  Data augmentation is an important facilitator of deep learning applications
in the time series domain. A gap is identified in the literature, demonstrating
sparse exploration of the transformer, the dominant sequence model, for data
augmentation in time series. A architecture hybridizing several successful
priors is put forth and tested using a powerful time domain similarity metric.
Results suggest the challenge of this domain, and several valuable directions
for future work.
