When we say its a combination of white noise processes or residuals then what exactly does that mean? What kind of error is that? Is it the randomness that is left after removing trend and seasonality?
Where does the noise in the equation come from? In out data we only have time on the x axis and Y as the value variable. There is no error term. What I mean to ask is does the MA model first regress y on y lag terms like the AR model and then calculate error between the actual and predicted y terms? Then regress y against the calculated error terms(residuals)?
When we say its a combination of white noise processes or residuals then what exactly does that mean? What kind of error is that? Is it the randomness that is left after removing trend and seasonality?
Where does the noise in the equation come from? In out data we only have time on the x axis and Y as the value variable. There is no error term. What I mean to ask is does the MA model first regress y on y lag terms like the AR model and then calculate error between the actual and predicted y terms? Then regress y against the calculated error terms(residuals)?
Bro, Can you answer your question?? I have same doubt.
White noise has mean 0 and variance sigma^2 since it follows iidrv normal
nice