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A novel BEMD-based method for forecasting tourist volume with search engine data

Author

Listed:
  • Ling Tang

    (Beihang University, China)

  • Chengyuan Zhang

    (Beihang University, China)

  • Tingfei Li

    (Beijing University of Chemical Technology, China)

  • Ling Li

    (Capital University of Economics and Business, China)

Abstract

As helpful big data, search engine data (SED) regarding tourism-related factors have currently been introduced to tourist volume prediction, but they have been shown to impact the tourism market on different timescales (or frequency band). This study develops a novel forecasting method using an emerging multiscale analysis—bivariate empirical mode decomposition (BEMD)—to investigate multiscale relationships. Three major steps are performed: (1) SED process to construct an informative index from sufficient SED using statistical analyses, (2) multiscale analysis to extract scale-aligned common factors from the bivariate data of tourist volumes and SED using BEMD, and (3) tourist volume prediction using an SED-based index. In the empirical study, the novel BEMD-based method with SED is used to forecast the tourist volume of Hainan in China, a global tourist attraction, and significantly outperforms both popular techniques (not considering SED or multiscales) and similar variants (considering SED or multiscales) in accuracy and robustness.

Suggested Citation

  • Ling Tang & Chengyuan Zhang & Tingfei Li & Ling Li, 2021. "A novel BEMD-based method for forecasting tourist volume with search engine data," Tourism Economics, , vol. 27(5), pages 1015-1038, August.
    • Handle: RePEc:sae:toueco:v:27:y:2021:i:5:p:1015-1038
    DOI: 10.1177/1354816620912995
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