Using Autoregressive Integrated Moving Average (ARIMA) Technique to Forecast the Production of Kharif Cereals in Odisha (India)
Current Journal of Applied Science and Technology,
Cereals are the most important kharif season crop in Odisha. The present study was carried out to forecast the production of kharif cereals in Odisha by using the forecast values of area and yield of kharif cereals obtained from the selected best fit Autoregressive Integrated Moving Average (ARIMA) model. The data from 1970-71 to 2010-11 are considered as training set data and used for model building and from 2011-12 to 2015-16 are considered as testing set data and used for cross-validation of the selected model on the basis of the absolute percentage error. The ARIMA models are fitted to the stationary data which may be the original data or the differenced data. The different ARIMA models are evaluated on the basis of Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF) at various lags. The possible ARIMA models are selected on the basis of significant coefficient of autoregressive and moving average components by using the training set data. The best fitted models are then selected on the basis of residual diagnostics test and model fit statistics. The ARIMA model found to be best fitted for area under kharif cereals and yield of kharif cereals are ARIMA (1,1,0) without constant and ARIMA (0,1,2) without constant respectively which are successfully cross-validated with the testing set data. The respective best fit ARIMA model has been used to forecast the area and yield of kharif cereals for the years 2016-17, 2017-18 and 2018-19. The forecast values of area shows a decrease, whereas, the forecast values of yield shows an increase. The decrease in area might have been the result of limited availability of area for cereals due to shifting towards non-food grain crops. The forecast values of production of kharif cereals obtained from the forecast values of area and yield of kharif cereals shows an increase which is due to the increase in forecast values of yield. Since there is limited scope for area expansion, the future production of kharif cereals can only be increased by increasing the yield to achieve the goal of food security for the growing population.
How to Cite
Nelson C. The prediction performance of the FRB-MIT_PENN model of the US economy. The American Economic Review. 1972;62(5):902-917.
Newbold P, Granger CWJ. Experience with forecasting univariate time series and the combination of forecasts. Journal of the Royal Statistical Society. Series A. 1974;137(2):131-165.
Vishawajith KP, Sahu PK, Dhekale BS, Mishra P. Modelling and forecasting sugarcane and sugar production in India. Indian Journal of Economics and Development. 2016;12(1):71-79.
Sahu PK, Vishwajith KP, Dhekale BS, Mishra P. Modelling and forecasting of area, production, yield and total seeds of rice and wheat in SAARC countries and the world towards food security. American Journal of Applied Mathematics and Statistics. Science and Education Publishing, USA. 2015;3(1):34-48.
Mishra P, Sahu PK, Uday JPS. ARIMA modeling technique in analyzing and forecasting fertilizer statistics in India. Trends in Biosciences Journal. 2014;7(2): 170-176.
Dhekale BS, Vishwajith KP, Sahu PK, Mishra P, Noman MD. Modeling and forecasting of tea production in West Bengal. Journal of Crop and Weed. 2014;10(2):94-103.
Mishra P, Fatih C, Niranjan HK, Tiwari S, Devi M, Dubey A. Modelling and forecasting of milk production in Chhattisgarh and India. Indian Journal of Animal Research; 2020.
Verma VK, Jheeba SS, Kumar P, Singh SP. Price forecasting of bajra (pearl millet) in Rajasthan: ARIMA model. International Journal of Agriculture Sciences. 2016;8(9):1103-1106.
Dasyam R, Bhattacharyya B, Mishra P. Statistical modeling to area, production and yield of potato in West Bengal. International Journal of Agriculture Sciences. 2016;8(53):2782-2787.
Dash A, Dhakre DS, Bhattacharjee D. Forecasting of food grain production in Odisha by fitting ARIMA model. Journal of Pharmacognosy and Phytochemistry. 2017;6(6):1126-1132.
Box GEP, Jenkins GM, Reinsel GC. Time series analysis: Forecasting and control. 4th Edition, John Wiley & Sons, Hoboken, New Jersey; 2007.
Ljung GM, Box GEP. On a measure of a lack of fit in time series models. Biometrika. 1978;65(2):297-303.
Lee R, Qian M, Shao Y. On rotational robustness of Shapiro-Wilk type tests for multivariate normality. Open Journal of Statistics. 2014;4(11):964-969.
Abstract View: 1054 times
PDF Download: 437 times