<p>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.</p>

CC BY-SA 4.0
2023-07-18T20:57:59.823Z
Could you please elaborate more on that part of "as far as the scaling factor is provided as a feature"? if you have a link with a code example/tutorial it would be great.
bardulia
CC BY-SA 4.0
2020-11-17T20:37:16.560Z
2020-11-17T20:37:16.560Z
F4RZ4D
<p>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.</p>
<p>One I can think of is min-max normalization</p>
<p><span class="math-container">$$z = \frac{x - min(x)}{max(x) - min(x)}$$</span></p>

CC BY-SA 4.0
2021-04-19T06:20:42.117Z
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.
Mike NZ
CC BY-SA 4.0
2021-12-04T05:55:54.800Z
@Leo would you share what neural network model using? At least similar popular model.
Cloud Cho
<neural-networks><machine-learning><time-series><data-preprocessing><normalisation>
What would be a typical pre-processing and data normalization pipeline for time series data (for non-linear models such as neural networks)?
Leo
# What would be a typical pre-processing and data normalization pipeline for time series data (for non-linear models such as neural networks)?

(Asked by: username_0 on 2020-09-23)

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

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

**Tags:** neural-networks, machine-learning, time-series, data-preprocessing, normalisation

> **Comments:**
> **username_1**: 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.  
> **username_2**: @username_0 would you share what neural network model using? At least similar popular model.  

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## 1 answers

### Answer by username_3 on 2020-11-17. Score: 1

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.

> **Comments:**
> **username_4**: Could you please elaborate more on that part of "as far as the scaling factor is provided as a feature"? if you have a link with a code example/tutorial it would be great.  

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