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

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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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Research works printed but not linked: 27

  1. Archana Anandakrishnan , Senthil Kumar , Alexander Statnikov , Tanveer Faruquie , and Di Xu . 2018 . Anomaly detection in finance: editors' introduction . In KDD 2017 Workshop on Anomaly Detection in Finance. PMLR, 1--7. Archana Anandakrishnan, Senthil Kumar, Alexander Statnikov, Tanveer Faruquie, and Di Xu. 2018. Anomaly detection in finance: editors' introduction. In KDD 2017 Workshop on Anomaly Detection in Finance. PMLR, 1--7.
  2. OD Anderson . 1976. Time-Series . 2 nd edn. OD Anderson. 1976. Time-Series. 2nd edn.
  3. 10.1080/01621459.1970.10481180 · DOI
  4. David Campos , Tung Kieu , Chenjuan Guo , Feiteng Huang , Kai Zheng , Bin Yang , and Christian S Jensen . 2021. Unsupervised Time Series Outlier Detection with Diversity-Driven Convolutional Ensembles-Extended Version. arXiv preprint arXiv:2111.11108 ( 2021 ). David Campos, Tung Kieu, Chenjuan Guo, Feiteng Huang, Kai Zheng, Bin Yang, and Christian S Jensen. 2021. Unsupervised Time Series Outlier Detection with Diversity-Driven Convolutional Ensembles-Extended Version. arXiv preprint arXiv:2111.11108 (2021). · arXiv
  5. Mathilde Caron , Ishan Misra , Julien Mairal , Priya Goyal , Piotr Bojanowski , and Armand Joulin . 2020. Unsupervised learning of visual features by contrasting cluster assignments. Advances in neural information processing systems , Vol. 33 ( 2020 ), 9912--9924. Mathilde Caron, Ishan Misra, Julien Mairal, Priya Goyal, Piotr Bojanowski, and Armand Joulin. 2020. Unsupervised learning of visual features by contrasting cluster assignments. Advances in neural information processing systems, Vol. 33 (2020), 9912--9924.
  6. Ting Chen , Simon Kornblith , Mohammad Norouzi , and Geoffrey Hinton . 2020 . A simple framework for contrastive learning of visual representations . In International conference on machine learning. PMLR, 1597--1607 . Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. 2020. A simple framework for contrastive learning of visual representations. In International conference on machine learning. PMLR, 1597--1607.
  7. Ailin Deng and Bryan Hooi . 2021 a. Graph Neural Network-Based Anomaly Detection in Multivariate Time Series. In AAAI Conference on Artificial Intelligence. Ailin Deng and Bryan Hooi. 2021a. Graph Neural Network-Based Anomaly Detection in Multivariate Time Series. In AAAI Conference on Artificial Intelligence.
  8. Jingkun Gao , Xiaomin Song , Qingsong Wen , Pichao Wang , Liang Sun , and Huan Xu . 2020 . RobustTAD: Robust time series anomaly detection via decomposition and convolutional neural networks . KDD Workshop MileTS (2020). Jingkun Gao, Xiaomin Song, Qingsong Wen, Pichao Wang, Liang Sun, and Huan Xu. 2020. RobustTAD: Robust time series anomaly detection via decomposition and convolutional neural networks. KDD Workshop MileTS (2020).
  9. Jean-Bastien Grill , Florian Strub , Florent Altché , Corentin Tallec , Pierre Richemond , Elena Buchatskaya , Carl Doersch , Bernardo Avila Pires , Zhaohan Guo, Mohammad Gheshlaghi Azar, et al. 2020 . Bootstrap your own latent-a new approach to self-supervised learning. Advances in neural information processing systems, Vol. 33 (2020), 21271--21284. Jean-Bastien Grill, Florian Strub, Florent Altché, Corentin Tallec, Pierre Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Avila Pires, Zhaohan Guo, Mohammad Gheshlaghi Azar, et al. 2020. Bootstrap your own latent-a new approach to self-supervised learning. Advances in neural information processing systems, Vol. 33 (2020), 21271--21284.
  10. Neha Kant and Manish Mahajan . 2019. Time-series outlier detection using enhanced k-means in combination with pso algorithm . In Engineering Vibration, Communication and Information Processing: ICoEVCI 2018 , India. Springer , 363--373. Neha Kant and Manish Mahajan. 2019. Time-series outlier detection using enhanced k-means in combination with pso algorithm. In Engineering Vibration, Communication and Information Processing: ICoEVCI 2018, India. Springer, 363--373.
  11. Paweł Karczmarek , Adam Kiersztyn , Witold Pedrycz , and Ebru Al. 2020. K-Means-based isolation forest. Knowledge-based systems , Vol. 195 ( 2020 ), 105659. Paweł Karczmarek, Adam Kiersztyn, Witold Pedrycz, and Ebru Al. 2020. K-Means-based isolation forest. Knowledge-based systems, Vol. 195 (2020), 105659.
  12. Eamonn Keogh , Dutta Roy Taposh , U Naik , and A Agrawal . 2021 . Multi-dataset Time-Series Anomaly Detection Competition. In ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. https://compete. hexagonml. com/practice/competition/39 . Eamonn Keogh, Dutta Roy Taposh, U Naik, and A Agrawal. 2021. Multi-dataset Time-Series Anomaly Detection Competition. In ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. https://compete. hexagonml. com/practice/competition/39.
  13. Taesung Kim , Jinhee Kim , Yunwon Tae , Cheonbok Park , Jang-Ho Choi , and Jaegul Choo . 2021 . Reversible instance normalization for accurate time-series forecasting against distribution shift . In International Conference on Learning Representations. Taesung Kim, Jinhee Kim, Yunwon Tae, Cheonbok Park, Jang-Ho Choi, and Jaegul Choo. 2021. Reversible instance normalization for accurate time-series forecasting against distribution shift. In International Conference on Learning Representations.
  14. Kwei-Herng Lai , Daochen Zha , Junjie Xu , Yue Zhao , Guanchu Wang , and Xia Hu . 2021 . Revisiting time series outlier detection: Definitions and benchmarks . In Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 1). Kwei-Herng Lai, Daochen Zha, Junjie Xu, Yue Zhao, Guanchu Wang, and Xia Hu. 2021. Revisiting time series outlier detection: Definitions and benchmarks. In Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 1).
  15. Shiyang Li , Xiaoyong Jin , Yao Xuan , Xiyou Zhou , Wenhu Chen , Yu-Xiang Wang , and Xifeng Yan . 2019b. Enhancing the locality and breaking the memory bottleneck of transformer on time series forecasting. Advances in neural information processing systems , Vol. 32 ( 2019 ). Shiyang Li, Xiaoyong Jin, Yao Xuan, Xiyou Zhou, Wenhu Chen, Yu-Xiang Wang, and Xifeng Yan. 2019b. Enhancing the locality and breaking the memory bottleneck of transformer on time series forecasting. Advances in neural information processing systems, Vol. 32 (2019).
  16. Fei Tony Liu , Kai Ming Ting, and Zhi-Hua Zhou . 2008 . Isolation forest. In 2008 eighth ieee international conference on data mining. IEEE , 413--422. Fei Tony Liu, Kai Ming Ting, and Zhi-Hua Zhou. 2008. Isolation forest. In 2008 eighth ieee international conference on data mining. IEEE, 413--422.
  17. Steffen Moritz Frederik Rehbach Sowmya Chandrasekaran Margarita Rebolledo and Thomas Bartz-Beielstein. 2018. GECCO Industrial Challenge 2018 Dataset: a water quality dataset for the ?Internet of Things: Online Anomaly Detection for Drinking Water Quality" competition at the Genetic and Evolutionary Computation Conference 2018 Kyoto Japan (2018). Steffen Moritz Frederik Rehbach Sowmya Chandrasekaran Margarita Rebolledo and Thomas Bartz-Beielstein. 2018. GECCO Industrial Challenge 2018 Dataset: a water quality dataset for the ?Internet of Things: Online Anomaly Detection for Drinking Water Quality" competition at the Genetic and Evolutionary Computation Conference 2018 Kyoto Japan (2018).
  18. Adam Paszke , Sam Gross , Francisco Massa , Adam Lerer , James Bradbury , Gregory Chanan , Trevor Killeen , Zeming Lin , Natalia Gimelshein , Luca Antiga , 2019 . Pytorch: An imperative style, high-performance deep learning library. Advances in neural information processing systems , Vol. 32 (2019). Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al. 2019. Pytorch: An imperative style, high-performance deep learning library. Advances in neural information processing systems, Vol. 32 (2019).
  19. Mathias Perslev , Michael Jensen , Sune Darkner , Poul Jørgen Jennum , and Christian Igel . 2019 . U-time: A fully convolutional network for time series segmentation applied to sleep staging . Advances in Neural Information Processing Systems , Vol. 32 (2019). Mathias Perslev, Michael Jensen, Sune Darkner, Poul Jørgen Jennum, and Christian Igel. 2019. U-time: A fully convolutional network for time series segmentation applied to sleep staging. Advances in Neural Information Processing Systems, Vol. 32 (2019).
  20. Lukas Ruff , Robert Vandermeulen , Nico Goernitz , Lucas Deecke , Shoaib Ahmed Siddiqui , Alexander Binder , Emmanuel Müller , and Marius Kloft . 2018 . Deep one-class classification . In International conference on machine learning. PMLR, 4393--4402 . Lukas Ruff, Robert Vandermeulen, Nico Goernitz, Lucas Deecke, Shoaib Ahmed Siddiqui, Alexander Binder, Emmanuel Müller, and Marius Kloft. 2018. Deep one-class classification. In International conference on machine learning. PMLR, 4393--4402.
  21. David MJ Tax and Robert PW Duin . 2004. Support vector data description. Machine learning , Vol. 54 , 1 ( 2004 ), 45--66. David MJ Tax and Robert PW Duin. 2004. Support vector data description. Machine learning, Vol. 54, 1 (2004), 45--66.
  22. Jiehui Xu , Haixu Wu , Jianmin Wang , and Mingsheng Long . 2021. Anomaly transformer: Time series anomaly detection with association discrepancy. arXiv preprint arXiv:2110.02642 ( 2021 ). Jiehui Xu, Haixu Wu, Jianmin Wang, and Mingsheng Long. 2021. Anomaly transformer: Time series anomaly detection with association discrepancy. arXiv preprint arXiv:2110.02642 (2021). · arXiv
  23. Chin-Chia Michael Yeh , Yan Zhu , Liudmila Ulanova , Nurjahan Begum , Yifei Ding , Hoang Anh Dau , Diego Furtado Silva , Abdullah Mueen , and Eamonn Keogh . 2016 . Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets . In 2016 IEEE 16th international conference on data mining (ICDM). Ieee, 1317--1322 . Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Hoang Anh Dau, Diego Furtado Silva, Abdullah Mueen, and Eamonn Keogh. 2016. Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets. In 2016 IEEE 16th international conference on data mining (ICDM). Ieee, 1317--1322.
  24. Zahra Zamanzadeh Darban , Geoffrey I Webb , Shirui Pan , Charu C Aggarwal , and Mahsa Salehi . 2022. Deep Learning for Time Series Anomaly Detection: A Survey. arXiv e-prints ( 2022 ), arXiv-2211. Zahra Zamanzadeh Darban, Geoffrey I Webb, Shirui Pan, Charu C Aggarwal, and Mahsa Salehi. 2022. Deep Learning for Time Series Anomaly Detection: A Survey. arXiv e-prints (2022), arXiv-2211.
  25. Chaoning Zhang , Kang Zhang , Chenshuang Zhang , Trung X Pham , Chang D Yoo , and In So Kweon . 2022 b. How does SimSiam avoid collapse without negative samples? a unified understanding with self-supervised contrastive learning . Proceedings of International Conference on Learning Representations (ICLR) (2022). Chaoning Zhang, Kang Zhang, Chenshuang Zhang, Trung X Pham, Chang D Yoo, and In So Kweon. 2022b. How does SimSiam avoid collapse without negative samples? a unified understanding with self-supervised contrastive learning. Proceedings of International Conference on Learning Representations (ICLR) (2022).
  26. 10.1109/ICDM50108.2020.00093 · DOI
  27. Bo Zong , Qi Song , Martin Renqiang Min , Wei Cheng , Cristian Lumezanu , Daeki Cho , and Haifeng Chen . 2018 . Deep autoencoding gaussian mixture model for unsupervised anomaly detection . In International conference on learning representations. Bo Zong, Qi Song, Martin Renqiang Min, Wei Cheng, Cristian Lumezanu, Daeki Cho, and Haifeng Chen. 2018. Deep autoencoding gaussian mixture model for unsupervised anomaly detection. In International conference on learning representations.

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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)
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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