Difference-Guided Representation Learning Network for Multivariate Time-Series Classification

Multivariate time series (MTSs) are widely found in many important application fields, for example, medicine, multimedia, manufacturing, action recognition, and speech recognition. The accurate classification of MTS has become an important research topic. Traditional MTS classification methods do not explicitly model the temporal difference information of time series, which is, in fact, important and reflects the dynamic evolution information. In this article, the difference-guided representation learning network (DGRL-Net) is proposed to guide the representation learning of time series by dynamic evolution information. The DGRL-Net consists of a difference-guided layer and a multiscale convolutional layer. First, in the difference-guided layer, we propose a difference gating LSTM to model the time dependency and dynamic evolution of the time series to obtain feature representations of both raw and difference series. Then, these two representations are used as two input channels of the multiscale convolutional layer to extract multiscale information. Extensive experiments demonstrate that the proposed model outperforms state-of-the-art methods on 18 MTS benchmark datasets and achieves competitive results on two skeleton-based action recognition datasets. Furthermore, the ablation study and visualized analysis are designed to verify the effectiveness of the proposed model.