现代电子技术2025，Vol.48Issue(16)：38-44,7.DOI:10.16652/j.issn.1004-373x.2025.16.007
TS-SEA:用于时间序列分类的时域-频域-季节性联合对比学习
TS-SEA:temporal-frequency-seasonal joint contrastive learning for time series classification
李坤 1谭珺 2桂宁 2朱赵炜3
作者信息
- 1. 新疆大学 软件学院,新疆 乌鲁木齐 830091
- 2. 中南大学 计算机学院,湖南 长沙 410083
- 3. 浙江理工大学 计算机科学与技术学院,浙江 杭州 311241
- 折叠
摘要
Abstract
Time series classification(TSC)is the task of categorizing sequential data into predefined classes according to their temporal patterns.Real-world time series usually contain complex coupling of trend terms,seasonal components,outliers,and noise,and its accurate decomposition is crucial to improve classification performance.Therefore,a time series classification method,TS-SEA,is proposed,which decomposes the time series into three views:temporal,frequency,and seasonal by means of FFT and STL.Based on these views,iterative learning is realized by means of contrast learning between encoders.The results indicate that in comparison with existing methods,the proposed TS-SEA method can exhibit the better performance when dealing with diverse time series applications.关键词
TS-SEA/时间序列分类/多视图联合学习/对比学习/傅里叶变换/时间序列分解Key words
TS-SEA/time series classification/multi-view joint learning/contrastive learning/Fourier transform/time series decomposition分类
信息技术与安全科学引用本文复制引用
李坤,谭珺,桂宁,朱赵炜..TS-SEA:用于时间序列分类的时域-频域-季节性联合对比学习[J].现代电子技术,2025,48(16):38-44,7.
