Answers.Body: <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>
  
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Answers.CreationDate: 2023-07-18T20:57:59.823Z
Answers.Text: 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.
Answers.UserDisplayName: bardulia
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Answers.CreationDate: 2020-11-17T20:37:16.560Z
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Body: <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>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-04-19T06:20:42.117Z
Comments.Text: 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.
Comments.UserDisplayName: Mike NZ
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Comments.CreationDate: 2021-12-04T05:55:54.800Z
Comments.Text: @Leo would you share what neural network model using? At least similar popular model.
Comments.UserDisplayName: Cloud Cho
Tags: <neural-networks><machine-learning><time-series><data-preprocessing><normalisation>
Title: What would be a typical pre-processing and data normalization pipeline for time series data (for non-linear models such as neural networks)?
UserDisplayName: Leo
ThreadText: # 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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