需求预测
独创性
计算机科学
预订
领域(数学)
透视图(图形)
管理科学
运筹学
数据科学
经济
人工智能
社会学
工程类
社会科学
定性研究
纯数学
数学
计算机网络
作者
Liyao Huang,Weimin Zheng
出处
期刊:Tourism Review
[Emerald (MCB UP)]
日期:2022-11-26
卷期号:78 (1): 218-244
被引量:11
标识
DOI:10.1108/tr-07-2022-0367
摘要
Purpose This study aims to provide a comprehensive review of hotel demand forecasting to identify its key fundamentals and evolution and future research directions and trends to advance the field. Design/methodology/approach Articles on hotel demand modeling and forecasting were identified and rigorously selected using transparent inclusion and exclusion criteria. A final sample of 85 empirical studies was obtained for comprehensive analysis through content analysis. Findings Synthesis of the literature highlights that hotel forecasting based on historical demand data dominates the research, and reservation/cancellation data and combined data gradually attracted research attention in recent years. In terms of model evolution, time series and AI-based models are the most popular models for hotel demand forecasting. Review results show that numerous studies focused on hybrid models and AI-based models. Originality/value To the best of the authors’ knowledge, this study is the first systematic review of the literature on hotel demand forecasting from the perspective of data source and methodological development and indicates future research directions.
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