What would be a typical pre-processing and data normalization pipeline for time series data (for non-linear models such as neural networks)? I've started to work on time series. I was wondering what would be the best data normalizing and pre-processing technique for non-linear models, specifically, neural networks.
One I can think of is min-max normalization
$$z = \frac{x - min(x)}{max(x) - min(x)}$$
I would say any normalization such as min-max or standard deviation is fine as far as the scaling factor is provided as a feature, since time-series of different scale might behave differently.