I guess I did not understand the use of aggregation in anomaly detection. I was expecting that the same aggregation with different time intervals will not affect the results, however, it seems that this is not the case.
I created 3 anomaly detection jobs using aggregation as discussed here each of which running bucket spans of 6hr.
For each job, I created Datafeed with 3 different fixed_intervals: 1hr, 3hr, 6hr. Note that the bucket span is divisible by all these intervals.
I was expecting the same results for each job, however, even though I get exactly the same time series chart for the jobs, i.e., the actual values of the buckets are the same for the jobs, the anomaly scores for the anomalies are different. In some cases, some buckets are not considered as an anomaly when other job marks it as an anomaly.
Any clarification would be greatly appreciated.