Morning Poster Session: PSA-GAN: Progressive Self Attention GANs for Synthetic Time Series

Realistic synthetic time series data of sufficient length enables practical applications in time series modeling tasks, such as forecasting, but remains to be 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 this score is able to predict the best-performing models and could therefore be a useful tool to develop time series GAN models for downstream use.