Learning Newsvendor Problem with Intertemporal Dependence and Moderate Non-stationarities

This work focuses on solving the data-driven contextual newsvendor problem with intertemporal dependence and non-stationarities. More specifically, we investigate learn the data-to-decision mapping for the newsvendor problem when observations of contexts and demands are available. The observations of both contexts and demands are generated sequentially in a fluctuate nature, thus exhibit an intertemporal dependence and even non-stationarities. However, most existing works that investigate the data-driven conditional Newsvendor problem adopt a common assumption that the data are independent and identically distributed (i.i.d.) to obtain performance guarantees such as generalization bounds. In this work, we develop performance guarantees in the form of out-of-sample generalization bounds for learning contextual newsvendor problem under comparatively more realistic assumptions including intertemporal dependence and moderate non-stationarities.