Dynamic spatiotemporal interactive graph neural network for multivariate time series forecasting

Ziheng Gao, Zhuolin Li, Haoran Zhang, Jie Yu, Lingyu Xu

Abstract: Multivariate time series (MTS) forecasting holds significant importance in decision-making for complex real-world phenomena. However, the presence of nonlinear temporal correlations within variables and dynamic spatial correlations between variables makes accurate MTS prediction challenging. Currently, there are many researchers who build various spatiotemporal graph neural networks (STGNNs) and apply them to this field. However, most existing methods construct the graph structure using a single type of information and separately capture temporal and spatial features. These factors can result in models that fail to extract complete spatiotemporal features, thereby limiting their performance. To overcome these limitations, we propose the dynamic spatiotemporal interactive graph neural network (DSTIGNN), a novel STGNN for MTS forecasting. The proposed dynamic graph inference module models the dynamic spatial association between variables by fusing two types of heterogeneous information and is combined with the dynamic graph convolution module to propagate information in spatial dimensions. Meanwhile, downsampling operations and multiple sample convolution modules are used to jointly capture multiresolution temporal correlations. Subsequently, these modules are integrated into a spatiotemporal interactive learning framework, enabling the synchronous capture of temporal and spatial features. We have performed numerous experiments on six benchmark datasets, and the experimental results demonstrate that DSTIGNN achieves state-of-the-art performance.