Towards Experienced Anomaly Detector through Reinforcement Learning

no code implementations • Thirty-Second AAAI Conference on Artificial Intelligence 2018 • Chengqiang Huang, Yulei Wu, Yuan Zuo, Ke Pei, Geyong Min

This abstract proposes a time series anomaly detector which 1) makes no assumption about the underlying mechanism of anomaly patterns, 2) refrains from the cumbersome work of threshold setting for good anomaly detection performance under specific scenarios, and 3) keeps evolving with the growth of anomaly detection experience.

Anomaly Detection reinforcement-learning +4