I am new to applying ML to time series data but I do have experience doing general supervised learning. I have time series that is multidimensional (so several variables over time) with one output variable. I tried following some online tutorials but I am confused on a few points that keep coming up.
Some tutorials stress checking whether the variables are stationary (Usually they do this using a Dickey-Fuller test).
- Do I have to check every feature in my time series for this or just the output variable?
- Can I keep in individual features that are not stationary?
- Do I have to check if my dataset (features or output variable) are autocorrelated?
- Do I have to exclude any that are overly autocorrelated?
- Any other do's and don'ts for time series predictive analysis?
It seems like if I just lag my features with the output then I can readily apply supervised learning models like multiple linear regression or random forests if I cross-validate in a special fashion. Is that really all I have to do to make my time series a supervised learning task?