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 just finished my masters thesis project for multivariate time series prediction.
The standard approach is to normalize the data using minmax or z-score (there is one research paper that found it really didn’t matter which normalization technique was used).
You will most likely need to transform the dataset to a supervised problem (X, y)
You will need to transform the data into a format used by most neural networks (samples, timesteps, feature)
How to Convert a Time Series to a Supervised Learning Problem in Python
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.
