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Timeseries Anomaly Detection using Temporal Hierarchical One-Class Network
Lifeng Shen · Zhuocong Li · James Kwok

Wed Dec 09 09:00 PM -- 11:00 PM (PST) @ Poster Session 4 #1171

Real-world timeseries have complex underlying temporal dynamics and the detection of anomalies is challenging. In this paper, we propose the Temporal Hierarchical One-Class (THOC) network, a temporal one-class classification model for timeseries anomaly detection. It captures temporal dynamics in multiple scales by using a dilated recurrent neural network with skip connections. Using multiple hyperspheres obtained with a hierarchical clustering process, a one-class objective called Multiscale Vector Data Description is defined. This allows the temporal dynamics to be well captured by a set of multi-resolution temporal clusters. To further facilitate representation learning, the hypersphere centers are encouraged to be orthogonal to each other, and a self-supervision task in the temporal domain is added. The whole model can be trained end-to-end. Extensive empirical studies on various real-world timeseries demonstrate that the proposed THOC network outperforms recent strong deep learning baselines on timeseries anomaly detection.

Author Information

Lifeng Shen (The Hong Kong University of Science and Technology)
Zhuocong Li (Tencent)
James Kwok (Hong Kong University of Science and Technology)

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