Timezone: »

 
Poster
Online false discovery rate control for anomaly detection in time series
Quentin Rebjock · Baris Kurt · Tim Januschowski · Laurent Callot

Thu Dec 09 08:30 AM -- 10:00 AM (PST) @
in Poster Session 6 »

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.

Keywords: Theory · Machine Learning

Author Information

Quentin Rebjock (Swiss Federal Institute of Technology Lausanne)
Baris Kurt (Amazon Research)
Tim Januschowski (Amazon Research)

- Director Pricing Platform, Zalando SE - Head of Time Series ML at AWS AI

Laurent Callot (Amazon)

More from the Same Authors