arXiv stat.ML
· Papers
Contextual Deconvolution for Variance-Stable Demand Sensing: Kernel-Modulated Operators in Promotional Retail
arXiv:2607.25664v1 Announce Type: cross Abstract: Machine learning demand forecasts optimize statistical accuracy yet leave excess operational volatility that inflates safety stock and amplifies the Bullwhip effect. We introduce textbf{Contextual Deconvolution} (CD), a two-stage estimator that reframes demand sensing