Generating Synthetic Time Series Data for Cyber-Physical Systems
<|reference_start|>Generating Synthetic Time Series Data for Cyber-Physical Systems: 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.<|reference_end|>
@article{sommers2024generating,
    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},
    archivePrefix={arXiv},
    eprint={2404.08601},
    primaryClass={cs.LG}
}