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Forecasting Tourism Demand Using Time Series, Artificial Neural Networks and Multivariate Adaptive Regression Splines:Evidence from Taiwan

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Listed:
  • Chang-Jui Lin
  • Hsueh-Fang Chen
  • Tian-Shyug Lee

Abstract

In the past few decades, international tourism has grown rapidly and has become a very interesting topic in tourism research. Taiwan, acting as a citizen in the global community, improved traveling facilities, and governments¡¯ strong promotion has drawn more and more visitors to visit Taiwan. This study tries to build the forecasting model of visitors to Taiwan using three commonly adopted ARIMA, artificial neural networks (ANNs), and multivariate adaptive regression splines (MARS). In order to evaluate the appropriateness of the proposed modeling approaches, the dataset of monthly visitors to Taiwan was used as the illustrative example. Analytic results demonstrated that ARIMA outperformed ANNs and MARS approaches in terms of RMSE, MAD, and MAPE and provided effective alternatives for forecasting tourism demand.

Suggested Citation

  • 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.
    • Handle: RePEc:jfr:ijba11:v:2:y:2011:i:2:p:14-24
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    References listed on IDEAS

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