anomaly-detector

Metric anomaly detection reduces alert fatigue by establishing rolling baselines and correlating related signals across services. It applies threshold, rate-of-change, and statistical rules to filter noise and surface true incidents with prioritized remediation recommendations.

To investigate a sudden latency spike, you detect anomalous metrics, identify affected services, and correlate deployment and infrastructure signals. This process generates a prioritized action and escalation recommendation to guide targeted mitigation steps.

Correlating metric anomalies requires access to time-series metrics, deployment and change history, and storage for rolling baselines. These inputs enable statistical and correlation analyses to produce concise anomaly reports with probable root causes.

Yes, alert thresholds can be automatically retuned through a self-improvement loop. The system measures alert-to-incident ratios, adjusts rules based on false positives, and updates baselines and correlation patterns from postmortems.

The best way to correlate infrastructure signals during post-incident analysis is to apply statistical and correlation analyses to time-series metrics and deployment history. This generates a prioritized anomaly report identifying probable root causes and severity.