DA-Net: Dual-attention network for multivariate time series classification

Abstract

Multivariate time series classification is one of the increasingly important issues in machine learning. Existing methods focus on establishing the global long-range dependencies or discovering the local critical sequence fragments. However, they often ignore the combined information from both global and local features. In this paper, we propose a novel network (called DA-Net) based on dual attention to mine the local–global features for multivariate time series classification. Specifically, DA-Net consists of two distinctive layers, i.e., the Squeeze-Excitation Window Attention (SEWA) layer and the Sparse Self-Attention within Windows (SSAW) layer. For the SEWA layer, we capture the local window-wise information by explicitly establishing window dependencies to prioritize critical windows. For the SSAW layer, we preserve rich activate scores with less computation to widen the window scope for capturing global long-range dependencies. Based on the two elaborated layers, DA-Net can mine critical local sequence fragments in the process of establishing global long-range dependencies. The experimental results show that DA-Net is able to achieve competing performance with state-of-the-art approaches on the multivariate time series classification.