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Poster
Prototype-oriented unsupervised anomaly detection for multivariate time series
yuxin li · Wenchao Chen · Bo Chen · Dongsheng Wang · Long Tian · Mingyuan Zhou

Wed Jul 26 05:00 PM -- 06:30 PM (PDT) @ Exhibit Hall 1 #503
in Poster Session 4 »

Unsupervised anomaly detection (UAD) of multivariate time series (MTS) aims to learn robust representations of normal multivariate temporal patterns. Existing UAD methods try to learn a fixed set of mappings for each MTS, entailing expensive computation and limited model adaptation. To address this pivotal issue, we propose a prototype-oriented UAD (PUAD) method under a probabilistic framework. Specifically, instead of learning the mappings for each MTS, the proposed PUAD views multiple MTSs as the distribution over a group of prototypes, which are extracted to represent a diverse set of normal patterns. To learn and regulate the prototypes, PUAD introduces a reconstruction-based unsupervised anomaly detection approach, which incorporates a prototype-oriented optimal transport method into a Transformer-powered probabilistic dynamical generative framework. Leveraging meta-learned transferable prototypes, PUAD can achieve high model adaptation capacity for new MTSs. Experiments on five public MTS datasets all verify the effectiveness of the proposed UAD method.

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Author Information

yuxin li (XIDIAN University)
Wenchao Chen (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.

Dongsheng Wang (Xidian University)
Long Tian (Xi'an University of Electronic Science and Technology)
Mingyuan Zhou (University of Texas at Austin)

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