Hybrid Time Series Models for Forecasting Maize Production in India
Current Journal of Applied Science and Technology,
In spite of the immense success of different linear and non-linear time series models in their respective domains, real-world data are rarely pure linear or non-linear in nature. Hence, a hybrid modelling framework with the capability of handling both linear and non-linear patterns can substantially improve the forecasting accuracy. With this backdrop, an effort has been made in this investigation to evaluate the suitability of hybrid models in compassion to single linear or non-linear models for forecasting maize production in India. Data from 1949-50 to 2016-17 have been utilised for the model building purpose while retaining the data from 2017-18 to 2019-20 for the post-sample accuracy assessment. Outcomes emanated from this investigation clearly reveals that the ARIMA-NLSVR model has outperformed all other candidate models employed in this study. It is noteworthy to mention that both the hybrid models have performed better than their individual counterparts. The superior forecasting ability of both the non-linear models over the linear ARIMA model has also been evident.
- Agricultural forecasting
- Hybrid models
- Maize production
How to Cite
Nuss ET, Tanumihardjo SA. Maize: A paramount staple crop in the context of global nutrition. Compr Rev Food Sci Food Saf. 2010;9(4):417-36.
Kaul J, Jain K, Olakh D. An overview on role of yellow maize in food, feed and nutrition security. Int J Curr Microbiol Appl Sci. 2019;8(2):3037-48.
Hellin J, Erenstein O. Maize-poultry value chains in India: implications for research and development. J New Seeds. 2009;10(4):245-63.
FICCI. India maize summit’15; 2015.
Kumar R, Srinivas K, Sivaramane N. Assessment of the maize situation, outlook and investment opportunities in India. National Academy of Agricultural Research Management, Hyderabad, India: Country report–regional assessment Asia (MAIZE-CRP); 2013.
Sarika, Iquebal, MA, Chattopadhyay C. Modelling and forecasting of pigeon pea (Cajanus cajan) production using autoregressive integrated moving average methodology. Ind J Agric Sci. 2011;81(6):520-3.
Suresh KK, Priya SRK. Forecasting sugarcane yield of Tamil Nadu using ARIMA models. Sugar Tech. 2011;13(1):23-6.
Kumari P, Mishra GC, Pant AK, Shukla G, Kujur SN. Autoregressive Integrated Moving Average (ARIMA) approach for prediction of rice (Oryza sativa L.) yield in India. BioScan. 2014;9(3):1063- 6.
Box GEP, Jenkins G. Time series analysis, forecasting and control. San Francisco, CA: Holden-Day; 1970.
Taskaya-Temizel T, Casey MC. A comparative study of autoregressive neural network hybrids. Neural Netw. 2005;18(5-6):781-9.
Hadipour A, Khoshand A, Rahimi K, Kamalan HR. Groundwater level forecasting by application of artificial neural network approach: A case study in Qom Plain, Iran. J Hydro-Environ Res. 2019;3(5):30-4.
Fung KF, Huang YF, Koo CH, Mirzaei M. Improved SVR machine learning models for agricultural drought prediction at downstream of Langat River Basin, Malaysia. J Water Clim Chang. 2020;11(4):1383-98.
Zhang GP. Time series forecasting using a hybrid ARIMA and neural network model. Neural Comput. 2003;50:159-75.
Faruk DÖ. A hybrid neural network and ARIMA model for water quality time series prediction. Eng Appl Artif Intell. 2010;23(4):586-94.
Khairalla M, AL-Jallad NT. Hybrid forecasting scheme for financial time-series data using neural network and statistical methods. Int J Adv Comput Sci Appl. 2017;8(9):319-27.
Rathod S, Mishra GC, Singh KN. Hybrid time series models for forecasting banana production in Karnataka State, India. J Indian Soc Agric Stat. 2017;71(3):193-200.
Shanmuganathan S, Samarasinghe S, editors. Artificial neural network modelling. Switzerland: Springer Nature; 2016.
Tosun E, Aydin K, Bilgili M. Comparison of linear regression and artificial neural network model of a diesel engine fueled with biodiesel-alcohol mixtures. Alex Eng J. 2016;55(4):3081-9.
Elman JL. Finding structure in time. Cog Sci. 1990;14:179-211.
Haykin S. Neural networks – a comprehensive foundation. Upper Saddle River: Prentice‐Hall; 1999.
Vapnik V, Golowich S, Smola A. Support vector method for function approximation, regression estimation, and signal processing. In: Mozer M, Jordan M, Petsche T, editors. Advances in neural information processing systems. Cambridge: MIT Press; 1997.
Vapnik V. The nature of statistical learning theory. 2nd Edition. New York: Springer-Verlag; 2000.
Brock WA, Dechert WD, Scheinkman JA, lebaron B. A test for independence based on the correlation dimension. Econom Rev. 1996;15:197-235.
Xu D, Zhang Q, Ding Y, Huang H. Application of a Hybrid ARIMA–SVR Model Based on the SPI for the Forecast of Drought—A Case Study in Henan Province, China. J Appl Meteorol Climatol. 2020;59(7):1239-59.
Pannakkong W, Huynh VN, Sriboonchitta S. A novel hybrid autoregressive integrated moving average and artificial neural network model for cassava export forecasting. Int J Comput Intell Syst. 2019;12(2):1047-61.
Shin JY, Kim KR, Ha JC. Seasonal forecasting of daily mean air temperatures using a coupled global climate model and machine learning algorithm for field-scale agricultural management. Agric For Meteorol. 2020;281:107858-73.
Abstract View: 44 times
PDF Download: 13 times