Generating Synthetic Time Series Data for Cyber-Physical Systems
Alexander Sommers Somayeh Bakhtiari Ramezani Logan Cummins Sudip Mittal Shahram Rahimi Maria Seale Joseph Jaboure
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.