{"title":"应对酒店预测中的复杂季节模式：一项比较研究","authors":"Apostolos Ampountolas","doi":"10.1057/s41272-024-00494-6","DOIUrl":null,"url":null,"abstract":"<p>Accurately forecasting demand poses challenges for revenue managers, especially amid supply and demand uncertainties increased by the recent global pandemic. In addition, demand forecasting is particularly challenging in the hotel industry due to anomalous days and repeating seasonal patterns. This study investigates techniques like TBATS, MSTL, and STL Decomposition against Linear Regression in hotel demand time series analysis, focusing on daily occupancy and average daily rate seasonalities. Using a 5-year dataset from an Upper Upscale branded property, the study employs in-sample data for model development and a rolling window approach for testing. Results highlight the robust performance of TBATS and MSTL across different forecasting horizons, consistently outperforming Seasonal-Trend Decomposition (STLF) and linear regression, providing insights crucial for revenue optimization and strategic decision-making in the hotel industry.</p>","PeriodicalId":46686,"journal":{"name":"Journal of Revenue and Pricing Management","volume":"13 1","pages":""},"PeriodicalIF":1.1000,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Addressing complex seasonal patterns in hotel forecasting: a comparative study\",\"authors\":\"Apostolos Ampountolas\",\"doi\":\"10.1057/s41272-024-00494-6\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Accurately forecasting demand poses challenges for revenue managers, especially amid supply and demand uncertainties increased by the recent global pandemic. In addition, demand forecasting is particularly challenging in the hotel industry due to anomalous days and repeating seasonal patterns. This study investigates techniques like TBATS, MSTL, and STL Decomposition against Linear Regression in hotel demand time series analysis, focusing on daily occupancy and average daily rate seasonalities. Using a 5-year dataset from an Upper Upscale branded property, the study employs in-sample data for model development and a rolling window approach for testing. Results highlight the robust performance of TBATS and MSTL across different forecasting horizons, consistently outperforming Seasonal-Trend Decomposition (STLF) and linear regression, providing insights crucial for revenue optimization and strategic decision-making in the hotel industry.</p>\",\"PeriodicalId\":46686,\"journal\":{\"name\":\"Journal of Revenue and Pricing Management\",\"volume\":\"13 1\",\"pages\":\"\"},\"PeriodicalIF\":1.1000,\"publicationDate\":\"2024-07-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Revenue and Pricing Management\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1057/s41272-024-00494-6\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"BUSINESS, FINANCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Revenue and Pricing Management","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1057/s41272-024-00494-6","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"BUSINESS, FINANCE","Score":null,"Total":0}
Addressing complex seasonal patterns in hotel forecasting: a comparative study
Accurately forecasting demand poses challenges for revenue managers, especially amid supply and demand uncertainties increased by the recent global pandemic. In addition, demand forecasting is particularly challenging in the hotel industry due to anomalous days and repeating seasonal patterns. This study investigates techniques like TBATS, MSTL, and STL Decomposition against Linear Regression in hotel demand time series analysis, focusing on daily occupancy and average daily rate seasonalities. Using a 5-year dataset from an Upper Upscale branded property, the study employs in-sample data for model development and a rolling window approach for testing. Results highlight the robust performance of TBATS and MSTL across different forecasting horizons, consistently outperforming Seasonal-Trend Decomposition (STLF) and linear regression, providing insights crucial for revenue optimization and strategic decision-making in the hotel industry.
期刊介绍：
The?Journal of Revenue and Pricing Management?serves the community of researchers and practitioners dedicated to improving understanding through insight and real life situations. Each article emphasizes meaningful answers to problems whether cutting edge science or real solutions. The journal places an emphasis disseminating the best articles from the best minds and benchmarked businesses within the field of Revenue Management and Pricing.Revenue management (RM) also known as Yield Management (YM) is a management activity that marries the diverse disciplines of operations research/management science analytics economics human resource management software development marketing economics e-commerce consumer behaviour and consulting to manage demand for a firm's products or services with the goal of profit maximisation. From a practitioner standpoint RM encompasses a range of activities related to demand management including pricing segmentation capacity and inventory allocation demand modelling and business process management.Journal of Revenue and Pricing Management?aims to:formulate and disseminate a body of knowledge called 'RM and pricing' to practitioners educators researchers and students;provide an international forum for a wide range of practical theoretical and applied research in the fields of RM and pricing;represent a multi-disciplinary set of views on key and emerging issues in RM and pricing;include a cross-section of methodologies and viewpoints on research including quantitative and qualitative approaches case studies and empirical and theoretical studies;encourage greater understanding and linkage between the fields of study related to revenue management and pricing;to publish new and original ideas on research policy and managementencourage and engage with professional communities to adopt the Journal as the place of knowledge excellence i.e. INFORMS Revenue Management & Pricing section AGIFORS and Revenue Management Society and Revenue Management and Pricing International Ltd.Published six times a year?Journal of Revenue and Pricing Management?publishes a wide range of peer-reviewed practice papers research articles and professional briefings written by industry experts - including:Practice papers - addressing the issues facing practitioners in industry and consultancyApplied research papers - from leading institutions on all areas of research of interest to practitioners and the implications for practiceCase studies - focusing on the real-life challenges and problems faced by major corporations how they were approached and what was learnedModels and theories - practical models and theories which are being used in revenue managementThoughts - assessment of the key issues new trends and future ideas by leading experts and practitionersApprentice - the publication of tomorrows ideas by students of todayBook/conference reviews - reviewing leading conferences and major new books on RM and pricingThe Journal is essential reading for senior professionals in private and public sector organisations and academic observers in universities and business schools - including:Pricing AnalystsRevenue ManagersHeads of Revenue ManagementHeads of Yield ManagementDirectors of PricingHeads of MarketingChief Operating OfficersCommercial DirectorsDirectors of SalesDirectors of OperationsHeads of ResearchPricing ConsultantsProfessorsLecturers
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
