I am currently working on a dataset where the feature for each observation is time-series.
For instance assume two observations: "person X and person Y", and the feature 'price paid for milk' as time-series data for each.
Now my aim is to normalize the data as a pre-processing step and feed it into a neural network. However, for normalization, do I normalize values with respect to the time-series of each observation, or normalize across the samples?
My understanding is that the first approach induces a bias whereas the second might magnify the effect of outliers/single events due to outliers.