DCdetector: Dual Attention Contrastive Representation Learning for Time Series Anomaly Detection

By Yiyuan Yang, Chaoli Zhang, Tian Zhou, Qingsong Wen, Liang Sun. Knowledge Discovery and Data Mining, 2023

376 citations recorded by the source corpus.

Time series anomaly detection is critical for a wide range of applications. It aims to identify deviant samples from the normal sample distribution in time series. The most fundamental challenge for this task is to learn a representation map that enables effective discrimination of anomalies. Reconstruction-based methods still dominate, but the representation learning with anomalies might hurt the performance with its large abnormal loss. On the other hand, contrastive learning aims to find a representation that can clearly distinguish any instance from the others, which can bring a more natural and promising representation for time series anomaly detection. In this paper, we propose DCdetector, a multi-scale dual attention contrastive representation learning model. DCdetector utilizes a novel dual attention asymmetric design to create the permutated environment and pure contrastive loss to guide the learning process, thus learning a permutation invariant representation with superior discrimination abilities. Extensive experiments show that DCdetector achieves state-of-the-art results on multiple time series anomaly detection benchmark datasets. Code is publicly available at https://github.com/DAMO-DI-ML/KDD2023-DCdetector.

Subfields: Artificial Intelligence, Epidemiology, Signal Processing

View Paper: https://www.semanticscholar.org/paper/6d1b7c27748ed1bbafe61ba3fe60465b28af5e0f

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