Forecasting tourist arrivals with machine learning and internet search index |
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| Affiliation: | 1. Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190, China;2. School of Economics and Management, University of Chinese Academy of Sciences, Beijing, 100190, China;3. Department of Systems Engineering and Engineering Management, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong;4. Center for Forecasting Science, Chinese Academy of Sciences, Beijing, 100190, China;1. Fashion Business School, London College of Fashion, University of the Arts London, 272 High Holborn, Holborn, London, UK;2. Research Institute of Energy Management and Planning, University of Tehran, Iran;3. Cardiff Business School, Cardiff University, UK;4. Faculty of Business and Law, De Montfort University, Leicester, UK;1. Institute of Tourism, Beijing Union University, Beijing, 100101, China;2. Department of Recreation, Park, and Tourism, College of Health and Human Development, Penn State University, University Park, PA, 16802, USA;3. School of Tourism and Environmental Sciences, Shaanxi Normal University, Xi''an, China;4. School of Hotel and Tourism Management, The Hong Kong Polytechnic University, Kowloon, Hong Kong;5. Beijing Open University, Beijing, 100081, China;1. Department of Economics, University of the West Indies, Cave Hill Campus, P.O Box 64, Bridgetown BB11000, Barbados;2. Caribbean Tourism Organization, Baobab Tower, Warrens, St. Michael BB22026, Barbados |
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| Abstract: | Previous studies have shown that online data, such as search engine queries, is a new source of data that can be used to forecast tourism demand. In this study, we propose a forecasting framework that uses machine learning and internet search indexes to forecast tourist arrivals for popular destinations in China and compared its forecasting performance to the search results generated by Google and Baidu, respectively. This study verifies the Granger causality and co-integration relationship between internet search index and tourist arrivals of Beijing. Our experimental results suggest that compared with benchmark models, the proposed kernel extreme learning machine (KELM) models, which integrate tourist volume series with Baidu Index and Google Index, can improve the forecasting performance significantly in terms of both forecasting accuracy and robustness analysis. |
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| Keywords: | Tourism demand forecasting Kernel extreme learning machine Search query data Big data analytics Composite search index |
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