# Quick and Easy Time Series Generation with Established Image-based GANs

arXiv: https://arxiv.org/abs/1902.05624 · HTML: https://mlsanity.com/paper/1902.05624/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Feb 2019.

## Built on by (most cited first, 9 of 9)

- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Utilizing Image Transforms and Diffusion Models for Generative Modeling of Short and Long Time Series](https://mlsanity.com/paper/2410.19538/) (2024)
- [Generative adversarial networks in time series: A survey and taxonomy](https://mlsanity.com/paper/2107.11098/) (2021)
- [Generative Adversarial Networks: A Survey Towards Private and Secure Applications](https://mlsanity.com/paper/2106.03785/) (2021)
- [A Diffusion Model for Regular Time Series Generation from Irregular Data with Completion and Masking](https://mlsanity.com/paper/2510.06699/) (2025)
- [A Spectral Enabled GAN for Time Series Data Generation](https://mlsanity.com/paper/2103.01904/) (2021)
- [Synthesis of standard 12-lead electrocardiograms using two dimensional generative adversarial network](https://mlsanity.com/paper/2106.03701/) (2021)
- [Leveraging Image-based Generative Adversarial Networks for Time Series Generation](https://mlsanity.com/paper/2112.08060/) (2021)
- [Synthetic ECG Generation for Data Augmentation and Transfer Learning in Arrhythmia Classification](https://mlsanity.com/paper/2411.18456/) (2024)

## Abstract (arXiv)

In the recent years Generative Adversarial Networks (GANs) have demonstrated significant progress in generating authentic looking data. In this work we introduce our simple method to exploit the advancements in well established image-based GANs to synthesise single channel time series data. We implement Wasserstein GANs (WGANs) with gradient penalty due to their stability in training to synthesise three different types of data; sinusoidal data, photoplethysmograph (PPG) data and electrocardiograph (ECG) data. The length of the returned time series data is limited only by the image resolution, we use an image size of 64x64 pixels which yields 4096 data points. We present both visual and quantitative evidence that our novel method can successfully generate time series data using image-based GANs.