Comparer des méthodes

	Modèle Bayésien ARIMA	Modèle SARIMA
Domaine	Économétrie	Économétrie
Famille	Regression model	Regression model
Année d'origine	1970s (ARIMA); Bayesian extension prominent from 1990s	1970 (first edition); 1976 (revised)
Auteur d'origine	Pole, West & Harrison (Bayesian treatment); Box & Jenkins (ARIMA foundation)	Box, Jenkins, and Reinsel
Type	Bayesian time series model	Seasonal time series model
Source fondatrice	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
Alias	Bayesian ARIMA, BARIMA, Bayesian Box-Jenkins model, Bayesian integrated time series model	SARIMA, seasonal ARIMA, Box-Jenkins seasonal model, ARIMA with seasonal component
Apparentées	6	5
Résumé	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.