{"title":"基于非加性预测组合的旅游需求预测","authors":"Yi-Chung Hu, Geng Wu, Peng Jiang","doi":"10.1177/10963480211047857","DOIUrl":null,"url":null,"abstract":"Accurately forecasting the demand for tourism can help governments formulate industrial policies and guide the business sector in investment planning. Combining forecasts can improve the accuracy of forecasting the demand for tourism, but limited work has been devoted to developing such combinations. This article addresses two significant issues in this context. First, the linear combination is the commonly used method of combining tourism forecasts. However, additive techniques unreasonably ignore interactions among the inputs. Second, the available data often do not adhere to specific statistical assumptions. Grey prediction has thus drawn attention because it does not require that the data follow any statistical distribution. This study proposes a nonadditive combination method by using the fuzzy integral to integrate single-model forecasts obtained from individual grey prediction models. Using China and Taiwan tourism demand as empirical cases, the results show that the proposed method outperforms the other combined methods considered here.","PeriodicalId":51409,"journal":{"name":"Journal of Hospitality & Tourism Research","volume":null,"pages":null},"PeriodicalIF":4.4000,"publicationDate":"2021-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"12","resultStr":"{\"title\":\"Tourism Demand Forecasting Using Nonadditive Forecast Combinations\",\"authors\":\"Yi-Chung Hu, Geng Wu, Peng Jiang\",\"doi\":\"10.1177/10963480211047857\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Accurately forecasting the demand for tourism can help governments formulate industrial policies and guide the business sector in investment planning. Combining forecasts can improve the accuracy of forecasting the demand for tourism, but limited work has been devoted to developing such combinations. This article addresses two significant issues in this context. First, the linear combination is the commonly used method of combining tourism forecasts. However, additive techniques unreasonably ignore interactions among the inputs. Second, the available data often do not adhere to specific statistical assumptions. Grey prediction has thus drawn attention because it does not require that the data follow any statistical distribution. This study proposes a nonadditive combination method by using the fuzzy integral to integrate single-model forecasts obtained from individual grey prediction models. Using China and Taiwan tourism demand as empirical cases, the results show that the proposed method outperforms the other combined methods considered here.\",\"PeriodicalId\":51409,\"journal\":{\"name\":\"Journal of Hospitality & Tourism Research\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.4000,\"publicationDate\":\"2021-09-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"12\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Hospitality & Tourism Research\",\"FirstCategoryId\":\"91\",\"ListUrlMain\":\"https://doi.org/10.1177/10963480211047857\",\"RegionNum\":3,\"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":"Journal of Hospitality & Tourism Research","FirstCategoryId":"91","ListUrlMain":"https://doi.org/10.1177/10963480211047857","RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"HOSPITALITY, LEISURE, SPORT & TOURISM","Score":null,"Total":0}
Tourism Demand Forecasting Using Nonadditive Forecast Combinations
Accurately forecasting the demand for tourism can help governments formulate industrial policies and guide the business sector in investment planning. Combining forecasts can improve the accuracy of forecasting the demand for tourism, but limited work has been devoted to developing such combinations. This article addresses two significant issues in this context. First, the linear combination is the commonly used method of combining tourism forecasts. However, additive techniques unreasonably ignore interactions among the inputs. Second, the available data often do not adhere to specific statistical assumptions. Grey prediction has thus drawn attention because it does not require that the data follow any statistical distribution. This study proposes a nonadditive combination method by using the fuzzy integral to integrate single-model forecasts obtained from individual grey prediction models. Using China and Taiwan tourism demand as empirical cases, the results show that the proposed method outperforms the other combined methods considered here.
期刊介绍:
The Journal of Hospitality & Tourism Research (JHTR) is an international scholarly research journal that publishes high-quality, refereed articles that advance the knowledge base of the hospitality and tourism field. JHTR focuses on original research, both conceptual and empirical, that clearly contributes to the theoretical development of our field. The word contribution is key. Simple applications of theories from other disciplines to a hospitality or tourism context are not encouraged unless the authors clearly state why this context significantly advances theory or knowledge. JHTR encourages research based on a variety of methods, qualitative and quantitative.