  It is abundantly clear that time dependent data is a vital source of
information in the world. The challenge has been for applications in machine
learning to gain access to a considerable amount of quality data needed for
algorithm development and analysis. Modeling synthetic data using a Generative
Adversarial Network (GAN) has been at the heart of providing a viable solution.
Our work focuses on one dimensional times series and explores the few shot
approach, which is the ability of an algorithm to perform well with limited
data. This work attempts to ease the frustration by proposing a new
architecture, Time Series GAN (TSGAN), to model realistic time series data. We
evaluate TSGAN on 70 data sets from a benchmark time series database. Our
results demonstrate that TSGAN performs better than the competition both
quantitatively using the Frechet Inception Score (FID) metric, and
qualitatively when classification is used as the evaluation criteria.
