henrikingo commented on PR #96:
URL: https://github.com/apache/otava/pull/96#issuecomment-3525938057

   Thanks a lot @Sowiks for this! You have valuable skill in being able to 
grasp the academic level math and then still explain your findings to normal 
people with simple pictures. Btw this is why I like this tigerbeetle demo 
dataset from 2023. In 200+ points it exercises many of  the phenomena you might 
encounter in this field, and so it captured your bug, or fix rather, too.
   
   Amazingly I vaguely remember how this happened at MongoDB back then. I 
remember asking about this kappa and the people who had read the jameson paper 
(I would read it much later) explained that we can choose a value for it 
freely. So we did and I never thought of it again. We thought of it as a 
parameter we could choose, not that we were supposed to use all values. Since 
the by-the-book algorithm ends in a monte carlo simulation, we  apparently 
accepted the fact that the reference implementation in R often produced 
different change points.
   
   So it seems with your fix the algorithm will perform even better than it 
ever did. (And even now Otava has outperformed all alternatives with a good 
margin!) It now seems to hit the blind spots that always annoyed me. In a way 
Piotr's approach applying small windows kind of achieves the same behavior.
   
   Do I understand correctly that running this Kappa from 0 to T is exactly the 
same as if I would start with two points, then append one point at a time to 
the timeseries, re-running otava between each step, and then keeping all change 
points found along the way? If yes, then it means that storing the previous 
results becomes the norm and we should pay more attention to a format and api 
for doing that.
   
   Will review code over the weekend but from the text and pictures I can 
already tell this is good stuff. Thanks for  contributing!


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