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analysisofthepredictioncapabilityofwebsearchdatabasedonthehetdcmethodpredictionofthevolumeofdailytourismvisitors
Peng Geng1; Liu Ying1; Wang Jiyuan1; Gu Jifa2
2017
Source Publicationjournalofsystemsscienceandsystemsengineering
ISSN1004-3756
Volume26Issue:2Pages:163
AbstractWeb 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.
Language英语
Document Type期刊论文
Identifierhttp://ir.amss.ac.cn/handle/2S8OKBNM/43224
Collection中国科学院数学与系统科学研究院
Affiliation1.中国科学院大学
2.中国科学院数学与系统科学研究院
Recommended Citation
GB/T 7714
Peng Geng,Liu Ying,Wang Jiyuan,et al. analysisofthepredictioncapabilityofwebsearchdatabasedonthehetdcmethodpredictionofthevolumeofdailytourismvisitors[J]. journalofsystemsscienceandsystemsengineering,2017,26(2):163.
APA Peng Geng,Liu Ying,Wang Jiyuan,&Gu Jifa.(2017).analysisofthepredictioncapabilityofwebsearchdatabasedonthehetdcmethodpredictionofthevolumeofdailytourismvisitors.journalofsystemsscienceandsystemsengineering,26(2),163.
MLA Peng Geng,et al."analysisofthepredictioncapabilityofwebsearchdatabasedonthehetdcmethodpredictionofthevolumeofdailytourismvisitors".journalofsystemsscienceandsystemsengineering 26.2(2017):163.
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