Universiti Teknologi Malaysia Institutional Repository

Wavelet-support vector machine for forecasting palm oil price

Shabri, Ani and Abdul Hamid, Mohd. Fahmi (2019) Wavelet-support vector machine for forecasting palm oil price. Malaysian Journal of Fundamental and Applied Sciences, 15 (3). pp. 398-406. ISSN 2289-5981


Official URL: https://mjfas.utm.my/index.php/mjfas/article/downl...


This study examines the feasibility of applying Wavelet-Support Vector Machine (W-SVM) model in forecasting palm oil price. The conjunction method wavelet-support vector machine (W-SVM) is obtained by the integration of discrete wavelet transform (DWT) method and support vector machine (SVM). In W-SVM model, wavelet transform is used to decompose data series into two parts; approximation series and details series. This decomposed series were then used as the input to the SVM model to forecast the palm oil price. This study also utilizes the application of partial correlation-based input variable selection as the preprocessing steps in determining the best input to the model. The performance of the W-SVM model was then compared with the classical SVM model and also artificial neural network (ANN) model. The empirical result shows that the addition of wavelet technique in W-SVM model enhances the forecasting performance of classical SVM and performs better than ANN.

Item Type:Article
Uncontrolled Keywords:Support vector machine, discrete wavelet transform, artificial neural network, partial correlation variable selection, palm oil price
Subjects:Q Science > QA Mathematics
ID Code:84991
Deposited By: Fazli Masari
Deposited On:29 Feb 2020 13:34
Last Modified:29 Feb 2020 13:34

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