Oracle International Corporation

Massachusetts Institute of Technology

Setareh Borjian

Kiran V Panchamgam

Georgia Perakis

Leann Thayaparan

Embodiments generate and train an optimized demand model for predicting a demand of an item. Embodiments receive a plurality of trees, each of the plurality of trees including one or more levels of splits and a plurality of nodes, each of the plurality of nodes corresponding to a demand feature that influences demand for the item. Embodiments store a first bound as a current bound for each of the plurality of trees. Starting at a top split of each of the plurality of trees, embodiments select a first demand feature that a greatest number of the plurality of trees split on. Embodiments optimize the first demand feature using the stored current bound to generate a second bound. Embodiments store the second bound as the current bound for each of the plurality of trees and move down each of the plurality of trees to a next level of splits.

Optimized tree ensemble based demand model

Embodiments generate an optimized demand model for a retail item. Embodiments train a tree ensemble machine learning model comprising a plurality of trees, the training comprising storing upper bounds for each of the trees, the trees comprising levels and branches that correspond to the demand features that influence demand for the item. Embodiments generate an objective function for the demand model. At a top split of each tree, embodiments determine optimal child nodes using the stored upper bounds and calculate a new feasible region for each tree. Using bounds on the new feasible region, embodiments move down each tree to a next level of splits and generate the optimized demand model when a leaf node of every tree has been reached.

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New Haven, CT, United States of America

Setareh Borjian