Suggestions on how to detect anomalies in around 200k time series, maybe with deep learning, maybe in a fast way?

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I have tried Time Series Anomaly Detection Using Deep Learning, with just a few thousands of time series, but it is very slow...
Any suggestion to get anomalies detected with 200k time series in a faster way?

Accepted Answer

Eamonn
Eamonn on 18 Jun 2022
Hello
The DAMP algorithm (in Matlab) can do anomlay detecion at 100,000+ Hz
It is the only time series anomlay detecion algorthm to process a dataset with one trillion datapoints.
OH, and it beat deep learning on all the benchmarks.
It will be here https://www.cs.ucr.edu/~eamonn/MatrixProfile.html next week, but in the meantime, if you want the code/paper, I am happy to share it.
  6 Comments
Sim
Sim on 21 Jun 2022
Edited: Sim on 21 Jun 2022
@Eamonn, email just sent to you :-)
P.S.: just in case I attach here again the ts.mat file (including both "w3" and "w4" time series)

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More Answers (1)

Image Analyst
Image Analyst on 18 Jun 2022
Are you talking about training or prediction?
For what it's worth, here is another anomaly detection demo from Mathworks.
Learn how to apply statistical and machine learning based anomaly detection techniques to industrial processes and machinery.
  3 Comments
Sim
Sim on 20 Jun 2022
Thanks a lot @Image Analyst, in this moment I do not have access to faster machines, but I could try with the parallel processing toolbox. However, by considering the paper/s showed by @Eamonn, it looks like that (for time series) deep learning is outperformed by another algorithm based on the Matrix Profile :-)

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