计算机工程与应用2024，Vol.60Issue(1)：189-197,9.DOI:10.3778/j.issn.1002-8331.2207-0444
增强局部注意力的时间序列分类方法
Time Series Classification Method with Local Attention Enhancement
摘要
Abstract
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 classifi-cation method based on local attention enhancement can accelerate convergence and effectively improve the classification effect of time series.关键词
时间序列分类/自注意力机制/位置感知/多尺度卷积Key words
time series classification/self-attention mechanism/position perception/multi-scale convolution分类
能源科技引用本文复制引用
李克文,柯翠虹,张敏,王晓晖,耿文亮..增强局部注意力的时间序列分类方法[J].计算机工程与应用,2024,60(1):189-197,9.基金项目
国家自然科学基金重大项目(51991365) （51991365）
山东省自然科学基金(ZR2021MF082). （ZR2021MF082）
