Feature selection for time series prediction

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

  1. 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 0.7-0.9 for non-differentiated variables, and lower values after differentiation.

  2. 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 ?

Leave a Reply

Your email address will not be published. Required fields are marked *