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Forecasting tourism demand with KPCA-based web search indexes

Author

Listed:
  • 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)

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.

Suggested Citation

  • Gang Xie & Xin Li & Yatong Qian & Shouyang Wang, 2021. "Forecasting tourism demand with KPCA-based web search indexes," Tourism Economics, , vol. 27(4), pages 721-743, June.
    • Handle: RePEc:sae:toueco:v:27:y:2021:i:4:p:721-743
    DOI: 10.1177/1354816619898576
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