MRM2: Multi-Relationship Modeling Module for Multivariate Time Series Classification

Multivariate Time Series Classification (MTSC) is a prevalent but challenging problem in data mining. With the development of Deep Neural Networks (DNN), hundreds of deep models for MTSC have been proposed. However, most prior works only explicitly model the relationship between time series and classes and ignore the diversity of the relationship, suffering from insufficient information exploitation. In this paper, we propose a novel module named Multi-Relationship Modeling Module(MRM2) for more effective MTSC. MRM2 uses the classified labels to explicitly model not only the relationship between time series and classes, but also the relationship among time series, enabling the backbone to generate distinguishable embeddings. In addition, MRM2 is versatile because it can be combined with the existing backbones of DNN for end-to-end training. Finally, we conduct a series of ablation studies and comparative experiments on the real multivariate time series archive UEA. Experimental results indicate that MRM2 can significantly improve classification performance in most cases. Codes are available on GitHub (1).