Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Modelo ARIMA Bayesiano× | Modelo SARIMA× | |
|---|---|---|
| Área | Econometria | Econometria |
| Família | Regression model | Regression model |
| Ano de origem≠ | 1970s (ARIMA); Bayesian extension prominent from 1990s | 1970 (first edition); 1976 (revised) |
| Autor original≠ | Pole, West & Harrison (Bayesian treatment); Box & Jenkins (ARIMA foundation) | Box, Jenkins, and Reinsel |
| Tipo≠ | Bayesian time series model | Seasonal time series model |
| Fonte seminal≠ | 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., & Reinsel, G. C. (1976). Time Series Analysis: Forecasting and Control (revised ed.). Holden-Day. ISBN: 978-0130607744 |
| Outros nomes | Bayesian ARIMA, BARIMA, Bayesian Box-Jenkins model, Bayesian integrated time series model | SARIMA, seasonal ARIMA, Box-Jenkins seasonal model, ARIMA with seasonal component |
| Relacionados≠ | 6 | 5 |
| Resumo≠ | 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. | SARIMA extends ARIMA by adding seasonal autoregressive and moving-average operators to capture repeating patterns at fixed intervals — such as monthly, quarterly, or annual cycles. Denoted SARIMA(p,d,q)(P,D,Q)s, it is the standard workhorse for univariate seasonal time series forecasting in econometrics, economics, and official statistics. |
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