I have two questions regarding your blog post on https://www.elastic.co/blog/machine-learning-anomaly-scoring-elasticsearch-how-it-works
- Question regarding influencer scoring
Assume having a multi-metric-job. As I understand, the influencer score for a specific influencer entity (e.g. influencer: "country", entity: "spain") is somehow derived from the anomalies that occur in all timeseries' with country=="spain". I hope this is correct so far.
My question: Does it also take into account the number of timeseries with country=="spain" that are "clean" (i.e. not affected by anomalies in this bucket)? I am asking because it might be a big difference if a influencer entity affects 10 of 10 timeseries or 10 of 1000.
- Question about bucket results
Note that the calculation behind the bucket score is more complex than just a simple average of all the individual anomaly record scores, but will have a contribution from the influencer scores in each bucket.
I don't really get it. Can you rephrase the sentence please? So do influencer scores contribute to the bucket result or don’t they contribute?
Thanks you very much in advance!