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

Multivariate time series classification with dual attention network