mlsanityData to 7 Oct 2026

Time Series Data Augmentation for Deep Learning: A Survey

arXiv 2002.12478 ↗First posted 27 Feb 2020cs.LG eess.SP stat.MLcited by 225

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Abstract (arXiv)

Deep learning performs remarkably well on many time series analysis tasks recently. The superior performance of deep neural networks relies heavily on a large number of training data to avoid overfitting. However, the labeled data of many real-world time series applications may be limited such as classification in medical time series and anomaly detection in AIOps. As an effective way to enhance the size and quality of the training data, data augmentation is crucial to the successful application of deep learning models on time series data. In this paper, we systematically review different data augmentation methods for time series. We propose a taxonomy for the reviewed methods, and then provide a structured review for these methods by highlighting their strengths and limitations. We also empirically compare different data augmentation methods for different tasks including time series classification, anomaly detection, and forecasting. Finally, we discuss and highlight five future directions to provide useful research guidance.

Key facts. Cited by 225 papers in the map (data through 7 Oct 2026). First posted on arXiv 27 Feb 2020.

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Citing papers per year: 2021: 20; 2022: 44; 2023: 48; 2024: 51; 2025: 36; 2026: 26.

225 papers.

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  1. 2021A Survey of Uncertainty in Deep Neural Networkscited by 675
  2. 2022Transformers in Time Series: A Surveycited by 489
  3. 2023Data-centric Artificial Intelligence: A Surveycited by 202
  4. 2022FiLM: Frequency improved Legendre Memory Model for Long-term Time Series Forecastingcited by 147
  5. 2021A Survey on Data Augmentation for Text Classificationcited by 111
  6. 2023Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and Prospectscited by 97
  7. 2025Time-MQA: Time Series Multi-Task Question Answering with Context Enhancementcited by 74
  8. 2022Synthetic Data -- what, why and how?cited by 66
  9. 2022Accelerating Dataset Distillation via Model Augmentationcited by 62
  10. 2023Time Series Contrastive Learning with Information-Aware Augmentationscited by 52
  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. 2024Position: What Can Large Language Models Tell Us about Time Series Analysiscited by 47
  2. 2022Data Augmentation techniques in time series domain: A survey and taxonomycited by 41
  3. 2023A Survey on Deep Learning based Time Series Analysis with Frequency Transformationcited by 40
  4. 2022What Makes Good Contrastive Learning on Small-Scale Wearable-based Tasks?cited by 39
  5. 2022Self-supervised Contrastive Representation Learning for Semi-supervised Time-Series Classificationcited by 39
  6. 2024A Comprehensive Survey on Data Augmentationcited by 38
  7. 2022Deep Learning Methods for Abstract Visual Reasoning: A Survey on Raven's Progressive Matricescited by 37
  8. 2022A Survey of Mix-based Data Augmentation: Taxonomy, Methods, Applications, and Explainabilitycited by 30
  9. 2022WOODS: Benchmarks for Out-of-Distribution Generalization in Time Seriescited by 29
  10. 2023Toward a Foundation Model for Time Series Datacited by 28
  11. 2024Universal Time-Series Representation Learning: A Surveycited by 28
  12. 2021Meta-Learning to Improve Pre-Trainingcited by 27
  13. 2022Beyond Just Vision: A Review on Self-Supervised Representation Learning on Multimodal and Temporal Datacited by 27
  14. 2022Unsupervised Model Selection for Time-series Anomaly Detectioncited by 24
  15. 2022The good, the bad and the ugly sides of data augmentation: An implicit spectral regularization perspectivecited by 24
  16. 2023A review of ensemble learning and data augmentation models for class imbalanced problems: combination, implementation and evaluationcited by 24
  17. 2024Revisiting VAE for Unsupervised Time Series Anomaly Detection: A Frequency Perspectivecited by 22
  18. 2022Model-Agnostic Augmentation for Accurate Graph Classificationcited by 21
  19. 2022COCOA: Cross Modality Contrastive Learning for Sensor Datacited by 21
  20. 2025A Survey of Reasoning and Agentic Systems in Time Series with Large Language Modelscited by 21
  21. 2024TimeSiam: A Pre-Training Framework for Siamese Time-Series Modelingcited by 20
  22. 2023Finding Order in Chaos: A Novel Data Augmentation Method for Time Series in Contrastive Learningcited by 18
  23. 2022Data Augmentation vs. Equivariant Networks: A Theory of Generalization on Dynamics Forecastingcited by 17
  24. 2021GLASS: Geometric Latent Augmentation for Shape Spacescited by 16
  25. 2022Boomerang: Local sampling on image manifolds using diffusion modelscited by 16
  26. 2023Label-efficient Time Series Representation Learning: A Reviewcited by 16
  27. 2023TSGM: A Flexible Framework for Generative Modeling of Synthetic Time Seriescited by 16
  28. 2024Adversarial Learning for Neural PDE Solvers with Sparse Datacited by 16
  29. 2025Deep Learning Advancements in Anomaly Detection: A Comprehensive Surveycited by 15
  30. 2023Unsupervised Representation Learning for Time Series: A Reviewcited by 14
  31. 2024Automated data processing and feature engineering for deep learning and big data applications: a surveycited by 14
  32. 2025Time Series Generation Under Data Scarcity: A Unified Generative Modeling Approachcited by 14
  33. 2022Data Augmentation for Electrocardiogramscited by 12
  34. 2023Unraveling the "Anomaly" in Time Series Anomaly Detection: A Self-supervised Tri-domain Solutioncited by 12
  35. 2024Parametric Augmentation for Time Series Contrastive Learningcited by 12
  36. 2023Unsupervised Deep Learning for IoT Time Seriescited by 11
  37. 2023ADGym: Design Choices for Deep Anomaly Detectioncited by 11
  38. 2023Improving Conversational Recommendation Systems via Counterfactual Data Simulationcited by 10

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