Online false discovery rate control for anomaly detection in time series
This addresses the challenge of ensuring reliable anomaly detection in time series data for applications like monitoring and security, representing an incremental improvement over existing methods.
The paper tackles the problem of controlling false discovery rates in online anomaly detection for time series, proposing novel rules that maintain high power even when anomalies are rare and test statistics are serially dependent, with theoretical and experimental validation.
This article proposes novel rules for false discovery rate control (FDRC) geared towards online anomaly detection in time series. Online FDRC rules allow to control the properties of a sequence of statistical tests. In the context of anomaly detection, the null hypothesis is that an observation is normal and the alternative is that it is anomalous. FDRC rules allow users to target a lower bound on precision in unsupervised settings. The methods proposed in this article overcome short-comings of previous FDRC rules in the context of anomaly detection, in particular ensuring that power remains high even when the alternative is exceedingly rare (typical in anomaly detection) and the test statistics are serially dependent (typical in time series). We show the soundness of these rules in both theory and experiments.