AIMS analyzes all performance parameters in close to real-time and generate normal behavior patterns for all of these with different time-resolutions. A normal behavior pattern means dynamic thresholds which are unique for every performance parameter that AIMS is monitoring. As seen in the screenshot above, the normal behavior pattern is the blue area that represents a dynamic upper and lower threshold.
AIMS starts by learning patterns for up to 48 hours and can then start comparing all hours of the day between two different days. Then AIMS continues to learn daily patterns while building weekly patterns, and to be able to compare weekly cycles AIMS need two full weeks of data. This is why the anomaly detection is enabled 14 days after connecting the agents, as these normal behavior patterns (dynamic thresholds) are an important component of the anomaly detection. After this AIMS continues to build patterns for months and finally years.