Graph Neural Networks for multivariate time-series forecasting via learning hierarchical spatiotemporal dependencies

Abstract

Multivariate time-series forecasting is one of the essential tasks to draw insights from sequential data. Spatiotemporal Graph Neural Networks (STGNN) have attracted much attention in this field due to their capability to capture the underlying spatiotemporal dependencies. However, current STGNN solutions succumb to a higher degree of error in their predictions due to insufficient modelling of the dependencies and dynamics at different levels. In this paper, a Graph Neural Networks-based model is proposed for multivariate time-series forecasting via learning hierarchical spatiotemporal dependencies (HSDGNN). Specifically, variables are organised as nodes in a graph while each node serves as a subgraph consisting of the attributes of variables. Then two-level convolutions are designed on the hierarchical graph to model the spatial dependencies with different granularities. The changes in graph topologies are also encoded for strengthening dependency modelling across time and spatial dimensions. The proposed model is tested using real-world datasets from different domains, including transportation, electricity, and meteorology. The experimental results demonstrate that HSDGNN can outperform state-of-the-art baselines by up to 15.3% in terms of prediction accuracy, without compromising model scalability.