radove opened a new issue, #100: URL: https://github.com/apache/otava/issues/100
I think it would be nice to have some descriptions about how the statistics of analysis.py works (compute_change_points) and the math behind it. This can be helpful for those investigating to use this as possible solution for various use-cases. Short Example: analysis.py is a smart change point detector built by combining: - A powerful segmentation algorithm (EDivisive) - A fast statistical test (t-test) - Sliding windows for scalability - A postprocessing step to enforce quality - Optional incremental processing It produces stable, noise-resistant change points for industrial or scientific signals. -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: [email protected] For queries about this service, please contact Infrastructure at: [email protected]
