессионная интегрированная скользящая средняя)× | |
|---|---|---|
| Область | Эконометрика | Эконометрика |
| Семейство | Regression model | Regression model |
| Год появления≠ | 1970s (ARIMA); Bayesian extension prominent from 1990s | 1970 |
| Автор метода≠ | Pole, West & Harrison (Bayesian treatment); Box & Jenkins (ARIMA foundation) | George Box and Gwilym Jenkins |
| Тип≠ | Bayesian time series model | Time series forecasting model |
| Основополагающий источник≠ | 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 ↗ |
| Другие названия | Bayesian ARIMA, BARIMA, Bayesian Box-Jenkins model, Bayesian integrated time series model | ARIMA, Box-Jenkins model, integrated ARMA, ARIMA(p,d,q) |
| Связанные | 6 | 6 |
| Сводка≠ | 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. |
| ScholarGateНабор данных ↗ |
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