  Generative Adversarial Networks (GANs) have proven to be a powerful tool for
generating realistic synthetic data. However, traditional GANs often struggle
to capture complex relationships between features which results in generation
of unrealistic multivariate time-series data. In this paper, we propose a
Graph-Attention-based Generative Adversarial Network (GAT-GAN) that explicitly
includes two graph-attention layers, one that learns temporal dependencies
while the other captures spatial relationships. Unlike RNN-based GANs that
struggle with modeling long sequences of data points, GAT-GAN generates long
time-series data of high fidelity using an adversarially trained autoencoder
architecture. Our empirical evaluations, using a variety of real-time-series
datasets, show that our framework consistently outperforms state-of-the-art
benchmarks based on \emph{Frechet Transformer distance} and \emph{Predictive
score}, that characterizes (\emph{Fidelity, Diversity}) and \emph{predictive
performance} respectively. Moreover, we introduce a Frechet Inception
distance-like (FID) metric for time-series data called Frechet Transformer
distance (FTD) score (lower is better), to evaluate the quality and variety of
generated data. We also found that low FTD scores correspond to the
best-performing downstream predictive experiments. Hence, FTD scores can be
used as a standardized metric to evaluate synthetic time-series data.
