Measure for uncertainty of the Anomaly Detection algorihtm

Hello,

We are trying to find anomalies in people's daily power usage using Elastic's Anomaly Detection.

Of course, in order to detect a day-night cylce, the algorithm first needs some data before it can recognize patterns.
However, the time it takes before such a cycle is picked up can differ a lot.
Sometimes the model needs only 2 days worth of data, and sometimes it does not happen at all. See the images below.

In the second image in the middle a clear point can be seen at which the model "understands" the data. Is there a way to automatically detect this change in behaviour?

I was thinking about analysing the rate of change in the model bounds, but have not found a way to retrieve these when there are no anomalies. I also noticed that the forecast-functionality does something similar when it checks whether enough data is provided. Is it possible to run such a check without the forecasting?

Thanks in advance,

Jorrit van der Laan

There will be an upcoming feature in which significant model changes will be published to ML's Annotations: https://github.com/elastic/elasticsearch/issues/55781

Looks interesting! Do you know when this upcoming feature would be released?

We cannot commit exactly, but possibly v7.9.