I have $500$ time-series that represent different occurences of the same class of events. As such they have similar properties but not the same length (lengths vary from 30 to 150). I suspect the general structure of the time-series of being a $AR(2)$ model.
From what I know, the packages in R and Python can compute the auto-regression coefficients only if being fed a single time-serious.
What I tried to do is computing the coefficients for each time-series and then taking the weighted average of those coefficients (weighted by each time-series length) but I'm not really convinced by this method.
Does anyone have any ideas about a proper and clean way to find auto-regression coefficients for mutiple time-series with different lengths?
Any help would be much appreciated. Thank you very much.