Time Series Synthesis using Generative Adversarial Networks

Abstract

Generative Adversarial Networks are widely used as a tool to generate synthetic data and have previously been applied directly to time-series data. However, relying solely on the binary adversarial loss is not sufficient to ensure the model learns the temporal dynamics of the data. TimeGAN [14] introduces an additional reconstruction and supervised loss to tackle this issue. We have been able to reproduce results similar to those of the original TimeGAN paper [14], after fixing several issues in the provided implementation by the authors of TimeGAN. Furthermore, we propose two novel improvements to the existing algorithm. Firstly we updated the implementation to Tensorflow 2 to ensure compatibility across systems. Secondly by scaling the epochs over the three training phases we are able to reduce the overall training time up to 29\% and produce results equal or better compared to the benchmark.