I have 90 markets with quarterly results, with data from 2014 Q1 to 2016 Q2. I'd like to predict 2016 Q4 results. With a time-series in R, as I understand, you need a single observation over multiple time-periods, but I have 90 observations.
One option I considered was to calculate the mean of the 90 observations, and predict a mean result. The problem is that the distribution of the results tends to change from the beginning of the year (tightly packed) to the end of the year (wide, with a long tail of higher results).
I don't necessarily need an accurate prediction of the individual observations, but I do need a good distribution to work with, as I will be creating reward grids based on quartiles.