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.
@article{arxiv.2404.08601,
title = {Generating Synthetic Time Series Data for Cyber-Physical Systems},
author = {Alexander Sommers and Somayeh Bakhtiari Ramezani and Logan Cummins and Sudip Mittal and Shahram Rahimi and Maria Seale and Joseph Jaboure},
journal= {arXiv preprint arXiv:2404.08601},
year = {2024}
}