mlsanityData to 7 Oct 2026

Generative adversarial networks in time series: A survey and taxonomy

arXiv 2107.11098 ↗First posted 23 Jul 2021cs.LG cs.AIcited by 20

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Abstract (arXiv)

Generative adversarial networks (GANs) studies have grown exponentially in the past few years. Their impact has been seen mainly in the computer vision field with realistic image and video manipulation, especially generation, making significant advancements. While these computer vision advances have garnered much attention, GAN applications have diversified across disciplines such as time series and sequence generation. As a relatively new niche for GANs, fieldwork is ongoing to develop high quality, diverse and private time series data. In this paper, we review GAN variants designed for time series related applications. We propose a taxonomy of discrete-variant GANs and continuous-variant GANs, in which GANs deal with discrete time series and continuous time series data. Here we showcase the latest and most popular literature in this field; their architectures, results, and applications. We also provide a list of the most popular evaluation metrics and their suitability across applications. Also presented is a discussion of privacy measures for these GANs and further protections and directions for dealing with sensitive data. We aim to frame clearly and concisely the latest and state-of-the-art research in this area and their applications to real-world technologies.

Key facts. Cited by 20 papers in the map (data through 7 Oct 2026). Builds on 42 of its 104 references that are in the map. First posted on arXiv 23 Jul 2021.

Builds on

42 of 104 references.

References · 42

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2016Improved Techniques for Training GANscited by 2,938
  3. 2016Deep Learning with Differential Privacycited by 2,682
  4. 2016Membership Inference Attacks against Machine Learning Modelscited by 2,221
  5. 2015Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networkscited by 2,048
  6. 2016Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Networkcited by 1,898
  7. 2015Scheduled Sampling for Sequence Prediction with Recurrent Neural Networkscited by 779
  8. 2016Generative Adversarial Text to Image Synthesiscited by 735
  9. 2017Wasserstein GANcited by 635
  10. 2016SeqGAN: Sequence Generative Adversarial Nets with Policy Gradientcited by 375
  11. 2017Real-valued (Medical) Time Series Generation with Recurrent Conditional GANscited by 329
  12. 2018Generative Adversarial Network in Medical Imaging: A Reviewcited by 257
30 more
  1. 2015Generative Moment Matching Networkscited by 252
  2. 2018Differentially Private Generative Adversarial Networkcited by 235
  3. 2018Pros and Cons of GAN Evaluation Measurescited by 224
  4. 2018Adversarial Audio Synthesiscited by 222
  5. 2020A Review on Generative Adversarial Networks: Algorithms, Theory, and Applicationscited by 209
  6. 2017Generating Multi-label Discrete Patient Records using Generative Adversarial Networkscited by 197
  7. 2019MAD-GAN: Multivariate Anomaly Detection for Time Series Data with Generative Adversarial Networkscited by 176
  8. 2016C-RNN-GAN: Continuous recurrent neural networks with adversarial trainingcited by 132
  9. 2021Pros and Cons of GAN Evaluation Measures: New Developmentscited by 121
  10. 2019Quant GANs: Deep Generation of Financial Time Seriescited by 107
  11. 2016Generative Models and Model Criticism via Optimized Maximum Mean Discrepancycited by 99
  12. 2020FedGAN: Federated Generative Adversarial Networks for Distributed Datacited by 89
  13. 2019Generative Models for Effective ML on Private, Decentralized Datasetscited by 85
  14. 2018MD-GAN: Multi-Discriminator Generative Adversarial Networks for Distributed Datasetscited by 49
  15. 2018EEG-GAN: Generative adversarial networks for electroencephalograhic (EEG) brain signalscited by 46
  16. 2017Synthetic Medical Images from Dual Generative Adversarial Networkscited by 32
  17. 2017Boundary-Seeking Generative Adversarial Networkscited by 27
  18. 2019Synthesis of Realistic ECG using Generative Adversarial Networkscited by 26
  19. 2020Decision-Aware Conditional GANs for Time Series Datacited by 11
  20. 2016Generating images with recurrent adversarial networkscited by 9
  21. 2019Quick and Easy Time Series Generation with Established Image-based GANscited by 9
  22. 2018Use of Neural Signals to Evaluate the Quality of Generative Adversarial Network Performance in Facial Image Generationcited by 7
  23. 2019Biosignal Generation and Latent Variable Analysis with Recurrent Generative Adversarial Networkscited by 4
  24. 2019Augmenting Physiological Time Series Data: A Case Study for Sleep Apnea Detectioncited by 4
  25. 2020Controlled time series generation for automotive software-in-the-loop testing using GANscited by 4
  26. 2017Health Data in an Open Worldcited by 3
  27. 2019Synthetic-Neuroscore: Using A Neuro-AI Interface for Evaluating Generative Adversarial Networkscited by 3
  28. 2018Waveform generation for text-to-speech synthesis using pitch-synchronous multi-scale generative adversarial networkscited by 2
  29. 2019Snore-GANs: Improving Automatic Snore Sound Classification with Synthesized Datacited by 2
  30. 2020GANs & Reels: Creating Irish Music using a Generative Adversarial Networkcited by 2

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 10; 2023: 5; 2024: 2; 2025: 1; 2026: 1.

20 papers.

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  1. 2023Vector Quantized Time Series Generation with a Bidirectional Prior Modelcited by 49
  2. 2022TTS-GAN: A Transformer-based Time-Series Generative Adversarial Networkcited by 41
  3. 2022Data Augmentation techniques in time series domain: A survey and taxonomycited by 41
  4. 2022Generating multivariate time series with COmmon Source CoordInated GAN (COSCI-GAN)cited by 23
  5. 2022TTS-CGAN: A Transformer Time-Series Conditional GAN for Biosignal Data Augmentationcited by 20
  6. 2023Unsupervised Representation Learning for Time Series: A Reviewcited by 14
  7. 2021Spiking Generative Adversarial Networks With a Neural Network Discriminator: Local Training, Bayesian Models, and Continual Meta-Learningcited by 8
  8. 2022Fighting Money Laundering with Statistics and Machine Learningcited by 5
  9. 2024A Survey of Transformer Enabled Time Series Synthesiscited by 5
  10. 2022ALGAN: Anomaly Detection by Generating Pseudo Anomalous Data via Latent Variablescited by 4
  11. 2025MSDformer: Multi-scale Discrete Transformer For Time Series Generationcited by 3
  12. 2022Mitigating Health Data Poverty: Generative Approaches versus Resampling for Time-series Clinical Datacited by 2
8 more
  1. 2023GlucoSynth: Generating Differentially-Private Synthetic Glucose Tracescited by 2
  2. 2022DVGAN: Stabilize Wasserstein GAN training for time-domain Gravitational Wave physicscited by 1
  3. 2022How to train your draGAN: A task oriented solution to imbalanced classificationcited by 0
  4. 2022Distribution estimation and change-point estimation for time series via DNN-based GANscited by 0
  5. 2023TCGAN: Convolutional Generative Adversarial Network for Time Series Classification and Clusteringcited by 0
  6. 2023A Spatiotemporal Deep Neural Network for Fine-Grained Multi-Horizon Wind Predictioncited by 0
  7. 2024Representation Learning of Multivariate Time Series using Attention and Adversarial Trainingcited by 0
  8. 2026Graph2TS: Structure-Controlled Time Series Generation via Quantile-Graph VAEscited by 0