Time Series Classification Method with Local Attention Enhancement

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.