521
Views
38
CrossRef citations to date
0
Altmetric
Articles

A novel hybrid model for tourist volume forecasting incorporating search engine data

, , &

References

  • Andre, J., Siarry, P., & Dognon, T. (2001). An improvement of the standard genetic algorithm fighting premature convergence in continuous optimization. Advances in Engineering Software, 32(1), 49–60. doi: 10.1016/S0965-9978(00)00070-3
  • Archer, B. H. (1987). Demand forecasting and estimation. In J. R. B. Ritchie & C. R. Goeldner (Eds.), Travel, tourism and hospitality research (pp. 77–85). New York: Wiley.
  • Athiyaman, A., & Robertson, R. W. (1992). Time series forecasting techniques: Short-term planning in tourism. International Journal of Contemporary Hospitality Management, 4(4), 8–11. doi: 10.1108/09596119210018864
  • Chang, C. C., & Lin, C. J. (2001). LIBSVM: A library for support vector machines. Department of Computer Science and Information Engineering, National Taiwan University. Retrieved May 20, 2004, from http://www.csie.ntu.edu.tw/_cjlin/papers/libsvm.pdf
  • Chen, K. Y. (2011). Combining linear and nonlinear model in forecasting tourism demand. Expert Systems with Applications, 38(8), 10368–10376. doi: 10.1016/j.eswa.2011.02.049
  • Chen, K. Y., & Wang, C. H. (2007). Support vector regression with genetic algorithms in forecasting tourism demand. Tourism Management, 28(1), 215–226. doi: 10.1016/j.tourman.2005.12.018
  • Chen, R., Liang, C. R., Hong, W. C., & Gu, D. X. (2015). Forecasting holiday daily tourist flow based on seasonal support vector regression with adaptive genetic algorithm. Applied Soft Computing, 26, 435–443. doi: 10.1016/j.asoc.2014.10.022
  • Chen, R., Liang, C. Y., Lu, W. X., Song, G. F., & Liang, Y. (2014). Forecasting tourism flow based on seasonal PSP-SVR model. Systems Engineering-Theory & Practice, 34(5), 1290–1296.
  • Cho, V. (2003). A comparison of three different approaches to tourist arrival forecasting. Tourism Management, 24(3), 323–330. doi: 10.1016/S0261-5177(02)00068-7
  • Choi, H., & Varian, H. (2012). Predicting present with google trends. Economic Record, 88(S1), 2–9. doi: 10.1111/j.1475-4932.2012.00809.x
  • Chu, F. L. (2009). Forecasting tourism demand with ARMA-based methods. Tourism Management, 30(5), 740–751. doi: 10.1016/j.tourman.2008.10.016
  • Drucker, H., Burges, C. J. C., Kaufman, L., Smola, A. J., & Vapnik, V. (1996). Support vector regression machines. Advances in Neural Information Processing Systems, 28(7), 779–784.
  • Duan, K., Keerthi, S., & Poo, A. (2001). Evaluation of simple performance measures for tuning SVM hyperparameters (technical report). Singapore: National University of Singapore, Department of Mechanical Engineering.
  • Fesenmaier, D. R., Cook, S. D., Zach, F., Gretzel, U., & Stienmetz, J. (2009). Travelers’ use of the internet. Washington, DC: Travel Industry Association of America.
  • Ghani, J., Choudhury, I., & Hassan, H. (2004). Application of Taguchi method in the optimization of end milling parameters. Journal of Materials Processing Technology, 145(1), 84–92. doi: 10.1016/S0924-0136(03)00865-3
  • Gu, J. R., Zhu, M. C., & Jiang, L. G. Y. (2011). Housing price forecasting based on genetic algorithm and support vector machine. Expert Systems with Applications, 38(4), 3383–3386. doi: 10.1016/j.eswa.2010.08.123
  • Hadavandi, E., Shavandi, H., Ghanbari, A., & Abbasian-Naghneh, S. (2012). Developing a hybrid artificial intelligence model for outpatient visits forecasting in hospitals. Applied Soft Computing, 12(2), 700–711. doi: 10.1016/j.asoc.2011.09.018
  • Hainan Tourism Bureau. (2015). Tourist volume and income of Hainan province, 2014. Retrieved from http://www.visithainan.gov.cn/Government/jiaodianxinwen/lvyouyaowen/201503/t20150328_59666.htm.
  • Hong, W. C. (2010). Application of chaotic ant swarm optimization in electric load forecasting. Energy Policy, 38(10), 5830–5839. doi: 10.1016/j.enpol.2010.05.033
  • Hong, W. C. (2011). Traffic flow forecasting by seasonal SVR with chaotic simulated annealing algorithm. Neurocomputing, 74(12–13), 2096–2107. doi: 10.1016/j.neucom.2010.12.032
  • Hong, W. C., Dong, Y., Chen, L. Y., & Wei, S. Y. (2011). SVR with hybrid chaotic genetic algorithms for tourism demand forecasting. Applied Soft Computing, 11(2), 1881–1890. doi: 10.1016/j.asoc.2010.06.003
  • Hotta, K. (2008). Robust face recognition under partial occlusion based on support vector machine with local Gaussian summation kernel. Image & Vision Computing, 26(11), 1490–1498. doi: 10.1016/j.imavis.2008.04.008
  • Hsu, C. W., Chang, C. C., & Lin, C. J. (2003). A practical guide to support vector classification (Tech. Rep.). Dept. Comput. Sci., National Taiwan University.
  • Hung, W. M., & Hong, W. C. (2009). Application of SVR with improved ant colony optimization algorithms in exchange rate forecasting. Control & Cybernetics, 38(3), 863–891.
  • Kabacoff, R. (2015). R in action: Data analysis and graphics with R. Shelter Island, NY: Manning Publications.
  • Keerthi, S. S. (2002). Efficient tuning of SVM hyper-parameters using radius/margin bound and iterative algorithms. IEEE Transactions on Neural Networks, 13(5), 1225–1229. doi: 10.1109/TNN.2002.1031955
  • Law, R. (2000). Back-propagation learning in improving the accuracy of neural network-based tourism demand forecasting. Tourism Management, 21(4), 331–340. doi: 10.1016/S0261-5177(99)00067-9
  • Li, J., & Yang, M. (2010). Study on online tourism information search behavior of domestic tourists in Xi’an. Economy and Geography, 30(7), 1212–1216.
  • Lim, C., & McAleer, M. (2002). Time series forecasts of international travel demand for Australia. Tourism Management, 23(4), 389–396. doi: 10.1016/S0261-5177(01)00098-X
  • Lu, C. J., Lee, T. S., & Chiu, C. C. (2009). Financial time series forecasting using independent component analysis and support vector regression. Decision Support Systems, 47(2), 115–125. doi: 10.1016/j.dss.2009.02.001
  • Mohandes, M. (2002). Support vector machines for short-term electrical load forecasting. International Journal of Energy Research, 26(4), 335–345. doi: 10.1002/er.787
  • Pai, P. F., & Hong, W. C. (2005). An improved neural network model in forecasting arrivals. Annals of Tourism Research, 32(4), 1138–1141. doi: 10.1016/j.annals.2005.01.002
  • Pai, P. F., Hong, W. C., & Chang, P. T. (2006). The application of support vector machines to forecast tourist arrivals in Barbados: An empirical study. International Journal of Management, 123(2), 375–385.
  • Pan, B., Wu, D. C., & Song, H. (2012). Forecasting hotel room demand using search engine data. Journal of Hospitality and Tourism Technology, 3(3), 196–210. doi: 10.1108/17579881211264486
  • Sapankevych, N., & Sankar, R. (2009). Time series prediction using support vector machines: A survey. IEEE Computational Intelligence Magazine, 4(2), 24–38. doi: 10.1109/MCI.2009.932254
  • Song, H., & Li, G. (2008). Tourism demand modelling and forecasting—a review of recent research. Tourism Management, 29(2), 203–220. doi: 10.1016/j.tourman.2007.07.016
  • Suykens, J. A. K. (2001). Support vector machines: A nonlinear modelling and control perspective. European Journal of Control, 7(2–3), 311–327. doi: 10.3166/ejc.7.311-327
  • Tavakkoli, A., Rezaeenour, J., & Hadavandi, E. (2015). A novel forecasting model based on support vector regression and bat meta-heuristic (Bat–SVR): Case study in printed circuit board industry. International Journal of Information Technology & Decision Making, 14(1), 195–215. doi: 10.1142/S0219622014500849
  • Vapnik, V. (1995). The nature of statistical learning theory. New York: Springer.
  • Vapnik, V., Golowich, S., & Smola, A. (1997). Support vector machine for function approximation, regression estimation, and signal processing. Advances in Neural Information Processing Systems, 9, 281–287.
  • Wang, J. Z., Zhu, S. L., Zhang, W. Y., & Lu, H. Y. (2010). Combined modeling for electric load forecasting with adaptive particle swarm optimization. Energy, 35(4), 1671–1678. doi: 10.1016/j.energy.2009.12.015
  • Wu, C. H., Ho, J. M., & Lee, D. T. (2004). Travel-time prediction with support vector regression. IEEE Transactions on Intelligent Transportation Systems, 5 (4), 276–281. doi: 10.1109/TITS.2004.837813
  • Yang, X., Pan, B., James, A., & Lv, B. (2015). Forecasting Chinese tourist volume with search engine data. Tourism Management, 46, 386–397. doi: 10.1016/j.tourman.2014.07.019
  • Yang, X. S. (2009). Harmony search as a metaheuristic algorithm. In Z. Geem (Eds.), Music-inspired harmony search algorithm (Vol. 191, pp. 1–14). Berlin: Springer.
  • Yang, X. S. (2010). A new metaheuristic bat-inspired algorithm. In J. Gonzalez, D. Pelta, C. Cruz, G. Terrazas, & N. Krasnogor (Eds.), Nature inspired cooperative strategies for optimization (NICSO 2010) (Vol. 284, pp. 65–74). Berlin: Springer.
  • Yang, X. S. (2012). Bat algorithm: A novel approach for global engineering optimization. Engineering Computations, 29(5), 464–483. doi: 10.1108/02644401211235834
  • Yang, Y., Pan, B., & Song, H. (2014). Predicting hotel demand using destination marketing organization’s WEB traffic data. Journal of Travel Research, 53(4), 433–447. doi: 10.1177/0047287513500391
  • Zhang, Y. J. (2011). The impact of financial development on carbon emissions: An empirical analysis in China. Energy Policy, 39(4), 2197–2203. doi: 10.1016/j.enpol.2011.02.026

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.