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Applied Economics Letters Volume 23, 2016 - Issue 6
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Original Articles

Combination forecasts of tourism demand with machine learning models

Oscar ClaveriaAQR-IREA, University of Barcelona (UB), Barcelona, SpainCorrespondence[email protected]
,
Enric MonteDepartment of Signal Theory and Communications, Polytechnic University of Catalunya (UPC), Barcelona, Spain
&
Salvador TorraRiskcenter-IREA, Department of Econometrics and Statistics, University of Barcelona (UB), Barcelona, Spain
Pages 428-431 | Received 08 Jun 2015, Accepted 28 Jul 2015, Published online: 11 Sep 2015

References

  • Akın, M. 2015. “A Novel Approach to Model Selection in Tourism Demand Modeling.” Tourism Management 48: 64–72. doi:10.1016/j.tourman.2014.11.004.
  • Banerjee, S., A. E. Gelfand, A. O. Finley, and H. Sang. 2008. “Gaussian Predictive Process Models for Large Spatial Data Sets.” Journal of the Royal Statistical Society: Series B (Statistical Methodology) 70: 825–848. doi:10.1111/rssb.2008.70.issue-4.
  • Bates, J. M., and C. W. J. Granger. 1969. “The Combination of Forecasts.” Journal of the Operational Research Society 20: 451–468. doi:10.1057/jors.1969.103.
  • Cang, S. 2014. “A Comparative Analysis of Three Types of Tourism Demand Forecasting Models: Individual, Linear Combination and Non-Linear Combination.” International Journal of Tourism Research 16: 596–607. doi:10.1002/jtr.v16.6.
  • Chen, K.-Y., and C.-H. Wang. 2007. “Support Vector Regression with Genetic Algorithms in Forecasting Tourism Demand.” Tourism Management 28: 215–226. doi:10.1016/j.tourman.2005.12.018.
  • Claveria, O., and S. Torra. 2014. “Forecasting Tourism Demand to Catalonia: Neural Networks vs. Time Series Models.” Economic Modelling 36: 220–228. doi:10.1016/j.econmod.2013.09.024.
  • Cristianini, N., and J. Shawhe-Taylor. 2000. An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods. Cambridge: Cambridge University Press.
  • Drucker, H., C. J. C. Burges, L. Kaufman, A. Smola, and V. Vapnik. 1997. “Support Vector Regression Machines.” In Advances in Neural Information Processing Systems, edited by M. Mozer, M. Jordan, and T. Petsche, Vol. 9, 155–161. Cambridge, MA: MIT Press.
  • Haykin, S. 2008. Neural Networks and Learning Machines. 3rd ed. Upper Saddle River, NJ: Prentice Hall.
  • Hong, W., Y. Dong, L. Chen, and S. Wei. 2011. “SVR with Hybrid Chaotic Genetic Algorithms for Tourism Demand Forecasting.” Applied Soft Computing 11: 1881–1890. doi:10.1016/j.asoc.2010.06.003.
  • Huang, W., Y. Nakamori, and S.-Y. Wang. 2005. “Forecasting Stock Market Movement Direction with Support Vector Machine.” Computers & Operations Research 32: 2513–2522. doi:10.1016/j.cor.2004.03.016.
  • Kim, K. 2003. “Financial Time Series Forecasting Using Support Vector Machines.” Neurocomputing 55: 307–319. doi:10.1016/S0925-2312(03)00372-2.
  • Lin, C., H. Chen, and T. Lee. 2011. “Forecasting Tourism Demand Using Time Series, Artificial Neural Networks and Multivariate Adaptive Regression Splines: Evidence from Taiwan.” International Journal of Business Administration 2: 14–24. doi:10.5430/ijba.v2n2p14.
  • MacKay, D. J. C. 2003. Information Theory, Inference, and Learning Algorithms. Cambridge: Cambridge University Press.
  • Padhi, S. S., and V. Aggarwal. 2011. “Competitive Revenue Management for Fixing Quota and Price of Hotel Commodities under Uncertainty.” International Journal of Hospitality Management 30: 725–734. doi:10.1016/j.ijhm.2010.12.007.
  • Rasmussen, C. E. 1996. “The Infinite Gaussian Mixture Model.” Advances in Neural Information Processing Systems 8: 514–520.
  • Ruth, K. 2008. “Macroeconomic Forecasting in the EMU. Does Disaggregate Modeling Improve Forecast Accuracy?” Journal of Policy Modeling 30: 417–429. doi:10.1016/j.jpolmod.2007.12.002.
  • Stock, J. H., and M. W. Watson. 2004. “Combination Forecasts of Output Growth in a Seven-Country Data Set.” Journal of Forecasting 23: 405–430. doi:10.1002/(ISSN)1099-131X.
  • Williams, C. K. I., and C. E. Rasmussen. 2006. Gaussian Processes for Machine Learning. Cambridge, MA: The MIT Press.
  • Wu, Q., R. Law, and X. Xu. 2012. “A Sparse Gaussian Process Regression Model for Tourism Demand Forecasting in Hong Kong.” Expert Systems with Applications 39: 4769–4774. doi:10.1016/j.eswa.2011.09.159.
  • Zhang, G., B. E. Putuwo, and M. Y. Hu. 1998. “Forecasting with Artificial Neural Networks: The State of the Art.” International Journal of Forecasting 14: 35–62. doi:10.1016/S0169-2070(97)00044-7.

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