Implementation of Time Series Stochastic Modelling for Zea mays Production in India

Abstract

This study deals with the implementation of time series stochastic modelling for Zea mays (Maize) production in India during the years from 1951 to 2018. The demand for maize is spiralling in India. Maize can be grown in all seasons viz., Kharif (monsoon), post monsoon, Rabi (winter) and spring. The study considers Autoregressive (AR), Moving Average (MA) and ARIMA processes to select the appropriate ARIMA model forZea mays 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 Zea mays production would increase to 32.12million tons in 2025 from 27.02million tons in 2019 in India.