Use of Empirical Mode Decomposition in Improving Neural Network Forecasting of Paddy Price

Authors

  • Siti Nabilah Syuhada Abdullah Department of Mathematical Sciences Faculty of Science Universiti Teknologi Malaysia 81310 UTM Johor Bahru, Malaysia
  • Ani Shabri Department of Mathematical Sciences Faculty of Science Universiti Teknologi Malaysia 81310 UTM Johor Bahru, Malaysia
  • Ruhaidah Samsudin School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia 81310 UTM Johor Bahru, Malaysia

DOI:

https://doi.org/10.11113/matematika.v35.n4.1263

Abstract

Since rice is a staple food in Malaysia, its price fluctuations pose risks to the producers, suppliers and consumers. Hence, an accurate prediction of paddy price is essential to aid the planning and decision-making in related organizations. The artificial neural network (ANN) has been widely used as a promising method for time series forecasting. In this paper, the effectiveness of integrating empirical mode decomposition (EMD) into an ANN model to forecast paddy price is investigated. The hybrid method is applied on a series of monthly paddy prices fromFebruary 1999 up toMay 2018 as recorded in the Malaysian Ringgit (MYR) per metric tons. The performance of the simple ANN model and the EMD-ANN model was measured and compared based on their root mean squared Error (RMSE), mean absolute error (MAE) and mean percentage error (MPE). This study finds that the integration of EMD into the neural network model improves the forecasting capabilities. The use of EMD in the ANN model made the forecast errors reduced significantly, and the RMSE was reduced by 0.012, MAE by 0.0002 and MPE by 0.0448.

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Published

2019-12-31