Forecasting tourism demand by incorporating neural networks into Grey–Markov models
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DOI: 10.1080/01605682.2017.1418150
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Cited by:
- Yi-Chung Hu, 2023. "Tourism combination forecasting using a dynamic weighting strategy with change-point analysis," Current Issues in Tourism, Taylor & Francis Journals, vol. 26(14), pages 2357-2374, July.
- Sule Birim & Ipek Kazancoglu & Sachin Kumar Mangla & Aysun Kahraman & Yigit Kazancoglu, 2024. "The derived demand for advertising expenses and implications on sustainability: a comparative study using deep learning and traditional machine learning methods," Annals of Operations Research, Springer, vol. 339(1), pages 131-161, August.
- 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.
- 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.
- 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).
- 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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