An improved gray prediction model for China's beef consumption forecasting

Bo Zeng; Shuliang Li; Wei Meng; Dehai Zhang

doi:10.1371/journal.pone.0221333

An improved gray prediction model for China's beef consumption forecasting

PLoS One. 2019 Sep 6;14(9):e0221333. doi: 10.1371/journal.pone.0221333. eCollection 2019.

Authors

Bo Zeng¹, Shuliang Li², Wei Meng^{1

2}, Dehai Zhang¹

Affiliations

¹ College of Business Planning, Chongqing Technology and Business University, Chongqing, PR China.
² Collaborative Innovation Center for Chongqing's Modern Trade Logistics & Supply Chain, Chongqing Technology and Business University, Chongqing, PR China.

Abstract

To balance the supply and demand in China's beef market, beef consumption must be scientifically and effectively forecasted. Beef consumption is affected by many factors and is characterized by gray uncertainty. Therefore, gray theory can be used to forecast the beef consumption, In this paper, the structural defects and unreasonable parameter design of the traditional gray model are analyzed. Then, a new gray model termed, EGM(1,1,r), is built, and the modeling conditions and error checking methods of EGM(1,1,r) are studied. Then, EGM(1,1,r) is used to simulate and forecast China's beef consumption. The results show that both the simulation and prediction precisions of the new model are better than those of other gray models. Finally, the new model is used to forecast China's beef consumption for the period from 2019-2025. The findings will serve as an important reference for the Chinese government in formulating policies to ensure the balance between the supply and demand for Chinese beef.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Animals
China
Food Industry / trends*
Forecasting
Models, Statistical*
Red Meat / statistics & numerical data*

Grants and funding

This work was supported by the National Natural Science Foundation of China (Grant Nos. 71771033); the Postdoctoral Science Foundation of China (Grant Nos. 2014M560712 and 2015T80975); Social Science Foundation of Ministry of Education in China (Grant Nos.18XJC630003) and the Science and Technology Research Program of Chongqing Municipal Education Commission (Grant Nos. KJ1706166;KJ1703057 and KJQN201800805). We would like to thank the anonymous referees for their constructive comments, which helped to improve the clarity and completeness of this paper.