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Quartz | Level 8

I have over 100 distributions (Auto dealerships) and I need to use a time series to forecast each dealership's sale for the next 12 months with monthly data I have for the past 10 years.  I want to develop a time series model, but first I want to compare dealerships to see if any have the dependent variable (which in this case is the prices) in common (perhaps Proc corr, I don't know) and see if I can group some so that I don't have to model 100 stores, but instead model only on say 60 or 50, or whatever the results show.. I can say 4 stores show similar trends and cycles, seasonality and increase/decrease in price, that I can group Sore 1/2/3/4 together..

How can this be done?  Is proc corr the right tool or perhaps a proc glm ??

Opal | Level 21

It will be interesting to see what our statisticians have to recommend.  I would have thought a discrimant function or factor analysis, or possibly a decision tree or neural net.  However, I am definitely not well versed in time series and don't know if there are similar types of analyses that also take time into account.


See also for the perspective of Time Series analysts.

I have seen some people use PROC VARCLUS to group similar variables into clusters of variables with similar behavior. They then model a representative variable from each cluster, or construct the "average" of each cluster and model that. 

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