Multivariate time series classification with dual attention network
This addresses classification challenges in domains like healthcare or finance, but it is incremental as it builds on existing attention-based methods.
The paper tackles multivariate time series classification by proposing DA-Net, a dual attention network that extracts both local and global features, achieving improved performance on benchmark datasets.
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.