文献类型：期刊文献

英文题名：Self-Supervised Time Series Classification Method Based on FRFT Cross-Channel Fusion

作者：Sun, ZhongHeng[1]; Wang, Yiyang[1]; Wang, Meihui[1]; Jiang, Cuiling[1]; Wan, Yongjing[1]

机构：[1] East China University of Science and Technology, School of Information Science and Engineering, Shanghai, 200237, China

年份：2025

外文期刊名：Proceedings of the International Joint Conference on Neural Networks

收录：EI(收录号：20260620032733)

语种：英文

外文关键词：Biomedical engineering - Classification (of information) - Self-supervised learning - Signal processing - Supervised learning - Time domain analysis - Time series

摘要：Recently, self-supervised representation learning has been widely applied to various time series tasks (e.g., electric device classification). However, building models for large-scale time series classification remains challenging due to the high cost of labeling time series data, which requires specialized expertise. Additionally, many datasets consist solely of unlabeled data or contain only a small number of labeled samples. Existing solutions for traditional time series tasks are not directly applicable to modern complex temporal problems due to two unique characteristics: (i) long-term temporal dependencies and (ii) complex cross-channel interactions. To address these challenges, we propose TC-FrC, a self-supervised time series classification model that leverages seasonal-trend decomposition for data augmentation and incorporates contrastive losses based on both temporal features and the Fractional Fourier Transform (FRFT). Specifically, we: (i) design a sparse attention mechanism within the temporal contrastive module to enhance feature extraction and improve robustness in long-term time series data, (ii) introduce a seasonal-trend decomposition approach to mitigate inter-class feature confusion, and (iii) develop a cross-channel FRFT-based feature fusion module, which transforms contextual features from the time domain to the fractional domain. We extensively evaluate TC-FrC on seven publicly available datasets, including HAR, Sleep-EDF, and Epilepsy, among others. Experimental results demonstrate that our method outperforms state-of-the-art baselines across various evaluation metrics. ? 2025 IEEE.