I’m working on an LSTMbased stock market forecasting problem and trying to figure out a way to select input variables.

When calculating correlation between variables (e.g. Close price of Tesla vs Close price of Microsoft), would differentiating the curves give a more accurate (or correct) correlation index ? I’m finding values in the range 0.70.9 for nondifferentiated variables, and lower values after differentiation.

Once I have a correlation matrix of all my variables, is there a way to figure out which ones would add information to the neural net and which ones would just add noise ?