henrikingo opened a new pull request, #154: URL: https://github.com/apache/otava/pull/154
Same as original algorithm but with deterministic Student T significance test. The motivation for this variation follows from fixing the bug explained at the top of https://github.com/apache/otava/pull/96 The intuition is that the split-merge approach introduced by Datastax is addressing the same problem that the _kappa_ variable does in the original paper. Now that we compute correctly over all values of kappa, the split-merge part should be unnecessary, as the original algorithm with kappa bug fixed, will find the same change points and more. Therefore the conclusion is we want to go back as much as possible to the original and real algorithm from the Matteson & James paper. But even then, we find that Student T as significance test is both much faster but also qualitatively produces better results for the use case we're in at least, that we want to continue using T test and not random permutations for the significance test. TBD: Whether weak change points are still helpful or not. By reading the problem they fix appears unrelated from the split-merge vs **kappa** symptoms. TODO: Incremental e-divisive is not supported in this mode. This was easy to implement on top of the split-merge variation. Not clear what is the correct way here. An easy solution is to rerun from the last change-point, but the problem is the last change point could itself be influenced by the new data appended. -- 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]
