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Cluster Computing

, Volume 22, Supplement 3, pp 6621–6632 | Cite as

Research on duplicate combined forecasting method based on supply chain coordination

  • Yanxin ZhuEmail author
  • Sujian Li
  • Yongfang Peng
Article
  • 84 Downloads

Abstract

The coordinated forecast of agricultural means supply chain not only needs cooperation and information sharing among different parties but also needs scientific forecast methods and means. This paper firstly builds the synergetic framework of demand forecast and analyzes the key factors of demand forecast coordination in different forecast stages and then confirms duplicate combined forecast method for time series based on the factors which influence the demand of agricultural means. GM(1,1) is used in the model to forecast the fluctuant items of long-term trend; BP neural network and ARMA are used to simulate periodically fluctuant items. Particle swarm algorithm is used to confirm the combined forecast model of periodically fluctuant items. Finally, a calculating example is used to compare the forecast precision of the combined forecast model, GM(1,1), BP neural network model and ARMA model. In conclusion, duplicate combined forecast model is applicable to forecasting the demand of agricultural means which are influenced by long-term trend and periodically fluctuant factors.

Keywords

Demand of agricultural means Supply chain coordination Grey forecast Combined forecast model Particle swarm algorithm 

Notes

Acknowledgement

The work was supported by the project of Social Science Foundation of Hebei Province-“Empirical Study and Policy Choice of Synergetic Development of Regional Logistics Capability and Regional Economic System in Hebei Province” (HB15YJ024), and 2018 Hebei Science and Technology Department of soft science research and science projects-“Research on the Implementing Path and Policy Innovation of Supply-side Structural Reform of Logistics Industry in Hebei Province”.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Mechanical Engineering CollegeUniversity of Science & Technology Beijing (USTB)BeijingChina
  2. 2.Commercial CollegeHebei GEO UniversityShijiazhuangChina

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