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Multi-view time series classification (MVTSC) aims to improve the performanceby fusing the distinctive temporal information from multiple views. Existingmethods mainly focus on fusing multi-view information at an early stage, e.g.,by learning a common feature subspace among multiple views. However, theseearly fusion methods may not fully exploit the unique temporal patterns of eachview in complicated time series. Moreover, the label correlations of multipleviews, which are critical to boost-ing, are usually under-explored for theMVTSC problem. To address the aforementioned issues, we propose a CorrelativeChannel-Aware Fusion (C2AF) network. First, C2AF extracts comprehensive androbust temporal patterns by a two-stream structured encoder for each view, andcaptures the intra-view and inter-view label correlations with a graph-basedcorrelation matrix. Second, a channel-aware learnable fusion mechanism isimplemented through convolutional neural networks to further explore the globalcorrelative patterns. These two steps are trained end-to-end in the proposedC2AF network. Extensive experimental results on three real-world datasetsdemonstrate the superiority of our approach over the state-of-the-art methods.A detailed ablation study is also provided to show the effectiveness of eachmodel component.

Correlative Channel-Aware Fusion for Multi-View Time Series Classification