Towards Experienced Anomaly Detector Through Reinforcement Learning


  • Chengqiang Huang University of Exeter
  • Yulei Wu University of Exeter
  • Yuan Zuo University of Exeter
  • Ke Pei Huawei Technologies Co. Ltd.
  • Geyong Min University of Exeter



Time Series Anomaly Detection, Recurrent Neural Network, Reinforcement Learning


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. Essentially, the anomaly detector is powered by the Recurrent Neural Network (RNN) and adopts the Reinforcement Learning (RL) method to achieve the self-learning process. Our initial experiments demonstrate promising results of using the detector in network time series anomaly detection problems.




How to Cite

Huang, C., Wu, Y., Zuo, Y., Pei, K., & Min, G. (2018). Towards Experienced Anomaly Detector Through Reinforcement Learning. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1).