{"title":"基于频域重构的多变量时间序列分类","authors":"Aiguo Li, Bowen Li","doi":"10.1016/j.dsp.2025.105598","DOIUrl":null,"url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"168 ","pages":"Article 105598"},"PeriodicalIF":3.0000,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"CFPNet: Multivariate time series classification based on frequency domain reconstruction\",\"authors\":\"Aiguo Li, Bowen Li\",\"doi\":\"10.1016/j.dsp.2025.105598\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>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.</div></div>\",\"PeriodicalId\":51011,\"journal\":{\"name\":\"Digital Signal Processing\",\"volume\":\"168 \",\"pages\":\"Article 105598\"},\"PeriodicalIF\":3.0000,\"publicationDate\":\"2025-09-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Digital Signal Processing\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1051200425006207\",\"RegionNum\":3,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Digital Signal Processing","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1051200425006207","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
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.
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,