I am working on a financial predict problem. which means it is a time series prediction problem.
I have three features, which have high correlation(each two's corr is about 0.6) And I do the linear regression fit.
I assume that the coefficient should be similiar among these three features, but i get a coefficient vector like this:
[0.01, 0.15, 0.01]
which means the second features have the biggest coff(features are normalized), and it can dominant the prediction result.
I dont know why. I think adding weak features can boost my prediction model, but i think the second feature is dominant in my model, and other features are worthless.
Why one of features can be dominant in the model, did I miss something?