However, a common problem is having data which is only recent and does not go back as far as other input data.

How to incorporate it?  More specifically, what to do with early missing input values?

I have read that using the average of the existing data for the missing data is one way to deal with it.

I would agree only if one could say that the state of the system was the same for all input values.

On the other hand, if one could say that the state is probably different or even much different, if that state change could be identified, then a simple Cardinal column indicating State A or B would be of use.

Suppose we have data for the latter State B but nothing for the earlier State A?

I propose that State A values are ZERO while State B are the actual data.

The result is that you have useful "new" weights which apply only to State B.  The Zeroes assigned for State A simply "turn off" those State B weights (as they should be.)

CAUTION:  One must be careful that the ZEROes are applied after all standardization or transformation of the inputs.  The last thing you need would be a big block of zeroes ruining the mean and standard deviation of the actual data.

IRONICALLY, when Standardized to Zero Mean, use of the Zeros is actually using the Mean.

Comments?

Thanks
Tom