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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
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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 

Notes

Acknowledgements

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).

References

  1. 1.
    Shao Y, Campbell JB, Taff GN, Zheng B (2015) An analysis of cropland mask choice and ancillary data for annual corn yield forecasting using MODIS data. Int J Appl Earth Obs Geoinf 38:78–87CrossRefGoogle Scholar
  2. 2.
    Shrestha R, Di L, Eugene GY, Kang L, Shao YZ, Bai YQ (2017) Regression model to estimate flood impact on corn yield using MODIS NDVI and USDA cropland data layer. J Integr Agric 16(2):398–407CrossRefGoogle Scholar
  3. 3.
    Schwager JD, Etzkorn M (2017) Practical considerations in applying regression analysis. A complete guide to the futures market: technical analysis, trading systems, fundamental analysis, options, spreads, and trading principles, 2nd edn, pp 673–681Google Scholar
  4. 4.
    Miao R, Khanna M, Huang H (2015) Responsiveness of crop yield and acreage to prices and climate. Am J Agr Econ 98(1):191–211CrossRefGoogle Scholar
  5. 5.
    Wang D, Yue C, Wei S, Lv J (2017) Performance analysis of four decomposition-ensemble models for one-day-ahead agricultural commodity futures price forecasting. Algorithms 10(3):108MathSciNetCrossRefzbMATHGoogle Scholar
  6. 6.
    Haile MG, Kalkuhl M, von Braun J (2015) Worldwide acreage and yield response to international price change and volatility: a dynamic panel data analysis for wheat, rice, corn, and soybeans. Am J Agr Econ 98(1):172–190CrossRefGoogle Scholar
  7. 7.
    Wu H, Wu H, Zhu M, Chen W, Chen W (2017) A new method of large-scale short-term forecasting of agricultural commodity prices: illustrated by the case of agricultural markets in Beijing. J Big Data 4(1):1CrossRefGoogle Scholar
  8. 8.
    Kristoufek L (2015) Detrended fluctuation analysis as a regression framework: estimating dependence at different scales. Phys Rev E 91(2):022802CrossRefGoogle Scholar
  9. 9.
    Khaidem L, Saha S, Dey SR (2016) Predicting the direction of stock market prices using random forest. arXiv preprint arXiv:1605.00003
  10. 10.
    Li X, Coble K, Tack J, Barnett B (2016) Estimating site-specific crop yield response using varying coefficient models. In: 2016 annual meeting, July 31–August 2, 2016, Boston, Massachusetts (No. 235798). Agricultural and Applied Economics AssociationGoogle Scholar
  11. 11.
    Swain S, Abeysundara S, Hayhoe K, Stoner AM (2017) Future changes in summer MODIS-based enhanced vegetation index for the South-Central United States. Ecol Inform 41:64–73CrossRefGoogle Scholar
  12. 12.
    Wong RK, Li Y, Zhu Z (2017) Partially linear functional additive models for multivariate functional data. J Am Stat Assoc (just-accepted)Google Scholar
  13. 13.
    Vlontzos G, Pardalos PM (2017) Assess and prognosticate green house gas emissions from agricultural production of EU countries, by implementing, DEA Window analysis and artificial neural networks. Renew Sustain Energy Rev 76:155–162CrossRefGoogle Scholar
  14. 14.
    Nizamuddin M, Akhand K, Roytman L, Kogan F, Goldberg M (2015) Using NOAA/AVHRR based remote sensing data and PCR method for estimation of Aus rice yield in Bangladesh. In: Sensing for agriculture and food quality and safety VII, vol 9488, p 94880O. International Society for Optics and PhotonicsGoogle Scholar
  15. 15.
    Green DM (2017) Amphibian breeding phenology trends under climate change: predicting the past to forecast the future. Glob Change Biol 23(2):646–656CrossRefGoogle Scholar
  16. 16.
    Guo Y, Wei H, Lu C, Gao B, Gu W (2016) Predictions of potential geographical distribution and quality of Schisandra sphenanthera under climate change. PeerJ 4:e2554CrossRefGoogle Scholar
  17. 17.
    Whittaker G, Barnhart BL, Srinivasan R, Arnold JG (2015) Cost of areal reduction of gulf hypoxia through agricultural practice. Sci Total Environ 505:149–153CrossRefGoogle Scholar
  18. 18.
    Lu W, Atkinson DE, Newlands NK (2017) ENSO climate risk: predicting crop yield variability and coherence using cluster-based PCA. Model Earth Syst Environ 3:1–17CrossRefGoogle Scholar
  19. 19.
    Self S, Deol S (2016) Impact of ethanol mandates on corn prices in the US, Canada, and Mexico. J Econ (03616576), 42(2) Google Scholar
  20. 20.
    Chen S, Chen X, Xu J (2016) Impacts of climate change on agriculture: evidence from China. J Environ Econ Manag 76:105–124CrossRefGoogle Scholar
  21. 21.
    Arora G, Wolter PT, Feng H, Hennessy D (2015) Role of ethanol plants in Dakotas’ land use change: analysis using remotely sensed data. In: 2015 AAEA and WAEA joint annual meeting, July 26–28, San Francisco, California (No. 205877). Agricultural and Applied Economics Association and Western Agricultural Economics AssociationGoogle Scholar
  22. 22.
    Eagle AJ, Olander LP, Locklier KL, Heffernan JB, Bernhardt ES (2017) Fertilizer management and environmental factors drive N2O and NO3 losses in corn: a meta-analysis. Soil Sci Soc Am J 81(5):1191–1202CrossRefGoogle Scholar
  23. 23.
    Amatya P, Yu M, Ewell F (2016) Economic analysis of optimal nitrogen application in corn production. Tex J Agric Nat Resour 21:101–108Google Scholar
  24. 24.
    Sharma LK, Bali SK, Dwyer JD, Plant AB, Bhowmik A (2017) A case study of improving yield prediction and sulfur deficiency detection using optical sensors and relationship of historical potato yield with weather data in maine. Sensors 17(5):1095CrossRefGoogle Scholar
  25. 25.
    Chen W, Hohl R, Tiong LK (2017) Rainfall index insurance for corn farmers in Shandong based on high-resolution weather and yield data. Agric Financ Rev 77(2):337–354CrossRefGoogle Scholar
  26. 26.
    Chen X, Shekiro J, Pschorn T, Sabourin M, Tucker MP, Tao L (2015) Techno-economic analysis of the deacetylation and disk refining process: characterizing the effect of refining energy and enzyme usage on minimum sugar selling price and minimum ethanol selling price. Biotechnol Biofuels 8(1):173CrossRefGoogle Scholar
  27. 27.
    Tack J, Barkley A, Lanier Nalley L (2015) Estimating yield gaps with limited data: an application to United States wheat. Am J Agr Econ 97(5):1464–1477CrossRefGoogle Scholar
  28. 28.
    Walker ZT, Coulter JA, Russelle MP et al (2017) Do soil tests help forecast nitrogen response in first-year corn following alfalfa on fine-textured soils? Soil Sci Soc Am J 81(6):1640–1651CrossRefGoogle Scholar

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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