Auto-Regressive
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.
Example
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
Expert Tutor at Learnsignal
Qualified professional with years of experience in teaching and helping students achieve their accounting qualifications.
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