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dc.contributorSchool of Hotel and Tourism Managementen_US
dc.contributor.advisorSong, Haiyan (SHTM)en_US
dc.creatorChen, Fengyi-
dc.identifier.urihttps://theses.lib.polyu.edu.hk/handle/200/13447-
dc.languageEnglishen_US
dc.publisherHong Kong Polytechnic Universityen_US
dc.rightsAll rights reserveden_US
dc.titlePredicting inbound tourism demand using improved grey modelsen_US
dcterms.abstractMost tourism products are intangible and cannot be stored, posing a challenge in how to plan and allocate tourism resources effectively to maximize economic benefits. This issue is a primary concern for both government and tourism enterprises. Utilizing a suitable forecasting model is an effective method to predict tourism demand. The number of tourist arrivals is a commonly used variable in these predictions. Given the convenience and objectivity of this data acquisition method, it has been the focus of extensive research. In this study, we aim to enhance the accuracy of classical grey models. Specifically, we develop a new GM(1,1) model using a C1 continuous monotonic piecewise rational linear/linear interpolation spline, offering a more reasonable formula for calculating the background value. Additionally, we introduce an improved GM2(2,1) model based on the original GM2(2,1) with 2-AGO, utilizing background and derivative values estimated by a C1 convexity-preserving rational quadratic interpolation spline. Using data on inbound tourists from various Chinese provinces and cities over different periods, we conducted 55 experiments. These tests involved comparing our new models against classical GM(1,1), GM2(2,1), ARMA, ARIMA models, and a BP neural network to assess their enhanced predictive accuracy in forecasting tourism demand. Specifically, there were 7 experiments comparing the classical GM(1,1) models with our new GM(1,1) model, 9 comparing the classical GM(2,1) models with our new GM2(2,1), 9 comparing ARMA and ARIMA models with our new GM(1,1), 10 comparing ARMA and ARIMA models with our new GM2(2,1), 10 comparing the BP neural network with both new GM(1,1) and new GM2(2,1), and another 10 comparing the GM(1,1) models with the GM2(2,1) models. Our findings indicate that our improved grey models are effective for predicting short-term tourist inbound data. This insight can help in understanding tourism demand trends at destinations and assist local governments and tourism departments in implementing relevant measures to support the tourism industry's development.en_US
dcterms.extentix, 150 pages : color illustrationsen_US
dcterms.isPartOfPolyU Electronic Thesesen_US
dcterms.issued2024en_US
dcterms.educationalLevelDHTMen_US
dcterms.educationalLevelAll Doctorateen_US
dcterms.LCSHTourism -- Forecastingen_US
dcterms.LCSHTourism -- Managementen_US
dcterms.LCSHHong Kong Polytechnic University -- Dissertationsen_US
dcterms.accessRightsrestricted accessen_US

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Please use this identifier to cite or link to this item: https://theses.lib.polyu.edu.hk/handle/200/13447