I have hourly demand data for taxi rides that spans several years into the past. I want to use it in order to forecast future demand (for the next day). Robert Nau warns against the usage of a mixed ARMA model
you should generally avoid using both AR and MA terms in the same nonseasonal ARIMA model: they may end up working against each other and merely canceling each other’s effects.
Not sure I understand why are they canceling each-other - can you explain the mathematical intuition?
Also, I saw that Hyndman isn't paying attention to Nau's advice when dealing with demand data (much like my data), and simply uses
auto.arima and searches for the best model (the one that's minimizing the AICc).
I think that the source of my confusion is that I don't understand in what circumstances AR and MA processes are cancelling each other, and when should we avoid them. Is this a manifestation of a multicollinearity problem? or is it something else I should worry about?