The Research of Forecasting the Number of Visitors for International Tourist Hotels in Taiwan-A Case of Kaohsiung Area

• Main Authors: Ya-Chu Chan, 詹雅竹
• Other Authors: Ying-Fang Huang
• Format: Others
• Language: zh-TW
• Published: 2014
• Online Access: http://ndltd.ncl.edu.tw/handle/phty73

碩士 === 國立高雄應用科技大學 === 工業工程與管理系碩士班 === 102 === In recent years, with the national quality of life improved, and Emphasis on leisure, tourism activities are increasingly flourish and grow rapidly, not only to stimulate the domestic economy, but also led to the rise of related industries, subjoin with frequent international business travelers to and from the accommodation needs. The relative increase, making tourist hotel became great potential for development of the industry. From 2004 to 2013 year a total increase of the decade of the 36 tourist hotels, the number of 7,221 rooms, accommodation increased by 3.9 million the number of people, we can see the obvious growth trend. Development of Kaohsiung in southern Taiwan city, sea and air transportation are very developed, suitable for the development of tourism industry, as well as large enterprises has driven in the ground for business travelers, so choose to Kaohsiung International Tourist Hotels as study areas. In this study, the data source is "Monthly Report on International Tourist Hotel Operations in Taiwan", published by the Tourism Bureau, Taken in 2006 to 2013 year the number of international tourist hotels in Kaohsiung passenger statistics, on an annual basis analysis, a total of eight pen observations. Gray theory GM (1,1), exponential smoothing, simple linear regression analysis to predict three prediction methods do. Finally, the mean absolute percentage error (MAPE) to measure the performance of the model, to find the best predictive model. The results show that the MAPE of GM(1,1) was 5.07%, The MAPE of Exponential Smoothing was 9.84%, And the MAPE of General Linear Regression was 5.44%. So choose GM (1,1) model to predict the number of hotels industry model, and predict to the number of visitors for International tourist hotels from 2014 to 2018 year in the future.