mlsanityData to 14 Oct 2026

Graph Neural Network-Based Anomaly Detection in Multivariate Time Series

arXiv 2106.06947 ↗First posted 13 Jun 2021cs.LG cs.AIcited by 301

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Abstract (arXiv)

Given high-dimensional time series data (e.g., sensor data), how can we detect anomalous events, such as system faults and attacks? More challengingly, how can we do this in a way that captures complex inter-sensor relationships, and detects and explains anomalies which deviate from these relationships? Recently, deep learning approaches have enabled improvements in anomaly detection in high-dimensional datasets; however, existing methods do not explicitly learn the structure of existing relationships between variables, or use them to predict the expected behavior of time series. Our approach combines a structure learning approach with graph neural networks, additionally using attention weights to provide explainability for the detected anomalies. Experiments on two real-world sensor datasets with ground truth anomalies show that our method detects anomalies more accurately than baseline approaches, accurately captures correlations between sensors, and allows users to deduce the root cause of a detected anomaly.

Key facts. Cited by 301 papers in the map (data through 14 Oct 2026). Builds on 17 of its 39 references that are in the map. First posted on arXiv 13 Jun 2021.

Builds on

17 of 39 references.

References · 17

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
  2. 2017Graph Attention Networkscited by 8,923
  3. 2013Auto-Encoding Variational Bayescited by 4,053
  4. 2019Fast Graph Representation Learning with PyTorch Geometriccited by 2,434
  5. 2016Convolutional Neural Networks on Graphs with Fast Localized Spectral Filteringcited by 2,089
  6. 2017Modeling Relational Data with Graph Convolutional Networkscited by 1,694
  7. 2017Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecastingcited by 1,136
  8. 2019GMAN: A Graph Multi-Attention Network for Traffic Predictioncited by 434
  9. 2018Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic Thresholdingcited by 410
  10. 2017A Dual-Stage Attention-Based Recurrent Neural Network for Time Series Predictioncited by 245
  11. 2017A Multimodal Anomaly Detector for Robot-Assisted Feeding Using an LSTM-based Variational Autoencodercited by 209
  12. 2020A review on outlier/anomaly detection in time series datacited by 189
5 more
  1. 2019MAD-GAN: Multivariate Anomaly Detection for Time Series Data with Generative Adversarial Networkscited by 176
  2. 2019Multi-Range Attentive Bicomponent Graph Convolutional Network for Traffic Forecastingcited by 69
  3. 2016Multivariate Industrial Time Series with Cyber-Attack Simulation: Fault Detection Using an LSTM-based Predictive Data Modelcited by 34
  4. 2020STP-UDGAT: Spatial-Temporal-Preference User Dimensional Graph Attention Network for Next POI Recommendationcited by 31
  5. 2015Bayesian Structure Learning for Stationary Time Seriescited by 6

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 13; 2022: 39; 2023: 66; 2024: 66; 2025: 64; 2026: 53.

301 papers.

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  1. 2021Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancycited by 394
  2. 2022TranAD: Deep Transformer Networks for Anomaly Detection in Multivariate Time Series Datacited by 214
  3. 2023A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detectioncited by 145
  4. 2022Deep Learning for Time Series Anomaly Detection: A Surveycited by 131
  5. 2023DCdetector: Dual Attention Contrastive Representation Learning for Time Series Anomaly Detectioncited by 103
  6. 2023Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and Prospectscited by 99
  7. 2021Towards a Rigorous Evaluation of Time-series Anomaly Detectioncited by 97
  8. 2024CATCH: Channel-Aware multivariate Time Series Anomaly Detection via Frequency Patchingcited by 94
  9. 2022Deep Isolation Forest for Anomaly Detectioncited by 92
  10. 2023A Comprehensive Survey on Deep Graph Representation Learningcited by 72
  11. 2022Empowering Graph Representation Learning with Test-Time Graph Transformationcited by 67
  12. 2023GADBench: Revisiting and Benchmarking Supervised Graph Anomaly Detectioncited by 58
38 more
  1. 2021LUNAR: Unifying Local Outlier Detection Methods via Graph Neural Networkscited by 42
  2. 2022Graph-Augmented Normalizing Flows for Anomaly Detection of Multiple Time Seriescited by 42
  3. 2022Calibrated One-class Classification for Unsupervised Time Series Anomaly Detectioncited by 40
  4. 2023ImDiffusion: Imputed Diffusion Models for Multivariate Time Series Anomaly Detectioncited by 40
  5. 2025TimeXL: Explainable Multi-modal Time Series Prediction with LLM-in-the-Loopcited by 39
  6. 2023Knowledge Distillation on Graphs: A Surveycited by 38
  7. 2023Taming Local Effects in Graph-based Spatiotemporal Forecastingcited by 37
  8. 2022Graph Neural Networks in IoT: A Surveycited by 28
  9. 2023Graph Deep Learning for Time Series Forecastingcited by 26
  10. 2024Large Language Model Guided Knowledge Distillation for Time Series Anomaly Detectioncited by 26
  11. 2023AnomalyBERT: Self-Supervised Transformer for Time Series Anomaly Detection using Data Degradation Schemecited by 25
  12. 2023Navigating the Metric Maze: A Taxonomy of Evaluation Metrics for Anomaly Detection in Time Seriescited by 24
  13. 2022Graph Anomaly Detection with Graph Neural Networks: Current Status and Challengescited by 23
  14. 2023Fully-Connected Spatial-Temporal Graph for Multivariate Time-Series Datacited by 23
  15. 2022Sparse Graph Learning from Spatiotemporal Time Seriescited by 22
  16. 2023A Comprehensive Survey of Deep Transfer Learning for Anomaly Detection in Industrial Time Series: Methods, Applications, and Directionscited by 22
  17. 2022Detecting Multivariate Time Series Anomalies with Zero Known Labelcited by 21
  18. 2022Multivariate Time Series Forecasting with Latent Graph Inferencecited by 20
  19. 2022A Survey of Graph-based Deep Learning for Anomaly Detection in Distributed Systemscited by 20
  20. 2023MST-GAT: A Multimodal Spatial-Temporal Graph Attention Network for Time Series Anomaly Detectioncited by 20
  21. 2023Towards Unifying Diffusion Models for Probabilistic Spatio-Temporal Graph Learningcited by 20
  22. 2024Graph Mixture of Experts and Memory-augmented Routers for Multivariate Time Series Anomaly Detectioncited by 20
  23. 2022Adaptive Memory Networks with Self-supervised Learning for Unsupervised Anomaly Detectioncited by 18
  24. 2023Graph Anomaly Detection in Time Series: A Surveycited by 18
  25. 2023Hierarchical Graph Neural Networks for Causal Discovery and Root Cause Localizationcited by 18
  26. 2023Correlation-aware Spatial-Temporal Graph Learning for Multivariate Time-series Anomaly Detectioncited by 18
  27. 2024BACKTIME: Backdoor Attacks on Multivariate Time Series Forecastingcited by 18
  28. 2023Nominality Score Conditioned Time Series Anomaly Detection by Point/Sequential Reconstructioncited by 17
  29. 2024Position: Quo Vadis, Unsupervised Time Series Anomaly Detection?cited by 17
  30. 2025Deep Learning Advancements in Anomaly Detection: A Comprehensive Surveycited by 17
  31. 2022Deep Generative model with Hierarchical Latent Factors for Time Series Anomaly Detectioncited by 16
  32. 2022Graph Neural Networks for Multivariate Time Series Regression with Application to Seismic Datacited by 15
  33. 2022Graph Learning for Anomaly Analytics: Algorithms, Applications, and Challengescited by 15
  34. 2023Towards Semi-supervised Universal Graph Classificationcited by 15
  35. 2023A Survey of Imbalanced Learning on Graphs: Problems, Techniques, and Future Directionscited by 15
  36. 2024Collaborate to Adapt: Source-Free Graph Domain Adaptation via Bi-directional Adaptationcited by 15
  37. 2023Label-Free Multivariate Time Series Anomaly Detectioncited by 14
  38. 2024UniGAD: Unifying Multi-level Graph Anomaly Detectioncited by 14

50 most cited of 301 All 301 (JSON)