title: A Forecasting Model for Feed Grain Demand Based on Combined Dynamic Model.
abstract: In order to improve the long-term prediction accuracy of feed grain demand, a dynamic forecast model of long-term feed grain demand is realized with joint multivariate regression model, of which the correlation between the feed grain demand and its influence factors is analyzed firstly; then the change trend of various factors that affect the feed grain demand is predicted by using ARIMA model. The simulation results show that the accuracy of proposed combined dynamic forecasting model is obviously higher than that of the grey system model. Thus, it indicates that the proposed algorithm is effective.
authors.affiliation: School of Information Science and Engineering, Henan University of Technology, Zhengzhou 450001, China.
authors.forename: Tiejun
authors.initials: T
authors.lastname: Yang
authors.affiliation: School of Information Science and Engineering, Henan University of Technology, Zhengzhou 450001, China.
authors.forename: Na
authors.identifier: 0000-0002-1205-479X
authors.initials: N
authors.lastname: Yang
authors.affiliation: School of Information Science and Engineering, Henan University of Technology, Zhengzhou 450001, China.
authors.forename: Chunhua
authors.identifier: 0000-0003-2672-5483
authors.initials: C
authors.lastname: Zhu
mesh_terms: D002681:China; D003198:Computer Simulation; D002523:Edible Grain / Q000191:economics / Q000600:supply & distribution*; D005544:Forecasting; D006801:Humans; D008962:Models, Theoretical; D017711:Nonlinear Dynamics; D012044:Regression Analysis