  Forecasting demand is one of the fundamental components of a successful
revenue management system in hospitality. The industry requires understandable
models that contribute to adaptability by a revenue management department to
make data-driven decisions. Data analysis and forecasts prove an essential role
for the time until the check-in date, which differs per day of week. This paper
aims to provide a new model, which is inspired by cubic smoothing splines,
resulting in smooth demand curves per rate class over time until the check-in
date. This model regulates the error between data points and a smooth curve,
and is therefore able to capture natural guest behavior. The forecast is
obtained by solving a linear programming model, which enables the incorporation
of industry knowledge in the form of constraints. Using data from a major hotel
chain, a lower error and 13.3% more revenue is obtained.
