In some cases, the forecasting model is unable to capture all the series patterns, and therefore some information is left over in model residuals (or forecasting error) 
. The goal of the moving average process is to capture patterns in the residuals, if they exist, by modeling the relationship between Yt, the error term, ∈t, and the past q error terms of the models (for example, 
). The structure of the MA process is fairly similar to the ones of the AR. The following equation defines an MA process with a q order:
The following terms are used in the preceding equation:
- MA(q) is the notation for an MA process with q-order
represents the mean of the series
are white noise error terms
is the corresponding coefficient of 
- q defines the number of past error terms to be used in the equation
Like the AR process, the MA equation holds only if the...
