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Sustainability 2017, 9(7), 1228; doi:10.3390/su9071228

Predicting Foreign Tourists for the Tourism Industry Using Soft Computing-Based Grey–Markov Models

1
College of Management & College of Tourism, Fujian Agriculture and Forestry University, Fuzhou City 35000, China
2
Department of Business Administration, Chung Yuan Christian University, Chung Li Dist., Taoyuan City 32023, Taiwan
Received: 7 June 2017 / Revised: 4 July 2017 / Accepted: 7 July 2017 / Published: 13 July 2017
(This article belongs to the Section Economic, Business and Management Aspects of Sustainability)
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Abstract

Accurate prediction of foreign tourist numbers is crucial for each country to devise sustainable tourism development policies. Tourism time series data often have significant temporal fluctuation, so Grey–Markov models based on a grey model with a first order differential equation and one variable, GM(1,1), can be appropriate. To further improve prediction accuracy from Grey–Markov models, this study incorporates soft computing techniques to estimate a modifiable range for a predicted value, and determine individual state bounds for the Markov chain. A new residual value is formulated by summing the transition probability matrices with different steps. The proposed grey prediction model was applied to foreign tourist forecasting using historical annual data collected from Taiwan Tourism Bureau and China National Tourism Administration. The experimental results show that the proposed grey prediction model performs well in comparison with other Grey–Markov models considered. View Full-Text
Keywords: foreign tourist; soft computing; grey prediction; Markov chain; neural network foreign tourist; soft computing; grey prediction; Markov chain; neural network
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Hu, Y.-C. Predicting Foreign Tourists for the Tourism Industry Using Soft Computing-Based Grey–Markov Models. Sustainability 2017, 9, 1228.

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