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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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Self-citationTian, Y., Xu, J., Zuo, J., & Duan, L. (2024) · IEEE International Conference on Tools with Artificial Intelligence (ICTAI) · doi:10.1109/ictai62512.2024.00086
“Compared to recent SOTA methods (AnomalyTrans [7], DCdetector [2] and DiffAD [5]), our AgileAD demonstrates either superior or competitive performance.”
InfluentialAnomaly Detection for Wind Turbine Based on Contrastive Learning and Causal Feature Selection
Song, J., Yang, Y., Wu, Y., Zhang, Y., Liu, C., Zhong, F., Zhang, M., & Zhang, Z. (2025) · Lecture notes in electrical engineering · doi:10.1007/978-981-96-2046-3_8
Self-citationDeep Time Series Anomaly Detection with Local Temporal Pattern Learning
Li, Y., Wang, Y., Xu, H., & Zhou, X. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10889109
“…following 20 competing methods, including LOF [33], OCSVM [34], ECOD [35], iForest [36], DIF [27], DSVDD [37], COUTA [31], GDN [10], LSTM-NDT [11], LSTM-ED [38], Omni [7], MSCRED [39], USAD [5], TcnED [40], Anomaly-former [9], TranAD [8], NeuTral [23], TS-CP2 [41], NCAD [22] and DCdetector [42].”
TSLA: A Multi-Task Time Series Language Model
Fang, L., Chen, Y., Yu, W., Liu, Y., Tang, L.-A., Torvik, V. I., & Chen, H. (2025) · ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp49660.2025.10888390
“Many groundbreaking studies have focused on time series perspective, like time series classification [38], forecasting [6, 13], and anomaly detection [37].”
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“Contrastive representation learning is one promising method that has recently been used for time-series anomaly detection [7] and has gained popularity in computer vision tasks [8,9].”
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“…for multivariate time series; InterFusion [31], utilizing multi-scale feature fusion; THOC [32], using hierarchical temporal representation learning; AnomalyTrans [33], based on Transformer architecture with association discrepancy mechanisms; DCdetector [34], employing dual encoder architecture.”
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“Building upon reconstruction methods, in recent years, numerous approaches have attempted to capture the correlations between metrics to enhance feature representation and improve the model’s ability to model normal data [10]–[13].”
InfluentialTime Series Anomaly Detection Based on Contrastive Learning
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“Time Series Anomaly Detection (TSAD) is crucial in modern Web services and Industrial IoT, widely applied in server monitoring, sensor data collection, and log analysis [1].”
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Li, Y., Wen, Z., Lin, M., Zhu, J., Liu, S., Hu, Z., & Zhu, M. (2025) · International Conference on Advanced Algorithms and Neural Networks (AANN) · doi:10.1109/aann66429.2025.11257686
“Existing deep methods can be classified into three types: prediction-based [7], contrastive-based [8], and reconstruction-based [2].”
Zhao, D., Li, H., Xu, L., Wang, Z., & Peng, H. (2025) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2025.3639899
“Unlike previous works that solely rely on the traditional attention mechanism for sequence modeling [6], [10], we employ a MLP to flatten the attention mechanism.”
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Li, Y., Zhou, Z., Sedlak, B., & Dustdar, S. (2025) · IEEE International Conference on Web Services (ICWS) · doi:10.1109/icws67624.2025.00096
“DCdetector [13] focuses on time series anomaly detection via contrastive representation learning, primarily targeting attribute-level anomalies and lacks structural modeling.”
Time Series Anomaly Detection via LLM-Based Reconstruction and Contrastive Representation Learning
Zhou, Y., Zheng, W., & Wang, H. (2025) · Lecture notes in computer science · doi:10.1007/978-981-95-3462-3_19
Time Series Anomaly Detection and Visual Analysis Method Based on Multi-Scale Fusion
Zhang, P., & Chen, Y. (2026) · Communications in computer and information science · doi:10.1007/978-981-95-4966-5_19
CLASP: Contrastive Learning with Asymmetric Stochastic Patching for Anomaly Detection
Xia, Z., Xu, K., Qiu, C., Zhou, Y., & Hang, H. (2025) · IEEE International Conference on Parallel and Distributed Systems (ICPADS) · doi:10.1109/icpads67057.2025.11322985
“DCdetector [5] employs a dual-attention mechanism for contrastive learning, enhancing the separation between normal and anomalous patterns.”
Zong, J., Lu, J., Guo, Y., Xu, X., Hou, H., & Liang, C. (2025) · International Conference on Smart Grid and Energy Internet (SGEI) · doi:10.1109/sgei68093.2025.11331059
MSADNet: Multi-Scale Adaptive Dual Attention Network for Multivariate Time Series Anomaly Detection
Lyu, X., Yang, Y., Zhang, C., & Huang, Y. (2025) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc58881.2025.11342760
“The baselines considered for comparison include: DAGMM [24], LSTM-VAE [2], BeatGAN [13], OmniAnomaly [11], USAD [9], TranAD [5], AnomalyTrans [4], DCdetector [25], and SiET [26].”
Self-citationFreTime:Dual-Branch Frequency-Time Representation Learning for Time Series
Zhu, J., Zuo, E., Wang, W., Sun, R., Yan, Z., Chen, C., Chen, C., & Lv, X. (2025) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc58881.2025.11342759
“Related works including DCDetec-tor [5], Pathformer [4], OneNet [20], Contiformer [21], and ST-GNN [22] demonstrate task-specific strengths but face challenges in generalization and efficiency.”
MARPP: Memory-Augmented Reconstruction for Pseudo-Periodic Time Series Anomaly Detection
Zuo, Y., Qu, M., Shang, Z., Zhang, L., & Gao, X. (2026) · International Conference on Ubiquitous Information Management and Communication (IMCOM) · doi:10.1109/imcom69009.2026.11360789
“To benchmark our model MARPP with existing methods, we have selected the following ten baselines for the purposes of evaluation: LOF [5], OCSVM [7], DAGMM [8], THOC [33], BeatGAN [15], OmniAnomaly [13], Anom-alyTransformer [17], DCDetector [32], AMSL [25] and MEMTO [26].”
InfluentialKnowledge-Guided Dual-View Self-Supervised Learning for Robust FHR Signal Classification
Peng, Y., Lu, Y., Shi, S., & Wang, J. (2025) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm66473.2025.11357104
Multivariate Time Series Anomaly Detection Based on Time-Frequency Combination
Huang, Z., & Wu, Z. (2025) · International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD) · doi:10.1109/icsmd67131.2025.11365385
“DCdetector [9] uses contrastive learning in anomaly detection to create an embedding space where normal data samples are close together while anomalies are far apart.”
Multiscale Spatio-Temporal Graph Convolutional Network for UAV Anomaly Detection
Hu, G., Zhou, Z., Dong, Z., Chen, S., Li, Y., & Li, Z. (2026) · IEEE Internet of Things Journal · doi:10.1109/jiot.2026.3661961
Zeng, Z., Wang, X., Xu, L., Ye, X., Hu, J. W., Kumbhar, F. H., & Dev, K. (2026) · IEEE Internet of Things Journal · doi:10.1109/jiot.2026.3663198
“In addition to extending these capabilities, the DCdetector [19] introduces a dual attention contrastive learning framework to better distinguish between normal and abnormal patterns.”
Advancing CNNs for Time Series Anomaly Detection: A Multi-Scale Contrastive Learning Framework
Gu, H., Xiong, H., Dai, Y., Xie, Z., He, Z., Wang, X. S., & Li, J. (2026) · IEEE International Conference on Big Data and Smart Computing (BigComp) · doi:10.1109/bigcomp68355.2026.00013
“…training and reconstruction losses; AnomalyTransformer [12], which uses a prior-association mechanism for better inter-pretability; DCdetector [14], which incorporates contrastive learning on patch-based views; SensitiveHUE [13], which adapts to various time series dynamics; and CATCH [18],…”
Anomoly Detection in Weather Patterns and Real-Time Web Application for Forecasting
Benita, J., Tharun, P., Venkat, N. S., Nayeem, S. M., & Bharath, T. (2025) · OITS International Conference on Information Technology (OCIT) · doi:10.1109/ocit66168.2025.11400002
SpectraBayes: Exploring Traffic Series Reconstruction in Frequency Domain for Anomaly Detection
Li, R., Tang, D., Wang, F., Zhu, X. Y., & Cao, X. (2026) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2026.3667876
“Recent studies suggest that merely increasing model complexity does not consistently improve anomaly detection [28], motivating approaches that explicitly capture anomaly-specific features, including inconsistency [29] and association discrepancies [7].”
Golchin, B., & Rekabdar, B. (2025) · IEEE International Conference on Cognitive Machine Intelligence (CogMI) · doi:10.1109/cogmi67134.2025.00034
Yang, C., Li, X., Xu, K., Lin, X., & Fu, G. (2026) · Scientific Reports · doi:10.1038/s41598-026-43236-x
“In addition, contrastive learning– based methods (DCdetector 9 ) and SimAD 10 , diffusion-based models (D3R 41 ), and large language model–based approaches using GPT-2 Adapter 42 are also considered.”
Fan, Y., Lin, W., Chen, K., Wang, X., Yang, J., & Qiao, Y. (2026) · IEEE Internet of Things Journal · doi:10.1109/jiot.2026.3674919
“…methods (CrossAD [56], SARAD [20], TFMAE [57], LSTM-VAE [58], and CATCH [40]), a density-estimation approach (MTGFlow [30], [45]), contrastive learning-based methods (DCdetector [35], CAROTS [31], and the calibrated one-class method COUTA [59]), and a causality-driven method (GCAD [21]).”
CrashAlertNet for Predicting Stock Market Crashes Using Deep Sequential Anomaly Detection Models
Sharma, D., Bhasin, N. K., Kadyan, S., Muda, I., Sreeram, G., & Saravanakumar, R. (2025) · International Conference on Artificial Intelligence and Quantum Computation-Based Sensor Application (ICAIQSA) · doi:10.1109/icaiqsa67794.2025.11440422
“Traditional forecasting approaches, such as statistical models and conventional machine learning algorithms, are often designed to capture normal market fluctuations[3].”
Evaluation of AI Methods in Real-World Cloud Environments
Giri Babu, K., Bhavana, J., & Rambabu, B. (2025) · International Conference on Recent Trends in Electrical, Electronics and Computing Technologies (ICRTEECT) · doi:10.1109/icrteect67512.2025.11448702
“Yu et al. [12] [14] present DCdetector, a Dual Attention Contrastive framework that uses two attention modules (temporal and feature) to enhance anomaly localization.”
Xing, H.-J., Qian, T., & Li, C.-G. (2026) · Knowledge-Based Systems · doi:10.1016/j.knosys.2026.116955
Gao, H., Xin, R., Chen, P., Zeng, S., Li, X., You, P., & Zhao, Z. (2026) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-032-21171-2_29
You, P., Chen, P., Xia, Y., Li, X., Bian, A., Zeng, S., & Gao, H. (2026) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-032-21171-2_30
Miao, Y., Wang, Q., Feng, T., Dong, R., Chang, P., & Wu, W. (2026) · Nondestructive Testing And Evaluation · doi:10.1080/10589759.2026.2653117
High-Dimensional Time Series Anomaly Detection over Heterogeneous Domains
Jacob, V. (2024) · theses.fr (ABES) · doi:10.70675/1e3d1cc8z7d7dz4429zaf3czf2f0d6c802ec
Semi-supervised deep learning for robust anomaly detection in smart manufacturing systems
Le, T. (2024) · theses.fr (ABES) · doi:10.70675/19d23228zf030z4df7za3abz596d36577818
A Neural Network-Enhanced Kalman Filter for Time Series Anomaly Detection in Cyber-Physical Systems
Ma, Z., Xu, W., Hao, Z., Yu, K., & Wu, X. (2026) · Sensors · doi:10.3390/s26082332
“DCdetector [34]: Employs dual-attention asymmetric design with contrastive learning for permutation-invariant representations.”
Benefit From Noise: Detecting Time-Series Anomaly by Distinguishing Prior and Posterior Noises
Xing, S., Niu, J., Ren, T., & Rodrigues, J. J. P. C. (2026) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2026.3682883
Anomaly-Aware Association Discrepancy for Temporal Anomaly Detection
Li, F., He, X., Wang, J., & Zhao, X. (2026) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp55912.2026.11460565
SALM: Stochastic Attention with Learnable Memory for Multivariate Time Series Anomaly Detection
Yang, Z., Chang, B., Huang, D., He, Q., Song, W., & Du, Y. (2026) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp55912.2026.11463490
BAMoE: Bi-Attention Synergy With Expert Routing For Time Series Anomaly Detection
Li, H., Xu, L., & Zhao, D. (2026) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp55912.2026.11462394
Li, F., Nie, J., & Li, Z. (2026) · Research Square · doi:10.21203/rs.3.rs-9352751/v1
Beyond the Window: Region-Based Anomaly Localization Network for Time Series Anomaly Detection
Ham, S., Jung, H., & Kim, E. Y. (2026) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp55912.2026.11461423
Chen, J., He, Q., Zhong, Y., Rao, Y., Qin, X., & Wang, L. (2026) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp55912.2026.11462720
Peng, S., Han, Y., Liu, L., Li, J., Li, R., Gu, Z., Liu, J., & Chu, X. (2026) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2026.3683936
“Advancing toward attention mechanisms, Yang et al. [29] introduced DCdetector, which applies dual-attention contrastive learning to enhance discriminative representations.”
LLM-Enhanced Reinforcement Learning for Time Series Anomaly Detection
Golchin, B., Rekabdar, B., & Justo, D. (2026) · IEEE International Conference on Semantic Computing (ICSC) · doi:10.1109/icsc67292.2026.00016
DualCTAD: Causal-Temporal Dual-Attention for Multivariate Time Series Anomaly Detection
Shang, S., Yang, J., Wang, J., & Xu, Z. S. (2026) · International Conference on Electronics Technology and Artificial Intelligence (ETAI) · doi:10.1109/etai68332.2026.11485430
“As observed from Table II, ten baselines include InterFusion [2], OmniAnomaly [3], MTAD-GAT [17], TranAD [18], Anomaly Trasformer [4], DCDetector [6], MAD-QSM [5], DiffAD [19], DiG [20], and DConAD [21], our model achieves the best F1 performance across three benchmark datasets (SMD, PSM and SMAP),…”
Mu, Y., Kong, Z., Zhang, M., Guo, H., & Wang, L. (2026) · Applied Intelligence · doi:10.1007/s10489-026-07248-4
卢芮伊, Yuan, W., Zhou, X., & Lan, S. (2026) · Lecture notes in computer science · doi:10.1007/978-981-95-7084-3_45
Chen, J., He, Q., Zhong, Y., Qin, X., & Wang, L. (2026) · Lecture notes in computer science · doi:10.1007/978-981-92-0372-7_36
MS-STNet: Multi-scale Spatio-Temporal Modeling for Multivariate Time Series Anomaly Detection
Wang, M., Li, J. F., Cui, Y., Li, F., Zhang, J. Z., & Wang, L. (2026) · Lecture notes in computer science · doi:10.1007/978-981-92-0372-7_37
TFD-CAD: Collaborative time-frequency dual-branch framework for time series anomaly detection
Chen, J., Liu, P., Li, S. Y., Li, P., Li, X., & Hua, R. (2026) · Journal of King Saud University - Computer and Information Sciences · doi:10.1007/s44443-026-00805-4
CSIDA: contextual structure-invariant domain adaptation for multivariate time-series
Xie, H., Ran, T., Wu, J., Yuan, L., & Zhang, X. (2026) · The Journal of Supercomputing · doi:10.1007/s11227-026-08541-2
Liu, Y., Liu, X., Yao, J., Wang, Y., Guo, B., & Wang, Y. (2026) · Journal of Computer and Communications · doi:10.4236/jcc.2026.145002
Edge AI-based Anomaly Behavior Analysis for Industrial Control Systems
Yang, Z., Shi, Q., Lin, Y.-Z., Salehi, S., Satam, P., & Shao, S. (2026) · International Symposium on Quality Electronic Design (ISQED) · doi:10.1109/isqed69900.2026.11534670
Yao, Y., & Yang, Z.-X. (2026) · Advanced Engineering Informatics · doi:10.1016/j.aei.2026.104827
Hu, G., Zhou, Z., Zhao, Y., Dong, Z., Li, Z., Wu, Y., & Li, Z. (2026) · Information Processing & Management · doi:10.1016/j.ipm.2026.104948
Adaptive Unsupervised Anomaly Detection for Low-Quality Multivariate Time-Series Data
Liu, J., Yang, D., Wang, J., Gao, H., & Li, J. F. (2026) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2026.3700672
“6) DCdetector [45]: DCdetector is a multi-scale dual attention contrastive representation learning model.”
DMSA: a dual-mask spatiatemporal autoencoder for multivariate time series anomaly detection
Bao, Y. (2026) · Applied Intelligence · doi:10.1007/s10489-026-07310-1
Han, D., Guo, R., Yu, X., & Ding, H. (2026) · Electronics · doi:10.3390/electronics15122503
Dai, Y., Quinn, B., Kearney, F., Liu, W., Spence, I. T. A., Rafferty, K., & Wang, H. (2026) · Information Processing & Management · doi:10.1016/j.ipm.2026.104961
Zhang, A., Shen, A., & Zhang, T. (2026) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2026.111290
TwinTCN: Correlation-Gated Temporal Convolutions with Twin Encoders
Ro, Y.-C., & Woo, S. S. (2026) · ACM Symposium on Applied Computing · doi:10.1145/3748522.3779816
Microservices Anomaly Detection Based on Adaptive Wavelet Convolutional Networks and Transformer
Tang, W., Jiang, C., Wang, G., & Liu, J. J. (2026) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2026.3701934
Zhang, X., Zhang, L., Wang, P., Gao, H., & Cao, Z. (2026) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-026-03180-8
Tu, J., Zhang, Z., Wu, J., & Wu, S. (2026) · Signal Image and Video Processing · doi:10.1007/s11760-026-05465-5
“Despite the significant difference in their anomaly rates, with NIPS-TS-SWAN having the highest anomaly rate of 32.6% and NIPS-TS-GECCO having the lowest anomaly rate of 1.1%, the EM-BMGAN model still demonstrates excellent performance that outperforms many existing methods[19, 20, 31–34].”
InfluentialMulti-Scale Spatiotemporal Dependency Learning for Anomaly Detection in Multivariate Time Series
Duan, S., Xin, L., Liu, Y., & Zhang, D. (2026) · International Conference on Image, Signal Processing and Pattern Recognition (ISPP) · doi:10.1109/ispp69262.2026.11542860
“We have chosen seven time series anomaly detection meth-ods for comparison, encompassing models such as Anoma-lyTransformer [12], and DCdetector [13], which are based on transformer architectures.”
Guo, J., Kong, Y., Wang, J., Li, S., & Ren, L. (2026) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2026.3705659
“Following the Anomaly Transformer, DCdetector [9] introduced time series patch methods for anomaly detection.”
Buckelew, J., Talusan, J. P., Sivaramakrishnan, V., Mukhopadhyay, A., Srivastava, A. K., & Dubey, A. (2026) · ACM/IEEE International Conference on Cyber-Physical Systems (ICCPS) · doi:10.1109/iccps68698.2026.00032
Hu, P., Li, D., Yang, Q., Wang, W., & Ba, W. (2026) · Chinese Control and Decision Conference (CCDC) · doi:10.1109/ccdc69976.2026.11560058
“DCdetector [15] constructs permutation-invariant and patch-wise contrastive mechanisms to amplify representation differences.”
Cloud–Edge Collaborative Anomaly Detection for Water Treatment Systems
Yang, Z., Shi, Q., & Shao, S. (2026) · Lecture notes in computer science · doi:10.1007/978-981-92-1947-6_43
Multi-Scale Spatiotemporal Dependency Learning for Anomaly Detection in Multivariate Time Series
Duan, S., Xin, L., Liu, Y., & Zhang, D. (2026) · International Conference on Artificial Intelligence and Industrial Technology Applications (AIITA) · doi:10.1109/aiita69518.2026.11567117
“We have chosen seven time series anomaly detection meth-ods for comparison, encompassing models such as Anoma-lyTransformer [12], and DCdetector [13], which are based on transformer architectures.”
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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