Prediction of corn price fluctuation based on multiple linear regression analysis model under big data
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SMART DATA AGGREGATION INSPIRED PARADIGM & APPROACHES IN IOT APPLNS
Prediction of corn price fluctuation based on multiple linear regression analysis model under big data Yan Ge1 • Haixia Wu2 Received: 7 September 2018 / Accepted: 20 December 2018 Ó Springer-Verlag London Ltd., part of Springer Nature 2019
Abstract 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. Keywords Univariate nonlinear regression analysis Big data Multiple regression analysis Price forecast
1 Introduction According to USDA’s forecast in December 2016, the global maize production in 2016 has increased significantly from 928 to 968 million tons, an increase of 40 million tons and a global increase of 4.2%. In the forecast, the rate of corn increase in China is underestimated, only increasing by 4.08%; this is much lower than the actual growth rate of 8.2%. Global cereal production also increased significantly in 2016, with global food, wheat and rice production up to 2.5%, 4.1% and 2.5%, respectively, based on USDA
& Haixia Wu [email protected] 1
School of Public Finance and Tax, Central University of Finance and Economics, Beijing 100081, China
2
International Business School, Shaanxi Normal University, Xi’an 710119, Shaanxi, China
projections [1]. High grain yield will ease the pressure of global food supply. According to USDA data, 1.4 million tons of wheat was added for feed use in 2016 in countries such as Korea, Japan, the Philippines, Thailand and Vietnam. Mexico also significantly increased its imports of feed wheat. Due to the relatively strong international corn prices
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