Gaussian processes for unconstraining demand

Ilan Price*, Jaroslav Fowkes, Daniel Hopman

*Corresponding author for this work

Research output: Contribution to JournalArticleAcademicpeer-review


One of the key challenges in revenue management is unconstraining demand data. Existing state of the art single-class unconstraining methods make restrictive assumptions about the form of the underlying demand and can perform poorly when applied to data which breaks these assumptions. In this paper, we propose an unconstraining method that uses Gaussian process (GP) regression. We develop a novel GP model by constructing and implementing a new non-stationary covariance function for the GP which enables it to learn and extrapolate the underlying demand trend. We show that this method can cope with important features of realistic demand data, including nonlinear demand trends, variations in total demand, lengthy periods of constraining, non-exponential inter-arrival times, and discontinuities/changepoints in demand data. In all such circumstances, our results indicate that GPs outperform existing single-class unconstraining methods.

Original languageEnglish
Pages (from-to)621-634
Number of pages14
JournalEuropean Journal of Operational Research
Issue number2
Publication statusPublished - 1 Jun 2019


This work was supported by the Oxford-Emirates Data Science Lab and partially funded by the Skye Foundation and the Weidenfeld-Hoffmann Trust.

FundersFunder number
Skye Foundation
Weidenfeld-Hoffmann Trust


    • Demand unconstraining
    • Gaussian process regression
    • OR in airlines
    • Revenue management


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