I have created a multi metric Anomaly Detection Job with High Mean Response Time as detector, bucket span was 4h and Training data was for 1year.
After a certain span the Upper Bound of the model has increased abruptly. Refer to the screenshot attached below:
Can you help me to understand why this abrupt increase of upper bound happened?
In the second row in the anomalies table (refer to the attached screenshot below) where Severity is 85 and it is having a high multi bucket effect there the typical value is abnormally high from the actual value. I observed that, this is happening for all the anomaly points which is having multi bucket effect, but the anomaly points without multi bucket effect is not showing any abnormal value. But in my data, I don’t have such abnormal value, the Maximum Response Time = 400 and Minimum Response Time = 0 in my time series.
It will be helpful if you can help me to understand why this typical value is so high? What do this Multi Bucket effect signifies? Does this Multi Bucket Effect has any correlation with the abnormally high typical value?
I would also like to know what is the difference between “High Mean Response Time” and “Mean Response Time” in the detector field of the anomaly job?