Forecasting tourism demand with KPCA-based web search indexes

Gang Xie, Xin Li, Yatong Qian and Shouyang Wang
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Gang Xie: 85320Academy of Mathematics and Systems Science, Chinese Academy of Sciences, China
Xin Li: 12507University of Science and Technology, China
Yatong Qian: 85320Academy of Mathematics and Systems Science, Chinese Academy of Sciences, China; University of Chinese Academy of Sciences, China
Shouyang Wang: 85320Academy of Mathematics and Systems Science, Chinese Academy of Sciences, China

Tourism Economics, 2021, vol. 27, issue 4, 721-743

Abstract: Search query data (SQD) can be helpful in predicting tourism demand by generating web search indexes. However, valuable nonlinear information in SQD may be neglected by researchers. To effectively capture the nonlinear information, we used kernel principal component analysis (KPCA) to extract web search indexes from SQD. Then, several models with KPCA-based web search indexes were developed for tourism demand forecasting. An empirical study was conducted with collected SQD and real data of tourist arrivals at Hong Kong. The results suggest that models with KPCA-based web search indexes are more accurate than other models because of the nonlinear data processing ability of the KPCA and demonstrate that KPCA-based web search indexes can be excellent predictors for tourism demand forecasting.

Keywords: kernel principal component analysis; search query data; tourism demand forecasting; web search index (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:sae:toueco:v:27:y:2021:i:4:p:721-743

DOI: 10.1177/1354816619898576

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