  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.
