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; then the cha...

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Detalles Bibliográficos
Autores principales: Yang, Tiejun, Yang, Na, Zhu, Chunhua
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2016
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5029050/
https://www.ncbi.nlm.nih.gov/pubmed/27698661
http://dx.doi.org/10.1155/2016/5329870
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author Yang, Tiejun
Yang, Na
Zhu, Chunhua
author_facet Yang, Tiejun
Yang, Na
Zhu, Chunhua
author_sort Yang, Tiejun
collection PubMed
description 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.
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spelling pubmed-50290502016-10-03 A Forecasting Model for Feed Grain Demand Based on Combined Dynamic Model Yang, Tiejun Yang, Na Zhu, Chunhua Comput Intell Neurosci Research Article 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. Hindawi Publishing Corporation 2016 2016-09-06 /pmc/articles/PMC5029050/ /pubmed/27698661 http://dx.doi.org/10.1155/2016/5329870 Text en Copyright © 2016 Tiejun Yang et al. https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Yang, Tiejun
Yang, Na
Zhu, Chunhua
A Forecasting Model for Feed Grain Demand Based on Combined Dynamic Model
title A Forecasting Model for Feed Grain Demand Based on Combined Dynamic Model
title_full A Forecasting Model for Feed Grain Demand Based on Combined Dynamic Model
title_fullStr A Forecasting Model for Feed Grain Demand Based on Combined Dynamic Model
title_full_unstemmed A Forecasting Model for Feed Grain Demand Based on Combined Dynamic Model
title_short A Forecasting Model for Feed Grain Demand Based on Combined Dynamic Model
title_sort forecasting model for feed grain demand based on combined dynamic model
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5029050/
https://www.ncbi.nlm.nih.gov/pubmed/27698661
http://dx.doi.org/10.1155/2016/5329870
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