Multi-View Time Series Classification via Global-Local Correlative Channel-Aware Fusion Mechanism

Multi-view time series classification aims to fuse the distinctive temporal information from different views to further enhance the classification performance. Existing methods mainly focus on fusing multi-view features at an early stage ( e.g., learning a common representation shared by multiple views). However, these early fusion methods may not fully exploit the view-specific distinctive patterns in high-dimension time series data. Moreover, the intra-view and interview label correlations, which are critical for multi-view classification, are usually ignored in previous works. In this paper, we propose a Global-Local Correlative Channel-A ware Fusion (GLCCF) model to address the aforementioned issues. Particularly, our model extracts global and local temporal patterns by a two-stream structure encoder, captures the intra-view and interview label correlations by constructing a graph based correlation matrix, and extracts the cross-view global patterns via a learnable channel-aware late fusion mechanism, which could be effectively implemented with a convo-lutional neural network. Extensive experiments on two real-world datasets demonstrate the superiority of our approach over the state-of-the-art methods. An ablation study is further provided to show the effectiveness of each model component. Introduction Time series classification has attracted increasing attention recently since temporal data contains more dynamic patterns which cannot be discovered easily.