mlsanityData to 7 Oct 2026

Leveraging Image-based Generative Adversarial Networks for Time Series Generation

arXiv 2112.08060 ↗First posted 15 Dec 2021cs.LG cs.CV stat.MLcited by 4

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Abstract (arXiv)

Generative models for images have gained significant attention in computer vision and natural language processing due to their ability to generate realistic samples from complex data distributions. To leverage the advances of image-based generative models for the time series domain, we propose a two-dimensional image representation for time series, the Extended Intertemporal Return Plot (XIRP). Our approach captures the intertemporal time series dynamics in a scale-invariant and invertible way, reducing training time and improving sample quality. We benchmark synthetic XIRPs obtained by an off-the-shelf Wasserstein GAN with gradient penalty (WGAN-GP) to other image representations and models regarding similarity and predictive ability metrics. Our novel, validated image representation for time series consistently and significantly outperforms a state-of-the-art RNN-based generative model regarding predictive ability. Further, we introduce an improved stochastic inversion to substantially improve simulation quality regardless of the representation and provide the prospect of transfer potentials in other domains.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 24 of its 76 references that are in the map. First posted on arXiv 15 Dec 2021.

Builds on

24 of 76 references.

References · 24

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12 more
  1. 2020Time Series Data Augmentation for Deep Learning: A Surveycited by 225
  2. 2018Adversarial Audio Synthesiscited by 222
  3. 2018Medical Image Synthesis for Data Augmentation and Anonymization using Generative Adversarial Networkscited by 146
  4. 2020An Empirical Survey of Data Augmentation for Time Series Classification with Neural Networkscited by 128
  5. 2015Learning to Diagnose with LSTM Recurrent Neural Networkscited by 99
  6. 2015Imaging Time-Series to Improve Classification and Imputationcited by 85
  7. 2020A GAN-Based Image Transformation Scheme for Privacy-Preserving Deep Neural Networkscited by 27
  8. 2019Forecasting with time series imagingcited by 22
  9. 2019Probabilistic Forecasting of Sensory Data with Generative Adversarial Networks - ForGANcited by 16
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Cited by year

Citing papers per year: 2022: 1; 2023: 0; 2024: 2; 2025: 1.

4 papers.

  1. 2024Utilizing Image Transforms and Diffusion Models for Generative Modeling of Short and Long Time Seriescited by 42
  2. 2025A Diffusion Model for Regular Time Series Generation from Irregular Data with Completion and Maskingcited by 7
  3. 2022Financial Time Series Data Augmentation with Generative Adversarial Networks and Extended Intertemporal Return Plotscited by 0
  4. 2024A Diffusion-based Data Generator for Training Object Recognition Models in Ultra-Range Distancecited by 0