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