Generating Synthetic Time Series Data for Cyber-Physical Systems

Alexander Sommers, Somayeh Bakhtiari Ramezani, Logan Cummins, Sudip Mittal, Shahram Rahimi +2 more
4/12/2024
cs.LG

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Abstract

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.

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Cite this paper

@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},
  year   = {2024},
  eprint = {2404.08601},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2404.08601v1}
}

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