Phillips 66
GENERALIZED MACHINE LEARNING APPLICATION TO ESTIMATE WHOLESALE REFINED PRODUCT PRICE SEMI-ELASTICITIES

Abstract:

Certain aspects of the present disclosure provide techniques for combining multiple machine learning applications in order to train a model of a decision support system to determine an optimal semi-elasticity or elasticity coefficient for a commodity in a highly competitive market structure (e.g., unbranded, wholesale fuels market). Data is obtained from sources and clustered using a plurality of clustering combinations. Once data clusters are generated, the relevant features from each cluster is identified. A correlation coefficient range is established, and for each cluster at each iteration of the correlation coefficient range, a set of regressions are implemented and statistical tests conducted in order to determine an optimal coefficient for each cluster. The set of regressions is also implemented on the selected optimal correlation coefficient and the correlation coefficient and corresponding metric is recorded, from which one correlation coefficient is distributed to a computing device associated with the decision support system.