mlsanityData to 7 Oct 2026

GT-GAN: General Purpose Time Series Synthesis with Generative Adversarial Networks

arXiv 2210.02040 ↗First posted 5 Oct 2022cs.LG cs.AIcited by 43

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Abstract (arXiv)

Time series synthesis is an important research topic in the field of deep learning, which can be used for data augmentation. Time series data types can be broadly classified into regular or irregular. However, there are no existing generative models that show good performance for both types without any model changes. Therefore, we present a general purpose model capable of synthesizing regular and irregular time series data. To our knowledge, we are the first designing a general purpose time series synthesis model, which is one of the most challenging settings for time series synthesis. To this end, we design a generative adversarial network-based method, where many related techniques are carefully integrated into a single framework, ranging from neural ordinary/controlled differential equations to continuous time-flow processes. Our method outperforms all existing methods.

Key facts. Cited by 43 papers in the map (data through 7 Oct 2026). Builds on 24 of its 43 references that are in the map. First posted on arXiv 5 Oct 2022.

Builds on

24 of 43 references.

References · 24

  1. 2018Neural Ordinary Differential Equationscited by 3,733
  2. 2017Improved Training of Wasserstein GANscited by 2,223
  3. 2015Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networkscited by 2,048
  4. 2016WaveNet: A Generative Model for Raw Audiocited by 1,710
  5. 2017Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecastingcited by 1,125
  6. 2019Graph WaveNet for Deep Spatial-Temporal Graph Modelingcited by 820
  7. 2018FFJORD: Free-form Continuous Dynamics for Scalable Reversible Generative Modelscited by 584
  8. 2013Generating Sequences With Recurrent Neural Networkscited by 549
  9. 2016Recurrent Neural Networks for Multivariate Time Series with Missing Valuescited by 548
  10. 2020Neural Controlled Differential Equations for Irregular Time Seriescited by 501
  11. 2017Real-valued (Medical) Time Series Generation with Recurrent Conditional GANscited by 329
  12. 2019GRU-ODE-Bayes: Continuous modeling of sporadically-observed time seriescited by 230
12 more
  1. 2018Adversarial Audio Synthesiscited by 222
  2. 2016Professor Forcing: A New Algorithm for Training Recurrent Networkscited by 214
  3. 2019Neural Jump Stochastic Differential Equationscited by 147
  4. 2016C-RNN-GAN: Continuous recurrent neural networks with adversarial trainingcited by 132
  5. 2021Graph-Guided Network for Irregularly Sampled Multivariate Time Seriescited by 110
  6. 2019Deep Signature Transformscited by 75
  7. 2020Learning Continuous System Dynamics from Irregularly-Sampled Partial Observationscited by 60
  8. 2019STG2Seq: Spatial-temporal Graph to Sequence Model for Multi-step Passenger Demand Forecastingcited by 59
  9. 2019Joint Modeling of Local and Global Temporal Dynamics for Multivariate Time Series Forecasting with Missing Valuescited by 37
  10. 2020Modeling Continuous Stochastic Processes with Dynamic Normalizing Flowscited by 37
  11. 2021Attentive Neural Controlled Differential Equations for Time-series Classification and Forecastingcited by 28
  12. 2021Continuous Latent Process Flowscited by 10

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 10; 2024: 9; 2025: 14; 2026: 9.

43 papers.

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  1. 2023Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and Prospectscited by 97
  2. 2023On the Constrained Time-Series Generation Problemcited by 52
  3. 2023Generative Modeling of Regular and Irregular Time Series Data via Koopman VAEscited by 48
  4. 2024Utilizing Image Transforms and Diffusion Models for Generative Modeling of Short and Long Time Seriescited by 42
  5. 2023TSGBench: Time Series Generation Benchmarkcited by 24
  6. 2023Reliable Generation of Privacy-preserving Synthetic Electronic Health Record Time Series via Diffusion Modelscited by 24
  7. 2025BRIDGE: Bootstrapping Text to Control Time-Series Generation via Multi-Agent Iterative Optimization and Diffusion Modelingcited by 18
  8. 2025Time Series Generation Under Data Scarcity: A Unified Generative Modeling Approachcited by 14
  9. 2023PCF-GAN: generating sequential data via the characteristic function of measures on the path spacecited by 13
  10. 2023TransFusion: Generating Long, High Fidelity Time Series using Diffusion Models with Transformerscited by 13
  11. 2024TimEHR: Image-based Time Series Generation for Electronic Health Recordscited by 8
  12. 2025A Diffusion Model for Regular Time Series Generation from Irregular Data with Completion and Maskingcited by 7
31 more
  1. 2023TS-Diffusion: Generating Highly Complex Time Series with Diffusion Modelscited by 6
  2. 2023Time-Transformer: Integrating Local and Global Features for Better Time Series Generation (Extended Version)cited by 6
  3. 2024Denoising-Aware Contrastive Learning for Noisy Time Seriescited by 5
  4. 2025New Money: A Systematic Review of Synthetic Data Generation for Financecited by 5
  5. 2022Time Series Forecasting with Hypernetworks Generating Parameters in Advancecited by 4
  6. 2025MSDformer: Multi-scale Discrete Transformer For Time Series Generationcited by 3
  7. 2025CTBench: Cryptocurrency Time Series Generation Benchmarkcited by 3
  8. 2025TSGM: Regular and Irregular Time-series Generation using Score-based Generative Modelscited by 3
  9. 2025Theoretical Foundation of Flow-Based Time Series Generation: Provable Approximation, Generalization, and Efficiencycited by 2
  10. 2025DS-Diffusion: Data Style-Guided Diffusion Model for Time-Series Generationcited by 2
  11. 2025Sequence Modeling with Spectral Mean Flowscited by 2
  12. 2025TimeFlow: Towards Stochastic-Aware and Efficient Time Series Generation via Flow Matching Modelingcited by 2
  13. 2026Diff-MN: Diffusion Parameterized MoE-NCDE for Continuous Time Series Generation with Irregular Observationscited by 2
  14. 2023Long-term Time Series Forecasting based on Decomposition and Neural Ordinary Differential Equationscited by 1
  15. 2024VFLGAN-TS: Vertical Federated Learning-based Generative Adversarial Networks for Publication of Vertically Partitioned Time-Series Datacited by 1
  16. 2024Bridging Dynamic Factor Models and Neural Controlled Differential Equations for Nowcasting GDPcited by 1
  17. 2025Stage-Diff: Stage-wise Long-Term Time Series Generation Based on Diffusion Modelscited by 1
  18. 2025Learning Time-Series Representations by Hierarchical Uniformity-Tolerance Latent Balancingcited by 1
  19. 2026E4GEN: Event-level Explainable Extreme-Enhanced Time-series Generationcited by 1
  20. 2024MGCP: A Multi-Grained Correlation based Prediction Network for Multivariate Time Seriescited by 0
  21. 2024Deep Temporal Deaggregation: Large-Scale Spatio-Temporal Generative Modelscited by 0
  22. 2024Simulating realistic short tandem repeat capillary electrophoretic signal using a generative adversarial networkcited by 0
  23. 2024A Wave is Worth 100 Words: Investigating Cross-Domain Transferability in Time Seriescited by 0
  24. 2025Modeling Neural Networks with Privacy Using Neural Stochastic Differential Equationscited by 0
  25. 2026Preserving Temporal Dynamics in Time Series Generationcited by 0
  26. 2026SRT: Super-Resolution for Time Series via Disentangled Rectified Flowcited by 0
  27. 2026A GAN-Based Framework for Robust Data Synthesis in Satellite Internet Observationscited by 0
  28. 2026Contrastive Representation-Guided Genetic Minority Oversampling for Imbalanced Time-Series Classificationcited by 0
  29. 2026CodeTS: Verifiable Text-to-Time Series Generation via Executable Codecited by 0
  30. 2026HALO: Enhancing Time Series Generation via Hyperspherical Latents and Masked AutoregRessive Modelingcited by 0
  31. 2026Wavelet Flow Matching for Time Seriescited by 0