Generating Synthetic Time Series Data for Cyber-Physical Systems

arXiv:2404.08601v1 Announce Type: new
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.

abstract applications architecture arxiv augmentation cs.lg cyber data deep learning domain exploration gap literature sequence model series synthetic synthetic time series data systems time series transformer type