Anomaly Detection Visualization

Anomaly detection identifies data that does not conform to expected (or usual) patterns. LogicMonitor uses machine learning algorithms to establish expected data patterns for datapoints to identify data that falls outside of these patterns. Anomaly detection provides another avenue of insight into resource behavior, allowing users to catch issues before they escalate into more severe events potentially.

The LogicMonitor platform leverages anomaly detection to:

• Automatically trigger alerts when anomalous values are present, as discussed in Enabling Dynamic Thresholds for Datapoints.
• Visually highlight expected data ranges (and any data that falls outside of these ranges) for any graph in the LogicMonitor interface, as discussed in this topic.