The DOI matches its Crossref record; 61 of the 61 references it cites were found in a registry or an index.
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“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]
The cited source says what the claim attributes to it.
In the citing paper
“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]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
Advances in neural information processing systems 7
(1996) · Computers & Mathematics with Applications · doi:10.1016/s0898-1221(96)90463-0 · Cited by 14,759
Momentum Contrast for Unsupervised Visual Representation Learning
He, K., Fan, H., Wu, Y., Xie, S., & Girshick, R. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00975 · Cited by 13,347
LOF: identifying density-based local outliers
Breunig, M. M., Kriegel, H.-P., Ng, R. T., & Sander, J. (2000) · Proceedings of the 2000 ACM SIGMOD international conference on Management of data · doi:10.1145/342009.335388 · Cited by 4,149
Exploring Simple Siamese Representation Learning
Chen, X., & He, K. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.01549 · Cited by 3,725
Unsupervised Feature Learning via Non-parametric Instance Discrimination
Wu, Z., Xiong, Y., Yu, S. X., & Lin, D. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00393 · Cited by 3,664
Box, G. E. P., & Pierce, D. A. (1970) · Journal of the American Statistical Association · doi:10.2307/2284333 · Cited by 3,212
DeepAR: Probabilistic forecasting with autoregressive recurrent networks
Salinas, D., Flunkert, V., Gasthaus, J. A., & Januschowski, T. (2019) · International Journal of Forecasting · doi:10.1016/j.ijforecast.2019.07.001 · Cited by 3,094
Robust Anomaly Detection for Multivariate Time Series through Stochastic Recurrent Neural Network
Su, Y., Zhao, Y., Niu, C., Liu, R., Sun, W., & Pei, D. (2019) · Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining · doi:10.1145/3292500.3330672 · Cited by 1,804
Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic Thresholding
Hundman, K., Constantinou, V., Laporte, C., Colwell, I., & Soderstrom, T. (2018) · Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining · doi:10.1145/3219819.3219845 · Cited by 1,771
Research works printed but not linked: 27
- 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.
- OD Anderson . 1976. Time-Series . 2 nd edn. OD Anderson. 1976. Time-Series. 2nd edn.
- 10.1080/01621459.1970.10481180 · DOI
- 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
- 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.
- 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.
- 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.
- 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).
- 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.
- 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.
- 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.
- 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.
- 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.
- 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).
- 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).
- 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.
- 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).
- 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).
- 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).
- 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.
- 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.
- 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
- 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.
- 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.
- 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).
- 10.1109/ICDM50108.2020.00093 · DOI
- 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.
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.”
BackgroundWang, F., Jiang, Y., Zhang, R., Wei, A., Xie, J., & Pang, X. (2025) · Sensors · doi:10.3390/s25010190 · Cited by 119
“DCdetector [13] employs a contrastive learning framework to learn representations of time series data.”
A Survey on Time-Series Pre-Trained Models
Ma, Q., Liu, Z., Zheng, Z., Huang, Z., Zhu, S., Yu, Z., & Kwok, J. T.-Y. (2024) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2024.3475809 · Cited by 133
“The TS2Vec [77], TimesNet [165], GPT4TS [99], DCdetector [145], and the benchmark methods SPOT [175], DSPOT [175], LSTM-VAE [176], DONUT [177], Spectral Residual (SR) [167], and Anomaly Transformer (AT) [178] are employed as comparison methods.”
BackgroundMethodDUET: Dual Clustering Enhanced Multivariate Time Series Forecasting
Qiu, X., Wu, X., Lin, Y. D., Guo, C., Hu, J., & Yang, B. (2025) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3690624.3709325 · Cited by 140
HDMixer: Hierarchical Dependency with Extendable Patch for Multivariate Time Series Forecasting
Huang, Q., Shen, L., Zhang, R., Cheng, J., Ding, S., Zhou, Z., & Wang, Y. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i11.29155 · Cited by 81
GinAR+: A Robust End-to-End Framework for Multivariate Time Series Forecasting With Missing Values
Yu, C., Wang, F., Shao, Z., Qian, T., Zhang, Z., Wei, W., An, Z., Wang, Q., & Xu, Y. (2025) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2025.3569649 · Cited by 67
“The main role of contrastive learning is to help existing models enhance the difference in representations of negative data pairs and the similarity of representations of positive data pairs [54], [55].”
Decomposition-based multi-scale transformer framework for time series anomaly detection
Zhang, W., & Luo, C. (2025) · Neural Networks · doi:10.1016/j.neunet.2025.107399 · Cited by 61
“Specifically, we focus on a subset of the UCR univariate time series (Yang et al., 2023), examining timestamps from 2000 to 5000.”
Temporal-Frequency Masked Autoencoders for Time Series Anomaly Detection
Fang, Y., Xie, J., Zhao, Y. L., Lü, C., Gao, Y., & Zheng, K. (2024) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde60146.2024.00099 · Cited by 62
“Conversely, DCdetec-tor [43] minimizes distances between different patch size-based association representations to avoid prior knowledge.”
BackgroundPatchAD: A Lightweight Patch-Based MLP-Mixer for Time Series Anomaly Detection
Zhong, Z., Yu, Z., Yang, Y., Wang, W., Yang, K., & Chen, C. L. P. (2025) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2025.3596745 · Cited by 62
“Detailed explanations for why these metrics are used can be found in Appendix D2, available online.”
InfluentialSelf-citationWen, M., Chen, Z., Xiong, Y., & Zhang, Y. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.129024 · Cited by 39
DACAD: Domain Adaptation Contrastive Learning for Anomaly Detection in Multivariate Time Series
Darban, Z. Z., Yang, Y., Webb, G. I., Aggarwal, C. C., Wen, Q., Pan, S., & Salehi, M. (2025) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2025.3569909 · Cited by 46
“DCdetector 14 [52] is distinctive for its use of a dual attention asymmetric design combined with contrastive learning.”
InfluentialSelf-citationGao, Z., Jiang, W., Wu, J., Wang, Y., & Zhu, H. (2025) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2025.112611 · Cited by 32
Gao, H., Xin, R., Chen, P., Li, X., Lü, N., & You, P. (2025) · Journal of Cloud Computing Advances Systems and Applications · doi:10.1186/s13677-025-00766-5 · Cited by 36
Yahya, M. A., Moya, A. R., & Ventura, S. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-025-11401-9 · Cited by 44
Ma, S., Nie, J., Guan, S., He, Z., & Gao, M. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3443910 · Cited by 25
“14) DCdetector [50]: A dual-attention contrastive representation learning method for time-series anomaly detection.”
Advances in Time-Series Anomaly Detection: Algorithms, Benchmarks, and Evaluation Measures
Paparrizos, J., Boniol, P., Liu, Q., & Palpanas, T. (2025) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3711896.3736565 · Cited by 34
SimAD: A Simple Dissimilarity-Based Approach for Time-Series Anomaly Detection
Zhong, Z., Yu, Z., Xi, X., Xu, Y., Cao, W., Yang, Y., Yang, K., & You, J. (2025) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2025.3590220 · Cited by 29
Self-citationGao, R., Chen, Z., Wu, X., Yu, Y., & Zhang, L. (2024) · Cluster Computing · doi:10.1007/s10586-024-04707-w · Cited by 24
Rethinking self-supervised learning for time series forecasting: A temporal perspective
Zhao, S., Zhou, X., Jin, M., Hou, Z., Yang, C., Li, Z., Wen, Q., Wang, Y., Wen, Y., & Yuan, X. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.112652 · Cited by 24
Self-citationMulGad: Multi-granularity contrastive learning for multivariate time series anomaly detection
Xiao, B.-W., Xing, H.-J., & Li, C.-G. (2025) · Information Fusion · doi:10.1016/j.inffus.2025.103008 · Cited by 24
Xie, Y., Zhang, H., & Babar, M. A. (2025) · International World Wide Web Conference (WWW) · doi:10.1145/3696410.3714941 · Cited by 21
Joint Selective State Space Model and Detrending for Robust Time Series Anomaly Detection
Chen, J., Tan, X., Rahardja, S., Yang, J., & Rahardja, S. (2024) · IEEE Signal Processing Letters · doi:10.1109/lsp.2024.3438078 · Cited by 23
“The three parameters Δ , B , C in Algorithm 1 could be computed as: where Softplus( · ) denotes the Softplus activation function, The AMA module then took x SSM as input and performed trend removal through average pooling:”
MethodChen, L., Xu, Y., Li, M., Hu, B., Guo, H., & Liu, Z. (2025) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2025.3538127 · Cited by 20
A lightweight All-MLP time–frequency anomaly detection for IIoT time series
Chen, L., Cao, X., He, T., Xu, Y., Liu, X., & Hu, B. (2025) · Neural Networks · doi:10.1016/j.neunet.2025.107400 · Cited by 20
Li, M., Liu, K., Chen, H., Bu, J., Wang, H., & Wang, H. (2025) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3690624.3709266 · Cited by 28
Unraveling the ‘Anomaly’ in Time Series Anomaly Detection: A Self-supervised Tri-domain Solution
Sun, Y., Pang, G., Ye, G., Chen, T., Hu, X., & Yin, H. (2024) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde60146.2024.00080 · Cited by 28
“Beyond this, several studies have explored the potential of view generation, crafting multiple contrasting perspectives through multi-scale augmentations [38], [69], [70].”
BackgroundMethodLu, R., Zheng, D., Yang, Q., Cao, W., & Zhu, C. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.110824 · Cited by 19
Deep anomaly detection for time series: A survey
Jia, X., Peng, X., Peng, W., Zhao, B., Li, H., & Shen, C. (2025) · Computer Science Review · doi:10.1016/j.cosrev.2025.100787 · Cited by 27
Fu, S., Gao, X., Li, B., Zhai, F., Lu, J., Xue, B., Yu, J., & Xiao, C. (2024) · Applied Soft Computing · doi:10.1016/j.asoc.2024.111671 · Cited by 22
CSCAD: Modeling cross-scale sequence correlations for multivariate time series anomaly detection
Lee, H., Zeng, Z., Qiu, Z., Zhu, W., & Xiao, R. (2025) · Information Processing & Management · doi:10.1016/j.ipm.2025.104315 · Cited by 17
RESTAD: Reconstruction and Similarity Based Transformer for Time Series Anomaly Detection
Ghorbani, R., Reinders, M. J. T., & Tax, D. M. J. (2024) · Machine learning for signal processing ... · doi:10.1109/mlsp58920.2024.10734755 · Cited by 17
“Subsequently, the normalized signal is segmented into non-overlapped sliding windows with a fixed length of 100 data points, a common setting based on previous related works [15, 9].”
MethodChen, L., Cao, X., He, T., Guo, H., & Liu, X. (2025) · Cluster Computing · doi:10.1007/s10586-025-05772-5 · Cited by 18
“Therefore, the discrepancy between local and global associations of Based on this mechanism, the ATrans model [7] and the DCd model [8] are proposed and demonstrate excellent performance.”
Zhao, M., Peng, H., Li, L., & Ren, Y. (2024) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2024.053765 · Cited by 18
He, Y., Chen, X., Miao, D., Zhang, H., Qin, X., Du, S., & Lu, P. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.126667 · Cited by 15
Contrastive learning for efficient anomaly detection in electricity load data
Choubey, M., Chaurasiya, R. K., & Yadav, J. S. (2025) · Sustainable Energy Grids and Networks · doi:10.1016/j.segan.2025.101639 · Cited by 15
Contrasting Estimation of Pattern Prototypes for Anomaly Detection in Urban Crowd Flow
Wang, Y., Luo, X., & Zhou, Z. (2024) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2024.3355143 · Cited by 14
“• DCdetector, as detailed in [25], stands as a state-of-the-art model for time series anomaly detection.”
BackgroundYao, M., Tao, D., Qi, P., & Gao, R. (2024) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2024.3486688 · Cited by 15
“Yang et al. [32] analyze representations in latent space, and point out that normal time series share strong correlations with other points.”
Self-supervised Spatial-Temporal Normality Learning for Time Series Anomaly Detection
Chen, Y., Xu, H., Pang, G., Qiao, H., Zhou, Y., & Shang, M. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-70365-2_9 · Cited by 15
AssureSense: A Framework for Enabling Sensor Fault Detection in Low-Power IoT Edge Devices
Attarha, S., & Förster, A. (2024) · IEEE Sensors Journal · doi:10.1109/jsen.2024.3451349 · Cited by 17
Zhang, J., Wang, X., Yang, Y., Miao, H., & Yang, S. R. (2025) · Journal of King Saud University - Computer and Information Sciences · doi:10.1007/s44443-025-00024-3 · Cited by 14
Self-citationTime Series Anomaly Detection Using Signal Processing and Deep Learning
Backhus, J., Rao, A. R., Venkatraman, C., & Gupta, C. (2025) · Applied Sciences · doi:10.3390/app15116254 · Cited by 15
Learning the feature distribution similarities for online time series anomaly detection
Fan, J., Yan, G., Zhang, X., Wang, Z., Wu, H., & Wu, J. (2024) · Neural Networks · doi:10.1016/j.neunet.2024.106638 · Cited by 14
Wang, H., & Zhang, H. (2025) · Mathematics · doi:10.3390/math13071209 · Cited by 15
Mamba Adaptive Anomaly Transformer with association discrepancy for time series
Sellam, A. Z., Benaissa, I., Ahmed, A. T., Patrono, L., & Distante, C. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.111685 · Cited by 14
Wang, Z., Liu, H., Kuang, L., Zhang, X., Chen, X., & Du, J. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.112775 · Cited by 12
Rectifying inaccurate unsupervised learning for robust time series anomaly detection
Chen, Z., Li, Z., Chen, X., Chen, X., Fan, H., & Hu, R. (2024) · Information Sciences · doi:10.1016/j.ins.2024.120222 · Cited by 12
Online Neural Networks for Change-Point Detection
Hushchyn, M., Arzymatov, K., & Деркач, Д. (2026) · Machine Learning · doi:10.1007/s10994-026-07000-6 · Cited by 12
A self-supervised contrastive change point detection method for industrial time series
Bao, X., Chen, L., Zhong, J., Wu, D., & Zheng, Y. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.108217 · Cited by 12
Chen, F., Zhang, Y., Qin, Z., Fan, L., Jiang, R., Liang, Y., Wen, Q., & Deng, S. (2024) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde60146.2024.00047 · Cited by 12
“Therefore, b) The figure shows F1 score of some SOTA methods: DCdetector [6], AnomalyTransformer [4], DVGCRN [5], OmniAnomaly [3], MSCRED [7], TranAD [8].”
BackgroundMethodInfluentialSelf-citationYao, M., Tao, D., Qi, P., & Gao, R. (2025) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2025.3536306 · Cited by 11
Li, Z., Guo, W., An, J., Wang, Q., Mei, Y., Juan, R., Wang, T., Li, Y., & Gao, Z. (2025) · IEEE Sensors Journal · doi:10.1109/jsen.2025.3543835 · Cited by 11
“Since anomalies typically occur consecutively in practice, like many previous works [2], [30], [34], we adopt the point-adjust strategy to finetune the metrics in this study.”
Chen, L., Liu, X., Zou, Y. Y., Tang, J., Liu, C., Hu, B., & Lv, M. (2025) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2025.3554286 · Cited by 11
“Note that the IIoT sensor signals X need to be instance normalized [17] before being processed by our model.”
Frequency-enhanced and decomposed transformer for multivariate time series anomaly detection
Li, S., Wang, Z., Wang, X., Yin, Z., & Yao, M. (2025) · Applied Intelligence · doi:10.1007/s10489-025-06441-1 · Cited by 10
MemMambaAD: Memory-augmented state space model for multivariate time series anomaly detection
Li, G., Ge, M., Wan, J., Han, D., Li, M., & Zhou, M. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.111308 · Cited by 10
Anomaly detection in medical via multimodal foundation models
Tang, Z. J., Tang, Z. J., & Wu, J. (2025) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2025.1644697 · Cited by 19
“Traditional approaches have primarily relied on rule-based systems, statistical models, and unsupervised learning algorithms such as one-class SVMs, autoencoders, and isolation forests (Yang et al., 2023).”
Multimodal Fusion Anomaly Detection Model for Agricultural Wireless Sensors
Zhou, Z. (2024) · Engineering Reports · doi:10.1002/eng2.13021 · Cited by 9
“[31] suggests that representation learning in anomalous situations may affect performance due to large anomaly loss, and therefore proposes a multi-scale dual attention contrastive representation learning model and trains the model using only contrastive loss to learn out a permutation invariant…”
Wang, X., Wang, L., & An, W. (2024) · Energy Economics · doi:10.1016/j.eneco.2024.107986 · Cited by 9
A Robust Framework for Evaluation of Unsupervised Time-Series Anomaly Detection
Güngör, O., Rios, A., Mudgal, P., Ahuja, N., & Rosing, T. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-78395-1_4 · Cited by 10
Multiscale Transformers With Contrastive Learning for UAV Anomaly Detection
Hu, G., Zhou, Z., Li, Z., Dong, Z., Fang, J., Zhao, Y., & Zhou, C. (2025) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2025.3571126 · Cited by 9
“Chen et al. [26] introduced Pathformer to address adaptive multiscale modeling, but it overlooks the neighborhood relationships among patches, which is important for learning the temporal correlations [27].”
Zhang, Z., Han, Y. M., & Geng, Z. (2025) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2025.3568019 · Cited by 9
“Currently, promising progress has been made in unsupervised time-series anomaly detection, including traditional methods and deep learning-based methods [4].”
Sánchez-Ferrera, A., Calvo, B., & Lozano, J. A. (2025) · ACM Computing Surveys · doi:10.1145/3770575 · Cited by 15
You, P., Chen, P., Li, X., & Bian, A. (2026) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2026.114104 · Cited by 9
Time-Series Multi-Instance Learning for Weakly Supervised Industrial Fault Detection
Liu, C., He, S., Li, S., Shi, Z., & Meng, W. (2025) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2024.3523591 · Cited by 8
ConvTrans-CL: Ocean time series temperature data anomaly detection based context contrast learning
Li, X., Chen, Y., Zhang, X., Peng, Y., Zhang, D., & Chen, Y. (2024) · Applied Ocean Research · doi:10.1016/j.apor.2024.104122 · Cited by 8
Zhang, S., Hu, X., & Liu, J. (2024) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme57554.2024.10687464 · Cited by 8
“Baseline We select several state-of-the-art anomaly detection methods as baselines: (1) Isolation Forest [5] (2) DCdetector [22] (3) LSTM-AE [23] (4) STAD-GAN [24] (5) Devised RNN (6) U-Transformer [25] (7) BTAD [26]”
MethodIntelligent deepwater energy development: Flow assurance monitoring and smart decision-making system
Wang, J., Yang, B., Jin, K., Zhang, L., Wang, L., Jiang, B., Yao, H., & Song, Y. (2025) · The Innovation Energy · doi:10.59717/j.xinn-energy.2025.100081 · Cited by 8
Kong, Y., Guo, J., Ren, L., Kang, G., Wang, Y., & Lü, J. (2025) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2025.3563540 · Cited by 8
“For instance, the computational cost of attention mechanism grows quadraticallywithsequencelength[7].”
InfluentialLi, C., Kiat, Y. C., Jing, J., & Long, C. (2025) · Expert Systems · doi:10.1111/exsy.70078 · Cited by 8
“We compare our proposed method T-VAE with multiple well-known methods, including MSCRED (Zhang et al. 2019), GDN (Deng and Hooi 2021), CAE-M, Tran-AD (Tuli et al. 2022), and DCdetector (Yang et al. 2023).”
Ding, X., Wang, J., Liu, Y., & Jung, U. (2025) · Applied Sciences · doi:10.3390/app15052861 · Cited by 11
Han, C. S., Kim, H., & Lee, K. M. (2025) · Sensors · doi:10.3390/s25082510 · Cited by 7
“DCdetector [39] utilized a two-tower architecture that simultaneously learns patch-wise and in-patch association representations to enhance the representation capability of normal data.”
InfluentialChen, L., Tang, J., Zou, Y. Y., Liu, X., Xie, X., & Deng, G. (2025) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2025.3565807 · Cited by 7
“Based on this work, several advanced TSAD models, such as AnomalyTrans (ATran) [21] and DCdetector (DCDet) [22], are proposed in the past two years, achieving high accuracy by capturing fine-grained short-and long-term contextual information.”
Tian, J., Li, M., Fang, L., & Chen, L. (2025) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2025.3588674 · Cited by 9
“…limitations of supervised and unsupervised methods, self-supervised anomaly detection methods emerged, which do not require labels and generate positive and negative samples through data augmentation to train the encoder, and the main methods include TimeAutoAD [45], CTAD [14], and DCdetector [15].”
InfluentialAnomaly Detection for UAV Flight Data via Reconstruction–Prediction Co-Learning Attention Network
Zhou, Z., Zhong, J., Zhang, Y., & Miao, Q. (2025) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2025.3593530 · Cited by 7
“3 highlights RPCA-Net’s architectural distinctions from representative AD models [23], [28].”
Jia, X., Cao, D., Zhuang, N., Peng, W., Zhao, B., Peng, X., Li, H., & Shen, C. (2025) · Neural Networks · doi:10.1016/j.neunet.2025.107903 · Cited by 7
Yan, P., Abdulkadir, A., Luley, P.-P., Rosenthal, M., Schatte, G. A., Grewe, B. F., & Stadelmann, T. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2307.05638 · Cited by 10
“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].”
BackgroundDetecting Multivariate Time Series Anomalies With Cascade Decomposition Consistency
Li, R., Liu, Z., Zhu, X., Li, L., & Cao, X. (2025) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2025.3547479 · Cited by 6
“Thus, exploring a generalizable anomaly representation of decomposed series is essential to enhance the discriminative ability of density-based methods [30].”
Contrastive Learning-Based Dual Autoencoder for Anomaly Detection in Loader Gearboxes
Lu, R., Zheng, D., Zhu, C., Cao, W., & Yang, Q. (2025) · IEEE Transactions on Circuits & Systems II Express Briefs · doi:10.1109/tcsii.2025.3590139 · Cited by 6
“Yang et al. [20] proposed a multi-scale dual attention contrastive representation learning AD model.”
Xing, Y., Wang, J., Cui, S., Liu, X., & Song, M. (2025) · Progress in Nuclear Energy · doi:10.1016/j.pnucene.2025.105982 · Cited by 6
Unsupervised Anomaly Detection in Multivariate Time Series across Heterogeneous Domains
Jacob, V., & Diao, Y. (2025) · Proceedings of the VLDB Endowment · doi:10.14778/3725688.3725699 · Cited by 9
A Contrastive-Learning-Based Abnormal Electricity Load Detection Method
Kou, L., Chen, L. F., Wang, M., Zhang, J., & Lin, Y. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3414304 · Cited by 6
“DCdetector [24] designs a dual attention structure based on contrastive learning to learn an alignment-invariant representation, enlarging the difference between normal and abnormal points and improving detection accuracy.”
BackgroundA multivariate time series anomaly detection method with Multi-Grain Dynamic Receptive Field
Chen, L., Gao, X., Liu, J., Zhang, Y., Diao, X., Wang, T., Lu, J., & Meng, Z. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.112768 · Cited by 7
Zhang, Y., Feng, J., Zhang, Q., Hu, J., & Zhang, W. (2025) · Digital Signal Processing · doi:10.1016/j.dsp.2025.105123 · Cited by 6
Li, J. W., Yu, Z., Jiang, Q., & Cao, Z. (2025) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2025.3548251 · Cited by 5
“Lastly, the model must exhibit high sensitivity to the distribution of anomalous data and give a clear distinction between outputs for normal and anomalous data [8], [9].”
Dai, Y., Spence, I. T. A., Rafferty, K., Quinn, B., Huang, J., & Wang, H. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3577931 · Cited by 5
“Previous studies globally modeled the entire time series, with contrastive learning applied only to two lengthy sequences from distinct sources [1], [10], [22], [24].”
InfluentialRobust and Explainable Detector of Time Series Anomaly via Augmenting Multiclass Pseudo-Anomalies
Obata, K., Matsubara, Y., & Sakurai, Y. (2025) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3711896.3737110 · Cited by 8
Yu, Y.-C., Ouyang, Y., & Lin, C.-A. (2025) · IEEE Access · doi:10.1109/access.2025.3610684 · Cited by 6
Zhang, W., Lin, X., Yu, W., Yao, G., Zhong, J., Li, Y., Han, R., Xu, S. Y., Shi, H., & Luo, C. (2025) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn64981.2025.11229301 · Cited by 5
“According to [9], contrastive learning with only positive samples can gain a non-trivial solution, preventing the model from collapsing and complicated reconstruction tasks.”
Yang, L., Yao, Y., Yang, D., Yang, W., & Hao, Y. (2026) · Knowledge-Based Systems · doi:10.1016/j.knosys.2026.115380 · Cited by 5
Xia, S., Sun, W., Zou, X., Chen, P., Ma, D., Xu, H., Chen, M., & Li, H. (2024) · PeerJ Computer Science · doi:10.7717/peerj-cs.2201 · Cited by 7
Yu, B., Yu, Y., Yang, Z., & Xiang, G. (2024) · Measurement Science and Technology · doi:10.1088/1361-6501/ad9495 · Cited by 5
Spatio-Temporal Anomaly Detection for 5G-Clusters: A Multi-Scale Fuzzy Contrastive Learning Approach
Luo, C., Li, Y., Ding, F., & Rui, S. (2025) · IEEE Transactions on Networking · doi:10.1109/ton.2025.3540091 · Cited by 5
“Recently, by possessing the ability to obtain discriminative features, contrastive learning was also applied in temporal data anomaly detection [15], [16].”
InfluentialTowards Unsupervised Time-Series Anomaly Detection for Virtual Cloud Networks
Ma, Z., Li, C., Zhang, K., & Tu, B. (2025) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2025.3561672 · Cited by 5
“We have introduced recent representative state-of-the-art methods [12], [13], [14], [15], [16], [17], [21], [22] in § V-A.”
InfluentialLi, K., Li, S., Li, Q., Jiao, Z., Fu, J., Gao, X., & Zhang, L. (2025) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2025.3587359 · Cited by 6
“In the past few years, contrastive learning [27] has achieved su ffi cient performance in dealing with unsupervised problems due to its outstanding contrastive optimization strategy, which has also been successfully applied in anomaly detection [28], [29].”
HYDRA: A Multi-Level Hierarchy-Driven Approach for Robust Anomaly Detection in Time Series
Huang, M., Liu, Q., Boniol, P., & Paparrizos, J. (2026) · Proceedings of the ACM on Management of Data · doi:10.1145/3802074 · Cited by 4
Diffusion Model in Normal Gathering Latent Space for Time Series Anomaly Detection
Han, J., Feng, S., Zhou, M. Q., Zhang, X., Ong, Y.-S., & Li, X. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-70352-2_17 · Cited by 4
DGAN: Flight Data Anomaly Detection Based on Dual-View Graph Attention Network
Yan, W., Li, X., Zheng, L., Shang, J., Wu, L., Liu, H., Li, X., & Qian, Y. X. (2025) · IEEE Sensors Journal · doi:10.1109/jsen.2025.3562842 · Cited by 4
“9) DCdetector [47]: A novel dual-attention asymmetric design is employed to create a permuted environment representation, and the model is trained solely with positive samples.”
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
