Comparison of ARIMA, SutteARIMA, and Holt-Winters, and NNAR Models to Predict Food Grain in India

The agriculture sector plays an essential function within the Indian economic system. Foodgrains provide almost all calories and proteins. This paper aims to compare ARIMA, SutteARIMA, Holt-Winters, NNAR models recommend effective model predict foodgrains production in India. execution of SutteARIMA predictive used this analysis was compared with established Neural Network Auto-Regressive (NNAR), Holt-Winters models, which have been widely applied for time series prediction. findings study reveal that both performed well real-life problems can effectively profitably be engaged food grain forecasting approach indicated superior performance over models. Indeed, actual predicted values are quite close predicting has verified by MAPE MSE relatively low model. Therefore, India’s from 2021 2025. forecasted amount respective crops as follows (in lakh tonnes) 1140.14 (wheat), 1232.27 (rice), 466.46 (coarse), 259.95 (pulses), a total 3069.80 (foodgrains)