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Auto-Regressive models are used in statistics, econometrics, and signal processing to represent random processes.

What is Auto-Regressive (AR) Process?

If a statistical model predicts future values based on past values, it is called Auto-Regressive. This model, for example, might try to forecast a stock’s future prices based on its historical performance. In regression analysis, we try to model the factor using a dependent and an independent variable. In the AR model, we try to forecast the variable using its own data series.


The equation gives the AR(p) process Φ(B)Xt = ωt;t = 1,…,n. • Φ (B) is known as the characteristic polynomial of the process and its roots determine when the process is stationary.

Why is Auto-Regressive important?

They’re commonly employed in technical analysis to predict future stock values. These models are predicated on assuming that the future will be similar to the past.
Owais Siddiqui
1 min read

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