I have done a ton of preprocessing and math on this data to arrive at two equally sized 3xN numpy arrays. A and B.
A = integers that have been classified as labels to predict B. B = time series data.
I also have C which is just B[1:]
A and B are equal at their respective time steps and I can't figure out a better way to make a list of the next time step of B, so I have A[0:-1], B[0:-1] and C[1:])
I know that A + or - B is always == to at least 1 integer in C.
Example:
if A = [2,3,4] and B = [5,2,1] I know that at least 1 of C will be a 7,8,1,3, or any other combination of those numbers + or - each other.
How can I make a simple function to do this operation and check if it IS == to C and then store it as a key in a dictionary? The goal would be to create a neural network once this is complete to evaluate new data.