@sheiser1 I did the dirtiest thing to print my sample but I hope you can see the patterns :
And here are my anomaly scores after more than 100 iterations:
Step 0-> Belongs to change from last SDR to first one
Step 9->Transition line from bits (1-10) to (10-19)
Step 17->Transition line from bits (10-19) to (19-28)
@rhyolight @sheiser1 the fact that annoys me is that I´m getting the 1.0 anomaly scores in these steps from the very first iteration...
3 more things to add:
My w/n is 10,47%
So if I´m getting a 1.0 score for the transitions metioned in my model, I don´t have any way to perceive if a new transition is added to my data as it will get almost the same score
Is overlap needed for possible transitions? The key point is to detect anomaly behaviour between transitions so if I train my model with a transition between 10 to 100 that happens very often, it should get low anomaly scores eventhough there is no SDR overlap between these numbers. Right?
Thanks again for your attention! Hope you haven´t got slept with my comment