I want to train the network based on two sets of data. For example, I want the network to predict the humidity based on past humidity trends AND past temperature trends. In this case, how should I organize the input layer? I would think that for just one time-series, I would use a regular sliding window on the series. With two series, do I just present both windows (from the humidity and temperature series) to the input layer that is twice the size of the window? if not, how else can I configure the input layer so that it doesn't confuse between the two sets of training data?
Do I just let the network sort this out by itself or is there a preferred method of presenting two (or more) sets of training data to the network? Thanks.