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arXiv CS.AI·Jun 29·5 min read
GNBAN: Graph Neural Basis Attention Networks for Long-Horizon Forecasting over Large Entity Sets
TL;DR
Long-horizon forecasting over large sets of entities is challenging due to the complexity of relationships and data. GNBAN, or Graph Neural Basis Attention Networks, was developed to enhance forecasting accuracy by leveraging graph neural networks and attention mechanisms.
✦ Why It Matters
Engineers can leverage GNBAN for more accurate long-term predictions in applications involving large datasets and complex relationships.
Key Takeaways
- Utilize GNBAN for scalable demand forecasting across large datasets.
- Leverage the interpretable decomposition of forecasts into trend and seasonal components.
- Apply the per-basis attention mechanism for specialized learning of temporal patterns.
- Evaluate GNBAN's performance against existing models on your retail data.
How It Works
GNBAN employs a heterogeneous graph representation of retail data, allowing it to model relationships between products, stores, and regions effectively. The architecture decomposes forecasts into trend, seasonal, and generic components, enhancing interpretability.
Its per-basis attention mechanism assigns learnable queries to each basis function, enabling them to independently retrieve relevant historical information, thus specializing in different temporal patterns.