增强局部注意力的时间序列分类方法OACSTPCD

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.