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Deep Variational Graph Convolutional Recurrent Network for Multivariate Time Series Anomaly Detection
Wenchao Chen · Long Tian · Bo Chen · Liang Dai · Zhibin Duan · Mingyuan Zhou

Wed Jul 20 03:30 PM -- 05:30 PM (PDT) @ Hall E #612

Anomaly detection within multivariate time series (MTS) is an essential task in both data mining and service quality management. Many recent works on anomaly detection focus on designing unsupervised probabilistic models toextract robust normal patterns of MTS. In this paper, we model sensor dependency and stochasticity within MTS by developing an embedding-guided probabilistic generative network. We combine it with adaptive variational graph convolutional recurrent network %and get variational GCRN (VGCRN) to model both spatial and temporal fine-grained correlations in MTS. To explore hierarchical latent representations, we further extend VGCRN into a deep variational network, which captures multilevel information at different layers and is robust to noisy time series. Moreover, we develop an upward-downward variational inference scheme that considers both forecasting-based and reconstruction-based losses, achieving an accurate posterior approximation of latent variables with better MTS representations. The experiments verify the superiority of the proposed method over current state-of-the-art methods.

Author Information

Wenchao Chen (Xi'dian University)
Long Tian (Xidian University)
Bo Chen (School of Electronic Engineering, Xidian University)

Bo Chen, Ph.D., Professor. Before joining the Department of Electronic Engineering in Xidian University in 2013, I was a post-doc researcher, research scientist and senior research scientist at the Department of Electrical and Computer Engineering in Duke University. In 2013 and 2014, I was elected into the Program for New Century Excellent Talents in University and the Program for Thousand Youth Talents respectively. I am interested in developing statistical machine learning methods for the complex and large-scale data. My current interests are in statistical signal processing, statistical machine learning, deep learning and their applications to radar target detection and recognition.

Liang Dai (Institute of Information Engineering, Chinese Academy of Sciences)
Zhibin Duan (Xidian University)
Mingyuan Zhou (University of Texas at Austin)

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