Title: Gaussian Processes for Demand Unconstraining,
Abstract: 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 a novel 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.