Agricultural price fluctuation model based on SVR

This paper introduces a highly intelligent and widely applicable model system for intelligent regression analysis of agricultural products price fluctuation, and it presents the main conditions which influence the agricultural price fluctuation including meteorological factors, input factors and resident consumption level. In the paper, the author systematically analyzes daily, monthly and annual price data of major agricultural products in China in the past 15 years. According to the factors that determine the price of agricultural products, this paper systematically studies the characteristics and laws of major agricultural products price in China since 2000, analyzes agricultural price fluctuation factors and design a least square Support Vector Regression (SVR) model for the prediction of the wholesale agricultural products price. Finally, the author describes the model design process and the model performance, and it is confirmed that the method putting forward in the research is applicable, and indicates the direction of the further research.

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