Tourism prediction using web search data based on CLSI-EMD-BP

LI Xiaoxuan, LÜ Benfu, ZENG Pengzhi, LIU Jinxuan

Systems Engineering - Theory & Practice ›› 2017, Vol. 37 ›› Issue (1) : 106-118.

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Systems Engineering - Theory & Practice ›› 2017, Vol. 37 ›› Issue (1) : 106-118. DOI: 10.12011/1000-6788(2017)01-0106-13

Tourism prediction using web search data based on CLSI-EMD-BP

  • LI Xiaoxuan1,2, LÜ Benfu1,2, ZENG Pengzhi1,2, LIU Jinxuan1,2
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Abstract

Predicting tourism traffic accurately plays an important role in making policies for tourist administration. It helps to distribute the resources reasonably and avoid the tourism congestions. To improve the tourism prediction accuracy, this study considered the noise interference and proposed a forecast model of CLSI-EMD-BP using web search data. This model firstly applied CLSI to combine the web search data into a search index, then it denoised the series with EMD. EMD extracted the high frequency noise from the original series. The low frequency series of search index would be used to predict the low frequency tourism series. Taking Jiuzhaigou as an example, this study trained the model and predicted the next 22 weeks tourism arrivals. The conclusion demonstrated that the forecast error of CLSI-EMD-BP model is lower remarkably than the baselines of time series model, the web search data model and the BP network model. This revealed that nosing processing is necessary as well as CLSI-EMD-BP forecast model can improve the prediction accuracy.

Key words

web search data / tourism prediction / composite leading search index (CLSI) / empirical mode decomposition (EMD) / BP network

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LI Xiaoxuan, LÜ Benfu, ZENG Pengzhi, LIU Jinxuan. Tourism prediction using web search data based on CLSI-EMD-BP. Systems Engineering - Theory & Practice, 2017, 37(1): 106-118 https://doi.org/10.12011/1000-6788(2017)01-0106-13

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Funding

National Natural Science Foundation of China (71202115,71202155,71172199)
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