Salīdzināt metodes

	Autoregresīvs modelis (AR)	ARIMA modelis (autoregresīvais integrētais slīdošais vidējais)
Nozare	Ekonometrija	Ekonometrija
Saime	Regression model	Regression model
Izcelsmes gads	1970s (popularised 1976)	1970
Autors	George E. P. Box and Gwilym M. Jenkins	George Box and Gwilym Jenkins
Tips	Time series model	Time series forecasting model
Pirmavots	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.
Citi nosaukumi	AR model, AR(p) model, autoregression, AR process	ARIMA, Box-Jenkins model, integrated ARMA, ARIMA(p,d,q)
Saistītās	6	6
Kopsavilkums	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.