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. [ABSTRACT FROM AUTHOR]
Copyright of Cluster Computing is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
