Minimally monitored anomaly source detection based on seasonality

Abstract

System, methods, and machine-readable media may facilitate minimally monitored anomaly source detection based on seasonality. Time series data individually corresponding to entities may be obtained. For each entity or for each cluster of entities, statistical parameter fingerprints may be generated. For each entity or cluster, the statistical parameter fingerprints may be updated using a weighted averaging technique that incorporates newly received time series data. The statistical parameter fingerprints for a given entity or cluster may be compared across the time horizons to detect discrepancies indicative of seasonality. Anomaly detection thresholds may be defined for each time horizon based on the corresponding statistical parameter fingerprints. An anomalous value in the time series data of an entity or cluster may be detected by determining that a value falls outside the anomaly detection threshold for at least one time horizon. Automated interventions may be performed based on a severity assessment.