Dear all, sorry for my stupid question but I am new to machine learning.
I was wondering if I should introduce lagged variables in my series to take into consideration past information.
If it helps, I am doing a classification on stock performance forecasting (either negative, neutral or positive). Therefore, each line correspond to a month with its different observations (predictors).
After normalising them, I don't know if these algorithms take into consideration past values, in other words if they recognise that some indicators are particularly high of low, compared to previous months and take a wise decision in function of that.
I had a doubt since for a tree, decisions points are made with the "best" threshold (gini). Did it then took into consideration all past values ?
Many thanks in advance,