A Recurrent Spatio-Temporal Graph Neural Network Based on Latent Time Graph for Multi-Channel Time Series Forecasting

With the advancement of technology, the field of multi-channel time series forecasting has emerged as a focal point of research. In this context, spatio-temporal graph neural networks have attracted significant interest due to their outstanding performance. An established approach involves integrating graph convolutional networks into recurrent neural networks. However, this approach faces difficulties in capturing dynamic spatial correlations and discerning the correlation of multi-channel time series signals. Another major problem is that the discrete time interval of recurrent neural networks limits the accuracy of spatio-temporal prediction. To address these challenges, we propose a continuous spatio-temporal framework, termed Recurrent Spatio-Temporal Graph Neural Network based on Latent Time Graph (RST-LTG). RST-LTG incorporates adaptive graph convolution networks with a time embedding generator to construct a latent time graph, which subtly captures evolving spatial characteristics by aggregating spatial information across multiple time steps. Additionally, to improve the accuracy of continuous time modeling, we introduce a gate enhanced neural ordinary differential equation that effectively integrates information across multiple scales. Empirical results on four publicly available datasets demonstrate that the RST-LTG model outperforms 19 competing methods in terms of accuracy.