I have a collection of values for two different variables X1 and X2 for each timestamp, as briefly represented below:
For each timestamp, length of X1 is equal to length of X2, and X1 is non-linearly dependant on X2.
Each timestamp is a data sample for my supervised regression machine learning model (response/target variable ignored here).
Question 1: What is the most promising technique to derive useful features from the dependant variables X1 and X2 in each data sample such that maximum information can be retained?
Question 2: Is this technique replicable for higher dimensions (X1,X2...Xn)?