mlsanityData to 7 Oct 2026

Real-valued (Medical) Time Series Generation with Recurrent Conditional GANs

arXiv 1706.02633 ↗First posted 8 Jun 2017stat.ML cs.LGcited by 329

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Cited by 40 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Generative Adversarial Networks (GANs) have shown remarkable success as a framework for training models to produce realistic-looking data. In this work, we propose a Recurrent GAN (RGAN) and Recurrent Conditional GAN (RCGAN) to produce realistic real-valued multi-dimensional time series, with an emphasis on their application to medical data. RGANs make use of recurrent neural networks in the generator and the discriminator. In the case of RCGANs, both of these RNNs are conditioned on auxiliary information. We demonstrate our models in a set of toy datasets, where we show visually and quantitatively (using sample likelihood and maximum mean discrepancy) that they can successfully generate realistic time-series. We also describe novel evaluation methods for GANs, where we generate a synthetic labelled training dataset, and evaluate on a real test set the performance of a model trained on the synthetic data, and vice-versa. We illustrate with these metrics that RCGANs can generate time-series data useful for supervised training, with only minor degradation in performance on real test data. This is demonstrated on digit classification from 'serialised' MNIST and by training an early warning system on a medical dataset of 17,000 patients from an intensive care unit. We further discuss and analyse the privacy concerns that may arise when using RCGANs to generate realistic synthetic medical time series data.

Key facts. Cited by 329 papers in the map (data through 7 Oct 2026). First posted on arXiv 8 Jun 2017.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 41; 2022: 55; 2023: 66; 2024: 53; 2025: 62; 2026: 52.

329 papers.

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  1. 2021Neural SDEs as Infinite-Dimensional GANscited by 159
  2. 2021TimeVAE: A Variational Auto-Encoder for Multivariate Time Series Generationcited by 139
  3. 2023Machine Learning for Synthetic Data Generation: A Reviewcited by 118
  4. 2023Predict, Refine, Synthesize: Self-Guiding Diffusion Models for Probabilistic Time Series Forecastingcited by 86
  5. 2022STaSy: Score-based Tabular data Synthesiscited by 85
  6. 2021WaveFake: A Data Set to Facilitate Audio Deepfake Detectioncited by 78
  7. 2023CoDi: Co-evolving Contrastive Diffusion Models for Mixed-type Tabular Synthesiscited by 78
  8. 2023Diffusion Models for Time Series Applications: A Surveycited by 72
  9. 2022Synthetic Data -- what, why and how?cited by 66
  10. 2021PSA-GAN: Progressive Self Attention GANs for Synthetic Time Seriescited by 58
  11. 2024Deep Learning for Multivariate Time Series Imputation: A Surveycited by 57
  12. 2023On the Constrained Time-Series Generation Problemcited by 52
38 more
  1. 2023Curated LLM: Synergy of LLMs and Data Curation for tabular augmentation in low-data regimescited by 52
  2. 2023Vector Quantized Time Series Generation with a Bidirectional Prior Modelcited by 49
  3. 2023Generative Modeling of Regular and Irregular Time Series Data via Koopman VAEscited by 48
  4. 2021Generating Synthetic Mixed-type Longitudinal Electronic Health Records for Artificial Intelligent Applicationscited by 47
  5. 2023Causal Recurrent Variational Autoencoder for Medical Time Series Generationcited by 44
  6. 2022A Multifaceted Benchmarking of Synthetic Electronic Health Record Generation Modelscited by 43
  7. 2022GT-GAN: General Purpose Time Series Synthesis with Generative Adversarial Networkscited by 43
  8. 2024Utilizing Image Transforms and Diffusion Models for Generative Modeling of Short and Long Time Seriescited by 42
  9. 2022TTS-GAN: A Transformer-based Time-Series Generative Adversarial Networkcited by 41
  10. 2022Data Augmentation techniques in time series domain: A survey and taxonomycited by 41
  11. 2024A Comprehensive Survey on Data Augmentationcited by 38
  12. 2023CausalTime: Realistically Generated Time-series for Benchmarking of Causal Discoverycited by 35
  13. 2024Time Series Diffusion in the Frequency Domaincited by 35
  14. 2024Time Weaver: A Conditional Time Series Generation Modelcited by 35
  15. 2022SOS: Score-based Oversampling for Tabular Datacited by 29
  16. 2024How Realistic Is Your Synthetic Data? Constraining Deep Generative Models for Tabular Datacited by 29
  17. 2021Conditional Generative Models for Counterfactual Explanationscited by 28
  18. 2021OCT-GAN: Neural ODE-based Conditional Tabular GANscited by 27
  19. 2021Towards Generating Real-World Time Series Datacited by 26
  20. 2023Feature Likelihood Divergence: Evaluating the Generalization of Generative Models Using Samplescited by 25
  21. 2022Invertible Tabular GANs: Killing Two Birds with OneStone for Tabular Data Synthesiscited by 24
  22. 2023TSGBench: Time Series Generation Benchmarkcited by 24
  23. 2023Reliable Generation of Privacy-preserving Synthetic Electronic Health Record Time Series via Diffusion Modelscited by 24
  24. 2023AutoDiff: combining Auto-encoder and Diffusion model for tabular data synthesizingcited by 24
  25. 2024TWIN-GPT: Digital Twins for Clinical Trials via Large Language Modelcited by 24
  26. 2022Generating multivariate time series with COmmon Source CoordInated GAN (COSCI-GAN)cited by 23
  27. 2023Regular Time-series Generation using SGMcited by 23
  28. 2022Data-IQ: Characterizing subgroups with heterogeneous outcomes in tabular datacited by 22
  29. 2023Synthetic ECG Signal Generation using Probabilistic Diffusion Modelscited by 22
  30. 2021Generative adversarial networks in time series: A survey and taxonomycited by 20
  31. 2022TTS-CGAN: A Transformer Time-Series Conditional GAN for Biosignal Data Augmentationcited by 20
  32. 2022Controllable Data Generation by Deep Learning: A Reviewcited by 20
  33. 2024A Survey on Statistical Theory of Deep Learning: Approximation, Training Dynamics, and Generative Modelscited by 20
  34. 2022The Health Gym: Synthetic Health-Related Datasets for the Development of Reinforcement Learning Algorithmscited by 19
  35. 2023NetDiffus: Network Traffic Generation by Diffusion Models through Time-Series Imagingcited by 18
  36. 2025Beyond the convexity assumption: Realistic tabular data generation under quantifier-free real linear constraintscited by 18
  37. 2021A Deep Adversarial Model for Suffix and Remaining Time Prediction of Event Sequencescited by 17
  38. 2023SurvivalGAN: Generating Time-to-Event Data for Survival Analysiscited by 17

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