MTHetGNN: A heterogeneous graph embedding framework for multivariate time series forecasting

Yueyang Wang, Ziheng Duan, Yida Huang, Haoyan Xu, Jie Feng, Anni Ren

Abstract: Highlights•A novel end-to-end heterogeneous graph-based deep learning model.•Capturing the internal temporal pattern of single-dimensional time series and the rich spatial relations among variables.•Capturing both the static and dynamic relations of multivariate time series (MTS).•Combining graph neural networks with MTS to take full advantage of complex relations among variables of MTS.