Corn Price Modeling and Forecasting Using Box-Jenkins Model

Main Article Content

Paibool Kitworawut
Vichai Rungreunganun


Corn harvesting is one of the most complicated problems which farmers need information to make decision prior farming. Corn price is the main factor for farming and there are many factors that affect the corn price vice versa. Knowledge of the factors affecting the corn price and the ability to forecast the corn price in advance would benefit farmers in the context of harvesting. The factors that affect the corn price in Thailand include chicken export rate, corn import rate, weather, soybean price, corn production, stock-to-use, season and planting area. The Cause Tree diagram has been constructed to demonstrate the linkage of such factors and all related data have been collected and analysed by using SPSS software. The Box-Jenkins model has been implemented to establish a time series forecasting model. And performance comparisons among the ARIMA model with Holt-Winters multiplicative seasonal model and Holt-Winters additive seasonal model methods. The results of this research indicated that the corn price can be forecast by using its two lag data with current period soybean price data. The resulting forecasting equation with the ARIMA model generate the lowest errors with Root Mean Square Error (RMSE) at 0.8678, Mean Absolute Percent Error (MAPE) at 12.1009 and Mean Absolute Error (MAE) of 4.7592.

Article Details

How to Cite
Kitworawut, P., & Rungreunganun, V. (2019). Corn Price Modeling and Forecasting Using Box-Jenkins Model. Applied Science and Engineering Progress, 12(4), 277–285. Retrieved from
Research Articles


[1] P. Milind and D. Isha, “Zea maize: A modern craze,” International Research Journal of Pharmacy, vol. 4, no. 6, pp. 39–43, May 2013.

[2] D. Pimentel and T. W. Patzek, “Ethanol production using corn, switchgrass, and wood; biodiesel production using soybean and sunflower,” Natural Resources Research, vol. 14, no. 1, pp. 65–76, Mar. 2005.

[3] K. Parker, M. Salas, and V. C. Nwosu, “High fructose corn syrup: Production, uses and public health concerns,” Biotechnology and Molecular Biology Review, vol. 5, no. 5, pp. 71–78, Dec. 2010.

[4] E. Whittaker, (2014, Apr.). The Fundamental Factors Affecting Corn Price from 1982–2013. Tufts University, Massachusetts, USA [Online]. Available:

[5] P. C. Westcott and L. A. Hoffman, Price Determination for Corn and Wheat: The Role of Market Factors and Government Programs. Washington, DC: Technical Bulletin, 1999.

[6] N. Condon, H. Klemick, and A. Wolverton. (2013, Oct.). Impacts of Ethanol Policy on Corn Prices: A Review and Meta-analysis of Recent Evidence. National Center for Environmental Economics. Washington, DC, USA [Online]. Available:

[7] G. E. P. Box and G. M. Jenkins, Time Series Analysis: Forecasting and Control. San Francisco: Holden-Day, 1970.

[8] J. W. Forrester, “Industrial dynamics,” Science, vol. 135, no. 3502, pp. 426–427, 1961.

[9] S. Makridakis, S. C. Wheelwright, and R. J. Hyndman, Forecasting: Methods and Applications. New York: John Wiley & Sons, 1998.

[10] S. Akhtar and S. Rozi, “An autoregressive integrated moving average model for short-term prediction of hepatitis C virus seropositivity among male volunteer blood donors in Karachi, Pakistan,” World Journal of Gastroenterology, vol. 15, no. 3, pp. 1607–1612, Apr. 2009.

[11] C. Christodoulos, C. Michalakelis, and D. Varoutas, “Forecasting with limited data: Combining ARIMA and diffusion models,” Technological Forecasting & Social Change, vol. 77, pp. 558–565, Jan. 2010.

[12] M. O. Nasiru and S. O. Olanrewaju, “Forecasting airline fatalities in the world using a univariate time series model,” International Journal of Statistics and Applications, vol. 5, no. 5, pp. 223– 230, May 2015.

[13] R. Weron, “Electricity price forecasting: A review of the state-of-the-art with a look into the future,” International Journal of Forecasting, vol. 30, no. 4, pp. 1030–1081.

[14] M. Williams and A. Hoel, “Modeling and forecasting vehicular traffic flow as a seasonal ARIMA process: Theoretical basis and empirical results,” Journal of Transportation Engineering, vol. 129, no. 6, pp. 664–672, Nov. 2003.

[15] W. Riansut and K. Thongrit, “Forecasting the prices of field corn in Thailand,” RMUTP Research Journal, vol. 11, no. 1, pp. 1–14, Jan. 2017.

[16] M. Kerdsomboon and M. Varaphakdi, “Forecasting of agricultural products and prices,” M.S. thesis, Department of Statistics, Faculty of Science, Chulalongkorn University, 1999 (in Thai).

[17] P. Kitworawut and V. Rungreunganun, “An application of analytical hierarchy process (AHP) for affect factor to corn price in Thailand market,” Journal of Advanced Agricultural Technologies, vol. 4, no. 3, pp. 280–284, Sep. 2017.