Wavelet-support vector machine for forecasting palm oil price

Ani Shabri, Mohd Fahmi Abdul Hamid

Abstract


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.

Keywords


Support vector machine, discrete wavelet transform, artificial neural network, partial correlation variable selection, palm oil price

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References


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DOI: https://doi.org/10.11113/mjfas.v15n3.1149

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