Skip to main content
SpringerLink
Log in

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

  • Smart Data Aggregation Inspired Paradigm & Approaches in IoT Applns
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  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 38:78–87

    Article  Google Scholar 

  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):398–407

    Article  Google Scholar 

  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 673–681

  4. Miao R, Khanna M, Huang H (2015) Responsiveness of crop yield and acreage to prices and climate. Am J Agr Econ 98(1):191–211

    Article  Google Scholar 

  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):108

    Article  MathSciNet  Google Scholar 

  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):172–190

    Article  Google Scholar 

  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):1

    Article  Google Scholar 

  8. Kristoufek L (2015) Detrended fluctuation analysis as a regression framework: estimating dependence at different scales. Phys Rev E 91(2):022802

    Article  Google Scholar 

  9. Khaidem L, Saha S, Dey SR (2016) Predicting the direction of stock market prices using random forest. arXiv preprint arXiv:1605.00003

  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 Association

  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 41:64–73

    Article  Google Scholar 

  12. Wong RK, Li Y, Zhu Z (2017) Partially linear functional additive models for multivariate functional data. J Am Stat Assoc (just-accepted)

  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 76:155–162

    Article  Google Scholar 

  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 Photonics

  15. Green DM (2017) Amphibian breeding phenology trends under climate change: predicting the past to forecast the future. Glob Change Biol 23(2):646–656

    Article  Google Scholar 

  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:e2554

    Article  Google Scholar 

  17. Whittaker G, Barnhart BL, Srinivasan R, Arnold JG (2015) Cost of areal reduction of gulf hypoxia through agricultural practice. Sci Total Environ 505:149–153

    Article  Google Scholar 

  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 3:1–17

    Article  Google Scholar 

  19. Self S, Deol S (2016) Impact of ethanol mandates on corn prices in the US, Canada, and Mexico. J Econ (03616576), 42(2)

  20. Chen S, Chen X, Xu J (2016) Impacts of climate change on agriculture: evidence from China. J Environ Econ Manag 76:105–124

    Article  Google Scholar 

  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 Association

  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):1191–1202

    Article  Google Scholar 

  23. Amatya P, Yu M, Ewell F (2016) Economic analysis of optimal nitrogen application in corn production. Tex J Agric Nat Resour 21:101–108

    Google Scholar 

  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):1095

    Article  Google Scholar 

  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):337–354

    Article  Google Scholar 

  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):173

    Article  Google Scholar 

  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):1464–1477

    Article  Google Scholar 

  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):1640–1651

    Article  Google Scholar 

Download references

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).

Author information

Authors and Affiliations

  1. School of Public Finance and Tax, Central University of Finance and Economics, Beijing, 100081, China

    Yan Ge

  2. International Business School, Shaanxi Normal University, Xi’an, 710119, Shaanxi, China

    Haixia Wu

Authors
  1. Yan Ge
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Haixia Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Haixia Wu.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ge, Y., Wu, H. Prediction of corn price fluctuation based on multiple linear regression analysis model under big data. Neural Comput & Applic 32, 16843–16855 (2020). https://doi.org/10.1007/s00521-018-03970-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-018-03970-4

Keywords

Search

Navigation