Attentional Gated Res2net for Multivariate Time Series Classification

Abstract

Multivariate time series classification is a critical problem in data mining with broad applications. We design a novel convolutional neural network architecture, Attentional Gated Res2Net, for robust multivariate time series classification. AGRes2Net uses hierarchical residual-like connections to achieve multi-scale receptive fields and to capture multi- granular temporal patterns. It further employs the gated mechanism to harness inter-relationship between feature maps. We propose two types of attention modules, namely channel-wise attention and block-wise attention, to leverage the multi-granular temporal patterns. Our experiments on six benchmark datasets demonstrate that AGRes2Net not only outperforms several baselines and state-of-the-art methods but also improves the classification accuracy of existing models when used as a plug-in.