  Generative adversarial networks (GANs) studies have grown exponentially in
the past few years. Their impact has been seen mainly in the computer vision
field with realistic image and video manipulation, especially generation,
making significant advancements. While these computer vision advances have
garnered much attention, GAN applications have diversified across disciplines
such as time series and sequence generation. As a relatively new niche for
GANs, fieldwork is ongoing to develop high quality, diverse and private time
series data. In this paper, we review GAN variants designed for time series
related applications. We propose a taxonomy of discrete-variant GANs and
continuous-variant GANs, in which GANs deal with discrete time series and
continuous time series data. Here we showcase the latest and most popular
literature in this field; their architectures, results, and applications. We
also provide a list of the most popular evaluation metrics and their
suitability across applications. Also presented is a discussion of privacy
measures for these GANs and further protections and directions for dealing with
sensitive data. We aim to frame clearly and concisely the latest and
state-of-the-art research in this area and their applications to real-world
technologies.
