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

Zhou Zhou, Ronisha Basker, Dit-Yan Yeung

Abstract: Highlights•A new hierarchical spatiotemporal dependency learning-based graph neural network.•The model leverages spatial-, temporal-, and intra-dependency learning processes.•The temporal correlations among dynamic graph topologies are considered.•The model is evaluated on real-world datasets from different engineering domains.