基于频域重构的多变量时间序列分类

IF 3 3区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC
Aiguo Li, Bowen Li
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引用次数: 0

摘要

基于变压器的深度学习模型极大地推动了多元时间序列分类领域的发展。然而,固有的自关注机制使得现有的Transformer方法容易出现频率偏差,并且在提取局部特征方面存在不足,最终限制了它们的表示能力。为了解决这些问题,我们提出了一种新的网络CFPNet,它通过在频域重建一个关键频率补丁来增强表征学习,从而有效地减轻频率偏置。此外,我们还引入了wave - kan编码器,该编码器将小波变换与Kolmogorov-Arnold网络相结合,以准确捕获局部依赖关系。在UEA(多元时间序列分类档案)的14个公共数据集以及来自金属材料的超声波信号定制数据集上进行的广泛实验表明,与最先进的方法相比,CFPNet实现了更高的分类精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

CFPNet: Multivariate time series classification based on frequency domain reconstruction

CFPNet: Multivariate time series classification based on frequency domain reconstruction
Transformer-based deep learning models have significantly advanced the field of multivariate time series classification. However, the intrinsic self-attention mechanism renders existing Transformer methods prone to frequency bias and inadequate in extracting local features, which ultimately limits their representational capacity. To address these issues, we propose CFPNet, a novel network that enhances representation learning by reconstructing a Crucial Frequency Patch in the frequency domain, thereby effectively mitigating frequency bias. Additionally, we introduce the Wav-KAN encoder, which integrates wavelet transforms with the Kolmogorov-Arnold Network to accurately capture local dependencies. Extensive experiments on fourteen public datasets from the UEA(Multivariate Time Series Classification Archive), as well as on a custom dataset of ultrasonic signals from metallic materials, demonstrate that CFPNet achieves superior classification accuracy compared to state-of-the-art methods.
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来源期刊
Digital Signal Processing
Digital Signal Processing 工程技术-工程:电子与电气
CiteScore
5.30
自引率
17.20%
发文量
435
审稿时长
66 days
期刊介绍: Digital Signal Processing: A Review Journal is one of the oldest and most established journals in the field of signal processing yet it aims to be the most innovative. The Journal invites top quality research articles at the frontiers of research in all aspects of signal processing. Our objective is to provide a platform for the publication of ground-breaking research in signal processing with both academic and industrial appeal. The journal has a special emphasis on statistical signal processing methodology such as Bayesian signal processing, and encourages articles on emerging applications of signal processing such as: • big data• machine learning• internet of things• information security• systems biology and computational biology,• financial time series analysis,• autonomous vehicles,• quantum computing,• neuromorphic engineering,• human-computer interaction and intelligent user interfaces,• environmental signal processing,• geophysical signal processing including seismic signal processing,• chemioinformatics and bioinformatics,• audio, visual and performance arts,• disaster management and prevention,• renewable energy,
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