Forecasting Tourist Volume Based on Clustering Method with Screening Keywords of Search Engine Data

Abstract

Abstract:

As one of the main sources of non-trade foreign exchange earnings, tourism industry has been developing rapidly, and its traffic forecasting is an important part of marketing and operations. However, the general prediction methods based on the Bureau of Statistics data, which are released with a lag, cannot reflect the latest trends of the tourism market. Therefore, the forecasting model based on search engine data and historical traffic statistics data is proposed in this paper, to explore the relationship between search engine data and tourism market forecast passenger traffic. Keywords related to the fluctuation of predictor variables are selected through clustering method to synthesize keywords indexes, so that the effective information between search data and tourism market trends can be further complementary. Then, historical statistics data and synthetic keyword indexes are used to establish autoregressive lag model. This model proves to be more accurate in comparison with the methods based merely on either search indexes or historical data. This paper provides a new method of forecasting tourist volume for tourism business management.

Key words: search engine data, keyword index, clustering method, forecasting tourist volume

Zhang Lingling, Zhang Xiao, Cui Yiwen. Forecasting Tourist Volume Based on Clustering Method with Screening Keywords of Search Engine Data[J]. , 2018, 30(8): 126-137.

References

[1] 章杰宽,朱普选. 动态粒子群算法优化灰色神经网络的旅游需求预测模型研究[J]. 管理评论, 2013,25(3):60-66
[2] Chen R., Liang C. Y., Hong W. C., et al. Forecasting Holiday Daily Tourist Flow Based on Seasonal Support Vector Regression with Adaptive Genetic Algorithm[J]. Applied Soft Computing, 2015,26(C):435-443
[3] Aguilo E., Alegre J., Sard M. The Persistence of The Sun and Sand Tourism Model[J]. Tourism Management, 2005,26(2):219-231
[4] Song H., Li G. Tourism Demand Modelling and Forecasting——A Review of Recent Research[J]. Tourism Management, 2008,29(2):203-220
[5] Ginsberg J., Mohebbi M. H., Patel R. S., et al. Detecting Influenza Epidemics Using Search Engine Query Data[J]. Nature, 2009,457(7232):1012-1014
[6] Zhi D. A., Engelberg J., Gao P. In Search of Attention[J]. Journal of Finance, 2011,66(5):1461-1499
[7] 宋双杰,曹晖,杨坤. 投资者关注与IPO异象——来自网络搜索量的经验证据[J]. 经济研究, 2011,(S1):145-155
[8] Xin Y., Pan B., Evans J. A., et al. Forecasting Chinese Tourist Volume with Search Engine Data[J]. Tourism Management, 2015,46:386-397
[9] Yuan Z., Wenbin B. The Impact of Investors' Attention on Stock Returns——Study Based on Baidu Index[C]. Service Systems and Service Management (ICSSSM), 201411th International Conference on. IEEE, 2014
[10] Pan B., Wu D. C., Song H. Forecasting Hotel Room Demand Using Search Engine Data[J]. Journal of Hospitality and Tourism Technology, 2012,3(3):196-210
[11] Yang Y., Pan B., Song H. Y. Predicting Hotel Demand Using Destination Marketing Organization's Web Traffic Data[J]. Journal of Travel Research, 2014,53(4):433-447
[12] Pan B., Litvin S. W., Goldman H. Real Users, Real Trips, and Real Queries:An Analysis of Destination Search on a Search Engine[C]. Annual Conference of Travel and Tourism Research Association (TTRA 2006), Dublin, Ireland, 2006
[13] Gustavsson P., Nordström J. The Impact of Seasonal Unit Roots and Vector ARMA Modelling on Forecasting Monthly Tourism Flows[J]. Tourism Economics, 2001,7(2):117-133
[14] Ho S. L., Xie M., Goh T. N. A Comparative Study of Neural Network and Box-Jenkins ARIMA Modeling in Time Series Prediction[J]. Computers & Industrial Engineering, 2002,42(2):371-375
[15] 王丽英,刘后平. 基于SARIMA模型的我国入境旅游人数时间序列分析[J]. 统计与咨询, 2008,(4):28-29
[16] Du P. J., Witt S. F. Univariate Versus Multivariate Time Series Forecasting:An Application to International Tourism Demand[J]. International Journal of Forecasting, 2003,19(3):435-451
[17] Lim C., McAleer M. A Seasonal Analysis of Asian Tourist Arrivals to Australia[J]. Applied Economics, 2000,32(4):499-509
[18] Lim C., McAleer M. Monthly Seasonal Variations:Asian Tourism to Australia[J]. Annals of Tourism Research, 2001,28(1):68-82
[19] Tideswell C., Mules T., Faulkner B. An Integrative Approach to Tourism Forecasting:A Glance in the Rearview Mirror[J]. Journal of Travel Research, 2001,40(2):162-171
[20] Kulendran N., Witt S. F. Leading Indicator Tourism Forecasts[J]. Tourism Management, 2003,24(5):503-510
[21] Shan J., Wilson K. Causality between Trade and Tourism:Empirical Evidence from China[J]. Applied Economics Letters, 2001,8(4):279-283
[22] Song H., Witt S. F. Forecasting International Tourist Flows to Macau[J]. Tourism Management, 2006,27(2):214-224
[23] Mello M. M. D., Nell K. S. The Forecasting Ability of a Cointegrated VAR System of the UK Tourism Demand for France, Spain and Portugal[J]. Empirical Economics, 2005,30(2):277-308
[24] Li G., Song H., Witt S. F. Modeling Tourism Demand:A Dynamic Linear AIDS Approach[J]. Journal of Travel Research, 2004,43(2):141-150
[25] Law R. Back-propagation Learning in Improving the Accuracy of Neural Network-based Tourism Demand Forecasting[J]. Tourism Management, 2000,21(4):331-340
[26] Palmer C. Ethnography:A Research Method in Practice[J]. International Journal of Tourism Research, 2001,3(4):301-312
[27] Chen R., Liang C. Y., Hong W. C., et al. Forecasting Holiday Daily Tourist Flow Based on Seasonal Support Vector Regression with Adaptive Genetic Algorithm[J]. Applied Soft Computing, 2015,26(C):435-443
[28] 陈荣,梁昌勇,陆文星,等. 基于季节SVR-PSO的旅游客流量预测模型研究[J]. 系统工程理论实践, 2014,34(5):1290-1296
[29] Burger C., Dohnal M., Kathrada M., et al. A Practitioners Guide to Time-series Methods for Tourism Demand Forecasting-A Case Study of Durban, South Africa[J]. Tourism Management, 2001,22(4):403-409
[30] Kon S. C., Turner L. W. Neural Network Forecasting of Tourism Demand[J]. Tourism Economics, 2005,11(3):301-328
[31] 赵黎明,吴文清,刘嘉焜. 基于小波分析的游客流量神经网络预测研究[J]. 系统工程学报, 2006,21(2):221-224
[32] Hernández-López M., Cáceres-Hernández J. J. Forecasting Tourists' Characteristics by a Genetic Algorithm with a Transition Matrix[J]. Tourism Management, 2007,28(1):290-297
[33] Pai P. F., Hong W. C., Chen P. T., et al. The Application of Support Vector Machines to Forecast Tourist Arrivals in Barbados:An Empirical Study[J]. International Journal of Management, 2006,23(2):375-385
[34] Wang C. H. Predicting Tourism Demand Using Fuzzy Time Series and Hybrid Grey Theory[J]. Tourism Management, 2004,25(3):367-374
[35] Clements M. P., Hendry D. F. Chapter 13. Testing Forecast Accuracy[M]. The United States:John Wiley & Sons, 2007
[36] Koning A. J., Franses P. H., Hibon M., et al. The M3 Competition:Statistical Tests of the Results[J]. International Journal of Forecasting, 2005,21(3):397-409
[37] Li X. M., Ding L. X., Deng Y. Y., et al. Hybrid Support Vector Machine and ARIMA Model in Building Cooling Prediction[C]. Computer Communication Control and Automation (3CA), 2010 International Symposium on. IEEE, 2010
[38] Fard A. K., Akbari-Zadeh M. R. A Hybrid Method Based on Wavelet, ANN and ARIMA Model for Short-term Load Forecasting[J]. Journal of Experimental & Theoretical Artificial Intelligence, 2014,26(2):167-182
[39] Pai P. F., Lin K. P., Lin C. S., et al. Time Series Forecasting by a Seasonal Support Vector Regression Model[J]. Expert Systems with Applications, 2010,37(6):4261-4265
[40] Karamustafa K., Ulama S. Measuring the Seasonality in Tourism with the Comparison of Different Methods[J]. Euromed Journal of Business, 2010,5(2):191-214
[41] Askitas N., Zimmermann K. F. Googlemetrie und Arbeitsmarkt[J]. Wirtschaftsdienst, 2009,89(7):489-496
[42] Francesco D. Predicting Unemployment in Short Samples with Internet Job Search Query Data[J]. Mpra Paper, University Library of Munich, Germany, 2009
[43] 王博永,杨欣. 基于网络搜索的房地产政策调控效果研究[J]. 管理评论, 2014,26(9):78-88
[44] 刘颖,吕本富,彭赓. 网络搜索对股票市场的预测能力:理论分析与实证检验[J]. 经济管理, 2011,(1):172-180
[45] 2013年中国搜索引擎市场调查研究报告.中国互联网络信息中心(CNNIC),http://wenku.baidu.com/link?url=pNuxA_ZtBuB_PFvZCslR3xR634y8G9FBQaif61DQwlsmk95MJIh07msE4qhVyldW98m_ztheGrXYewjGo5uEfIVjAN7ffS9SHtMg9U5mb2e, 2013
[46] Wu L., Brynjolfsson E. The Future of Prediction:How Google Searches Foreshadow Housing Prices and Quantities[DB/OL]. https://aisel.aisnet.org/cgi/viewcontent.cgi?article=1058&context=icis2009, 2009
[47] D'Amuri F., Marcucci J. The Predictive Power of Google Searches in Forecasting Unemployment[R]. Bank of Italy Temi Di Discussione, No. 891, 2012
[48] Choi H., Liu P. Reading Tea Leaves in the Tourism Industry:A Case Study in the Gulf Oil Spill[DB/OL]. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1893078, 2011
[49] 吕本富,万红杰,刘超群,等. 微博情感能影响旅游客流量?——基于新浪微博和园博会的实证研究[J]. 管理现代化, 2015,35(3):93-95
[50] Choi H., Varian H. Predicting the Present with Google Trends[J]. Economic Record, 2012,88(s1):2-9
[51] Fong S. Using Hierarchical Time Series Clustering Algorithm and Wavelet Classifier for Biometric Voice Classification[J]. Biomed Research International, 2012,(2):1-12
[52] Luca G. D., Zuccolotto P. A Tail Dependence-based Dissimilarity Measure for Financial Time Series Clustering[J]. Advances in Data Analysis & Classification, 2011,5(4):323-340
[53] D'Urso P., Cappelli C., Lallo D. D., et al. Clustering of Financial Time Series[J]. Physica A Statistical Mechanics & Its Applications, 2013,392(9):2114-2129
[54] Macqueen J. Some Methods for Classification and Analysis of Multi Variate Observations[C]. In 5th Berkeley Symp. Math. Statist. Prob., 1967,1(14):281-297
[55] 张庆新,崔展博,马睿,等. 基于k-means聚类与径向基神经(RBF)网络的电力系统日负荷预测[J]. 科学技术与工程, 2013,13(34):10177-10181
[56] Ren P., Liao Z., Ge P. Real-Time Forecast of Tourists Distribution Based on the Improved K-Means Method[J]. Mathematical Problems in Engineering, 2014,(4):1-10
[57] 褚洪洋,柴跃廷,刘义. 基于层次分裂算法的价格指数序列聚类[J]. 清华大学学报(自然科学版), 2015,(11):1178-1183
[58] Ryan C. Maori and Tourism:A Relationship of History, Constitutions and Rites[J]. Journal of Sustainable Tourism, 1997,5(4):257-278
[59] Yang X., Reeh T., Kreisel W. Cross-Cultural Perspectives on Promoting Festival Tourism——An Examination of Motives and Perceptions of Chinese Visitors Attending the Oktoberfest in Munich (Germany)[J]. Journal of China Tourism Research, 2011,7(4):377-395
[60] Ridderstaat J., Oduber M., Croes R., et al. Impacts of Seasonal Patterns of Climate on Recurrent Fluctuations in Tourism Demand:Evidence from Aruba[J]. Tourism Management, 2014,41(2):245-256
[61] Liu Y., Chen Y., Wu S., et al. Composite Leading Search Index:A Preprocessing Method of Internet Search Data for Stock Trends Prediction[J]. Annals of Operations Research, 2015,234(1):77-94
[62] Peng G., Liu Y., Wang J., et al. Analysis of The Prediction Capability of Web Search Data Based on The He-Tdc Method-Prediction of The Volume of Daily Tourism Visitors[J]. 系统科学与系统工程学报(英文版), 2017,26(2):163-182