A Forecasting Model for Feed Grain Demand Based on Combined Dynamic Model.

Tiejun Yang,Na Yang,Chunhua Zhu

doi:10.1155/2016/5329870

Tiejun Yang, Na Yang + Show 1 more

Open Access

https://doi.org/10.1155/2016/5329870

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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.

Highlights

The grain used in feeding is the second largest grain used in China; its quantity and proportion of the total grain consumption grow stably
The forecasting methods of feed grain demand in existing literature can be divided into two kinds: one is using some quantitative methods such as time series regression, model of consumer demand system, and farming grain consumption, based on the analysis about the situation of the feeding food consumption over the past few years to analysis and forecast [1, 2]; the other is from the perspective of nutrition standards analysis of meat, eggs, milk, per capita consumption of aquatic products to predict the future demand for animal products and use the ratio of feed to meat to predict the feed grain demand [3, 4]
The feed grain demand is affected by population growth, urbanization level, per capita income, and other factors [5, 6], which suggest that there should be a comprehensive survey about correlation degree between the feed grain demand and its influence factors for improving the prediction accuracy, and the corresponding prediction model should be generalized

Summary

A Forecasting Model for Feed Grain Demand Based on Combined Dynamic Model

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; 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. It indicates that the proposed algorithm is effective

Introduction

Relational Coefficient Analysis of Influence Factors to Feed Grain Demand

Prediction for Main Factors That Influence the Feed Grain

Simulation Analysis

Conclusion