I'm having trouble finding a time series technique to deal with a data set I am working on. It contains multiple subjects and multiple variables, not all of which will likely be part of the time series. It looks something like this:
Subject Date T1 T2 V1 V2 V3 A 1/1/2012 1 5 9 13 17 A 2/1/2012 2 6 10 14 18 ... B 1/1/2012 3 7 11 15 19 B 2/1/2012 4 8 12 16 20 ...
Where T1, T2 are likely time series, and V1, V2, and V3 are likely not. I'm sure that this distinction is probably unnecessary, since techniques like Box-Jenkins should detect autoregression in any variable.
Ultimately, I want to be able to do forecasting on other subjects that were probably not used to build this model.
If you know of any R package(s) that can take this on, please let me know. Some example code would also be greatly appreciated. Thank you for any insight you can provide.
Edit: I am looking into dynamic linear regression using the
dynlm package, but am having trouble coding it to include the dates and subjects.