Materials to read about anomaly detection
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Description
Not really a code related question, but more of a methods question.
Is there some material on the basic concepts that have been used to develop the luminol package? For example, whats the basic idea behind detecting the anomaly, how to interpret the score, how does the algorithm handles the seasonality and trend in the data. Should we make the time series stationary before using it? Or how does the package manages to work with data with non-stationary time series?
Thanks
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Research direction
The issue names no files, tests, or entry points. Review the package's existing documentation and anomaly-detection methods, then document the algorithm concepts, score interpretation, seasonality and trend handling, and expectations for non-stationary time series.
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Assessment
- Tech stack
- python
- Domain
- machine-learning
- Issue type
- Documentation
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100