The GM (1, 1) model, grounded in gray system theory, utilizes first-order cumulative data for forecasting. While offering simplicity and efficiency, its applicability is confined to such data. In light of the constraints inherent in the conventional gray GM (1, 1) prediction model when confronted with stochastic data fluctuations, the residual correction methodology was deployed to enhance the predictive efficacy of the GM (1, 1) model. Subsequently, an augmented model underwent refinement through the application of the Markov chain, giving rise to a sophisticated and optimized gray Markov chain prediction model. The efficacy of this novel model was substantiated through a case study involving the prediction of Macao's aggregate tourism revenue. A comparative analysis was conducted between the outcomes generated by the traditional gray prediction model, those of the refined prediction model, and the empirical data pertaining to tourism. This scrutiny validated the proficiency and precision of the optimized prediction model. The process of model optimization manifested a discernible enhancement in both predictive accuracy and stability, thereby broadening the prospective applications of gray prediction models. This endeavor aspired to furnish a scientifically grounded point of reference for the advancement of tourism within the Guangdong-Hong Kong-Macao Greater Bay Area and, indeed, throughout China. Moreover, it introduced a fresh methodology that held promise as a decision-making support mechanism for the developmental trajectory of Macao's tourism industry.
Citation: Xiaolong Chen, Hongfeng Zhang, Cora Un In Wong. Optimization study of tourism total revenue prediction model based on the Grey Markov chain: a case study of Macau[J]. AIMS Mathematics, 2024, 9(6): 16187-16202. doi: 10.3934/math.2024783
The GM (1, 1) model, grounded in gray system theory, utilizes first-order cumulative data for forecasting. While offering simplicity and efficiency, its applicability is confined to such data. In light of the constraints inherent in the conventional gray GM (1, 1) prediction model when confronted with stochastic data fluctuations, the residual correction methodology was deployed to enhance the predictive efficacy of the GM (1, 1) model. Subsequently, an augmented model underwent refinement through the application of the Markov chain, giving rise to a sophisticated and optimized gray Markov chain prediction model. The efficacy of this novel model was substantiated through a case study involving the prediction of Macao's aggregate tourism revenue. A comparative analysis was conducted between the outcomes generated by the traditional gray prediction model, those of the refined prediction model, and the empirical data pertaining to tourism. This scrutiny validated the proficiency and precision of the optimized prediction model. The process of model optimization manifested a discernible enhancement in both predictive accuracy and stability, thereby broadening the prospective applications of gray prediction models. This endeavor aspired to furnish a scientifically grounded point of reference for the advancement of tourism within the Guangdong-Hong Kong-Macao Greater Bay Area and, indeed, throughout China. Moreover, it introduced a fresh methodology that held promise as a decision-making support mechanism for the developmental trajectory of Macao's tourism industry.
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Xiaolong Chen, Hongfeng Zhang, Cora Un In Wong. Optimization study of tourism total revenue prediction model based on the Grey Markov chain: a case study of Macau[J]. AIMS Mathematics, 2024, 9(6): 16187-16202. doi: 10.3934/math.2024783
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