Optimize identify anomaly periods algorithm #11
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Previously, we were creating each sliding window one-by-one which took forever on large datasets. Now we use a cumulative sum approach that gives identical results but runs way faster on extremly huge datasets. Signed-off-by: Kaveh Shahedi <kaveh.shahedi@ericsson.com>
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bhufmann
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Feb 28, 2025
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Thanks for this contribution. It improves performance significantly.
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What it does
The original implementation created and processed sliding windows one by one, resulting in hour-long processing times for million-row datasets. The new implementation maintains identical detection logic but uses a vectorized cumulative sum approach to calculate all window means at once, dramatically reducing computation time.
This PR also aims to fix the excessive calculation time reported in a previous pull request.
How to test
Initialize the
AnomalyDetecionmodule with your custom outputs (e.g., CPU Usage, Memory Usage, etc.). In the case of having a huge dataset (e.g., millions of data points), you can now observe the significant performance improvement when indicating the anomalies.Follow-ups
N/A
Review checklist