Spectral Temporal Graph Neural Network for Multivariate Time-series Forecasting
Defu Cao,Yujing Wang,Juanyong Duan,Ce Zhang,Xia Zhu,Congrui Huang,Yunhai Tong,Bixiong Xu,Jing Bai,Jie Tong,Qi Zhang
In this paper, we propose Spectral Temporal Graph Neural Network (StemGNN) to further improve the accuracy of multivariate time-series forecasting. StemGNN captures inter-series correlations and temporal dependencies jointly in the spectral domain. It combines Graph Fourier Transform (GFT) which models inter-series correlations and Discrete Fourier Transform (DFT) which models temporal dependencies in an end-to-end framework. After passing through GFT and DFT, the spectral representations hold clear patterns and can be predicted effectively by convolution and sequential learning modules. Moreover, StemGNN learns inter-series correlations automatically from the data without using pre-defined priors. We conduct extensive experiments on ten real-world datasets to demonstrate the effectiveness of StemGNN.
