Generating Synthetic Time Series Data for Cyber-Physical Systems
This work addresses data augmentation for deep learning in time series, but it appears incremental as it builds on existing priors without demonstrating clear advancements.
The paper tackled the problem of generating synthetic time series data for cyber-physical systems by proposing a hybrid transformer-based architecture, but results indicated the challenge of the domain with no concrete performance numbers provided.
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.