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DCdetector: Dual Attention Contrastive Representation Learning for Time Series Anomaly Detection
Open accessAbstract
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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“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]
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“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]
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“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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Cited by
Sorted by citations
Deep Learning for Time Series Anomaly Detection: A Survey
Darban, Z. Z., Webb, G. I., Pan, S., Aggarwal, C. C., & Salehi, M. (2024) · ACM Computing Surveys · doi:10.1145/3691338 · Cited by 853
“DCdetector [187] employs a deep CNN with a dual attention mechanism.”
Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and Prospects
Zhang, K., Wen, Q., Zhang, C., Cai, R., Jin, M., Liu, Y., Zhang, J. Y., Liang, Y., Pang, G., Song, D., et al. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3387317 · Cited by 295
“Recently, DCdetec-tor [126] proposes a dual attention contrastive representation framework for time series anomaly detection.”
BackgroundSelf-citationFoundation Models for Time Series Analysis: A Tutorial and Survey
Liang, Y., Wen, H., Nie, Y., Jiang, Y., Jin, M., Song, D., Pan, S., & Wen, Q. (2024) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3637528.3671451 · Cited by 368
Self-citationYan, P., Abdulkadir, A., Luley, P.-P., Rosenthal, M., Schatte, G. A., Grewe, B. F., & Stadelmann, T. (2024) · IEEE Access · doi:10.1109/access.2023.3349132 · Cited by 256
“Yang et al. propose a contrastive learning structure with dual attention to learn a permutation invariant representation of the data with superior discrimination characteristics between normal points and anomalies [104].”
BackgroundCARLA: Self-supervised contrastive representation learning for time series anomaly detection
Darban, Z. Z., Webb, G. I., Pan, S., Aggarwal, C. C., & Salehi, M. (2024) · Pattern Recognition · doi:10.1016/j.patcog.2024.110874 · Cited by 183
“It involves the introduction of a subsequence anomaly within a time series window w i , represented as w ( t | t ∈ [ s, e ]), where s and e denotes the anomaly’s starting and end time points.”
Background
Metadata
- 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)
- Venue
- Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
- Publisher
- ACM
- Published
- 2023-08-04
- Pages
- 3033-3045
- 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
- bronze
- doi.org
- 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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