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Poster

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

Paul Jeha · Michael Bohlke-Schneider · Pedro Mercado · Shubham Kapoor · Rajbir Nirwan · Valentin Flunkert · Jan Gasthaus · Tim Januschowski

Keywords: [ generative modeling ] [ gan ] [ time series ] [ forecasting ]


Abstract:

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 several downstream forecasting tasks over baselines that only use real data. We also introduce a Frechet-Inception Distance-like score for time series, Context-FID, assessing the quality of synthetic time series samples. We find that Context-FID is indicative for downstream performance. Therefore, Context-FID could be a useful tool to develop time series GAN models.

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