  Data augmentation is a crucial component in training neural networks to
overcome the limitation imposed by data size, and several techniques have been
studied for time series. Although these techniques are effective in certain
tasks, they have yet to be generalized to time series benchmarks. We find that
current data augmentation techniques ruin the core information contained within
the frequency domain. To address this issue, we propose a simple strategy to
preserve spectral information (SimPSI) in time series data augmentation. SimPSI
preserves the spectral information by mixing the original and augmented input
spectrum weighted by a preservation map, which indicates the importance score
of each frequency. Specifically, our experimental contributions are to build
three distinct preservation maps: magnitude spectrum, saliency map, and
spectrum-preservative map. We apply SimPSI to various time series data
augmentations and evaluate its effectiveness across a wide range of time series
benchmarks. Our experimental results support that SimPSI considerably enhances
the performance of time series data augmentations by preserving core spectral
information. The source code used in the paper is available at
https://github.com/Hyun-Ryu/simpsi.
