Building an Automated and Self-Aware Anomaly Detection System
This addresses the problem of monitoring gaps and false positives in operational metrics for organizations, though it appears incremental as it builds on existing anomaly detection methods.
The paper tackles the challenge of proactively monitoring diverse time series metrics for anomalies by developing an automated system that self-adjusts models without manual intervention, demonstrating superior performance over alternatives on benchmark datasets.
Organizations rely heavily on time series metrics to measure and model key aspects of operational and business performance. The ability to reliably detect issues with these metrics is imperative to identifying early indicators of major problems before they become pervasive. It can be very challenging to proactively monitor a large number of diverse and constantly changing time series for anomalies, so there are often gaps in monitoring coverage, disabled or ignored monitors due to false positive alarms, and teams resorting to manual inspection of charts to catch problems. Traditionally, variations in the data generation processes and patterns have required strong modeling expertise to create models that accurately flag anomalies. In this paper, we describe an anomaly detection system that overcomes this common challenge by keeping track of its own performance and making changes as necessary to each model without requiring manual intervention. We demonstrate that this novel approach outperforms available alternatives on benchmark datasets in many scenarios.