A moving average process, often known as the moving average model, asserts that the relationship between the present value and the present and past error terms is linear.
<h3>What distinguishes the AR and MA processes?</h3>
Regressing the variable on its own lagged (i.e., previous) values is what the AR component entails. In the MA component, the error term is modeled as a linear combination of error terms that occur simultaneously and at distinct points in the past.
<h3>How are orders for AR and MA calculated?</h3>
You can hazard a guess as to how many AR and/or MA terms are required by examining the differenced series' autocorrelation function (ACF) and partial autocorrelation (PACF) graphs.
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