{"title":"利用多尺度时空特征预测酒店需求","authors":"","doi":"10.1016/j.ijhm.2024.103895","DOIUrl":null,"url":null,"abstract":"<div><p>Accurate demand forecasting is critical to hotel revenue management and related decision-making. Considering the heterogeneity and dynamics of spatial effects across different time scales, this study introduces a novel model which can deeply extract these features to improve the forecasting performance of hotel demand. Specifically, the model constructs input variables with different periodicities and then integrates a Transformer neural network and long short-term memory to extract multi-scale and dynamic spatiotemporal features to generate accurate forecasts. The effectiveness of the model is verified through an empirical case in Xiamen, China. Results suggest our model significantly outperforms benchmarks in terms of accuracy and robustness. The findings extend the application of spatial-temporal modeling in hotel demand forecasting. Hotel managers can use our forecasts to optimize operations, improve revenues, and control risks. The extracted spatiotemporal features can also help managers examine cooperation and competition relationships with neighbor hotels.</p></div>","PeriodicalId":48444,"journal":{"name":"International Journal of Hospitality Management","volume":null,"pages":null},"PeriodicalIF":9.9000,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Hotel demand forecasting with multi-scale spatiotemporal features\",\"authors\":\"\",\"doi\":\"10.1016/j.ijhm.2024.103895\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Accurate demand forecasting is critical to hotel revenue management and related decision-making. Considering the heterogeneity and dynamics of spatial effects across different time scales, this study introduces a novel model which can deeply extract these features to improve the forecasting performance of hotel demand. Specifically, the model constructs input variables with different periodicities and then integrates a Transformer neural network and long short-term memory to extract multi-scale and dynamic spatiotemporal features to generate accurate forecasts. The effectiveness of the model is verified through an empirical case in Xiamen, China. Results suggest our model significantly outperforms benchmarks in terms of accuracy and robustness. The findings extend the application of spatial-temporal modeling in hotel demand forecasting. Hotel managers can use our forecasts to optimize operations, improve revenues, and control risks. The extracted spatiotemporal features can also help managers examine cooperation and competition relationships with neighbor hotels.</p></div>\",\"PeriodicalId\":48444,\"journal\":{\"name\":\"International Journal of Hospitality Management\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":9.9000,\"publicationDate\":\"2024-09-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Hospitality Management\",\"FirstCategoryId\":\"91\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S027843192400207X\",\"RegionNum\":1,\"RegionCategory\":\"管理学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"HOSPITALITY, LEISURE, SPORT & TOURISM\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Hospitality Management","FirstCategoryId":"91","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S027843192400207X","RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"HOSPITALITY, LEISURE, SPORT & TOURISM","Score":null,"Total":0}
Hotel demand forecasting with multi-scale spatiotemporal features
Accurate demand forecasting is critical to hotel revenue management and related decision-making. Considering the heterogeneity and dynamics of spatial effects across different time scales, this study introduces a novel model which can deeply extract these features to improve the forecasting performance of hotel demand. Specifically, the model constructs input variables with different periodicities and then integrates a Transformer neural network and long short-term memory to extract multi-scale and dynamic spatiotemporal features to generate accurate forecasts. The effectiveness of the model is verified through an empirical case in Xiamen, China. Results suggest our model significantly outperforms benchmarks in terms of accuracy and robustness. The findings extend the application of spatial-temporal modeling in hotel demand forecasting. Hotel managers can use our forecasts to optimize operations, improve revenues, and control risks. The extracted spatiotemporal features can also help managers examine cooperation and competition relationships with neighbor hotels.
期刊介绍:
The International Journal of Hospitality Management serves as a platform for discussing significant trends and advancements in various disciplines related to the hospitality industry. The publication covers a wide range of topics, including human resources management, consumer behavior and marketing, business forecasting and applied economics, operational management, strategic management, financial management, planning and design, information technology and e-commerce, training and development, technological developments, and national and international legislation.
In addition to covering these topics, the journal features research papers, state-of-the-art reviews, and analyses of business practices within the hospitality industry. It aims to provide readers with valuable insights and knowledge in order to advance research and improve practices in the field.
The journal is also indexed and abstracted in various databases, including the Journal of Travel Research, PIRA, Academic Journal Guide, Documentation Touristique, Leisure, Recreation and Tourism Abstracts, Lodging and Restaurant Index, Scopus, CIRET, and the Social Sciences Citation Index. This ensures that the journal's content is widely accessible and discoverable by researchers and practitioners in the hospitality field.