Realities beyond the Grey-Markov Model for Forecasting International Tourism Demand
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This study applied a forecasting method to forecast Korean tourism demand from three major source countries (Japan, China and United States), which combines the first-order one variable grey differential equation model from grey system theory and Markov chain model from stochastic process theory. Then it tested the real performance of the model in terms of international tourism demand during 2013 through 2017. Even though all of the relative errors of the forecasting values are less than 5% meaning the Grey-Markov chain model gives higher precision in forecasting tourism demand, this study revealed that the real international tourism demand is not closed to the values forecasted by using the mathematical model. There are some other factors which took a role breaking the mathematical forecasting rules based basically upon historical records. This study informs international tourists’ movement in terms of Korean tourism demand is heavily influenced by political issues associated with China and Japan.
Abstract
This study applied a forecasting method to forecast Korean tourism demand from three major source countries (Japan, China and United States), which combines the first-order one variable grey differential equation model from grey system theory and Markov chain model from stochastic process theory. Then it tested the real performance of the model in terms of international tourism demand during 2013 through 2017. Even though all of the relative errors of the forecasting values are less than 5% meaning the Grey-Markov chain model gives higher precision in forecasting tourism demand, this study revealed that the real international tourism demand is not closed to the values forecasted by using the mathematical model. There are some other factors which took a role breaking the mathematical forecasting rules based basically upon historical records. This study informs international tourists’ movement in terms of Korean tourism demand is heavily influenced by political issues associated with China and Japan.
참고문헌 (23)
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