A Reservation Aggregation Framework Design for Demand Estimation

Abstract

Effective management practices in the tourism and hotel area have seldom been more important than at the present time. Pricing decisions cannot be taken without serious thought. IT has provided the opportunity for a customer to make a quick market search and it offers decision support systems that can be used in the hotel management. The heart of yield management system consists of the predicting machine, which estimates the number of incoming reservations. Incoming reservations arrive randomly in time. The time series calculi as well as the estimators known from control engineering require properly defined time rows (with a constant period). This requirement is usually not fulfilled, so the input data are not exploited properly. This paper outlines a procedure that aggregates the reservations into a time series that is useful for demand prediction. The algorithm prepares the data systematically for further processing. Any method that process time rows can be used for subsequent prediction: time series, linear models or time extrapolation.