Analysis of the prediction capability of web search data based on the HE-TDC method ‒ prediction of the volume of daily tourism visitors

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• 作者：Geng Peng ; Ying Liu ; Jiyuan Wang ; Jifa Gu
• 关键词：Tourism visitor volume prediction ; web ; search data ; HE ; TDC method ; Jiuzhai Valley ; time series ; Hurst exponent
• 刊名：Journal of Systems Science and Systems Engineering
• 出版年：2017
• 出版时间：April 2017
• 年：2017
• 卷：26
• 期：2
• 页码：163-182
• 全文大小：
• 刊物类别：Engineering
• 刊物主题：Complexity; Economic Theory/Quantitative Economics/Mathematical Methods; Operation Research/Decision Theory;
• 出版者：Springer Berlin Heidelberg
• ISSN：1861-9576
• 卷排序：26

文摘

Web search query data are obtained to reflect social spots and serve as novel economic indicators. When faced with high-dimensional query data, selecting keywords that have plausible predictive ability and can reduce dimensionality is critical. This paper presents a new integrative method that combines Hurst Exponent (HE) and Time Difference Correlation (TDC) analysis to select keywords with powerful predictive ability. The method is called the HE-TDC screening method and requires keywords with predictive ability to satisfy two characteristics, namely, high correlation and fluctuation memorability similar to the predicting target series. An empirical study is employed to predict the volume of tourism visitors in the Jiuzhai Valley scenic area. The study shows that keywords selected using HE-TDC method produce a model with better robustness and predictive ability.