Abstract
The time series is a set of values arranged in a specific order of time. Prediction and analysis of food grain is a vital role in agriculture statistics. The Agriculture Statistics System is very complete and provides data on a wide range of topics such as crop area and production, land use, irrigation, land holdings, agricultural prices and market intelligence, livestock, fisheries, forestry etc. Agricultural credit and subsidies also consider important supporting factors for agricultural growth. India is the world's largest producer of millets and second-largest producer of wheat, rice, and pulses. The present research work focused on production of food grains in India using time series data ranging from 1990- 91 to 2018-19. In this paper, Autoregressive Integrated Moving Average Model (ARIMA), Multilayer Perceptron (MLP) and Radial Basis Function (RBF) for predicting foodgrains of India were compared. Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) were compared. The results were displayed numerically and graphically.
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