Forecasting Rice Production of Bangladesh Using ARIMA and Artificial Neural Network Models

Authors

  • Abira Sultana Department of Statistics, University of Dhaka, Dhaka-1000, Bangladesh
  • Murshida Khanam Department of Statistics, University of Dhaka, Dhaka-1000, Bangladesh

DOI:

https://doi.org/10.3329/dujs.v68i2.54612

Keywords:

ARIMA, ANN, AIC, BIC, Nodes, Hidden Layer, Learning rate.

Abstract

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)

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Published

2020-10-29

How to Cite

Sultana, A., & Khanam, M. (2020). Forecasting Rice Production of Bangladesh Using ARIMA and Artificial Neural Network Models. Dhaka University Journal of Science, 68(2), 143–147. https://doi.org/10.3329/dujs.v68i2.54612

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Articles