Grey Markov method and its application based on kernel and degree of greyness

被引：3
|
作者：
Wang J. [1 ]
Zha Y. [1 ]
Wang X. [1 ]
Xiong F. [1 ]
机构：
[1] School of Business, Jiangnan University, Wuxi
来源：
Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics | 2020年 / 42卷 / 02期
关键词：
Air freight; Gray; Gray Markov prediction; Nuclear;
D O I：
10.3969/j.issn.1001-506X.2020.02.19
中图分类号：
学科分类号：
摘要：
When dealing with prediction problems, it is often in the case that the original data are interval numbers with high random fluctuation. In order to improve the accuracy of interval grey number prediction, a grey Markov prediction model based on kernel and gray scale is proposed. This method builds a prediction model based on the interval grey number kernel sequence to realize the prediction of interval grey number kernel, and the upper and lower bounds of the grey number range are derived from the grey number kernel. On the premise of keeping the interval grey number independent and complete, the interval grey number prediction model is constructed, and the Markov prediction model is used to correct the prediction results. In the trend prediction of air cargo volume, this model shows that the result of Markov chain correction is an underestimation compared with the interval gray number prediction data. The results can help strengthen the macro understanding of the air cargo market and provide a reference for economic decision. © 2020, Editorial Office of Systems Engineering and Electronics. All right reserved.
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页码：398 / 404
页数：6
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