arXiv paper proposes accuracy-preserving stability regularization for large-scale retail demand forecasting; evaluates training-time penalty on consecutive movements to improve forecast stability without sacrificing point accuracy.
Read the original at arxiv.org→arXiv:2607.13331v1 Announce Type: new Abstract: Retail demand forecasts are reused across replenishment, capacity, labor, and transportation planning cycles. Point-error objectives do not constrain abrupt movement...
Original headline: "Accuracy-Preserving Stability Regularization for Large-Scale Retail Demand Forecasting"