Wavelet-support vector machine for forecasting palm oil price


  • Ani Shabri Universiti Teknologi Malaysia
  • Mohd Fahmi Abdul Hamid UNITAR International University




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


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.


Abdullah, R., & Lazim, M. A. (2006). Production and price forecast for malaysian palm oil. Oil Palm Industry Economic Journal, 6(1), 39–45.

Abedinia, O., Amjady, N., & Zareipour, H. (2016). A new feature selection technique for load and price forecast of electrical power systems. IEEE Transactions on Power Systems, 32(1), 62-74. https://doi.org/10.1109/ TPWRS.2016.2556620

Ahmad, M. H., Ping, P. Y., & Mahamed, N. (2014). Volatility modelling and forecasting of Malaysian crude palm oil prices. Applied Mathematical Sciences, 8(121–124), 6159–6169. https://doi.org/10.12988/ams.2014. 48650

Al Wadi, S., Ismail, M. T., Alkhahazaleh, M. H., & Karim, S. A. A. (2011). Selecting wavelet transforms model in forecasting financial time series data based on ARIMA Model. Applied Mathematical Sciences, 5(7), 315–326. Retrieved from http://m-hikari.com/ams/ams-2011/ams-5-8-2011/alwadiAMS5-8-2011.pdf

Arshad, F. M., & Ghaffar, R. A. (1986). Crude Palm Oil Price Forecasting: Box-Jenkins Approach, Pertanika, 9(3), 359–367.

Assis, K., Amran, A., Remali, Y., & Affendy, H. (2010). A comparison of univariate time series methods for forecasting cocoa bean prices. Trends in Agricultural Economics. Retrieved from http://www.researchersworld. com/vol1/Paper_7.pdf

Chen, B.-J., Chang, M.-W., & Lin, C.-J. (2004). Load forecasting using support vector machines: A study on EUNITE Competition 2001. IEEE Transactions on Power Systems, 19(4), 1821–1830. https://doi.org/ 10.1109/TPWRS.2004.835679

Deo, R. C., Wen, X., & Qi, F. (2016). A wavelet-coupled support vector machine model for forecasting global incident solar radiation using limited meteorological dataset. Applied Energy, 168, 568–593. https://doi.org/ 10.1016/j.apenergy.2016.01.130

Drucker, H., Burges, C. J. C., Kaufman, L., Smola, A., & Vapnik, V. (1997). Support vector regression machines. Advances in Neural Information Processing Systems, 1, 155–161. https://doi.org/

Drucker, H., Wu, D., & Vapnik, V. N. (1999). Support vector machines for spam categorization. IEEE Transactions on Neural Networks, 10(5), 1048–1054. https://doi.org/10.1109/72.788645

Eynard, J., Grieu, S., & Polit, M. (2011). Wavelet-based multi-resolution analysis and artificial neural networks for forecasting temperature and thermal power consumption. Engineering Applications of Artificial Intelligence, 24(3), 501–516. https://doi.org/10.1016/j.engappai. 2010.09.003

Gençay, R., Whitcher, B., & Selçuk, F. (2002). An Introduction to Wavelets and other Filtering Methods in Finance and Economics. San Diego, California, USA: Academic Press.

Hamid, M. H., & Shabri, A. (2017). Wavelet regression model in forecasting crude oil price, AIP Conference Proceedings, 1842, 030019. https://doi.org/10.1063/1.4982857

Hsieh, T.-J., Hsiao, H.-F., & Yeh, W.-C. (2011). Forecasting stock markets using wavelet transforms and recurrent neural networks: An integrated system based on artificial bee colony algorithm. Applied Soft Computing, 11(2), 2510–2525. https://doi.org/10.1016/j.asoc.2010.09.007

Hsu, C.-W., Chang, C.-C., & Lin, C.-J. (2003). A practical guide to support vector classification. 1–16.

Jammazi, R., Lahiani, A., & Nguyen, D. K. (2015). A wavelet-based nonlinear ARDL model for assessing the exchange rate pass-through to crude oil prices. Journal of International Financial Markets, Institutions and Money, 34, 173–187. https://doi.org/10.1016/j.intfin.2014.11.011

Karia, A. A., Bujang, I., & Ahmad, I. (2013). Forecasting crude palm oil prices using artificial intelligence approach. American Journal of Operatins Research, 3(2013), 259–267.

Khamis, A., & Wahab, N. A. (2016). Comparative study on predicting crude palm oil prices using regression and neural network models, International Journal of Science and Technology, 5(3), 88–94.

Khandelwal, I., Adhikari, R., & Verma, G. (2015). Time series forecasting using hybrid ARIMA and ANN models based on DWT decomposition. Procedia Computer Science, 48, 173–179. https://doi.org/10.1016/j.procs. 2015.04.167

Khin, A. A., Mohamed, Z., Malarvizhi, C. A. N., & Thambiah, S. (2013). Price forecasting methodology of the Malaysian palm oil market. The Internation Journal of Applied Economics and Finance, 7(1), 23–36. https://doi.org/10.3923/ijaef.2013.23.36

Kim, K.-J. (2003). Financial time series forecasting using support vector machines, Neurocomputing, 55(1-2), 307–319. https://doi.org/10.1016/ S0925-2312(03)00372-2

Kisi, O., & Cimen, M. (2011). A wavelet-support vector machine conjunction model for monthly streamflow forecasting. Journal of Hydrology, 399(1–2), 132–140. https://doi.org/10.1016/j.jhydrol.2010.12.041

Lippmann, R. (1987). An introduction to computing with neural nets. IEEE ASSP Magazine, (April), 4(2) 4-22. https://doi.org/ 10.1109/MASSP.1987.1165576

Liu, F., & Fan, M. (2006). A hybrid support vector machines and discrete wavelet transform model in futures price forecasting In: Wang J., Yi

Z., Zurada J.M., Lu BL., Yin H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3973. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11760191_71

Liu, X., Zhu, Y., Zhang, Y., & Wang, X. (2011). Prediction based on wavelet transform and support, In: Liu C., Chang J., Yang A. (eds) Information Computing and Applications. ICICA 2011. Communications in Computer and Information Science, vol. 243. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27503-6_85

Lütkepohl, H., & Xu, F. (2009). The role of the log transformation in forecasting economic variables. Empirical Economics, 42(3), 619-638.

Mallat, S. (1989). A theory for multiresolution signal decomposition: the wavelet representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 11(7), 674–693. https://doi.org/10.1109/ 34.192463

May, R., Dandy, G., & Maier, H. (2011). Review of input variable selection methods for artificial neural networks. In: Suzuki, K. (ed.), Artificial Neural Networks - Methodological Advances and Biomedical Applications, InTech. https://doi.org/10.5772/644

Min, J. H., & Lee, Y. (2005). Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters, Expert Systems with Applications, 28(4), 603–614. https://doi.org/10.1016/j.eswa. 2004.12.008

Nochai, R., & Nochai, T. (2006). ARIMA Model for forecasting oil. Proceedings of the 2nd IMT-GT Regional Conference on Mathematics, Statistics and Application, Penang, Malaysia, 13-15. Retrieved from http://web.vu.lt/ef/v.karpuskiene/files/2015/10/Arima-Palm-OIL-Price.pdf

Noori, R., Khakpour, A., Omidvar, B., & Farokhnia, A. (2010). Comparison of ANN and principal component analysis-multivariate linear regression models for predicting the river flow based on developed discrepancy ratio statistic. Expert Systems with Applications, 37(8), 5856–5862. https://doi.org/10.1016/j.eswa.2010.02.020

Nor, A. H. S. M., Sarmidi, T., & Hosseinidoust, E. (2014). Forecasting of palm oil price in Malaysia using linear and nonlinear methods, AIP Conference Proceedings 1613(1), 138–152. https://doi.org/10.1063/1.4894340

Ramesh, B. N., & Arulmozhivarman, P. (2013). Improving forecast accuracy of wind speed using wavelet transform and neural networks. Journal of Electrical Engineering and Technology, 8(3), 559–564. https://doi.org/10.5370/JEET.2013.8.3.559

Rumelhart, D. E., McClelland, J. L., & PDP Research Group. (1986). Parallel distributed processing: Explorations in the microstructure cognition. Psychological and biological models. Cambridge: MIT Press.

Shabri, A., & Samsudin, R. (2014). Daily crude oil price forecasting using hybridizing wavelet and artificial neural network model, Mathematical Problems in Engineering, 2014, Article No. 201402. http://dx.doi.org/ 10.1155/2014/201402

Shamsudin, M. N., & Arshad, F. M. (1999). Short term forecasting of Malaysian crude palm oil prices, 1–12. Retrieved from http://econ.upm.edu.my/ ~fatimah/pipoc.html

Shamsudin, M. N., Mohamed, Z. A., & Arshad, F. M. (1988). Selected factors affecting palm oil price. Malaysian Journal of Agriculture Economics, 5, 20–29.

Stolojescu, C., Railean, I., Moga, S., & Isar, A. (2010). Comparison of wavelet families with application to WiMAX traffic forecasting. Proceedings of the International Conference on Optimisation of Electrical and Electronic Equipment, OPTIM, 932–937. https://doi.org/10.1109

/OPTIM.2010. 5510403

Tang, L. B., Tang, L. X., & Sheng, H. Y. (2009). Forecasting volatility based on wavelet support vector machine. Expert Systems with

Applications, 36(2 PART 2), 2901–2909. https://doi.org/10.1016/j.eswa.2008.01.047

Tang, Z., & Fishwick, P. A. (1993). Feedforward neural nets as models for time series forecasting. INFORMS Journal on Computing, 5(4), 374–385. https://doi.org/10.1287/ijoc.5.4.374

Tay, F. E. H., & Cao, L. (2001). Application of support vector machines in ÿnancial time series forecasting, Omega 29(4), 309–317.

Wong, F. S. (1991). Time series forecasting using backpropagation neural networks. Neurocomputing, 2(4), 147–159. https://doi.org/10.1016


Wu, C. H., Ho, J. M., & Lee, D. T. (2004). Travel-time prediction with support vector regression. IEEE Transactions on Intelligent Transportation Systems, 5(4), 276–281. https://doi.org/10.1109/TITS.2004.837813

Yang, M., Sang, Y. F., Liu, C., & Wang, Z. (2016). Discussion on the choice of decomposition level for wavelet based hydrological time series modeling. Water (Switzerland), 8(5), 1–11. https://doi.org/ 10.3390/w8050197

Zhang, G., Eddy Patuwo, B., & Y. Hu, M. (1998). Forecasting with artificial neural networks: The state of the art. International Journal of Forecasting, 14(1), 35–62. https://doi.org/10.1016/S0169-2070(97)00044-7