Design and Development of ARIMA Model for Bajra (Pennisetum glaucum) Production in India

Abstract

This study deals with design and development of autoregressive integrated moving average (ARIMA) model for Bajra (Pennisetum glaucum) production in India based on Bajra (P. glaucum) production during the years from 1951 to 2018. The study considers Autoregressive (AR), Moving Average (MA) and Autoregressive Integrated Moving Average (ARIMA) processes to select the appropriate ARIMA model for Bajra (P. glaucum) production in India. Based on ARIMA (p, d, q) and its components Autocorrelation Function (ACF), Partial Autocorrelation Function (PACF), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), Normalized BIC and Box-Ljung Q statistics estimated, ARIMA (0,1,1) was selected. Based on the chosen model, it could be predicted that Bajra (P. glaucum) production would increase from 9.21 million tons in 2018 to 10.52 million tons in 2025 in India.