  Many real-world tasks are plagued by limitations on data: in some instances
very little data is available and in others, data is protected by privacy
enforcing regulations (e.g. GDPR). We consider limitations posed specifically
on time-series data and present a model that can generate synthetic time-series
which can be used in place of real data. A model that generates synthetic
time-series data has two objectives: 1) to capture the stepwise conditional
distribution of real sequences, and 2) to faithfully model the joint
distribution of entire real sequences. Autoregressive models trained via
maximum likelihood estimation can be used in a system where previous
predictions are fed back in and used to predict future ones; in such models,
errors can accrue over time. Furthermore, a plausible initial value is required
making MLE based models not really generative. Many downstream tasks learn to
model conditional distributions of the time-series, hence, synthetic data drawn
from a generative model must satisfy 1) in addition to performing 2). We
present TsT-GAN, a framework that capitalises on the Transformer architecture
to satisfy the desiderata and compare its performance against five
state-of-the-art models on five datasets and show that TsT-GAN achieves higher
predictive performance on all datasets.
