One of the topics in machine learning that is becoming more and more relevant
is multivariate time series classification. Current techniques concentrate on
identifying the local important sequence segments or establishing the global
long-range dependencies. They frequently disregard the merged data from both
global and local features, though. Using dual attention, we explore a novel
network (DA-Net) in this research to extract local and global features for
multivariate time series classification. The two distinct layers that make up
DA-Net are the Squeeze-Excitation Window Attention (SEWA) layer and the Sparse
Self-Attention within Windows (SSAW) layer. DA- Net can mine essential local
sequence fragments that are necessary for establishing global long-range
dependencies based on the two expanded layers.

利用双重关注 (dual attention) 的方法，我们在本研究中探索了一种新型网络 (DA-Net)，用于提取多元时间序列分类的局部和全局特征。DA-Net 由 Squeeze-Excitation Window Attention (SEWA) 层和 Sparse Self-Attention within Windows (SSAW) 层组成，可以提取必要的局部序列片段，并建立基于这两个扩展层的全局长距离依赖关系。