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Cited by 387Anomaly Detection Techniques and ApplicationsTime Series Analysis and ForecastingSoftware System Performance and Reliability

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

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Yang, Yiyuan, Zhang, Chaoli, Zhou, Tian, Wen, Qingsong, Sun, Liang·2023·Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
DOI 10.1145/3580305.3599295
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Abstract

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.

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  • Contrastive-based methods such as Skip-CPC and Dcdetector have achieved the best performance on four anomaly detection datasets.

    “Furthermore, the latest research shows that contrastive-based methods, such as Skip-CPC [113] and Dcdetector [126], have achieved the best performance on four datasets.”

    From Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and Prospects (2024) · cites this paper as [126]

    Section 4.4.1 says DCdetector achieved state-of-the-art F1 results on most of the four multivariate benchmark datasets, not all four. Table 1 shows its F1 score was lower than AnomalyTrans on SMD. The separate citation to Skip-CPC is not assessed here. (LLM, conf=0.90)

    Experimental — accuracy not yet measured

  • DCdetector proposes a dual attention contrastive representation framework for time series anomaly detection, with in-patch and patch-wise representations designed to gain two views of the input samples, as normal samples behave differently from abnormal ones in such two views.

    “Recently, DCdetector [126] proposes a dual attention contrastive representation framework for time series anomaly detection. The in-patch and patch-wise representations are designed to gain two views of the input samples, as normal samples behave differently from abnormal ones in such two views.”

    From Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and Prospects (2024) · cites this paper as [126]

  • In time series anomaly detection, the adjustment strategy is widely adopted: if a time point in a certain successive abnormal segment is detected, all anomalies in this abnormal segment are viewed to be correctly detected.

    “Note that in time series anomaly detection task, the adjustment strategy is widely adopted: if a time point in a certain successive abnormal segment is detected, all anomalies in this abnormal segment are viewed to be correctly detected [81], [126], [163].”

    From Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and Prospects (2024) · cites this paper as [126]

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Authors
Yang, Y., Zhang, C., Zhou, T., Wen, Q., & Sun, L.
Affiliations
  • University of Oxford (GB)
  • Alibaba Group (China) (CN)
  • Alibaba Group (United States) (US)
ORCID
Venue
Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
Publisher
ACM
Published
2023-08-04
Pages
3033-3045
DOI
10.1145/3580305.3599295
OpenAlex
W4385562572
Cited by
387
References
57
Citations by year
2026: 160 · 2025: 161 · 2024: 65 · 2023: 1
FWCI
68.57
Type
conference paper
Language
en
Open access
Licence
www.acm.org/publications/policies/copyright_policy#Background (vor)
Locations
  • ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) (publishedVersion)
  • arXiv (Cornell University) (submittedVersion, cc-by-nc-sa)
  • arXiv (Cornell University)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Anomaly Detection Techniques and Applications
  • Physical Sciences › Computer Science › Signal Processing › Time Series Analysis and Forecasting
  • Physical Sciences › Computer Science › Computer Networks and Communications › Software System Performance and Reliability
Keywords
time series anomaly detection, contrastive learning, representation learning, dual attention mechanism, multi-scale representation

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