moving average

Model

It is a model that uses the dependency between an observation and a residual error from a moving average model applied to lagged observations.

It is proportion to the difference of the observed values and the moving average values.

Indicates that regression error is actually a linear combination of error terms whose values occurred contemporaneously at various times in the past.

We do a regression model as: $\epsilon (t)+{\theta }_1\epsilon (t-1)+\cdots +{\theta }_q\epsilon (t-q).$