Time Series Synthesis using Generative Adversarial Networks
J.M. Dannenberg (TU Delft - Electrical Engineering, Mathematics and Computer Science)
A. Kunar – Mentor (TU Delft - Electrical Engineering, Mathematics and Computer Science)
Y. Chen – Mentor (TU Delft - Electrical Engineering, Mathematics and Computer Science)
Z. Zhao – Mentor (TU Delft - Electrical Engineering, Mathematics and Computer Science)
D.M.J. Tax – Graduation committee member (TU Delft - Electrical Engineering, Mathematics and Computer Science)
More Info
expand_more
Git repository containing code used for this research. 3 different TimeGAN implementations and proposed improvements.
https://gitlab.com/JanMarkD/timeganOther than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.
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.