mlsanityData to 7 Oct 2026

Deep Learning for Time Series Anomaly Detection: A Survey

arXiv 2211.05244 ↗First posted 9 Nov 2022cs.LG cs.AIcited by 130

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Abstract (arXiv)

Time series anomaly detection has applications in a wide range of research fields and applications, including manufacturing and healthcare. The presence of anomalies can indicate novel or unexpected events, such as production faults, system defects, or heart fluttering, and is therefore of particular interest. The large size and complex patterns of time series have led researchers to develop specialised deep learning models for detecting anomalous patterns. This survey focuses on providing structured and comprehensive state-of-the-art time series anomaly detection models through the use of deep learning. It providing a taxonomy based on the factors that divide anomaly detection models into different categories. Aside from describing the basic anomaly detection technique for each category, the advantages and limitations are also discussed. Furthermore, this study includes examples of deep anomaly detection in time series across various application domains in recent years. It finally summarises open issues in research and challenges faced while adopting deep anomaly detection models.

Key facts. Cited by 130 papers in the map (data through 7 Oct 2026). Builds on 62 of its 218 references that are in the map. First posted on arXiv 9 Nov 2022.

Builds on

62 of 218 references.

References · 62

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38 more
  1. 2018Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic Thresholdingcited by 407
  2. 2021Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancycited by 387
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  4. 2019Deep Learning for Anomaly Detection: A Surveycited by 357
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 13; 2024: 14; 2025: 51; 2026: 52.

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  1. 2023A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detectioncited by 145
  2. 2023DCdetector: Dual Attention Contrastive Representation Learning for Time Series Anomaly Detectioncited by 103
  3. 2024A Survey of Large Language Models for Financial Applications: Progress, Prospects and Challengescited by 86
  4. 2025Time-MQA: Time Series Multi-Task Question Answering with Context Enhancementcited by 74
  5. 2025TAB: Unified Benchmarking of Time Series Anomaly Detection Methodscited by 58
  6. 2023A Survey on Deep Learning based Time Series Analysis with Frequency Transformationcited by 40
  7. 2024Universal Time-Series Representation Learning: A Surveycited by 28
  8. 2024TS-Reasoner: Domain-Oriented Time Series Inference Agents for Reasoning and Automated Analysiscited by 28
  9. 2025A Survey of Reasoning and Agentic Systems in Time Series with Large Language Modelscited by 21
  10. 2023Graph Anomaly Detection in Time Series: A Surveycited by 18
  11. 2023Correlation-aware Spatial-Temporal Graph Learning for Multivariate Time-series Anomaly Detectioncited by 17
  12. 2023CARLA: Self-supervised Contrastive Representation Learning for Time Series Anomaly Detectioncited by 15
38 more
  1. 2023AI for IT Operations (AIOps) on Cloud Platforms: Reviews, Opportunities and Challengescited by 13
  2. 2023Time Series Anomaly Detection using Diffusion-based Modelscited by 11
  3. 2025Can Multimodal LLMs Perform Time Series Anomaly Detection?cited by 11
  4. 2024Graph Spatiotemporal Process for Multivariate Time Series Anomaly Detection with Missing Valuescited by 10
  5. 2025Harnessing Vision-Language Models for Time Series Anomaly Detectioncited by 9
  6. 2025Towards Foundation Models for Zero-Shot Time Series Anomaly Detection: Leveraging Synthetic Data and Relative Context Discrepancycited by 9
  7. 2025Anomaly Detection and Generation with Diffusion Models: A Surveycited by 8
  8. 2024DACAD: Domain Adaptation Contrastive Learning for Anomaly Detection in Multivariate Time Seriescited by 7
  9. 2025Time-RA: Towards Time Series Reasoning for Anomaly Diagnosis with LLM Feedbackcited by 7
  10. 2023Entropy Causal Graphs for Multivariate Time Series Anomaly Detectioncited by 6
  11. 2025mTSBench: Benchmarking Multivariate Time Series Anomaly Detection and Model Selection at Scalecited by 6
  12. 2025Multi-Agent LLM Orchestration Achieves Deterministic, High-Quality Decision Support for Incident Responsecited by 6
  13. 2023Beyond Sharing: Conflict-Aware Multivariate Time Series Anomaly Detectioncited by 5
  14. 2026PaAno: Patch-Based Representation Learning for Time-Series Anomaly Detectioncited by 5
  15. 2026Tracing Agentic Failure from the Flow of Successcited by 5
  16. 2025A Review on Self-Supervised Learning for Time Series Anomaly Detection: Recent Advances and Open Challengescited by 4
  17. 2025GenIAS: Generator for Instantiating Anomalies in time Seriescited by 4
  18. 2025Incremental Causal Graph Learning for Online Cyberattack Detection in Cyber-Physical Infrastructurescited by 4
  19. 2025A Survey on Video Anomaly Detection via Deep Learning: Human, Vehicle, and Environmentcited by 4
  20. 2025Structured Temporal Causality for Interpretable Multivariate Time Series Anomaly Detectioncited by 4
  21. 2025Deep Joint Distribution Optimal Transport for Universal Domain Adaptation on Time Seriescited by 3
  22. 2025M2AD: Multi-Sensor Multi-System Anomaly Detection through Global Scoring and Calibrated Thresholdingcited by 3
  23. 2025CICADA: Cross-Domain Interpretable Coding for Anomaly Detection and Adaptation in Multivariate Time Seriescited by 3
  24. 2025Rethinking Metrics and Benchmarks of Video Anomaly Detectioncited by 3
  25. 2026Beyond a Single Perspective: Text Anomaly Detection with Multi-View Language Representationscited by 3
  26. 2026OATS: Online Data Augmentation for Time Series Foundation Modelscited by 3
  27. 2026AnomaMind: Agentic Time Series Anomaly Detection with Tool-Augmented Reasoningcited by 3
  28. 2024Joint Selective State Space Model and Detrending for Robust Time Series Anomaly Detectioncited by 2
  29. 2024Towards Unbiased Evaluation of Time-series Anomaly Detectorcited by 2
  30. 2024BlockScan: Detecting Anomalies in Blockchain Transactionscited by 2
  31. 2024PSformer: Parameter-efficient Transformer with Segment Attention for Time Series Forecastingcited by 2
  32. 2025DINAMO: Dynamic and INterpretable Anomaly MOnitoring for Large-Scale Particle Physics Experimentscited by 2
  33. 2025Quantum Autoencoder for Multivariate Time Series Anomaly Detectioncited by 2
  34. 2025An Unsupervised Deep Explainable AI Framework for Localization of Concurrent Replay Attacks in Nuclear Reactor Signalscited by 2
  35. 2023Convolutional and Deep Learning based techniques for Time Series Ordinal Classificationcited by 1
  36. 2024ECATS: Explainable-by-design concept-based anomaly detection for time seriescited by 1
  37. 2024Self-Supervised Spatial-Temporal Normality Learning for Time Series Anomaly Detectioncited by 1
  38. 2024Angel or Devil: Discriminating Hard Samples and Anomaly Contaminations for Unsupervised Time Series Anomaly Detectioncited by 1

50 most cited of 130 All 130 (JSON)