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arXiv · 2404.08601

Generating Synthetic Time Series Data for Cyber-Physical Systems

Alexander Sommers·Somayeh Bakhtiari Ramezani·Logan Cummins·Sudip Mittal·Shahram Rahimi·Maria Seale·Joseph Jaboure
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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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Alexander Sommers, Somayeh Bakhtiari Ramezani, Logan Cummins, Sudip Mittal, Shahram Rahimi, Maria Seale, Joseph Jaboure. 2024-04-12. Generating Synthetic Time Series Data for Cyber-Physical Systems. https://arxiv.org/abs/2404.08601

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