mlsanityData to 7 Oct 2026

A review on outlier/anomaly detection in time series data

arXiv 2002.04236 ↗First posted 11 Feb 2020cs.LG stat.MLcited by 187

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Abstract (arXiv)

Recent advances in technology have brought major breakthroughs in data collection, enabling a large amount of data to be gathered over time and thus generating time series. Mining this data has become an important task for researchers and practitioners in the past few years, including the detection of outliers or anomalies that may represent errors or events of interest. This review aims to provide a structured and comprehensive state-of-the-art on outlier detection techniques in the context of time series. To this end, a taxonomy is presented based on the main aspects that characterize an outlier detection technique.

Key facts. Cited by 187 papers in the map (data through 7 Oct 2026). First posted on arXiv 11 Feb 2020.

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Citing papers per year: 2021: 13; 2022: 31; 2023: 44; 2024: 39; 2025: 37; 2026: 23.

187 papers.

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  1. 2022Transformers in Time Series: A Surveycited by 489
  2. 2021Graph Neural Network-Based Anomaly Detection in Multivariate Time Seriescited by 300
  3. 2024Deep Time Series Models: A Comprehensive Survey and Benchmarkcited by 191
  4. 2023A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detectioncited by 145
  5. 2022Deep Learning for Time Series Anomaly Detection: A Surveycited by 130
  6. 2021Learning Graph Structures with Transformer for Multivariate Time Series Anomaly Detection in IoTcited by 104
  7. 2023DCdetector: Dual Attention Contrastive Representation Learning for Time Series Anomaly Detectioncited by 103
  8. 2023Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and Prospectscited by 97
  9. 2021An Evaluation of Anomaly Detection and Diagnosis in Multivariate Time Seriescited by 68
  10. 2023Generalized Video Anomaly Event Detection: Systematic Taxonomy and Comparison of Deep Modelscited by 59
  11. 2023A Survey on Time-Series Pre-Trained Modelscited by 52
  12. 2022TFAD: A Decomposition Time Series Anomaly Detection Architecture with Time-Frequency Analysiscited by 48
38 more
  1. 2023ImDiffusion: Imputed Diffusion Models for Multivariate Time Series Anomaly Detectioncited by 38
  2. 2023GATGPT: A Pre-trained Large Language Model with Graph Attention Network for Spatiotemporal Imputationcited by 35
  3. 2024Heterogeneous Contrastive Learning for Foundation Models and Beyondcited by 27
  4. 2024Large Language Models can Deliver Accurate and Interpretable Time Series Anomaly Detectioncited by 26
  5. 2022Unsupervised Model Selection for Time-series Anomaly Detectioncited by 24
  6. 2024Networking Systems for Video Anomaly Detection: A Tutorial and Surveycited by 21
  7. 2024See it, Think it, Sorted: Large Multimodal Models are Few-shot Time Series Anomaly Analyzerscited by 20
  8. 2024Root Cause Analysis of Outliers with Missing Structural Knowledgecited by 19
  9. 2023Graph Anomaly Detection in Time Series: A Surveycited by 18
  10. 2022Review of automated time series forecasting pipelinescited by 17
  11. 2023Correlation-aware Spatial-Temporal Graph Learning for Multivariate Time-series Anomaly Detectioncited by 17
  12. 2023Nominality Score Conditioned Time Series Anomaly Detection by Point/Sequential Reconstructioncited by 17
  13. 2024RobustTSF: Towards Theory and Design of Robust Time Series Forecasting with Anomaliescited by 17
  14. 2024Position: Quo Vadis, Unsupervised Time Series Anomaly Detection?cited by 17
  15. 2025Out-of-Distribution Generalization in Time Series: A Surveycited by 15
  16. 2025Improving Time Series Forecasting via Instance-aware Post-hoc Revisioncited by 14
  17. 2023Set Features for Fine-grained Anomaly Detectioncited by 13
  18. 2023AI for IT Operations (AIOps) on Cloud Platforms: Reviews, Opportunities and Challengescited by 13
  19. 2023Unraveling the "Anomaly" in Time Series Anomaly Detection: A Self-supervised Tri-domain Solutioncited by 12
  20. 2023Unsupervised Deep Learning for IoT Time Seriescited by 11
  21. 2023Time Series Anomaly Detection using Diffusion-based Modelscited by 11
  22. 2025VUS: Effective and Efficient Accuracy Measures for Time-Series Anomaly Detectioncited by 11
  23. 2022Time-Series Anomaly Detection with Implicit Neural Representationcited by 10
  24. 2022Diverse Counterfactual Explanations for Anomaly Detection in Time Seriescited by 10
  25. 2022Deep Contrastive One-Class Time Series Anomaly Detectioncited by 10
  26. 2022TSFool: Crafting Highly-Imperceptible Adversarial Time Series through Multi-Objective Attackcited by 9
  27. 2022Is it worth it? Comparing six deep and classical methods for unsupervised anomaly detection in time seriescited by 9
  28. 2023DDMT: Denoising Diffusion Mask Transformer Models for Multivariate Time Series Anomaly Detectioncited by 9
  29. 2023Successive Data Injection in Conditional Quantum GAN Applied to Time Series Anomaly Detectioncited by 8
  30. 2021gen2Out: Detecting and Ranking Generalized Anomaliescited by 7
  31. 2022An efficient aggregation method for the symbolic representation of temporal datacited by 7
  32. 2025DConAD: A Differencing-based Contrastive Representation Learning Framework for Time Series Anomaly Detectioncited by 7
  33. 2025Robust and Explainable Detector of Time Series Anomaly via Augmenting Multiclass Pseudo-Anomaliescited by 7
  34. 2025Time-RA: Towards Time Series Reasoning for Anomaly Diagnosis with LLM Feedbackcited by 7
  35. 2021Quantile-based fuzzy C-means clustering of multivariate time series: Robust techniquescited by 6
  36. 2023Correlated Attention in Transformers for Multivariate Time Seriescited by 6
  37. 2023Entropy Causal Graphs for Multivariate Time Series Anomaly Detectioncited by 6
  38. 2024Online Model-based Anomaly Detection in Multivariate Time Series: Taxonomy, Survey, Research Challenges and Future Directionscited by 6

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