  Generative Adversarial Networks (GANs) should produce synthetic data that
fits the underlying distribution of the data being modeled. For real valued
time-series data, this implies the need to simultaneously capture the static
distribution of the data, but also the full temporal distribution of the data
for any potential time horizon. This temporal element produces a more complex
problem that can potentially leave current solutions under-constrained,
unstable during training, or prone to varying degrees of mode collapse. In
FETSGAN, entire sequences are translated directly to the generator's sampling
space using a seq2seq style adversarial auto encoder (AAE), where adversarial
training is used to match the training distribution in both the feature space
and the lower dimensional sampling space. This additional constraint provides a
loose assurance that the temporal distribution of the synthetic samples will
not collapse. In addition, the First Above Threshold (FAT) operator is
introduced to supplement the reconstruction of encoded sequences, which
improves training stability and the overall quality of the synthetic data being
generated. These novel contributions demonstrate a significant improvement to
the current state of the art for adversarial learners in qualitative measures
of temporal similarity and quantitative predictive ability of data generated
through FETSGAN.
