Realistic synthetic time series data of sufficient length enables practical applications in time series modeling tasks, such as forecasting, but remains a challenge. In this paper we present PSA-GAN, a generative adversarial network (GAN) that generates long time series samples of high quality using progressive growing of GANs and self-attention. We show that PSA-GAN can be used to reduce the error in two downstream forecasting tasks over baselines that only use real data. We also introduce a Frechet-Inception Distance-like score, Context-FID, assessing the quality of synthetic time series samples. In our downstream tasks, we find that the lowest scoring models correspond to the best-performing ones. Therefore, Context-FID could be a useful tool to develop time series GAN models.
@article{arxiv.2108.00981,
title = {PSA-GAN: Progressive Self Attention GANs for Synthetic Time Series},
author = {Jeha Paul and Bohlke-Schneider Michael and Mercado Pedro and Kapoor Shubham and Singh Nirwan Rajbir and Flunkert Valentin and Gasthaus Jan and Januschowski Tim},
journal= {arXiv preprint arXiv:2108.00981},
year = {2022}
}