Dynamic multi-fusion spatio-temporal graph neural network for multivariate time series forecasting

Abstract

Multivariate time series data with multiple points is a kind of spatio-temporal data, and in recent years, spatio-temporal graph neural networks (STGNNs) have become one of the most effective methods for spatio-temporal data prediction. By leveraging graph neural networks and sequence models, STGNNs simultaneously capture the hidden temporal and spatial patterns in spatio-temporal data. However, there exist two key limitations: (1) Most existing STGNNs whether based on static graphs or dynamic graphs, fail to introduce time periodicity into graph structure learning and ignore the importance of multi-relational graph structure learning. (2) Most multivariate time series prediction models with multiple points ignore the relation between multiple attributes. To this end, we propose a novel framework called the Dynamic Multi-fusion Spatio-temporal Graph Neural Network (DMF-STNet), where multi-fusion embodies two meanings: spatio-temporal fusion and multi-attribute fusion, as well as multi-graph fusion and multi-layer fusion. The dynamic periodic graph is proposed to mine time periodicity that is hidden in graph structures, and we further offer multi-relational graph structure learning methods to capture static and dynamic relations. Moreover, we introduce a dual-stage feature aggregation method to aggregate hidden features from both primary and auxiliary attributes. Extensive experiments on three real-world datasets demonstrate the efficacy of our proposed DMF-STNet, and the visualization study proves the interpretability of learned weights.