I am building a CNN and am wondering if inputting derived or computed inputs are generally bad for the effectiveness of CNNs? Or just NNs in general?
By derived or computed values I mean data that is not "raw" and instead is computed based on the raw data. For example, in a very simple form, using time-series data as the "raw" data and computing a 30 day SMA as a "derived/computed" value, and as another input.
Is this bad practice at boosting the effectiveness of the network? If it is not a bad practice, are there any tips on what kind of computed values someone should consider when adding new inputs?
The goal of my NN is for building predictions in time-series data.