Time Series Classification Method with Local Attention Enhancement

被引：0
|
作者：
Li, Kewen [1 ]
Ke, Cuihong [1 ]
Zhang, Min [1 ]
Wang, Xiaohui [1 ]
Geng, Wenliang [1 ]
机构：
[1] School of Computer Science and Technology, China University of Petroleum (East China), Shandong, Qingdao,266580, China
来源：
Computer Engineering and Applications | 2024年 / 60卷 / 01期
关键词：
Existing time series classification methods are generally based on a circular network structure to solve the point value coupling problem of time series; which cannot be computed in parallel; resulting in a waste of computing resources. Therefore; this paper proposes a time series classification method with local attention enhancement. The mixed distance information is fitted to increase the position information perception of time series; the mixed distance information is incorporated into the self-attention matrix calculation to expand the self-attention mechanism. Multi-scale convolution attention is constructed to obtain multi-scale local forward information to solve the attention confusion problem in point value calculation of standard self-attention mechanism. The improved self-attention mechanism is used to construct the sequential self-attention classification module; and the time series classification task is processed by parallel computation. The experimental results show that; compared with the existing time series classification methods; the time series classification method based on local attention enhancement can accelerate convergence and effectively improve the classification effect of time series. © 2024 Journal of Computer Engineering and Applications Beijing Co; Ltd; Science Press. All rights reserved;
D O I：
10.3778/j.issn.1002-8331.2207-0444
中图分类号：
学科分类号：
摘要：
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页码：189 / 197
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