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Forecasting tourism demand by incorporating neural networks into Grey–Markov models

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  • Yi-Chung Hu
  • Peng Jiang
  • Ping-Chuan Lee

Abstract

Tourism demand plays a significant role in the formulation of tourism development policies by governments. While the GM(1,1) is the most frequently used grey prediction model, the Grey–Markov model has been applied to forecast tourism demand because it has advantages compared with the GM(1,1) model when the time series data fluctuate significantly. To further improve the predictive accuracy of the Grey–Markov model, two neural networks (NNs) are considered. One of the NNs is used to build an NNGM(1,1) such that the GM(1,1) does not need to determine the background value, and the other is used to estimate the degree to which a predicted value obtained from the NNGM(1,1) can be adjusted. We applied the proposed model to forecast the number of foreign tourists using historical annual data from Taiwan Tourism Bureau and China National Tourism Administration. The results showed that the proposed model outperforms other Grey–Markov models.

Suggested Citation

  • Yi-Chung Hu & Peng Jiang & Ping-Chuan Lee, 2019. "Forecasting tourism demand by incorporating neural networks into Grey–Markov models," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 70(1), pages 12-20, January.
    • Handle: RePEc:taf:tjorxx:v:70:y:2019:i:1:p:12-20
    DOI: 10.1080/01605682.2017.1418150
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    Cited by:

    1. Wei Jiang & Jianzhong Zhou & Yanhe Xu & Jie Liu & Yahui Shan, 2019. "Multistep Degradation Tendency Prediction for Aircraft Engines Based on CEEMDAN Permutation Entropy and Improved Grey–Markov Model," Complexity, Hindawi, vol. 2019, pages 1-18, October.
    2. Yi-Chung Hu, 2022. "Demand forecasting of green metal materials using non-equidistant grey prediction with robust nonlinear interval regression analysis," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 24(8), pages 9809-9831, August.
    3. Rajesh, R. & Agariya, Arun Kumar & Rajendran, Chandrasekharan, 2021. "Predicting resilience in retailing using grey theory and moving probability based Markov models," Journal of Retailing and Consumer Services, Elsevier, vol. 62(C).
    4. Jung-Kai Tsai & Chih-Hsing Hung, 2021. "Improving AdaBoost Classifier to Predict Enterprise Performance after COVID-19," Mathematics, MDPI, vol. 9(18), pages 1-10, September.

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