  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.
