COMPARISON OF PERFORMANCE BETWEEN THE TIME SERIES FORECASTING MODELS ARIMA AND ARIMAX IN FORECASTING THE RICE YIELD - G. C. MISHRA, K. SUPRIYA and (The Journal of Research, PJTSAU, 2019)
Rice being the staple food for more than 50% of the world population, it is very essential to forecast the production of rice so as to meet the need of the rapidly growing population. Forecasting is also essential for better planning and decision making. Many forecasting technics have evolved and it is the matter of prediction accuracy. In this study, two time series forecasting models, Autoregressive Integrated moving average (ARIMA) and Autoregressive integrated moving average with exogenous variables (ARIMAX) were compared to forecast the rice yield during both kharif and rabi seasons of Rajendranagar region of Telangana state. The exogenous variables used in the study are percentage of dead hearts and percentage of white ears which are the damage symptoms of rice yield due to yellow stem borer (Scirpophaga incertulas). To compare the effectiveness of these two models 26 years rice yield data of both kharif and rabi seasons pertaining to Rajendranagar region of Telangana state was used i.e., from 1990-2016. The results showed that Autoregressive integrated moving average model with exogenous variables (ARIMAX) performed reasonably well compared to the other model i.e., Autoregressive integrated moving average model (ARIMA) and hence can be applied for real life predictions and modeling problems.