mlsanityData to 7 Oct 2026

Data Augmentation techniques in time series domain: A survey and taxonomy

arXiv 2206.13508 ↗First posted 25 Jun 2022cs.LG cs.AIcited by 41

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Abstract (arXiv)

With the latest advances in Deep Learning-based generative models, it has not taken long to take advantage of their remarkable performance in the area of time series. Deep neural networks used to work with time series heavily depend on the size and consistency of the datasets used in training. These features are not usually abundant in the real world, where they are usually limited and often have constraints that must be guaranteed. Therefore, an effective way to increase the amount of data is by using Data Augmentation techniques, either by adding noise or permutations and by generating new synthetic data. This work systematically reviews the current state-of-the-art in the area to provide an overview of all available algorithms and proposes a taxonomy of the most relevant research. The efficiency of the different variants will be evaluated as a central part of the process, as well as the different metrics to evaluate the performance and the main problems concerning each model will be analysed. The ultimate aim of this study is to provide a summary of the evolution and performance of areas that produce better results to guide future researchers in this field.

Key facts. Cited by 41 papers in the map (data through 7 Oct 2026). Builds on 56 of its 133 references that are in the map. First posted on arXiv 25 Jun 2022.

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56 of 133 references.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 6; 2024: 14; 2025: 12; 2026: 9.

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  1. 2023Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and Prospectscited by 97
  2. 2024A Comprehensive Survey on Data Augmentationcited by 38
  3. 2024EntAugment: Entropy-Driven Adaptive Data Augmentation Framework for Image Classificationcited by 13
  4. 2024Relaxed Equivariance via Multitask Learningcited by 13
  5. 2024A Survey on Multimodal Wearable Sensor-based Human Action Recognitioncited by 10
  6. 2023Data Augmentation for Time-Series Classification: An Extensive Empirical Study and Comprehensive Surveycited by 9
  7. 2024Time-Causal VAE: Robust Financial Time Series Generatorcited by 8
  8. 2024Macroeconomic Forecasting with Large Language Modelscited by 6
  9. 2024A Survey of Transformer Enabled Time Series Synthesiscited by 5
  10. 2025WatchGuardian: Enabling User-Defined Personalized Just-in-Time Intervention on Smartwatchcited by 4
  11. 2023D3A-TS: Denoising-Driven Data Augmentation in Time Seriescited by 3
  12. 2024ReAugment: Model Zoo-Guided RL for Few-Shot Time Series Augmentation and Forecastingcited by 3
29 more
  1. 2025Synthetic Data Generation for Augmenting Small Samplescited by 3
  2. 2025PhysioSync: Temporal and Cross-Modal Contrastive Learning Inspired by Physiological Synchronization for EEG-Based Emotion Recognitioncited by 3
  3. 2024Evaluating the Role of Data Enrichment Approaches Towards Rare Event Analysis in Manufacturingcited by 2
  4. 2025Data Augmentation of Time-Series Data in Human Movement Biomechanics: A Scoping Reviewcited by 2
  5. 2025A Comprehensive Survey on Network Traffic Synthesis: From Statistical Models to Deep Learningcited by 2
  6. 2024Explainable Anomaly Detection: Counterfactual driven What-If Analysiscited by 1
  7. 2024Synthetic ECG Generation for Data Augmentation and Transfer Learning in Arrhythmia Classificationcited by 1
  8. 2025A Joint Learning Framework with Feature Reconstruction and Prediction for Incomplete Satellite Image Time Series in Agricultural Semantic Segmentationcited by 1
  9. 2025On-the-Fly Data Augmentation via Gradient-Guided and Sample-Aware Influence Estimationcited by 1
  10. 2026TransConv-DDPM: Enhanced Diffusion Model for Generating Time-Series Data in Healthcarecited by 1
  11. 2026Adapt Data to Model: Adaptive Transformation Optimization for Domain-shared Time Series Foundation Modelscited by 1
  12. 2023Attention-stacked Generative Adversarial Network (AS-GAN)-empowered Sensor Data Augmentation for Online Monitoring of Manufacturing Systemcited by 0
  13. 2023Improving the Security of Smartwatch Payment with Deep Learningcited by 0
  14. 2023Collinear datasets augmentation using Procrustes validation setscited by 0
  15. 2024Generating Synthetic Time Series Data for Cyber-Physical Systemscited by 0
  16. 2024Enhancing Indoor Temperature Forecasting through Synthetic Data in Low-Data Environmentscited by 0
  17. 2024HSDA: High-frequency Shuffle Data Augmentation for Bird's-Eye-View Map Segmentationcited by 0
  18. 2025Swarm Characteristic Classification using Robust Neural Networks with Optimized Controllable Inputscited by 0
  19. 2025Augmentation of EEG and ECG Time Series for Deep Learning Applications: Integrating Changepoint Detection into the iAAFT Surrogatescited by 0
  20. 2025Industrial Energy Disaggregation with Digital Twin-generated Dataset and Efficient Data Augmentationcited by 0
  21. 2025Efficient Online Continual Learning in Sensor-Based Human Activity Recognitioncited by 0
  22. 2025Stitch and Tell: A Structured Multimodal Data Augmentation Method for Spatial Understandingcited by 0
  23. 2026Generation of synthetic delay time series for air transport applicationscited by 0
  24. 2026A Dataset of Dengue Hospitalizations in Brazil (1999 to 2021) with Weekly Disaggregation from Monthly Countscited by 0
  25. 2026Synthetic Time Series Generation via Complex Networkscited by 0
  26. 2026Characterizing the Generalization Error of Random Feature Regression with Arbitrary Data-Augmentationcited by 0
  27. 2026Robust Transformer-Based One-Step Stock Index Forecasting via Shifted Data Augmentationcited by 0
  28. 2026Contrastive Representation-Guided Genetic Minority Oversampling for Imbalanced Time-Series Classificationcited by 0
  29. 2026Phenomenon-Graph JEPA: Label-Efficient Representation Learning for Contactless Cardiorespiratory Sensingcited by 0