Answers.Body: <p>If you want &quot;the model to be trained on online data, meaning when new data arrives it should be able to use that data, so that I dont need to train it over and over again.&quot;, then DAMP can do this, at over 100,000 Hz  [a]
  [a] <a href="https://www.cs.ucr.edu/%7Eeamonn/DAMP_long_version.pdf" rel="nofollow noreferrer">https://www.cs.ucr.edu/~eamonn/DAMP_long_version.pdf</a></p>
  
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Answers.CreationDate: 2022-07-29T05:57:29.547Z
Answers.LastActivityDate: 2022-07-29T05:57:29.547Z
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Body: <p>I would like to implement an anomaly detection algorithm on multiple timeseries' from different network users. Since each user has different behavior and network traffic usage, my question is how can I implement an anomaly detection algorithm for this case? If possible I would like to have the model to be trained on online data, meaning when new data arrives it should be able to use that data, so that I dont need to train it over and over again.
  When dealing with new users, it should consider other users as reference and not immediately trigger an anomaly.</p>
  <p>I was thinking about training an ensemble model of LSTMs with different temporal properties such as sequences of minutes, hours, days, weeks, months in order to predict successfully short-term anomalys and long-term occurences. Does anyone else had the same problem in the past?</p>
  
Tags: <neural-networks><time-series><anomaly-detection>
Title: How to handle anomaly detections with multiple different timeseries' from network traffic?
UserDisplayName: V.Hunon
ThreadText: # How to handle anomaly detections with multiple different timeseries' from network traffic?
  
  (Asked by: username_0 on 2022-07-24)
  
  I would like to implement an anomaly detection algorithm on multiple timeseries' from different network users. Since each user has different behavior and network traffic usage, my question is how can I implement an anomaly detection algorithm for this case? If possible I would like to have the model to be trained on online data, meaning when new data arrives it should be able to use that data, so that I dont need to train it over and over again.
  When dealing with new users, it should consider other users as reference and not immediately trigger an anomaly.
  
  I was thinking about training an ensemble model of LSTMs with different temporal properties such as sequences of minutes, hours, days, weeks, months in order to predict successfully short-term anomalys and long-term occurences. Does anyone else had the same problem in the past?
  
  **Tags:** neural-networks, time-series, anomaly-detection
  
  
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  ## 1 answers
  
  ### Answer by username_1 on 2022-07-29. Score: 0
  
  If you want "the model to be trained on online data, meaning when new data arrives it should be able to use that data, so that I dont need to train it over and over again.", then DAMP can do this, at over 100,000 Hz [a]
  [a] [https://www.cs.ucr.edu/~eamonn/DAMP\_long\_version.pdf](https://www.cs.ucr.edu/%7Eeamonn/DAMP_long_version.pdf)
  
  
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