I'm currently working on a prediction problem which deals with prediction of rainfall across US. The data I'm dealing with a time horizon which ranges from 1970 through 2019, at monthly intervals. I have historic rainfall recorded at a zip level and I have had these zip codes mapped to a metropolitan statistical area (MSA) and combined statistical area (CBSA) across each state in the US. For example, there are about 30 MSAs in California for which I need to predict the rainfall for future time periods.
I have my doubts on the approach - generally I am used to passing an array into any ML algorithm (Persistence, ARIMA, ETS, CNN/RNN) which gives me the results. In this case I feel it is not feasible to pass 30 MSAs*50 states on an average for the same time frame, which is more than passing 2000 arrays into the model for the same time period.
Is there a better way of approaching this problem in terms of modeling and recording the output?