  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.
