Forecasting Tourism Demand Using Time Series, Artificial Neural Networks and Multivariate Adaptive Regression Splines:Evidence from Taiwan
Chang-Jui Lin,
Hsueh-Fang Chen and
Tian-Shyug Lee
International Journal of Business Administration, 2011, vol. 2, issue 2, 14-24
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.
Keywords: Tourism demand forecasting; ARIMA; Artificial neural networks; Multivariate adaptive regression splines (search for similar items in EconPapers)
Date: 2011
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Persistent link: https://EconPapers.repec.org/RePEc:jfr:ijba11:v:2:y:2011:i:2:p:14-24
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