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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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Items 101–200 of 387
Dynamic graph contrastive learning for multivariate time series anomaly detection
Zhang, A., Chen, P., Gu, Y., & Zhang, T. (2025) · The Journal of Supercomputing · doi:10.1007/s11227-025-07455-9 · Cited by 4
Liu, M., Xia, Q., & Qiu, S. (2024) · Advances in Space Research · doi:10.1016/j.asr.2024.08.054 · Cited by 4
ALAD: A New Unsupervised Time Series Anomaly Detection Paradigm Based on Activation Learning
Ding, F., Li, B., Ben, X., Zhao, J., & Zhou, H. (2024) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2024.3453762 · Cited by 4
“…t ) 6: for all l = 1 , . . . , L do 7: //forward pass 8: //use learning rule to train weights layer by layer 10: end for 14: end for 15: e ← e + 1 16: end while 17: pressure measurements in pigs (Bleeding) [47], and a subset of the UCR dataset, which includes data from various natural sources [48].”
Zhao, H., Qiu, S., Liu, M., & Cao, X. (2024) · Science China Technological Sciences · doi:10.1007/s11431-024-2679-4 · Cited by 4
VisionGuard: Secure and Robust Visual Perception of Autonomous Vehicles in Practice
Han, X., Wang, H., Zhao, K., Deng, G., Xu, Y., Liu, H., Qiu, H., & Zhang, T. (2024) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3658644.3670296 · Cited by 4
MTAD-Kanformer: multivariate time-series anomaly detection via kan and transformer
Xie, X., Zheng, W., Xiong, S., & Wang, T. (2025) · Applied Intelligence · doi:10.1007/s10489-025-06650-8 · Cited by 4
Noise-Resilient Point-wise Anomaly Detection in Time Series Using Weak Segment Labels
Wang, Y., Cheng, H., Xiong, J., Wen, Q., Jia, H., Song, R., Zhang, L., Zhu, Z., & Liu, Y. (2025) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3690624.3709257 · Cited by 3
Self-citationCanonico, R., Lista, F., Navarro, A., Sperlí, G., & Vignali, A. (2025) · Computers & Security · doi:10.1016/j.cose.2025.104509 · Cited by 3
Frequency-Domain Anomaly Detection for Encrypted Traffic in Industrial Control Systems
Wu, Z., Li, H., Hua, Y., Tran, N. H. K., & Gan, H. (2025) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2025.3582394 · Cited by 3
Self-Supervised Disentangled Representation Learning for Time Series Anomaly Detection
Zhang, L., Zhu, J., Han, G., Jin, B., Wang, P., & Wei, X. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3588748 · Cited by 3
“Restrictions apply. methods [8] to get such informative representations.”
InfluentialiBACon: imBalance-Aware Contrastive Learning for Time Series Forecasting
Zhang, J., Dai, Q., & Ye, R. (2025) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2025.3589693 · Cited by 4
“For instance, in [33], the authors propose a dual-branch attention structure grounded in contrastive learning, hypothesizing that learning consistent representations for anomalies is challenging, whereas this is not the case for normal data.”
Time-Series Anomaly Detection for Sensor Data: Models, Metrics, and Methodologies—A Review
Sayyaf, M. I., Pascacio, P., Zhu, N., & Renaudin, V. (2025) · IEEE Sensors Journal · doi:10.1109/jsen.2025.3616395 · Cited by 5
“…that the papers that share their source code [3], [4], [5], [16], [20], [21], [22], [26], [38], [39], [40], [57], [59], [64], [67], [68], [72], [75], [78], [81], [82] demonstrated a significantly higher influence in the field, as evidenced by the citation metrics and adoption as benchmarks, as…”
Cui, K., Gao, L., Deng, X., Liu, S., Yi, L., He, S., & Lu, H. (2025) · IEEE Transactions on Networking · doi:10.1109/ton.2025.3614179 · Cited by 3
Wu, W., & Pan, C. (2025) · Frontiers in Public Health · doi:10.3389/fpubh.2025.1624345 · Cited by 4
Sun, S., Liu, X., Li, H., Wang, Z., & Zhu, Y. (2025) · Applied Intelligence · doi:10.1007/s10489-025-06927-y · Cited by 3
“These methods leverage powerful deep learning models such as Transformers [14, 15], Graph Neural Networks (GNNs) [16], Variational Auto-encoders (VAEs) [17], Generative Adversarial Networks (GANs) [18 ], and Diffusion models [ 19].”
Xiu, Z., He, Q., Zhong, Y., & Chen, J. (2025) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn64981.2025.11228289 · Cited by 3
“For the four multivariate datasets, we use a patch size of [3 , 5 , 7] and a window size of 105.”
MSR-GAN: multi-scales decomposition representations for unsupervised anomaly detection
Xu, D., Xia, T., Hou, J., Xiang, Y., & Xuan, Q. (2025) · Applied Intelligence · doi:10.1007/s10489-025-06606-y · Cited by 3
CBMAD: Anomaly Detection in IoT Network Traffic via Consistent Bidirectional Mamba Autoencoder
Yu, Y.-C., Ouyang, Y., & Lin, C.-A. (2025) · IEEE International Conference on High Performance Switching and Routing (HPSR) · doi:10.1109/hpsr64165.2025.11038914 · Cited by 3
“This includes contrasting local and series associations [19] or capturing correlations across segments of the input time window [20]- [22].”
InfluentialAhmadian, A., & Lindsten, F. (2025) · Pattern Recognition Letters · doi:10.1016/j.patrec.2025.07.011 · Cited by 3
TriP-LLM: A Tri-Branch Patch-Wise Large Language Model Framework for Time-Series Anomaly Detection
Yu, Y.-C., Ouyang, Y., & Lin, C.-A. (2025) · IEEE Access · doi:10.1109/access.2025.3613663 · Cited by 3
“We compared our method against several recent SOTA methods known for their strong performance in time-series anomaly detection tasks, including USAD [46], TranAD [21], AnomTrans [22], TimesNet [47], DIF [48], DCdetector [23], PatchAD [49], and the Mamba-based method CBMAD [27].”
Responsible Workload Sequence Anomaly Detection with Bi-View Feature Fusion
Liu, C., Wang, D., Li, D., Wang, K., & Zhang, J. (2025) · Journal of Network and Systems Management · doi:10.1007/s10922-025-09923-2 · Cited by 3
Liang, Q., & Yin, X. (2025) · Expert Systems · doi:10.1111/exsy.70102 · Cited by 3
FreCT: Frequency-Augmented Convolutional Transformer for Robust Time Series Anomaly Detection
Zhang, W., Ding, X., Yao, G., Lin, X., Guan, R., Du, C., Han, R., Xuan, X., & Luo, C. (2025) · Lecture notes in computer science · doi:10.1007/978-981-96-9921-6_2 · Cited by 3
Yujie, Z., Liu, X., Liu, R., Li, H., Duan, Y., Li, X., Sun, Y., & Li, L. (2026) · Journal of Sustainable Metallurgy · doi:10.1007/s40831-026-01432-z · Cited by 4
Li, A., Wei, G., Zhang, C., & Zhang, J. (2025) · IEEE Transactions on Dielectrics and Electrical Insulation · doi:10.1109/tdei.2025.3595516 · Cited by 2
“Yang et al. [25] employed contrastive learning to distinguish between normal and anomalous data, eliminating the need for reconstruction or prediction.”
Self-citationKARMAD: KAN-Based Adversarial Robust Model for Anomaly Detection
Chen, F., Qiu, X., Ye, Y., Jing, R., Chen, Y., & Gao, D. (2025) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde65448.2025.00256 · Cited by 2
“We compare KARMAD with state-of-the-art (SOTA) time series anomaly detection models in recent years, including DAGMM [40], DeepSVDD [30], OmniAnomaly [1], MS-CRED [41], USAD [14], THOC [31], GDN [33], CAE-M [42], TranAD [15], GRELEN [35], DCdetector [44], MT-GFlow [34], PeFAD [43] and TADNet [36].”
InfluentialFC-AEN: Fully Convolutional Adaptive Ensemble Network for Automatic Depression Detection
Wang, Y., Lin, Z., Teng, Y., Cheng, Y., Jiang, H., & Yang, Y. (2025) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2025.3602264 · Cited by 2
“Similarly, Zhang and Yan [15] utilized patch embedding and spatiotemporal cross attention for multivariate time series prediction, and Yang et al. [16] employed patch embedding and dual attention for time series anomaly detection.”
Self-citationLi, X., & Yang, Y.-B. (2025) · Applied Intelligence · doi:10.1007/s10489-025-06886-4 · Cited by 2
Self-citationSource-Free Time-Series Domain Adaptation With Prior Evaluation of Model Salience
Cai, R., Jin, M., Wen, Q., Zhang, K., & Liu, Y. (2025) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2025.3641830 · Cited by 2
“Crucially, the unique characteristics of time series, which di ff er fundamentally from those of images, are often inadequately considered; these include temporal dependency [23], sequence consistency [24], and local patterns [25].”
Self-citationDUVET: Dual-View Enhanced Transformer for Anomaly Detection in Aviation Safety Monitoring
Li, C., Li, X., Chen, H., Zheng, L., Li, F., Zhao, X., Sun, H., Zhang, R., & Shang, J. (2026) · IEEE Sensors Journal · doi:10.1109/jsen.2025.3647934 · Cited by 3
“…memory neural network-based method: LSTM-VAE [11]; the dissimilarity-based method: SimAD [38]; the contrastive learning-based method: DCdetector [22]; the graph neural network-based and GDN [16]; the adversarial generation method: MAD-GAN [14]; and the transformer-based methods: TranAD [17] and…”
InfluentialJin, L., Gao, X., Wang, J., & Yuan, S. (2026) · Scientific Reports · doi:10.1038/s41598-025-29849-8 · Cited by 2
Unsupervised Textless Prompt With LLMs for Multivariate Time Series Anomaly Detection
Zhong, G., Liu, J., Liu, F., Jiang, J., Wang, B., Zhang, F., & Chen, C. L. P. (2026) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2025.3649202 · Cited by 3
“M ULTIVARIATE time series anomaly detection, one of the typical research-intensive points in time series analysis [1], [2], [3], [4], has been widely applied in various domains, such as data-center operation [5], space exploration [6], industrial control systems [7], [8], and so on.”
SCAT: A Spectral-Convolutional Anomaly Transformer for Multivariate Time Series Anomaly Detection
Zhang, S., Chen, S., & Li, J. (2026) · Electronics · doi:10.3390/electronics15030628 · Cited by 2
LaGraph: Laplacian-Guided Graph Learning for Time Series Anomaly Detection
Zeng, S., Chao, G., Wei, J., Yu, Y., Wang, Z., & Chu, D. (2026) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2026.3665696 · Cited by 2
“Latent representation-basedmethodsdifferentiatenormalandabnor-malbehaviorsthroughdiscriminativeorcontrastivelearning objectivestolearneffectiverepresentation[24],[25].”
Khan, M. A., Khalid, O., & Rais, R. N. B. (2026) · Sensors · doi:10.3390/s26061918 · Cited by 2
“Transformer-and attention-based architectures, including MPFormer [23] and DCde-tector [24], achieve strong centralized detection performance but incur quadratic complexity in sequence length and high memory consumption, limiting their practicality on resource-constrained gateways.”
Multivariate time series anomaly detection using neighbor relation-guided Transformer
Zhang, J., Wu, Z., & Gan, H. (2026) · Pattern Recognition · doi:10.1016/j.patcog.2026.113835 · Cited by 2
Learnable product quantization for anomaly detection
Zhang, S., Chen, W., Lu, B., & Lai, H. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.127532 · Cited by 2
Skip-Step Contrastive Predictive Coding for Time Series Anomaly Detection
Zhang, K., Wen, Q., Zhang, C., Sun, L., & Liu, Y. (2024) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp48485.2024.10447104 · Cited by 2
“The results of the baselines are collected from [16, 17].”
BackgroundMethodSelf-citationMultidimensional Time Series Analysis for Anomaly Pattern Detection and Interpretation
Deng, Z., Kang, J., & Wang, X. (2024) · IEEE International Conference on Power, Electronics and Computer Applications (ICPECA) · doi:10.1109/icpeca60615.2024.10471100 · Cited by 2
“SPOT[11]used extreme value optimization theory to automatically find thresholds for detecting anomalies in univariate data, while DCdetector [12]identified anomalies and sudden events in time series by detecting changes in distribution, such as sudden trend shifts or distribution drifts over time.”
BackgroundObied, M. A., Zakaria, W., Ghaleb, F. F. M., Hassanien, A. E., & Abdelfattah, A. M. H. (2024) · CCF Transactions on Pervasive Computing and Interaction · doi:10.1007/s42486-024-00160-1 · Cited by 2
“The authors in Yang et al. (2023) introduce a DCdetector model for time-series anomaly detection, emphasizing contrastive representation learning.”
Cluster-Wide Task Slowdown Detection in Cloud System
Chen, F., Zhang, Y., Fan, L., Liang, Y., Pang, G., Wen, Q., & Deng, S. (2024) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3637528.3671936 · Cited by 3
Self-citationBi-Branching Feature Interaction Representation Learning for Multivariate Time Series
Wang, W., Zuo, E., Wang, R., Zhong, J., Chen, C., Chen, C., & Lv, X. (2024) · Applied Soft Computing · doi:10.1016/j.asoc.2024.112383 · Cited by 2
Research on Time Series Anomaly Detection Algorithm Based on Transformer Coupled with GAN
Ye, M., Wang, Z., & Fei, L. (2024) · IEEE International Conference on Information, Communication and Networks (ICICN) · doi:10.1109/icicn62625.2024.10761510 · Cited by 2
“…and compare its performance against that of the GAN-Transformer model, we conduct a series of comparative experiments with selected models: VAE [22], LSTM-VAE [23], BeatGAN [10], OmniAnomaly [15], InterFusion [24], THOC [25], DAEMON [19], Anomaly Transformer [6], DCdetector [7] and DiffAD [26].”
Wang, X., Xu, Q., Xu, K., Yu, T., Ding, B., Feng, D., & Dou, Y. (2024) · IEEE Open Journal of the Computer Society · doi:10.1109/ojcs.2024.3521217 · Cited by 4
“…and ITAD [34]; three density-estimation models: LOF [35], MPPCACD [36], and DAGMM [37]; three autoregression models: VAR [38], LSTM [39], and CL-MPPCA [40]; and six reconstruction models: LSTM-VAE [41], OmniAnomaly [42], BeatGAN [43], InterFusion [44], Anomaly Transformer [15], and DCdetector [4].”
InfluentialDetecting Both Seen and Unseen Anomalies in Time Series
Liu, C., He, S., Li, S., Shi, Z., & Meng, W. (2025) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/3717071 · Cited by 3
Prospective Multi-Graph Cohesion for Multivariate Time Series Anomaly Detection
Chen, J., Feng, M., & Wirjanto, T. S. (2025) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/3701551.3703494 · Cited by 2
DifNet: Difference-based multi-resolution decomposition for time series anomaly detection
Wang, H., Li, J., Chen, Y., Zou, X., Zeng, Z., Wu, J., Pan, C., Lu, Y., Gu, R., He, X., et al. (2025) · Applied Intelligence · doi:10.1007/s10489-025-06551-w · Cited by 2
Unsupervised Time Series Anomaly Prediction with Importance-based Generative Contrastive Learning
Zhao, K., Zhuang, Z., Guo, C., Miao, H., Jensen, C. S., Cheng, Y., & Yang, B. (2025) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3711896.3737174 · Cited by 2
Anomaly Detection Method of Maglev Train Suspension System Based on SSA–MLP–Attention Hybrid Model
Che, H., Yang, B., Long, Z., & Ma, Z. (2025) · IEEE Transactions on Transportation Electrification · doi:10.1109/tte.2025.3608347 · Cited by 2
“In response to the above difficulties, there is not a good way to solve them, and the literature [20], [21], [22], [23], [24], [25], [26], [27], [28] has done research related to anomaly detection in suspension systems, but none of them are well solved.”
Xia, S., Sun, Y., Pan, X., Yuan, Y., Zhang, S., Hu, S., Lei, T., Li, Y., & Feng, J. (2025) · International Conference for High Performance Computing, Networking, Storage and Analysis (SC) · doi:10.1145/3712285.3759794 · Cited by 2
Song, Y., Huang, H., Wei, Q., Liu, L., & Wei, Z. (2025) · Proceedings of the 2025 6th International Conference on Computer Information and Big Data Applications · doi:10.1145/3746709.3746772 · Cited by 1
Xu, D., Wu, X., Chen, N., & Liu, C. (2024) · Lecture notes in computer science · doi:10.1007/978-981-97-5672-8_8 · Cited by 1
Meng, F., Ma, L., Chen, Y., He, W., Wang, Z., & Wang, Y. (2024) · Mathematics · doi:10.3390/math12172612 · Cited by 1
Dual Temporal Masked Modeling for KPI Anomaly Detection via Similarity Aggregation
Yu, T., Gao, Z., Xu, K., Wang, X., Shi, P., Ding, B., & Feng, D. (2024) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2024.3486167 · Cited by 1
“[42] indicating the distance between real situations and predicted events, our method consistently maintains a leading position.”
InfluentialPASTA : Neural Architecture Search for Anomaly Detection in Multivariate Time Series
Trirat, P., & Lee, J.-G. (2024) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2024.3508845 · Cited by 1
“As in recent studies [43], [119], we also compare PASTA to the well-performing models (InterFusion and TimesNet) using the additional metrics [110].”
Advanced Deep Learning Models for Groundwater Contaminant Dynamics at the Savannah River Site
Soni, J., Upadhyay, H., Wainwright, H. M., Xu, Z., & Lagos, L. E. (2024) · IEEE International Conference on Future Machine Learning and Data Science (FMLDS) · doi:10.1109/fmlds63805.2024.00072 · Cited by 1
“Yang et al. [16] proposed a DCdetector model, which uses dual-branch attention in contrastive learning for time series anomaly detection.”
STAD: Joint Spatial-Temporal Dimension and Channel Correlation for Time Series Anomaly Detection
Zhou, M., Wang, X., Han, D., Wan, J., & Li, J. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10888777 · Cited by 1
“We compared STAD with six models: PatchTST [28], TimesNet [29], THOC [30], MEMTO [31], Anomaly Transformer [32], and DCdetector [33].”
Anomaly Detection Method for Meteorological Data Based on Neural Networks
Guan, A., Zhu, C., Kong, L., Nan, Y., Lu, P., Liu, S., & Zhao, Y. L. (2025) · Russian Meteorology and Hydrology · doi:10.3103/s1068373924600338 · Cited by 1
Anomaly Detection for Track Dynamic Inspection Data by Information Fusion Attention and TransDetect
Zhang, Y., Yin, H., Liu, X., Huang, H., & Hou, X. (2025) · Signal Image and Video Processing · doi:10.1007/s11760-025-04305-2 · Cited by 1
Self-Supervised Spatio-Temporal Representation Learning for Microservice Anomaly Detection
Li, Y., Guo, Y., Chen, Y., Cao, Z. L., & Liang, S. (2025) · World Conference on Computing and Communication Technologies (WCCCT) · doi:10.1109/wccct65447.2025.11027933 · Cited by 1
DDConv: Disentangled Dual Branch Convolutional Network for Anomaly Detection in Server Machines
Yao, Y., & Yang, Z.-X. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3583424 · Cited by 1
“11) DCdetector [29]: It is a contrastive-learning-based method which detects anomalies based on differences of both in-patch representations and patch-wise representations.”
You, P., Wang, X., Chen, P., Wu, L., Chen, J., Li, X., Zeng, S., & Gao, H. (2025) · The Journal of Supercomputing · doi:10.1007/s11227-025-07665-1 · Cited by 1
Dual Transformers With Latent Amplification for Multivariate Time Series Anomaly Detection
Choi, Y., Sohn, K., & Kim, I.-J. (2025) · IEEE Access · doi:10.1109/access.2025.3594473 · Cited by 1
“Among these, AnomalyTransformer [8] (denoted as A.T.), DCdetector [37], and MEMTO [10] are state-of-the-art Transformer-based models.”
SCAN: Selective Contrastive Learning Against Noisy Data for Acoustic Anomaly Detection
Liu, Z., Hou, Y., Wang, W., Michiels, S., & Hughes, D. (2025) · IEEE Signal Processing Letters · doi:10.1109/lsp.2025.3599796 · Cited by 1
Semi-supervised method for anomaly detection in HTTP traffic
Wasundara, M. I., Zhou, J., Yang, Y., Zhao, D., & Xiang, J. (2025) · EURASIP Journal on Information Security · doi:10.1186/s13635-025-00210-w · Cited by 1
Self-citationUnsupervised Dual-Domain Memory Model for Time Series Anomaly Detection
Zhou, M., Wang, X., Li, J., Han, D., & Li, G. (2025) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3746027.3754892 · Cited by 1
AAAD: Asynchronous Inter-Variable Relationship-Aware Anomaly Detection for Multivariate Time Series
Liu, H., Huang, X., Jia, M., Zhang, L., Jia, T., Wu, Z., & Li, Y. (2025) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme59968.2025.11210144 · Cited by 1
“…(LOF [24], OC-SVM [25]), methods that perform temporal modeling (THOC [14], Om-niAnomaly (Omni) [5], Anomaly Transformer (AT) [17], DCdecoder [27], MAN-QSM [26], MEMTO [23], CTAD [29], GPT4TS [28], AnomalyLLM [30]), and methods that incorporate spatiotemporal modeling (MTAD-GAT [16], STGAT…”
Enhanced Anomaly Detection in Solar Power Plants Using Hybrid Machine Learning Techniques
Ahirwar, A., & Nandanwar, S. R. (2025) · International Conference on Engineering Innovations and Technologies (ICoEIT) · doi:10.1109/icoeit63558.2025.11210880 · Cited by 1
“1 shows a hybrid microgrid system integrating solar and wind energy with battery storage and the utility grid to supply power to a load efficiently [7].”
Kotenko, I. V., Melnik, M. V., & Abramenko, G. (2025) · Communications in computer and information science · doi:10.1007/978-3-032-04758-8_20 · Cited by 1
A novel time-frequency augmented perception method for multivariate time series anomaly detection
Wang, X., Ma, W., & Li, Z. (2025) · Applied Intelligence · doi:10.1007/s10489-025-06966-5 · Cited by 1
Hierarchical Runtime Reliability Anomaly Detection for Edge Services Rejuvenation
Wang, L., Liu, J., He, Q., Chen, F., & Xia, X. (2025) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2025.3632794 · Cited by 1
“(cid:2) DCdetector [32]: DCdetector utilizes a contrastive learning-based dual-branch attention structure to learn a permutationinvariantrepresentation,whichcanenlargethe differencebetweennormalpointsandanomalies,improv-ingdetectionaccuracy.”
Contrastive Learning for Robust Time Series Anomaly Detection in Cyber-Physical Systems
Ma, Y., Wang, H., Guan, H., Tao, X., & Yin, H. (2025) · Lecture notes in computer science · doi:10.1007/978-981-96-9849-3_29 · Cited by 1
5G Mobile Networked Clusters Anomaly Detection Under Distribution Shifts: A Causal Perspective
Liu, X., Luo, C., & Shao, R. (2025) · IEEE Transactions on Cognitive Communications and Networking · doi:10.1109/tccn.2025.3632390 · Cited by 1
“Yang et al. [31] proposed DCdetector, featuring a dual-attention contrastive architecture with multi-scale patch design that generates multi-view temporal representations and enhances discriminative power through contrastive loss optimization.”
Wang, Y., Zhang, J. J., & Yang, M. (2025) · Scientific Reports · doi:10.1038/s41598-025-33186-1 · Cited by 1
“We also compare against prominent models from 2023, namely DCdetector 35 , TimesNet 36 , PatchTST 15 , the linear models DLinear and NLinear 37 , TFAD 26 , and a standard AutoEncoder (AE) 38 .”
Cho, K., Lee, J., & Kim, S. B. (2026) · Information Sciences · doi:10.1016/j.ins.2026.123094 · Cited by 2
Liu, H., Ren, Q., Tang, Y., & Li, S. (2025) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc58881.2025.11342752 · Cited by 1
“In contrast, methods like Anomaly Transformer [10] and DCdetector [11] introduce association-based criteria, further enhance detection performance.”
A Two-Stage Anomaly-Aware Framework for Robust Traffic Forecasting with Memory-Guided GNNs
Li, P.-X., Chou, C.-R., Tsai, J.-W., & Hsieh, H.-P. (2026) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/3773966.3779379 · Cited by 1
Attention-Driven Deep Learning Framework for Intelligent Anomaly Detection in ETL Processes
Nie, C., Wang, H., & Chiang, C. (2025) · International Symposium on Computer Engineering and Intelligent Communications (ISCEIC) · doi:10.1109/isceic67854.2025.11405696 · Cited by 1
Nicolae, A., & Korodi, A. (2026) · Applied Sciences · doi:10.3390/app16052457 · Cited by 1
Zhu, L. H., Ma, S., Xu, C., Xu, X., & Du, H. (2026) · Applied Intelligence · doi:10.1007/s10489-026-07171-8 · Cited by 1
Huang, X., Xiao, H., Zeng, Z., Qiu, Z., Zhu, W., Zhang, S., & Xiao, R. (2026) · The Journal of Supercomputing · doi:10.1007/s11227-026-08403-x · Cited by 2
RFETR: Residual Frequency-Enhanced Two-stage Reconstruction for Time Series Anomaly Detection
Jing, S., Wu, J., Zhan, P., & Zhang, T. (2026) · International Conference on Communication Networks and Machine Learning (CNML) · doi:10.1109/cnml68938.2026.11453190 · Cited by 1
“…Anomaly Transformer [5] discovers the ``adjacent-concentration'' property where anomalies concentrate attention on neighboring points, designing Association Discrepancy as the detection criterion; DCdetector [6] uses dual-attention contrastive learning to build compact normal representations.”
InfluentialWu, K., Xu, C., Zhang, R., Lu, W., Ma, Y., Zhang, E., & Tan, K. (2026) · Entropy · doi:10.3390/e28040448 · Cited by 1
Cheng, H., Mu, W., Liu, F., Zhu, W., & W, C. (2026) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp55912.2026.11462718 · Cited by 1
Luo, Z., Sheng, J., Xu, L., & Yi, Y. (2026) · Pattern Recognition · doi:10.1016/j.patcog.2026.113924 · Cited by 1
ADAD: Multivariate Time Series Anomaly Detection via Adaptive Series Decomposition
Yao, Y., & Yang, Z.-X. (2026) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2026.3695421 · Cited by 1
“5) DCdetector [28]: It is a contrastive learning based method which detects anomalies based on differences in both in-patch representations and patch-wise representations.”
InfluentialMSMGNet: Multiscale Modeling of Interseries Dependency for Time Series Anomaly Detection
Wang, S., Zhou, Y., Wei, Y., & Yong, P. (2026) · IEEE Sensors Journal · doi:10.1109/jsen.2026.3697065 · Cited by 1
Zhong, B., Li, J., Pu, Z., & Zhang, R. (2026) · Applied Sciences · doi:10.3390/app16115450 · Cited by 1
Alhadidi, M., & Alkasassbeh, M. S. (2026) · International Conference on Computational Intelligence Approaches and Applications (ICCIAA) · doi:10.1109/icciaa68481.2026.11543640 · Cited by 1
“We compare BiPAM against methods spanning multiple paradigms: autoencoder-based (AdMemAE [23], USAD [10]), Transformer-based (Anomaly Transformer [12], TranAD [17], DCdetector [24]), frequency-domain (TFAD [25], TFCL [20]), and hybrid approaches (TimesNet [26], GDN [11]).”
Online Multivariate Time Series Anomaly Detection Method Based on Contrastive Learning
Dong, X., Liu, H., Du, J., Wang, Z., & Wang, C. (2024) · Lecture notes in computer science · doi:10.1007/978-981-97-5618-6_39 · Cited by 1
Industrial Anomaly Detection Based on Factorized Temporal-Channel Fusion
Xing, Y., Liu, D., & Bao, Y. (2024) · IEEE Sensors Journal · doi:10.1109/jsen.2024.3452955 · Cited by 1
“Recent methods based on contrastive learning, such as PatchAD [20] and DCdetector [21], utilize multibranch architectures, employing different branches to perform different representations, and subsequently detect anomalies through contrastive loss.”
BackgroundMethodMSAnomaly: Time Series Anomaly Detection with Multi-scale Augmentation and Fusion
Yin, T., Zhang, Z., Hou, S., Huan, Z., Zheng, L., Zhou, Y., Xie, J., & Yan, M. (2024) · Lecture notes in computer science · doi:10.1007/978-981-96-0840-9_25 · Cited by 1
“For the baseline, relevant data from the paper DCdetector [24] or the official code were used.”
MethodInfluentialHarnessing Contrastive Learning and Neural Transformation for Time Series Anomaly Detection
Chen, J., Feng, M., & Wirjanto, T. S. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10890321 · Cited by 5
“2) Baseline Methods and Evaluation Metrics: We compare our approach to several recent anomaly detection meth-ods, including MAD-GAN [3], USAD [9], MSCRED [10], TranAD [11], NeuTralAD [26], AnomalyTransformer [12], COCA [30], DCdetector [31] and CST-GL [32].”
FEFM: Feature Extraction and Fusion Module for Enhanced Time Series Anomaly Detection
Jeon, S. H., Kim, K., & Choi, Y. S. (2025) · ACM Symposium on Applied Computing · doi:10.1145/3672608.3707794 · Cited by 1
Lu, R., Zheng, D., Yang, Q., & Cao, W. (2024) · International Conference on Neuromorphic Computing (ICNC) · doi:10.1109/icnc64304.2024.10987651 · Cited by 1
“Besides, these abnormal data often exhibit inconsistency and unpredictability [5].”
Li, J., Gao, R., Yan, L., Yao, Q., Peng, X., & Hu, J. (2025) · Electronics · doi:10.3390/electronics14102089 · Cited by 1
“To address these issues, inspired by the literature [16,17], we propose a novel recommendation model for session recommendation (EGDLTP-CCL).”
Hu, S., Zhang, J., Li, Z., Liao, L., Gan, Y., & Kiran, R. U. (2025) · Lecture notes in computer science · doi:10.1007/978-981-96-9881-3_40 · Cited by 1
RAID: Root Cause Anomaly Identification and Diagnosis
Ky, J. R., Mathieu, B., Lahmadi, A., Wang, M., Marrot, N., & Boutaba, R. (2025) · Lecture notes in computer science · doi:10.1007/978-3-662-72243-5_25 · Cited by 1
“Contrastive learning further enhances AD, with models like COCA [24], ContrastAD [16], and CARLA [8] improving representation learning, while DCdetector [27] refines spatial-temporal feature extraction.”
Shi, H., Ye, X., & Manoharan, S. (2025) · IEEE International Conference on Computing (ICOCO) · doi:10.1109/icoco67189.2025.11334136 · Cited by 1
“Emerging methods such as AnomalyTrans [17], DCdetector [18], STAT [19], and GDformer [1] enhance the anomaly recognition effect through attention mechanisms and correlation differences.”
A Wind Power Forecasting Method Based on Large Language ModelsPreprint
Liu, D., & 橘高, 義. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5251506 · Cited by 1
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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