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

• Ling Tang ^[1] ; Chengyuan Zhang ^[1] ; Tingfei Li ^[2] ; Ling Li ^[3]
  1. [1] Beihang University

     Beihang University

     China

     
  2. [2] Beijing University of Chemical Technology

     Beijing University of Chemical Technology

     China

     
  3. [3] Capital University of Economics and Business

     Capital University of Economics and Business

     China

     

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• Localización: Tourism economics: the business and finance of tourism and recreation, ISSN 1354-8166, Vol. 27, Nº. 5, 2021, págs. 1015-1038
• Idioma: inglés
• Texto completo no disponible (Saber más ...)
• Resumen

  • 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 mul- tiscales) and similar variants (considering SED or multiscales) in accuracy and robustness.