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Use of empirical mode decomposition in improving neural network forecasting of paddy price

Abdullah, Siti Nabilah Syuhada and Shabri, Ani and Samsudin, Ruhaidah (2019) Use of empirical mode decomposition in improving neural network forecasting of paddy price. Matematika, 35 . pp. 53-64. ISSN 0127-9602

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Official URL: http://dx.doi.org/10.11113/matematika.v35.n4.1263


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.

Item Type:Article
Uncontrolled Keywords:hybrid model, univariate data
Subjects:Q Science > QA Mathematics
ID Code:89431
Deposited By: Widya Wahid
Deposited On:22 Feb 2021 09:47
Last Modified:22 Feb 2021 09:47

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