mlsanityData to 13 Oct 2026

DCdetector: Dual Attention Contrastive Representation Learning for Time Series Anomaly Detection

arXiv 2306.10347 ↗First posted 17 Jun 2023cs.LG cs.AIcited by 103

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Abstract (arXiv)

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.

Key facts. Cited by 103 papers in the map (data through 13 Oct 2026). Builds on 40 of its 97 references that are in the map. First posted on arXiv 17 Jun 2023.

Builds on

40 of 97 references.

References · 40

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28 more
  1. 2019Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series Forecastingcited by 607
  2. 2018The UCR Time Series Archivecited by 427
  3. 2020A Unifying Review of Deep and Shallow Anomaly Detectioncited by 425
  4. 2018Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic Thresholdingcited by 408
  5. 2021Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancycited by 390
  6. 2021Graph Neural Network-Based Anomaly Detection in Multivariate Time Seriescited by 300
  7. 2019Unsupervised Embedding Learning via Invariant and Spreading Instance Featurecited by 231
  8. 2018Unsupervised Anomaly Detection via Variational Auto-Encoder for Seasonal KPIs in Web Applicationscited by 223
  9. 2017Improved Texture Networks: Maximizing Quality and Diversity in Feed-forward Stylization and Texture Synthesiscited by 215
  10. 2017A Multimodal Anomaly Detector for Robot-Assisted Feeding Using an LSTM-based Variational Autoencodercited by 209
  11. 2020A review on outlier/anomaly detection in time series datacited by 187
  12. 2019MAD-GAN: Multivariate Anomaly Detection for Time Series Data with Generative Adversarial Networkscited by 176
  13. 2020Multivariate Time-series Anomaly Detection via Graph Attention Networkcited by 165
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  15. 2022Deep Learning for Time Series Anomaly Detection: A Surveycited by 130
  16. 2021Learning Graph Structures with Transformer for Multivariate Time Series Anomaly Detection in IoTcited by 104
  17. 2019U-Time: A Fully Convolutional Network for Time Series Segmentation Applied to Sleep Stagingcited by 88
  18. 2022Local Evaluation of Time Series Anomaly Detection Algorithmscited by 79
  19. 2022How Does SimSiam Avoid Collapse Without Negative Samples? A Unified Understanding with Self-supervised Contrastive Learningcited by 65
  20. 2020Time Series Change Point Detection with Self-Supervised Contrastive Predictive Codingcited by 58
  21. 2020RobustTAD: Robust Time Series Anomaly Detection via Decomposition and Convolutional Neural Networkscited by 54
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  24. 2021Unsupervised Time Series Outlier Detection with Diversity-Driven Convolutional Ensembles -- Extended Versioncited by 35
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Cited by year (2026 to 13 Oct)

Citing papers per year: 2023: 7; 2024: 28; 2025: 36; 2026: 32.

103 papers.

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  1. 2024Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Expertscited by 286
  2. 2024Foundation Models for Time Series Analysis: A Tutorial and Surveycited by 237
  3. 2024DUET: Dual Clustering Enhanced Multivariate Time Series Forecastingcited by 105
  4. 2024CATCH: Channel-Aware multivariate Time Series Anomaly Detection via Frequency Patchingcited by 93
  5. 2024Towards a General Time Series Anomaly Detector with Adaptive Bottlenecks and Dual Adversarial Decoderscited by 38
  6. 2024Universal Time-Series Representation Learning: A Surveycited by 29
  7. 2024Large Language Model Guided Knowledge Distillation for Time Series Anomaly Detectioncited by 26
  8. 2023A Comprehensive Survey of Deep Transfer Learning for Anomaly Detection in Industrial Time Series: Methods, Applications, and Directionscited by 22
  9. 2024Generative Semi-supervised Graph Anomaly Detectioncited by 22
  10. 2024MultiRC: Joint Learning for Time Series Anomaly Prediction and Detection with Multi-scale Reconstructive Contrastcited by 19
  11. 2023CARLA: Self-supervised Contrastive Representation Learning for Time Series Anomaly Detectioncited by 15
  12. 2023Unraveling the "Anomaly" in Time Series Anomaly Detection: A Self-supervised Tri-domain Solutioncited by 12
38 more
  1. 2024PatchAD: A Lightweight Patch-based MLP-Mixer for Time Series Anomaly Detectioncited by 12
  2. 2025CrossAD: Time Series Anomaly Detection with Cross-scale Associations and Cross-window Modelingcited by 12
  3. 2025FreCT: Frequency-augmented Convolutional Transformer for Robust Time Series Anomaly Detectioncited by 10
  4. 2025Towards Foundation Models for Zero-Shot Time Series Anomaly Detection: Leveraging Synthetic Data and Relative Context Discrepancycited by 10
  5. 2025Multivariate Time Series Anomaly Detection by Capturing Coarse-Grained Intra- and Inter-Variate Dependenciescited by 8
  6. 2025Selective Learning for Deep Time Series Forecastingcited by 8
  7. 2024DACAD: Domain Adaptation Contrastive Learning for Anomaly Detection in Multivariate Time Seriescited by 7
  8. 2025DConAD: A Differencing-based Contrastive Representation Learning Framework for Time Series Anomaly Detectioncited by 7
  9. 2025Robust and Explainable Detector of Time Series Anomaly via Augmenting Multiclass Pseudo-Anomaliescited by 7
  10. 2025STAR: Boosting Time Series Foundation Models for Anomaly Detection through State-aware Adaptercited by 7
  11. 2024SimAD: A Simple Dissimilarity-based Approach for Time Series Anomaly Detectioncited by 6
  12. 2024TimeInf: Time Series Data Contribution via Influence Functionscited by 6
  13. 2024TSINR: Capturing Temporal Continuity via Implicit Neural Representations for Time Series Anomaly Detectioncited by 5
  14. 2025Mamba Adaptive Anomaly Transformer with association discrepancy for time seriescited by 5
  15. 2025Decomposition-based multi-scale transformer framework for time series anomaly detectioncited by 5
  16. 2026PaAno: Patch-Based Representation Learning for Time-Series Anomaly Detectioncited by 5
  17. 2023Understanding the Role of Textual Prompts in LLM for Time Series Forecasting: an Adapter Viewcited by 4
  18. 2024Attention as Robust Representation for Time Series Forecastingcited by 4
  19. 2025A Review on Self-Supervised Learning for Time Series Anomaly Detection: Recent Advances and Open Challengescited by 4
  20. 2025AXIS: Explainable Time Series Anomaly Detection with Large Language Modelscited by 4
  21. 2025Structured Temporal Causality for Interpretable Multivariate Time Series Anomaly Detectioncited by 4
  22. 2023Contaminated Multivariate Time-Series Anomaly Detection with Spatio-Temporal Graph Conditional Diffusion Modelscited by 3
  23. 2023Open-Set Multivariate Time-Series Anomaly Detectioncited by 3
  24. 2023Learning Multi-Pattern Normalities in the Frequency Domain for Efficient Time Series Anomaly Detectioncited by 3
  25. 2025CICADA: Cross-Domain Interpretable Coding for Anomaly Detection and Adaptation in Multivariate Time Seriescited by 3
  26. 2025An Encode-then-Decompose Approach to Unsupervised Time Series Anomaly Detection on Contaminated Training Data--Extended Versioncited by 3
  27. 2026Towards Multimodal Time Series Anomaly Detection with Semantic Alignment and Condensed Interactioncited by 3
  28. 2026Post-Training in Time Series Foundation Models: A Unifying Frameworkcited by 3
  29. 2024Joint Selective State Space Model and Detrending for Robust Time Series Anomaly Detectioncited by 2
  30. 2024Intelligent Cross-Organizational Process Mining: A Survey and New Perspectivescited by 2
  31. 2024Cluster-Wide Task Slowdown Detection in Cloud Systemcited by 2
  32. 2025PatchTrAD: A Patch-Based Transformer focusing on Patch-Wise Reconstruction Error for Time Series Anomaly Detectioncited by 2
  33. 2025Task-Aware Mixture-of-Experts for Time Series Analysiscited by 2
  34. 2025ScatterAD: Temporal-Topological Scattering Mechanism for Time Series Anomaly Detectioncited by 2
  35. 2025RED-F: Reconstruction-Elimination based Dual-stream Contrastive Forecasting for Multivariate Time Series Anomaly Predictioncited by 2
  36. 2024Self-supervised New Activity Detection in Sensor-based Smart Environmentscited by 1
  37. 2024RESTAD: REconstruction and Similarity based Transformer for time series Anomaly Detectioncited by 1
  38. 2024Towards Dynamic Trend Filtering through Trend Point Detection with Reinforcement Learningcited by 1

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