mlsanityData to 14 Oct 2026

Anomaly Detection in Univariate Time-series: A Survey on the State-of-the-Art

arXiv 2004.00433 ↗First posted 1 Apr 2020cs.LG stat.MLcited by 56

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Abstract (arXiv)

Anomaly detection for time-series data has been an important research field for a long time. Seminal work on anomaly detection methods has been focussing on statistical approaches. In recent years an increasing number of machine learning algorithms have been developed to detect anomalies on time-series. Subsequently, researchers tried to improve these techniques using (deep) neural networks. In the light of the increasing number of anomaly detection methods, the body of research lacks a broad comparative evaluation of statistical, machine learning and deep learning methods. This paper studies 20 univariate anomaly detection methods from the all three categories. The evaluation is conducted on publicly available datasets, which serve as benchmarks for time-series anomaly detection. By analyzing the accuracy of each method as well as the computation time of the algorithms, we provide a thorough insight about the performance of these anomaly detection approaches, alongside some general notion of which method is suited for a certain type of data.

Key facts. Cited by 56 papers in the map (data through 14 Oct 2026). First posted on arXiv 1 Apr 2020.

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Citing papers per year: 2021: 7; 2022: 14; 2023: 15; 2024: 6; 2025: 10; 2026: 4.

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  1. 2024Deep Time Series Models: A Comprehensive Survey and Benchmarkcited by 197
  2. 2022Deep Learning for Time Series Anomaly Detection: A Surveycited by 131
  3. 2022Unsupervised Time-Series Representation Learning with Iterative Bilinear Temporal-Spectral Fusioncited by 96
  4. 2021Causal Inference for Time series Analysis: Problems, Methods and Evaluationcited by 42
  5. 2023Navigating the Metric Maze: A Taxonomy of Evaluation Metrics for Anomaly Detection in Time Seriescited by 24
  6. 2023MST-GAT: A Multimodal Spatial-Temporal Graph Attention Network for Time Series Anomaly Detectioncited by 20
  7. 2022TiSAT: Time Series Anomaly Transformercited by 15
  8. 2023AI for IT Operations (AIOps) on Cloud Platforms: Reviews, Opportunities and Challengescited by 15
  9. 2021Online false discovery rate control for anomaly detection in time seriescited by 13
  10. 2023Time Series Anomaly Detection using Diffusion-based Modelscited by 12
  11. 2023Unsupervised Deep Learning for IoT Time Seriescited by 11
  12. 2022Is it worth it? Comparing six deep and classical methods for unsupervised anomaly detection in time seriescited by 9
38 more
  1. 2025Robust and Explainable Detector of Time Series Anomaly via Augmenting Multiclass Pseudo-Anomaliescited by 7
  2. 2025mTSBench: Benchmarking Multivariate Time Series Anomaly Detection and Model Selection at Scalecited by 7
  3. 2022MOSPAT: AutoML based Model Selection and Parameter Tuning for Time Series Anomaly Detectioncited by 6
  4. 2021Time Series Analysis and Modeling to Forecast: a Surveycited by 5
  5. 2022A Meta-level Analysis of Online Anomaly Detectorscited by 5
  6. 2023Towards Total Online Unsupervised Anomaly Detection and Localization in Industrial Visioncited by 5
  7. 2022Contrastive predictive coding for Anomaly Detection in Multi-variate Time Series Datacited by 4
  8. 2022Stacked Residuals of Dynamic Layers for Time Series Anomaly Detectioncited by 4
  9. 2025A Review on Self-Supervised Learning for Time Series Anomaly Detection: Recent Advances and Open Challengescited by 4
  10. 2021Outlier detection in multivariate functional data through a contaminated mixture modelcited by 3
  11. 2021Time Series Anomaly Detection with label-free Model Selectioncited by 3
  12. 2022Out-of-Distribution Detection in Time-Series Domain: A Novel Seasonal Ratio Scoring Approachcited by 3
  13. 2024Machine Learning-based vs Deep Learning-based Anomaly Detection in Multivariate Time Series for Spacecraft Attitude Sensorscited by 3
  14. 2025M2AD: Multi-Sensor Multi-System Anomaly Detection through Global Scoring and Calibrated Thresholdingcited by 3
  15. 2022Improved Anomaly Detection by Using the Attention-Based Isolation Forestcited by 2
  16. 2023SoftED: Metrics for Soft Evaluation of Time Series Event Detectioncited by 2
  17. 2023Detection of Anomalies in Multivariate Time Series Using Ensemble Techniquescited by 2
  18. 2025Representation Learning of Limit Order Book: A Comprehensive Study and Benchmarkingcited by 2
  19. 2026Bian Que: An Agentic Framework with Flexible Skill Arrangement for Online System Operationscited by 2
  20. 2021Outlier Detection using AI: A Surveycited by 1
  21. 2023CNTS: Cooperative Network for Time Seriescited by 1
  22. 2023Time-series Anomaly Detection based on Difference Subspace between Signal Subspacescited by 1
  23. 2023Impact of Deep Learning Libraries on Online Adaptive Lightweight Time Series Anomaly Detectioncited by 1
  24. 2023Practical Anomaly Detection over Multivariate Monitoring Metrics for Online Servicescited by 1
  25. 2023MadSGM: Multivariate Anomaly Detection with Score-based Generative Modelscited by 1
  26. 2024Attention and Autoencoder Hybrid Model for Unsupervised Online Anomaly Detectioncited by 1
  27. 2024Impact of Recurrent Neural Networks and Deep Learning Frameworks on Real-time Lightweight Time Series Anomaly Detectioncited by 1
  28. 2025Unsupervised Feature Construction for Anomaly Detection in Time Series -- An Evaluationcited by 1
  29. 2025Benchmarking Unsupervised Strategies for Anomaly Detection in Multivariate Time Seriescited by 1
  30. 2021Memory-augmented Adversarial Autoencoders for Multivariate Time-series Anomaly Detection with Deep Reconstruction and Predictioncited by 0
  31. 2022Robust Projection based Anomaly Extraction (RPE) in Univariate Time-Seriescited by 0
  32. 2022DEGAN: Time Series Anomaly Detection using Generative Adversarial Network Discriminators and Density Estimationcited by 0
  33. 2022tegdet: An extensible Python Library for Anomaly Detection using Time-Evolving Graphscited by 0
  34. 2022Label-Efficient Interactive Time-Series Anomaly Detectioncited by 0
  35. 2023Towards Meaningful Anomaly Detection: The Effect of Counterfactual Explanations on the Investigation of Anomalies in Multivariate Time Seriescited by 0
  36. 2023Interruptions detection in video conferencescited by 0
  37. 2024MTAD: Tools and Benchmarks for Multivariate Time Series Anomaly Detectioncited by 0
  38. 2024Understanding Time Series Anomaly State Detection through One-Class Classificationcited by 0

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