Prediction of corn price fluctuation based on multiple linear regression analysis model under big data

  • Yan Ge
  • Haixia WuEmail author
Smart Data Aggregation Inspired Paradigm & Approaches in IoT Applns


This paper mainly analyzes the changing trend of corn price and the factors that affect the price of corn. Using the data and regression analysis, the univariate nonlinear and multivariate linear regression models are established to predict the corn price, respectively. First, this paper establishes a univariate nonlinear regression model with time as the independent variable, and corn price is used as the dependent variable through the analysis of the trend of big data related to Chinese corn price from 2005 to 2016 by MATLAB, which is the computer-based analysis and processing method. The variation of the maize price with time was fitted. To a certain extent, the price trend of corn is predicted. However, the estimated price of corn in 2017 with this model will deviate from the actual value. According to the changes of related policies in our country, we analyzed the deviation of the original model, and the relationship between supply and demand is the main underlying factor that affects the price of corn. This paper selects maize-related big data from 2005 to 2016, we set its production consumption, import and export volume as independent variables, and we still use maize price as the dependent variable to establish a multiple linear regression model. At this stage, the time series analysis of the independent variable has obtained the forecast value of each independent variable in 2017, and then the model is used to predict the corn in 2017 more accurately.


Univariate nonlinear regression analysis Big data Multiple regression analysis Price forecast 



This paper is funded by the National Key Research and Development Program of China “The Assessment of Application Effect of Fertilizer and Pesticide Reduction Technologies on Wheat in Northern China” (2018YFD020040810); National Natural Science Foundation of China Youth Fund Project “Transmission Mechanism and Forecasting Effect of Financial Factors on Corn Price Fluctuation: Based on the perspective of food finance” (Project No. 71603153); Key Research and Development Program of Shaanxi Province “Study on Long-term Multidimensional Poverty, Poverty Factors and Poverty Reduction Policies for Children in Shaanxi” (2018KW-065); Fundamental Scientific Research Funds of Central Universities in Shaanxi Normal University; the Special Fund Project “Evaluation of Comprehensive Benefits of Fertilizer and Pesticide Reduction Technology in Dryland Wheat in the Loess Plateau” (GK201803092).


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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Public Finance and TaxCentral University of Finance and EconomicsBeijingChina
  2. 2.International Business SchoolShaanxi Normal UniversityXi’anChina

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