### Setting up data for DeepAR, targets and categories for simultaneous data?

I would like to try out DeepAR for an engineering problem that I have some sensor datasets for, but I am unsure how to set it up for ingestion into DeepAR to get a predictive model.
The data is essentially the positions, orientations, and a few other timeseries sensor readings of an assortment of objects (animals, in this case, actually) over time. Data is both noisy and sometimes missing.
So, in this case, there are N individuals and for each individual, there are Z variables of interest per individual. None of the variables are "static" (color, size, etc), they are all expected to be time-varying on the same time scale.
Ultimately, I would like to try and predict all Z targets for all N individuals.
How do I set up the timeseries to feed into DeepAR?
The premise is that all these individuals are implicitly interacting in the observed space, so all the target values have some interdependence on each other, which is what I would like to see if DeepAR can take into account to make predictions.
Should I be using a category vector of length 2, such that the first cat variable corresponds to the individual, and the second corresponds to one of the variables associated with the individual?
Then there would be N*Z targets in my input dataset, each with `cat = [ n , z ]`, where there are N distinct values for n, and z for Z?