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

Ling Tang, Chengyuan Zhang, Tingfei Li and Ling Li
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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

Tourism Economics, 2021, vol. 27, issue 5, 1015-1038

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.

Keywords: big data; bivariate empirical mode decomposition; multiscale analysis; search engine data; tourist volume forecasting (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:sae:toueco:v:27:y:2021:i:5:p:1015-1038

DOI: 10.1177/1354816620912995

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