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
The increasing complexity and scale of retail operations, coupled with the growing availability of computational power and advanced AI techniques, necessitate more sophisticated forecasting models.
This AI advancement offers a scalable and interpretable method for long-horizon demand forecasting over massive datasets, which is crucial for optimizing supply chains and inventory management in large enterprises.
Traditional forecasting methods are increasingly replaced by a new generation of graph-based neural networks capable of handling vast, correlated time series data with improved accuracy and interpretability.
- · Large retail enterprises
- · Logistics and supply chain companies
- · AI/ML model developers
- · Cloud computing providers
- · Developers of classical statistical forecasting methods
- · Companies relying on outdated forecasting systems
- · Businesses with unoptimized inventory
Retailers can achieve significant cost savings and efficiency gains through more accurate demand predictions.
Optimized supply chains lead to reduced waste, better resource allocation, and a diminished environmental footprint.
The widespread adoption of such AI models could further democratize sophisticated forecasting, enabling smaller businesses to compete more effectively through advanced operational intelligence.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG