Open AccessForecasting Rice Production of Bangladesh Using ARIMA and Artificial Neural Network Models
Author(s) -
Abira Sultana,
Murshida Khanam
Publication year - 2020
Publication title -
the dhaka university journal of science
Language(s) - English
Resource type - Journals
eISSN - 2408-8528
pISSN - 1022-2502
DOI - 10.3329/dujs.v68i2.54612
Subject(s) - autoregressive integrated moving average , artificial neural network , backpropagation , mean absolute percentage error , univariate , mean squared error , production (economics) , moving average , statistics , time series , autoregressive model , moving average model , box–jenkins , econometrics , computer science , econometric model , artificial intelligence , mathematics , economics , multivariate statistics , macroeconomics
Forecasting behavior of Econometric 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 Artificial Neural Network (ANN) using univariate time series data of annual rice production (1972 to 2013) of Bangladesh. Here, suitable ARIMA has been chosen from several selected ARIMA models with the help of AIC and BIC values. A simple ANN model using backpropagation algorithm with appropriate number of nodes or neurons in a single hidden layer, adjustable threshold value and learning rate, has been constructed. Based on the RMSE, MAE and MAPE values, the results showed that the estimated error of ANN is much higher than the estimated error of chosen ARIMA. So, according to this study, it can be said that the ARIMA model is better than ANN model for forecasting the rice production in Bangladesh.
Dhaka Univ. J. Sci. 68(2): 143-147, 2020 (July)