Forecasting behavior of economic and machine learning models has recently attracted much attention in the research sector. In this study an attempt has been made to compare the forecasting behavior of Autoregressive Integrated Moving Average (ARIMA) and Neural Network Autoregressive (NNAR) modes using univariate model time series data of annual paddy production (1980-2022) in Malaysia. The data was obtained from the open website of Department of Statistic Malaysia (DOSM). Through the evaluation of forecasting accuracy suing Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE), the results showed that the eliminated error of NNAR is much smaller than the estimated error of ARIMA for paddy production. So, the best model to forecast paddy production is NNAR(1,1).
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