论文标题：A novel BEMD-based method for forecasting tourist volume with search engine data

　　发表时间：2020.03

　　论文所有作者：Tang Ling, Zhang Chengyuan, Li Ling

　　期刊名及所属分类：Tourism Economics(国际SSCI or SCI)

　　英文摘要：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.

　　中文摘要：搜索引擎数据(SED)作为一种有用的大数据，目前已被引入到旅游相关因素的预测中，但它们已被证明在不同的时间尺度(或频带)影响旅游市场。本研究利用新兴的多尺度分析方法——双变量经验模态分解(BEMD)开发了一种新的预测方法来研究多尺度关系。本研究主要采取三个步骤:(1)利用统计分析方法，利用充分的SED过程构建信息指数;(2)利用多尺度分析方法，从旅游客流量和SED usina BEMD二元数据中提取尺度一致的公共因子;(3)利用SED指数进行客流量预测。在实证研究中，将基于bemd的新方法与SED相结合，对全球旅游胜地中国海南的客流量进行预测，结果表明，该方法的预测精度和稳健性均显著优于目前流行的方法(不考虑SED或多尺度)和类似的方法(考虑SED或多尺度)。