  Multivariate time series forecasting, which analyzes historical time series
to predict future trends, can effectively help decision-making. Complex
relations among variables in MTS, including static, dynamic, predictable, and
latent relations, have made it possible to mining more features of MTS.
Modeling complex relations are not only essential in characterizing latent
dependency as well as modeling temporal dependence but also brings great
challenges in the MTS forecasting task. However, existing methods mainly focus
on modeling certain relations among MTS variables. In this paper, we propose a
novel end-to-end deep learning model, termed Multivariate Time Series
Forecasting via Heterogeneous Graph Neural Networks (MTHetGNN). To characterize
complex relations among variables, a relation embedding module is designed in
MTHetGNN, where each variable is regarded as a graph node, and each type of
edge represents a specific static or dynamic relationship. Meanwhile, a
temporal embedding module is introduced for time series features extraction,
where involving convolutional neural network (CNN) filters with different
perception scales. Finally, a heterogeneous graph embedding module is adopted
to handle the complex structural information generated by the two modules.
Three benchmark datasets from the real world are used to evaluate the proposed
MTHetGNN. The comprehensive experiments show that MTHetGNN achieves
state-of-the-art results in the MTS forecasting task.
