# Understanding data normalization for SVMs

For SVMs, they operate best when the data is in the ranges of $[0,1]$ or $[-1, 1]$. Naturally you want to normalize data so that your model works well.

My question is: what do you do about features derived from normalized data?

For example, if I have a data frame with a column called "wait times" in seconds, and I want to add a 50 day moving average to this data to smooth it, do I:

1. Normalize the "wait times" to the range $[-1, 1]$ and then apply the moving average function
2. Apply the moving average function and then normalize the "wait times" and "ma" column to the range $[-1, 1]$.

I'm having trouble finding any sort of literature on this. I feel like (2) is correct because (1) causes information loss, but I am honestly not sure because I cannot prove it to myself handily. It would be very helpful if someone could explain to me which is right so that I know in the future how best to handle this.

Thank you!