# Improving Astronomical Time-series Classification via Data Augmentation with Generative Adversarial Networks

arXiv: https://arxiv.org/abs/2205.06758 · HTML: https://mlsanity.com/paper/2205.06758/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 20 references that are in the map.
- First posted on arXiv 13 May 2022.

## Builds on (most cited first, 4 of 4)

- [GAN-based Synthetic Medical Image Augmentation for increased CNN Performance in Liver Lesion Classification](https://mlsanity.com/paper/1803.01229/) (2018)
- [An Empirical Survey of Data Augmentation for Time Series Classification with Neural Networks](https://mlsanity.com/paper/2007.15951/) (2020)
- [Sig-Wasserstein GANs for Time Series Generation](https://mlsanity.com/paper/2111.01207/) (2021)
- [Alert Classification for the ALeRCE Broker System: The Real-time Stamp Classifier](https://mlsanity.com/paper/2008.03309/) (2020)

## Built on by (most cited first, 6 of 6)

- [Data Augmentation for Time-Series Classification: An Extensive Empirical Study and Comprehensive Survey](https://mlsanity.com/paper/2310.10060/) (2023)
- [Light curve completion and forecasting using fast and scalable Gaussian processes (MuyGPs)](https://mlsanity.com/paper/2208.14592/) (2022)
- [DK-Root: A Joint Data-and-Knowledge-Driven Framework for Root Cause Analysis of QoE Degradations in Mobile Networks](https://mlsanity.com/paper/2511.11737/) (2025)
- [Identifying Light-curve Signals with a Deep Learning Based Object Detection Algorithm. II. A General Light Curve Classification Framework](https://mlsanity.com/paper/2311.08080/) (2023)
- [Image Data Augmentation for the TAIGA-IACT Experiment with Conditional Generative Adversarial Networks](https://mlsanity.com/paper/2503.03982/) (2025)
- [Image-Based Multi-Survey Classification of Light Curves with a Pre-Trained Vision Transformer](https://mlsanity.com/paper/2507.11711/) (2025)

## Abstract (arXiv)

Due to the latest advances in technology, telescopes with significant sky coverage will produce millions of astronomical alerts per night that must be classified both rapidly and automatically. Currently, classification consists of supervised machine learning algorithms whose performance is limited by the number of existing annotations of astronomical objects and their highly imbalanced class distributions. In this work, we propose a data augmentation methodology based on Generative Adversarial Networks (GANs) to generate a variety of synthetic light curves from variable stars. Our novel contributions, consisting of a resampling technique and an evaluation metric, can assess the quality of generative models in unbalanced datasets and identify GAN-overfitting cases that the Fr'echet Inception Distance does not reveal. We applied our proposed model to two datasets taken from the Catalina and Zwicky Transient Facility surveys. The classification accuracy of variable stars is improved significantly when training with synthetic data and testing with real data with respect to the case of using only real data.