Abstract—Forecasting is the basis of planning, and the key to tourism supply chain. The international tourists visiting Taiwan from Mainland China, Japan, and South Korea is the major international tourist source markets for Taiwan. Recurrent neural network model is a fairly new and promising neural network technology. Genetic algorithms can be optimized the neural network structure. The ARIMAX model is recently utilized time series model for tourism demand forecasting. Accordingly, this work presents recurrent neural network model to forecast numbers of international tourists to Taiwan from Mainland China, Japan, and South Korea to help the Taiwanese tourism industry. This work also compares the forecast accuracy of the ARIMAX model with that of the ARIMAX model. Empirical results demonstrate that the recurrent neural network model with genetic algorithms is better than the ARIMAX model for Mainland China and South Korea, and worse than for Japan. The findings of this study can contribute to management and policy-decision issues related to the tourism industry for Taiwan in tourism supply chains.
Index Terms—Recurrent neural network, genetic algorithm, arimax, tourism demand, forecasting.
Yi-Hui Liang is with the Department of Information Management, I-Shou University (ISU), Taiwan (e-mail: german@isu.edu.tw).
Cite: Yi-Hui Liang, "Forecasting International Tourism Demand Using the Recurrent Neural Network Model with Genetic Algorithms and ARIMAX Model in Tourism Supply Chains," International Journal of Machine Learning and Computing vol. 12, no. 5, pp. 164-168, 2022.
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