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SimPSI: A Simple Strategy to Preserve Spectral Information in Time Series Data Augmentation

THIRTY-EIGHTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, VOL 38 NO 13(2024)

Korea Adv Inst Sci & Technol KAIST

Cited 0|Views48
Abstract
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.
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Dynamic Time Warping
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要点】:本文提出了一种简单策略SimPSI,用于在时间序列数据增强中保留频谱信息,有效提升了时间序列基准任务中的神经网络性能。

方法】:SimPSI通过结合原始和增强输入频谱,并利用一个指示每个频率重要性的保留图(包括幅度谱、显著性图和保谱图三种)进行加权混合,以保护频谱信息。

实验】:作者将SimPSI应用于多种时间序列数据增强方法,并在多个时间序列基准上评估其效果,实验结果证实了SimPSI在保留核心频谱信息方面的有效性。具体数据集名称未在摘要中提及,但源代码可在https://github.com/Hyun-Ryu/simpsi 找到。