Booking horizon forecasting with dynamic updating: A case study of hotel reservation data

被引：42

作者：

Haensel, Alwin ^{[1
,2
]}

Koole, Ger ^{[1
]}

机构：

[1] Vrije Univ Amsterdam, Dept Math, NL-1081 HV Amsterdam, Netherlands

[2] Bookit BV, Amstelveen, Netherlands

来源：

INTERNATIONAL JOURNAL OF FORECASTING | 2011年 / 27卷 / 03期

关键词：

Demand forecasting; Dynamic forecast updating; Dimension reduction; Penalized least squares; Time series; Revenue management; REVENUE MANAGEMENT; FUTURE;

D O I：

10.1016/j.ijforecast.2010.10.004

中图分类号：

F [经济];

学科分类号：

02 ;

摘要：

A highly accurate demand forecast is fundamental to the success of every revenue management model. As is often required in both practice and theory, we aim to forecast the accumulated booking curve, as well as the number of reservations expected for each day in the booking horizon. To reduce the dimensionality of this problem, we apply singular value decomposition to the historical booking profiles. The forecast of the remaining part of the booking horizon is dynamically adjusted to the earlier observations using the penalized least squares and historical proportion methods. Our proposed updating procedure considers the correlation and dynamics of bookings both within the booking horizon and between successive product instances. The approach is tested on real hotel reservation data and shows a significant improvement in forecast accuracy. (C) 2011 International Institute of Forecasters. Published by Elsevier B.V. All rights reserved.

引用

页码：942 / 960

页数：19

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