Building an Automated and Self-Aware Anomaly Detection System

• URL: http://arxiv.org/abs/2011.05047v1
• Date: Tue, 10 Nov 2020 11:19:07 GMT
• Title: Building an Automated and Self-Aware Anomaly Detection System
• Authors: Sayan Chakraborty, Smit Shah, Kiumars Soltani, Anna Swigart, Luyao Yang, Kyle Buckingham
• Abstract summary: It can be challenging to proactively monitor a large number of diverse and constantly changing time series for anomalies. 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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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