Comparer des méthodes

	Modèle autorégressif (AR)	Modèle ARIMA (Modèle Autorégressif Intégré à Moyenne Mobile)
Domaine	Économétrie	Économétrie
Famille	Regression model	Regression model
Année d'origine	1970s (popularised 1976)	1970
Auteur d'origine	George E. P. Box and Gwilym M. Jenkins	George Box and Gwilym Jenkins
Type	Time series model	Time series forecasting model
Source fondatrice	Box, G. E. P., & Jenkins, G. M. (1976). Time Series Analysis: Forecasting and Control (revised ed.). Holden-Day. ISBN: 978-0816211043	Box, G. E. P., & Jenkins, G. M. (1970). Time Series Analysis: Forecasting and Control. Holden-Day.
Alias	AR model, AR(p) model, autoregression, AR process	ARIMA, Box-Jenkins model, integrated ARMA, ARIMA(p,d,q)
Apparentées	6	6
Résumé	An autoregressive model of order p — AR(p) — expresses the current value of a time series as a linear function of its own p most recent past values plus a white-noise error. It is the building block of the Box-Jenkins family of time-series models and is widely used for forecasting stationary economic and financial series.	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.