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This paper introduces a scalable approach for estimating Demand Transfer (DT) coefficients in large item universes using restricted logit modeling, improving demand forecasting for store assortment optimization. Experiments show accurate DT coefficient estimation when substitution behavior assumptions hold.
The paper proposes a Predict-then-Correct (PtC) framework using a few-shot continuous contextual bandit to adaptively correct base ML forecasts in retail demand forecasting, achieving significant improvements in error metrics and inventory costs over baselines.
RouteCost is a multi-stage framework for pre-order shipping cost estimation in e-commerce, combining time-aware demand forecasting, fee-card baseline pricing, residual correction, and box consolidation inference. The method improves predictive quality over 250,000 orders and 260 products while maintaining interpretability.
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.