MTHetGNN: A Heterogeneous Graph Embedding Framework for Multivariate Time Series Forecasting

Understand

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.