GNBAN: Graph Neural Basis Attention Networks for Long-Horizon Forecasting over Large Entity Sets

arXiv:2606.27863v1 Announce Type: new Abstract: Demand forecasting at the bottom of a retail hierarchy requires predicting tens of thousands of correlated long-horizon series across products, stores, and regions. Modern systems must scale across massive catalogs, capture shared demand dynamics, and remain interpretable enough to be trusted. Classical statistical methods need a separate model per series and are hard to manage at scale; deep autoregressive models struggle as the joint state grows to tens of thousands of dimensions; and recent graph-based forecasters, while capturing cross-entity

Source: arXiv cs.LG — read the full report at the original publisher.

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