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Tourism Demand Forecasting: Econometric Model based on Multivariate Adaptive Regression Splines, Artificial Neural Network and Support Vector Regression

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  • Chang-Jui Lin
  • Tian-Shyug Lee

Abstract

This paper develops tourism demand econometric models based on the monthly data of tourists to Taiwan and adopts Multivariate Adaptive Regression Splines (MARS), Artificial Neural Network (ANN) and Support Vector Regression (SVR), MARS, ANN and SVR to develop forecast models and compare the forecast results. The results showed that SVR model is the optimal model, with a mean error rate of 3.61%, ANN model is the sub-optimal model, with a mean error rate of 7.08%, and MARS is the worst model, with a mean error rate of 11.26%.

Suggested Citation

  • Chang-Jui Lin & Tian-Shyug Lee, 2013. "Tourism Demand Forecasting: Econometric Model based on Multivariate Adaptive Regression Splines, Artificial Neural Network and Support Vector Regression," Advances in Management and Applied Economics, SCIENPRESS Ltd, vol. 3(6), pages 1-1.
    • Handle: RePEc:spt:admaec:v:3:y:2013:i:6:f:3_6_1
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    Cited by:

    1. Yuan, Fong-Ching, 2020. "Intelligent forecasting of inbound tourist arrivals by social networking analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 558(C).
    2. Binglei Xie & Yu Sun & Xiaolong Huang & Le Yu & Gangyan Xu, 2020. "Travel Characteristics Analysis and Passenger Flow Prediction of Intercity Shuttles in the Pearl River Delta on Holidays," Sustainability, MDPI, vol. 12(18), pages 1-23, September.

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