Deep Baseline Network for Time Series Modeling and Anomaly Detection

被引:0
作者
Ge, Cheng [1 ]
Chen, Xi [1 ]
Wang, Ming [1 ]
Wang, Jin [1 ]
机构
[1] Alibaba Grp, Shanghai, Peoples R China
来源
2022 21ST IEEE INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS, ICMLA | 2022年
关键词
deep baseline network; time series; anomaly detection; local regression; neural network;
D O I
10.1109/ICMLA55696.2022.00185
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Deep learning has seen increasing applications in time series in recent years. For time series anomaly detection scenarios, such as in finance, Internet of Things, data center operations, etc., time series usually show very flexible baselines depending on various external factors. Anomalies unveil themselves by lying far away from the baseline. However, the detection is not always easy due to some challenges including baseline shifting, lacking of labels, noise interference, real time detection in streaming data, result interpretability, etc. In this paper, we develop a novel deep architecture to properly extract the baseline from time series, namely Deep Baseline Network (DBLN). By using this deep network, we can easily locate the baseline position and then provide reliable and interpretable anomaly detection result. Empirical evaluation on both synthetic and public real-world datasets shows that our purely unsupervised algorithm achieves superior performance compared with state-of-art methods and has good practical applications.
引用
收藏
页码:1137 / 1142
页数:6
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