A Prediction Study on Tourist Amount Based on Web Search Data —A Case from Hainan
The web search data, which records hundreds of millions of searchersˇ concerns and interests, reflects the trends of their behavior and provides essential data basis for the prediction of tourist amount. In this paper, firstly, a systematic theoretical framework has been built to reveal the correlation between web search and touristsˇ travel. Secondly, at the theoretical frameworkˇ basis, an empirical study on Hainan verified the cointegration relationship between search index and tourist amount. Finally, an prediction model has been established to predict consecutive 4 monthsˇ Hainan tourist amount. The results show that compared with the traditional autoregression AR model, adding search index modelˇs Mean Absolute Percent Error( MAPE) decrease from 6.54％ to 1.34％, goodness of fit reaches 0.975, and realizes ¨predict the present〃 making up China National Tourism Administrationˇs(CNTA) data release delay for about 1 month, confirming the prediction ability of search index for tourist amount. This paperˇs conclusions can be provided as references for CNTA monitoring the change of tourist amount and tourism service offering adequate ancillary services. The new prediction method considering search index can also be applied to other webbased soceconomical activity.
searchdata searchindex tourism Hainantouristamount prediction co-integrationanalysis
Yang Xin Peng Geng Yuan Qinyu Lv Benfu
Management School Chinese Academy of Sciences Beijing, China Management SchoolChinese Academy of SciencesBeijing, China
国际会议
广州
英文
1-6
2011-05-13（万方平台首次上网日期，不代表论文的发表时间）