Detect anomalies with Moogsoft Cloud
Moogsoft Cloud uses advanced analytics in metric policies to identify performance anomalies on time series metrics. Each metric anomaly is considered an event of operational significance.
Moogsoft metric policies use the following anomaly detectors:
The Adaptive Detector identifies anomalies based on a statistical calculation against a median absolute deviation, which varies over time and determines the high and low thresholds.
The Threshold Detector identifies anomalies based on a fixed upper and lower threshold you define.
You can fine-tune how Moogsoft detects anomalies for individual metrics with scope queries for metrics with special characteristics. For example, you might want to first create a basic policy for metrics with very large or very small data ranges, then progressively update the anomaly detection logic in the policy to more specifically define the data points which are considered anomalous.