Comparar métodos

	Modelo ARIMA (Autoregressive Integrated Moving Average)	Modelo autorregresivo (AR)
Campo	Econometría	Econometría
Familia	Regression model	Regression model
Año de origen	1970	1970s (popularised 1976)
Autor original	George Box and Gwilym Jenkins	George E. P. Box and Gwilym M. Jenkins
Tipo	Time series forecasting model	Time series model
Fuente seminal	Box, G. E. P., & Jenkins, G. M. (1970). Time Series Analysis: Forecasting and Control. Holden-Day.	Box, G. E. P., & Jenkins, G. M. (1976). Time Series Analysis: Forecasting and Control (revised ed.). Holden-Day. ISBN: 978-0816211043
Alias	ARIMA, Box-Jenkins model, integrated ARMA, ARIMA(p,d,q)	AR model, AR(p) model, autoregression, AR process
Relacionados	6	6
Resumen	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.	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.