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.

时间序列数据增强方法中，保留频域信息对提高性能至关重要，本研究提出了一种简单的策略（SimPSI）来保留时间序列数据的频域信息，通过混合原始和增强的输入谱并加权，核心是使用保留映射（magnitude spectrum、saliency map和spectrum-preservative map）来指示每个频率的重要性得分。实验证明，SimPSI显著提升了时间序列数据增强的性能。

SimPSI：简洁策略用于保留时序数据增强中的频谱信息