  Graph Neural Networks (GNN) have gained significant traction in the
forecasting domain, especially for their capacity to simultaneously account for
intra-series temporal correlations and inter-series relationships. This paper
introduces a novel Hierarchical GNN (DeepHGNN) framework, explicitly designed
for forecasting in complex hierarchical structures. The uniqueness of DeepHGNN
lies in its innovative graph-based hierarchical interpolation and an end-to-end
reconciliation mechanism. This approach ensures forecast accuracy and coherence
across various hierarchical levels while sharing signals across them,
addressing a key challenge in hierarchical forecasting. A critical insight in
hierarchical time series is the variance in forecastability across levels, with
upper levels typically presenting more predictable components. DeepHGNN
capitalizes on this insight by pooling and leveraging knowledge from all
hierarchy levels, thereby enhancing the overall forecast accuracy. Our
comprehensive evaluation set against several state-of-the-art models confirm
the superior performance of DeepHGNN. This research not only demonstrates
DeepHGNN's effectiveness in achieving significantly improved forecast accuracy
but also contributes to the understanding of graph-based methods in
hierarchical time series forecasting.
