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