Forecasting of crude palm oil price using hybridizing wavelet and group method of data handling model
DOI:
https://doi.org/10.11113/mjfas.v13n4.735Abstract
Forecasting of Crude Palm Oil (CPO) is one of the most important and the largest vegetable oil traded in the world market. This study investigates the forecasting of Crude Palm Oil (CPO) price using a hybrid model of Group Method of Data Handling (GMDH) with wavelet decomposition. The original monthly data of CPO time series were decomposed into the spectral band. After that, these decomposed subseries were given as input time series data to GMDH model to forecast the CPO price of monthly time series data. The result performance of hybridized GMDH model is compared with the original GMDH model. The measurements results from the mean absolute error (MAE) and the root mean square error (RMSE) showed that the hybrid GMDH model with wavelet decomposition gives more accurate result of predictions compared with the original GMDH model.
References
Abdullah, R. 2011. World Palm Oil supply, demand, price and prospects: Focus on Malaysian and Indonesian Palm oil industries. Oil Palm Industry Economic Journal, 11(2): 13–25.
Anastasakis, L., L Anastasakis, N., Mort, and N Mort. 2001. The development of self-organization techniques in modelling: A review of the group method of data handling (GMDH). Group (813). Retrieved from http://eprints.whiterose.ac.uk/83130/
Badyalina, B. 2014. Streamflow estimation at ungauged site using wavelet group method of data handling in Peninsular Malaysia. International Journal of Mathematical Analysis, 8(11): 513–24.
Basheer, H., and Khamis, A. 2016. A Hybrid group method of data handling (GMDH) with the wavelet decomposition for time series forecasting. ARPN Journal of Engineering and Applied Sciences, 11(18): 10792–10800.
Cannas, B., Alessandra F., and Linda S. 2006. Data preprocessing for river flow forecasting using neural networks : Wavelet transforms and data partitioning. Physics and Chemistry of the Earth ,31: 1164–1171.
Dag, O. and Ceylan, Y. 2016. GMDH : An R Package for short term forecasting via GMDH-Type neural network algorithms. The R Journal, 8(2016): 379–86.
Danandeh Mehr, Ali, Ercan Kahya, and Ehsan Olyaie. 2013. Streamflow prediction using linear genetic programming in comparison with a neuro-wavelet technique. Journal of Hydrology, 505: 240–49.
Dash, P. K., Maya Nayak, M. R. Senapati, and I.W.C. Lee. 2007. Mining for similarities in time series data using wavelet-based feature vectors and neural networks. Engineering Applications of Artificial Intelligence, 20(2): 185–201.
Ivakhnenko, G.A., and Ivakhnenko, A.G. 1995. The review of problems solvable by algorithms of group method of data handling (GMDH). Pattern Recognition and Image Analysis, 5(4): 527–35.
Mohammadi, S., Fatimah, M. A., Bilash, K. B., and Abdulla, I. 2015. System dynamics analysis of the determinants of the malaysian palm oil price. American Journal of Applied Sciences, 12(5): 355–62.
Mottaghitalb, M. 1996. Genetic algorithms and group method of data handling-type neural networks applications in poultry science. In Roeva, O. (Ed.) Real-World Applications of Genetic Algorithms (pp 219-234). Croatia: In Tech.
Malaysian Palm Oil Board. 2014. Review Overview of the Malaysian Oil Palm Industry 2014. Malaysia : MPOB.
Nourani, V., Alami, M. T., and Aminfar, M. H. 2009. A combined neural-wavelet model for prediction of ligvanchai watershed precipitation. Engineering Applications of Artificial Intelligence, 22: 466–72.
Okkan, U., and Serbes, Z. A. 2013. The combined use of wavelet transform and black box models in reservoir inflow modeling. Journal of Hydrology and Hydromechanics, 61(2): 112–19.
Park, H. -S., and Pedrycz, W. 2007. Evolutionary design of hybrid self-organizing fuzzy polynomial neural networks with the aid of information granulation. Expert Systems with Application, 33: 830–46.
Rasillo-Calle, F., Pelkmans, L., and Walter, A. 2009. A Global overview of vegetable oils, with reference to biodiesel. IEA Bioenergy, Task 40 (June): 1–89.
Ratrout, N. T. 2014. Short-Term traffic flow prediction using group method data handling (GMDH)-Based Abductive Networks. Arabian Journal for Science and Engineering 39(2): 631–46.
Mallat, S. G. 1989. A Theory for multiresolution signal decomposition : The wavelet representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 11(7): 674–93.
Shabri, Ani, and Ruhaidah Samsudin. 2014. Daily Crude oil price forecasting using hybridizing wavelet and artificial neural network model. Mathematical Problems in Engineering, 2014: 1–10.
Shinohara, Yasuhide, Tadashi Dohi, and Shunji Osaki. 1999. Predictive Evaluation for software testing progress via GMDH networks. Electronics and Communications in Japan, 82(5): 22–28.
Sime Darby Plantation. 2014. Palm Oil Facts & Figures.
Strang, G. and Nguyen, T. 1996. Wavelet Toolbox. Wellesley-Cambridge Press.
Mungmee, P., Buaklom, P., Pungprasert, P., Sinlapaprechar, P., Suksa-ard, C., and Raweewan, M. 2013. A framework study of factors effecting crude palm oil price behavior in Thailand Morrakot Raweewan. Proceedings of the 4th International Conference on Engineering, Project, and Production Management (EPPM 2013), 23-25 October 2013. Bangkok, Thailand, 647–53.
United States Department of Agriculture (USDA). 2015. Report ISSN: 1949-1522 Released June 30, 2015, by the National Agricultural Statistics Service (NASS), Agricultural Statistics Board, United States Department of Agriculture (USDA).
