Study of tourism flow forecasting based on a seasonally adjusted particle swarm optimization-support vector regression model

被引：0

作者：

Weng, Gangmin ^{[1
]}

Li, Lingyan ^{[1
]}

机构：

[1] School of Economics and Management, Yanshan University, Qinhuangdao

来源：

Journal of Information and Computational Science | 2015年 / 12卷 / 07期

关键词：

Comparison of mean square error; Particle swarm optimization; Seasonal adjustment; Support vector regression; Tourism flow forecasting;

D O I：

10.12733/jics20105860

中图分类号：

学科分类号：

摘要：

Forecasting tourism flow accurately is one of the most important priorities for tourism destinations. Tourism flow has a distinct seasonal trend. Using a seasonal adjustment method to preprocess the sample data can eliminate seasonal influence, and can improve the accuracy of tourism flow forecasting. Support vector regression is a good method for machine learning, and is very suitable for predicting tourism flow. Supplemented by the use of the particle swarm optimization algorithm to choose appropriate parameters, SVR can get prediction results more accurately. Thus, we construct a PSO-SVR tourism flow forecasting model which takes seasonal influences into account. Sanya (Hainan, China) is taken as an example for empirical research. The fitting degree of different prediction models indicates that the predictive accuracy of the seasonally adjusted PSO-SVR model is significantly higher than that of the SVR model, the seasonally adjusted SVR model and the PSO-SVR model. That is to say, the seasonally adjusted PSO-SVR model is the most effective tool for predicting tourism flow. Copyright © 2015 Binary Information Press.

引用

页码：2747 / 2757

页数：10

共 20 条

[1]

Smeral E., Witt S.F., Witt C.A., Econometric forecasts: Tourism trends to 2000, Annals of Tourism Research, 19, 3, pp. 450-466, (1992)

[2]

Yang M.G., Yang X.X., Prediction for inbound tourism flow of Chongqing based on grey prediction model, Journal of Southwest China Normal University (Natural Science Edition), 35, 3, pp. 259-263, (2010)

[3]

Zhu X.H., Yang X.C., Forecast model and their comparison on international tourism passenger source of China, Geography and Geo-Information Science, 20, 5, pp. 84-87, (2004)

[4]

Vapnik V.N., The Nature of Statistical Learning Theory, (1999)

[5]

Chen K.Y., Evolutionary support vector regression modeling for Taiwan Stock Exchange Market Weighted Index forecasting, Journal of American Academy of Business, 8, 1, pp. 241-247, (2006)

[6]

Wang R.X., Liu Y., Study on network traffic forecast model of SVR optimized by GAFSA, Application Research of Computers, 30, 3, pp. 856-860, (2013)

[7]

Xiao Z., Li L.L., Forecast of highway traffic volume using PSO-SVM, Management Review, 23, 12, pp. 32-37, (2011)

[8]

Xia G.E., Shao P.J., Application of modified support vector regression in financial time sequence prediction, Financial Theory and Practice, 11, pp. 95-98, (2008)

[9]

Feng X.J., Wei X., Huang Y.L., Predicting the airport passenger throughput based on a support vector regression model, Computer Engineering, 31, 14, pp. 172-173, (2005)

[10]

Huang C.F., A hybrid stock selection model using genetic algorithms and support vector regression original research article, Applied Soft Computing, 2, 12, pp. 807-818, (2012)

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