# 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

$$z = \frac{x - min(x)}{max(x) - min(x)}$$

• are we assuming that the min and max do not change over time? specifically, from the training data to the test data. If there is trending or drift then z could end up negative or above 1 for large portions of test data using this normalisation method. Apr 19 at 6:20

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.

I just finished my masters thesis project for multivariate time series prediction.

1. 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).

2. You will most likely need to transform the dataset to a supervised problem (X, y)

3. 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

Multivariate Time Series Forecasting with LSTMs in Keras