GenAD: General unsupervised anomaly detection using multivariate time series for large‐scale wireless base stations
Xiaolei Hua, Lin Zhu, Shenglin Zhang, Zeyan Li, Su Wang, Chao Deng, Junlan Feng, Zhao Zhang, Wei Wu · Electronics Letters · 2022
Abstract The reliability of wireless base stations is essential to guarantee the user experiences in wireless networks, thereby employing the anomaly detection on multivariate time series is indispensable for network operators to monitor the behaviours of large‐scale wireless base stations. In this paper, a general unsupervised anomaly detection model is proposed using multivariate time series for large‐scale wireless base stations, called GenAD. Firstly, multi‐correlation attention and time‐series attention are employed to learn the representations of the complex correlations and various temporal patterns of multivariate series. Secondly, a general model on large‐scale wireless base stations is pre‐trained with self‐supervision, which can be easily transferred to a specific station with a small amount of training data. Experimentals show that GenAD boosts F1‐score by total 9% on real‐world datasets.