A Study on Production of Food Grains in the State of Telangana By Using Box Jenkins’s Methodology

In the real mean of research, statistical modeling of non-stationary, non-linear statistics has grown to be a significant challenge. ANN and ARIMA are two of the most widely utilized models. The Artificial Neural Network (ANN) and Box-Jenkin's methods for forecasting the actual production of Food grains crop value in Telangana are compared in this book. The primary goal of this investigation is to create a forecasting model that can accurately anticipate Telangana's agricultural production. In order to predict the annual production of the Food grains crop in Telangana, a statistical forecasting model utilizing Box-Jenkin's approach and artificial neural networks was created throughout this research. The model's ability to forecast was assessed using Mean Absolute Percent Error (MAPE) and Root Mean Squared Error (RMSE). According to the annual projections, Food grains crop production should be 90% accurate over a ten-year period with a regular variance of 1% error measure.