Username   Password       Forgot your password?  Forgot your username? 


Grain Ration Consumption Forecasting based on Multivariate Regression Model Combined with Gliding Data Barycenter

Volume 14, Number 8, August 2018, pp. 1666-1673
DOI: 10.23940/ijpe.18.08.p2.16661673

Chunhua Zhu and Jiaojiao Wang

College of Information Science and Engineering, Henan University of Technology, Zhengzhou, 450001, China

(Submitted on May 9, 2018; Revised on June 16, 2018; Accepted on July 19, 2018)


To address the existing and limited original data and lower prediction robustness, a new multivariate regression forecasting model combined with gliding data barycenter was proposed. In this new forecasting method, the original data was interpolated and the corresponding data barycenter was optimized. Then, the important impact factors of ration consumption were analysed and chosen for the multivariate regression model. In simulation experiments, the training data of 35 years (1981-2015) were used, and the results have shown that the proposed method can greatly improve prediction accuracy and robustness.


References: 12

            1. Z. Q. Li, J. Z. Wu, and D. J. Wang , “Change Analysis and Demand Forecast of Grain Consumption in China,” Food and Nutrition in China, Vol. 18, No. 3, pp. 38-42, 2012
            2. W. Jia and F. Qin, “China’s Grain Demand Forecast,” Food and Nutrition in China, Vol. 19, No. 1, pp. 40-44, 2013
            3. Q. Y. Luo, J. Mi, and M. J. Gao, “Research on Forecasting for Long-term Grain Consumption Demands in China,” Chinas Agricultural Resources and Zoning, Vol. 35, No. 5, pp. 1-6, 2014
            4. Y. Pan and L. Z. Liu, “Analysis and Prediction of Food Consumption of Rural Residents in China,” Population and Rconomy, No. 3, pp. 1-8, 2005
            5. J. Mi, Q. Y. Luo, and M. J. Gao, “Review on the Method of Forecasting Grain Demand,” China Agricultural Resources and Regional Planning, Vol. 34, No. 3, pp. 28-33, June 2013
            6. Y. S. Zhou, Y. H. Xiao, and R. S. Huang, “Prediction of Grain Yield in Guangxi based on Multivariate Linear Regression,” Journal of Southern Agriculture, Vol. 42, No. 9, pp. 1165-1167, 2011
            7. J. L. Zhang, “Research on Gliding Data Barycenter Forecasting Method and its Application,” Mathematical Statistics and Management, Vol. 29, No. 6, pp. 1036-1041, November 2010
            8. H. Y. Chen, J. B. Zhao, and C. L. Liu, “Properties of Combination Forecasting Model Based on Gray Incidence,” Journal of Southeast University (Natural Science Edition), Vol. 34, No. 1, pp. 23-27, January 2004
            9. Q. F. Li, G. L. Kang, and X. F. Li, “Factors Influencing Grain Production in Henan Province based on Gray Correlation,” Asia Agricultural Reaearch, Vol. 1, No. 5, pp. 23-27, May 2009
            10. X. M. Xu, W. Erika, and M. B. Angela, “Management of Raspberry and Strawberry Grey Mould in Open Field and Under Protection,” Agronomy for Sustainable Development, Vol. 32, pp. 531-543, 2012
            11. S. M. Wang, “The Main Factors Affecting Grain Consumption,” Contemporary Economy, No. 22, pp. 40-41, 2015
            12. National Bureau of Statistics, “Statistical Yearbook 2015 of China,” China Statistics Press, 2016


                      Please note : You will need Adobe Acrobat viewer to view the full articles.Get Free Adobe Reader

                      Download this file (02-IJPE-08-02.pdf)02-IJPE-08-02.pdf[Grain Ration Consumption Forecasting based on Multivariate Regression Model Combined with Gliding Data Barycenter]869 Kb
                      This site uses encryption for transmitting your passwords.