A Spectral Enabled GAN for Time Series Data Generation

TL;DR

This paper introduces uTSGAN, a unified training approach for time series data generation that outperforms the previous TSGAN in most benchmarks, especially with less training time and improved quality.

Contribution

The paper proposes a unified training framework for TSGAN, enhancing its performance and training efficiency while preserving few-shot generation capabilities.

Findings

uTSGAN outperforms TSGAN on 80% of datasets with same epochs.

uTSGAN achieves better FID scores across datasets.

uTSGAN requires less training time in 60% of cases.

Abstract

Time dependent data is a main source of information in today's data driven world. Generating this type of data though has shown its challenges and made it an interesting research area in the field of generative machine learning. One such approach was that by Smith et al. who developed Time Series Generative Adversarial Network (TSGAN) which showed promising performance in generating time dependent data and the ability of few shot generation though being flawed in certain aspects of training and learning. This paper looks to improve on the results from TSGAN and address those flaws by unifying the training of the independent networks in TSGAN and creating a dependency both in training and learning. This improvement, called unified TSGAN (uTSGAN) was tested and comapred both quantitatively and qualitatively to its predecessor on 70 benchmark time series data sets used in the community. uTSGAN showed to outperform TSGAN in 80\% of the data sets by the same number of training epochs and 60\% of the data sets in 3/4th the amount of training time or less while maintaining the few shot generation ability with better FID scores across those data sets.