Using a Grey–Markov model optimized by Cuckoo search algorithm to forecast the annual foreign tourist arrivals to China

Sun, Xiao; Sun, Wangshu; Wang, Jianzhou; Zhang, Yixin; Gao, Yining

doi:10.1016/j.tourman.2015.07.005

Cited by 113 publications

(78 citation statements)

References 40 publications

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“…Two real data sets were used to compare foreign tourist forecasting using the proposed SC-MCGM(1,1) model with different transitions (m = 1,2,3,4) against the original GM (1,1), MCGM(1,1), and several models proposed by Sun et al [1], including segmented GM(1,1), SGM(1,1) using Markov chain, and MCSGM(1,1) using a Cuckoo search algorithm. They are denoted by SGM (1,1), MCSGM(1,1), and CMCSGM(1,1), respectively.…”

Section: Resultsmentioning

confidence: 99%

“…Therefore, to obtain a and b without requiring z (1) k , an NNGM(1,1) model was established using a single layer perceptron (SLP) accompanied by the cost function…”

Section: Nngm(11) For Generating Predicted Valuesmentioning

confidence: 99%

“…National authorities should carefully consider the changing number of foreign tourists. The variety of international tourism has raised a challenging task for foreign tourist prediction [1]. Grey prediction models [2] have drawn much attention because they can characterize an unknown system from limited data [3][4][5], without requiring conformance to statistical assumptions, such as normal distributions.…”

Section: Introductionmentioning

confidence: 99%

“…Many studies have demonstrated the Grey-Markov model, denoted by MCGM (1,1), can significantly improve prediction accuracy over the original GM (1,1) [1,[15][16][17][18][19][20]. MCGM(1,1) uses GM (1,1) to identify the trend of historical data, and then applies the Markov chain to correct the residuals. Hsu and Wen [21] and Hsu [22] used Markov chain sign estimation to modify residuals for the air passenger market and global integrated circuit industry, respectively.…”

Section: Introductionmentioning

confidence: 99%

“…Mao and Sun [23] applied MCGM(1,1) for fire accident prediction. Sun et al [1] proposed a MCGM(1,1) variant using the Cuckoo search algorithm for foreign tourist arrival prediction, and Wang [24] showed MCGM(1,1) effectiveness for tourism demand prediction. Xie et al [20] proposed a novel Markov model to estimate the probability that one energy component could transit to another energy component.…”

Section: Introductionmentioning

confidence: 99%

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Predicting Foreign Tourists for the Tourism Industry Using Soft Computing-Based Grey–Markov Models

2017

Sustainability

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Accurate prediction of foreign tourist numbers is crucial for each country to devise sustainable tourism development policies. Tourism time series data often have significant temporal fluctuation, so Grey-Markov models based on a grey model with a first order differential equation and one variable, GM(1,1), can be appropriate. To further improve prediction accuracy from Grey-Markov models, this study incorporates soft computing techniques to estimate a modifiable range for a predicted value, and determine individual state bounds for the Markov chain. A new residual value is formulated by summing the transition probability matrices with different steps. The proposed grey prediction model was applied to foreign tourist forecasting using historical annual data collected from Taiwan Tourism Bureau and China National Tourism Administration. The experimental results show that the proposed grey prediction model performs well in comparison with other Grey-Markov models considered.

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Section: Resultsmentioning

confidence: 99%

“…Therefore, to obtain a and b without requiring z (1) k , an NNGM(1,1) model was established using a single layer perceptron (SLP) accompanied by the cost function…”

Section: Nngm(11) For Generating Predicted Valuesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Predicting Foreign Tourists for the Tourism Industry Using Soft Computing-Based Grey–Markov Models

2017

Sustainability

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Interval time series forecasting: A systematic literature review

Wang,

Gurmani,

Tao

et al. 2023

Journal of Forecasting

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Interval time series forecasting can be used for forecasting special symbolic data comprising lower and upper bounds and plays an important role in handling the complexity, instability, and uncertainty of observed objects. The purpose of this research is to identify the most widely used definition of interval time series; classify existing research into mature research, current research focus, and research gaps within the defined framework; and recommend future directions for interval forecasting research. To achieve this goal, we have conducted a systematic literature review, comprising search strategy planning, screening mechanism determination, document analysis, and report generation. During the search strategy planning stage, eight literature search libraries are selected to obtain the most extensive studies (total of 525 targets). In the screening‐mechanism determination stage, through the inclusion and exclusion mechanism, the literature that is repetitive, of low‐relevance, and from other fields are discarded, and 125 studies are finally selected. In the document analysis stage, tag‐based methods and classification grids are selected to analyze the shortlisted studies. The results show that there are still numerous research gaps in interval time series forecasting, such as the establishment of hybrid models, application of multisource information, development and application of evaluation techniques, and expansion of application scenarios. In the report‐generation stage, the problems that have been solved and encountered in interval forecasting are summarized, and future research directions are proposed. Finally, the most significant contribution of this research is to provide an overview of interval time series forecasting for easy reference by researchers and to facilitate further research.

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Grey-Markov Model for the Prediction of the Electricity Production and Consumption

Elgharbi

Mustapha

Ibrihich

et al. 2019

Big Data and Networks Technologies

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Using a Grey–Markov model optimized by Cuckoo search algorithm to forecast the annual foreign tourist arrivals to China

Cited by 113 publications

References 40 publications

Predicting Foreign Tourists for the Tourism Industry Using Soft Computing-Based Grey–Markov Models

Predicting Foreign Tourists for the Tourism Industry Using Soft Computing-Based Grey–Markov Models

Interval time series forecasting: A systematic literature review

Grey-Markov Model for the Prediction of the Electricity Production and Consumption

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