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“A regional perspective on the accuracy of machine learning forecasts of tourism demand based on data characteristics”

Author

Listed:
  • Oscar Claveria

    (AQR-IREA AQR-IREA, University of Barcelona (UB). Tel. +34-934021825; Fax. +34-934021821. Department of Econometrics, Statistics and Applied Economics, University of Barcelona, Diagonal 690, 08034 Barcelona, Spain)

  • Enric Monte

    (Department of Signal Theory and Communications, Polytechnic University of Catalunya (UPC))

  • Salvador Torra

    (Riskcenter-IREA, Department of Econometrics and Statistics, University of Barcelona (UB))

Abstract
In this work we assess the role of data characteristics in the accuracy of machine learning (ML) tourism forecasts from a spatial perspective. First, we apply a seasonal-trend decomposition procedure based on non-parametric regression to isolate the different components of the time series of international tourism demand to all Spanish regions. This approach allows us to compute a set of measures to describe the features of the data. Second, we analyse the performance of several ML models in a recursive multiple-step-ahead forecasting experiment. In a third step, we rank all seventeen regions according to their characteristics and the obtained forecasting performance, and use the rankings as the input for a multivariate analysis to evaluate the interactions between time series features and the accuracy of the predictions. By means of dimensionality reduction techniques we summarise all the information into two components and project all Spanish regions into perceptual maps. We find that entropy and dispersion show a negative relation with accuracy, while the effect of other data characteristics on forecast accuracy is heavily dependent on the forecast horizon.

Suggested Citation

  • Oscar Claveria & Enric Monte & Salvador Torra, 2018. "“A regional perspective on the accuracy of machine learning forecasts of tourism demand based on data characteristics”," IREA Working Papers 201805, University of Barcelona, Research Institute of Applied Economics, revised Mar 2018.
    • Handle: RePEc:ira:wpaper:201805
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    References listed on IDEAS

    as
    1. Gunter, Ulrich & Önder, Irem, 2015. "Forecasting international city tourism demand for Paris: Accuracy of uni- and multivariate models employing monthly data," Tourism Management, Elsevier, vol. 46(C), pages 123-135.
    2. Andrawis, Robert R. & Atiya, Amir F. & El-Shishiny, Hisham, 2011. "Forecast combinations of computational intelligence and linear models for the NN5 time series forecasting competition," International Journal of Forecasting, Elsevier, vol. 27(3), pages 672-688.
    3. Elias Soukiazis & Sara Proença, 2008. "Tourism as an alternative source of regional growth in Portugal: a panel data analysis at NUTS II and III levels," Portuguese Economic Journal, Springer;Instituto Superior de Economia e Gestao, vol. 7(1), pages 43-61, April.
    4. Chang-Jui Lin & Hsueh-Fang Chen & Tian-Shyug Lee, 2011. "Forecasting Tourism Demand Using Time Series, Artificial Neural Networks and Multivariate Adaptive Regression Splines:Evidence from Taiwan," International Journal of Business Administration, International Journal of Business Administration, Sciedu Press, vol. 2(2), pages 14-24, May.
    5. Medeiros, Marcelo C. & McAleer, Michael & Slottje, Daniel & Ramos, Vicente & Rey-Maquieira, Javier, 2008. "An alternative approach to estimating demand: Neural network regression with conditional volatility for high frequency air passenger arrivals," Journal of Econometrics, Elsevier, vol. 147(2), pages 372-383, December.
    6. Guizzardi, Andrea & Stacchini, Annalisa, 2015. "Real-time forecasting regional tourism with business sentiment surveys," Tourism Management, Elsevier, vol. 47(C), pages 213-223.
    7. Sudipto Banerjee & Alan E. Gelfand & Andrew O. Finley & Huiyan Sang, 2008. "Gaussian predictive process models for large spatial data sets," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(4), pages 825-848, September.
    8. Robert Lehmann & Klaus Wohlrabe, 2015. "Forecasting GDP at the Regional Level with Many Predictors," German Economic Review, Verein für Socialpolitik, vol. 16(2), pages 226-254, May.
    9. Andrawis, Robert R. & Atiya, Amir F. & El-Shishiny, Hisham, 2011. "Forecast combinations of computational intelligence and linear models for the NN5 time series forecasting competition," International Journal of Forecasting, Elsevier, vol. 27(3), pages 672-688, July.
    10. Claveria, Oscar & Torra, Salvador, 2014. "Forecasting tourism demand to Catalonia: Neural networks vs. time series models," Economic Modelling, Elsevier, vol. 36(C), pages 220-228.
    11. Tsui, Wai Hong Kan & Ozer Balli, Hatice & Gilbey, Andrew & Gow, Hamish, 2014. "Forecasting of Hong Kong airport's passenger throughput," Tourism Management, Elsevier, vol. 42(C), pages 62-76.
    12. Sen Cheong Kon & Lindsay W. Turner, 2005. "Neural Network Forecasting of Tourism Demand," Tourism Economics, , vol. 11(3), pages 301-328, September.
    13. Witt, Stephen F. & Witt, Christine A., 1995. "Forecasting tourism demand: A review of empirical research," International Journal of Forecasting, Elsevier, vol. 11(3), pages 447-475, September.
    14. Nesreen Ahmed & Amir Atiya & Neamat El Gayar & Hisham El-Shishiny, 2010. "An Empirical Comparison of Machine Learning Models for Time Series Forecasting," Econometric Reviews, Taylor & Francis Journals, vol. 29(5-6), pages 594-621.
    15. Dr. Murat çuhadar & Iclal Cogurcu & Ceyda Kukrer, 2014. "Modelling and Forecasting Cruise Tourism Demand to Izmir by Different Artificial Neural Network Architectures," International Journal of Business and Social Research, LAR Center Press, vol. 4(3), pages 12-28, March.
    16. Chou, Ming Che, 2013. "Does tourism development promote economic growth in transition countries? A panel data analysis," Economic Modelling, Elsevier, vol. 33(C), pages 226-232.
    17. Akın, Melda, 2015. "A novel approach to model selection in tourism demand modeling," Tourism Management, Elsevier, vol. 48(C), pages 64-72.
    18. Terasvirta, Timo & van Dijk, Dick & Medeiros, Marcelo C., 2005. "Linear models, smooth transition autoregressions, and neural networks for forecasting macroeconomic time series: A re-examination," International Journal of Forecasting, Elsevier, vol. 21(4), pages 755-774.
    19. Li, Gang & Song, Haiyan & Cao, Zheng & Wu, Doris Chenguang, 2013. "How competitive is Hong Kong against its competitors? An econometric study," Tourism Management, Elsevier, vol. 36(C), pages 247-256.
    20. Bergmeir, Christoph & Hyndman, Rob J. & Benítez, José M., 2016. "Bagging exponential smoothing methods using STL decomposition and Box–Cox transformation," International Journal of Forecasting, Elsevier, vol. 32(2), pages 303-312.
    21. Oscar Claveria & Enric Monte & Salvador Torra, 2016. "Combination forecasts of tourism demand with machine learning models," Applied Economics Letters, Taylor & Francis Journals, vol. 23(6), pages 428-431, April.
    22. Peng, Bo & Song, Haiyan & Crouch, Geoffrey I., 2014. "A meta-analysis of international tourism demand forecasting and implications for practice," Tourism Management, Elsevier, vol. 45(C), pages 181-193.
    23. Hassani, Hossein & Silva, Emmanuel Sirimal & Antonakakis, Nikolaos & Filis, George & Gupta, Rangan, 2017. "Forecasting accuracy evaluation of tourist arrivals," Annals of Tourism Research, Elsevier, vol. 63(C), pages 112-127.
    24. Zhang, Guoqiang & Eddy Patuwo, B. & Y. Hu, Michael, 1998. "Forecasting with artificial neural networks:: The state of the art," International Journal of Forecasting, Elsevier, vol. 14(1), pages 35-62, March.
    25. Chor Foon Tang & Salah Abosedra, 2016. "Tourism and growth in Lebanon: new evidence from bootstrap simulation and rolling causality approaches," Empirical Economics, Springer, vol. 50(2), pages 679-696, March.
    26. Warren Torgerson, 1952. "Multidimensional scaling: I. Theory and method," Psychometrika, Springer;The Psychometric Society, vol. 17(4), pages 401-419, December.
    27. Dr. Murat çuhadar & Iclal Cogurcu & Ceyda Kukrer, 2014. "Modelling and Forecasting Cruise Tourism Demand to Izmir by Different Artificial Neural Network Architectures," International Journal of Business and Social Research, MIR Center for Socio-Economic Research, vol. 4(3), pages 12-28, March.
    28. Oscar Claveria & Enric Monte & Salvador Torra, 2016. "Modelling cross-dependencies between Spain’s regional tourism markets with an extension of the Gaussian process regression model," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 7(3), pages 341-357, August.
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    More about this item

    Keywords

    STL decomposition; non-parametric regression; time series features; forecast accuracy; machine learning; tourism demand; regional analysis. JEL classification:C45; C51; C53; C63; E27; L83.;
    All these keywords.

    JEL classification:

    • C4 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics

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