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This paper introduces a training-time stability regularization penalty to improve forecast stability without sacrificing accuracy, evaluated on M5 retail demand data, showing improvements in Forecast Stability Score while maintaining RMSE within 0.72%.
STAGformer introduces a spatio-temporal agent graph transformer with linear complexity for bike-sharing demand forecasting, outperforming baselines on NYC and Chicago datasets.
GNBAN is a new graph-based neural architecture for long-horizon retail demand forecasting that combines heterogeneous graph learning with an interpretable basis-decomposition forecasting head, achieving 4-5% improvement on Walmart and Favorita benchmarks.
This paper presents a forecast-then-optimize algorithmic pricing tool for fashion e-commerce sales campaigns, using gradient-boosted trees for daily-demand forecasting and multi-objective optimization. A/B tests across 12 markets show the system achieves 6% higher profit while maintaining sales and revenue, and it has been deployed at Zalando.