All Title Author
Keywords Abstract

Forecasting on Crude Palm Oil Prices Using Artificial Intelligence Approaches

DOI: 10.4236/ajor.2013.32023, PP. 259-267

Keywords: Crude Palm Oil Prices, Neuro Fuzzy, Neural Networks, Fractionally Integrated, Forecast

Full-Text   Cite this paper   Add to My Lib


An accurate prediction of crude palm oil (CPO) prices is important especially when investors deal with ever-increasing risks and uncertainties in the future. Therefore, the applicability of the forecasting approaches in predicting the CPO prices is becoming the matter into concerns. In this study, two artificial intelligence approaches, has been used namely artificial neural network (ANN) and adaptive neuro fuzzy inference system (ANFIS). We employed in-sample forecasting on daily free-on-board CPO prices in Malaysia and the series data stretching from a period of January first, 2004 to the end of December 2011. The predictability power of the artificial intelligence approaches was also made in regard with the statistical forecasting approach such as the autoregressive fractionally integrated moving average (ARFIMA) model. The general findings demonstrated that the ANN model is superior compared to the ANFIS and ARFIMA models in predicting the CPO prices.


[1]  O. Kisi, J. Shiri and B. Nikoofar, “Forecasting Daily Lake Levels Using Artificial Intelligence Approaches,” Computers & Geosciences, Vol. 41, 2012, pp. 169-180. doi:10.1016/j.cageo.2011.08.027
[2]  M. Firat and M. Gungor, “Hydrological Time-Series Modelling Using An Adaptive Neuro-Fuzzy Inference System,” Hydrological Processes, Vol. 22, No. 13, 2008, pp. 2122-2132. doi:10.1002/hyp.6812
[3]  B. Samanta, “Prediction of Chaotic Time Series Using Computational Intelligence,” Expert System with Applications, Vol. 38, No. 9, 2011, pp. 11406-11411. doi:10.1016/j.eswa.2011.03.013
[4]  J. J. Flores, M. Graff and H. Rodriguez, “Evolutive Design of ARMA and ANN Models for Time Series Forecasting,” Renewable Energy, Vol. 44, 2012, pp. 225-230. doi:10.1016/j.renene.2012.01.084
[5]  W. C. Wang, K. W. Chau, C. T. Cheng and L. Qiu, “A Comparison of Performance of Several Artificial Intelligence Methods for Forecasting Monthly Discharge Time Series,” Journal of Hydrology, Vol. 374, No. 3-4, 2009, pp. 294-306. doi:10.1016/j.jhydrol.2009.06.019
[6]  A.A. Karia and I. Bujang, “Progress Accuracy of CPO Price Prediction: Evidence from ARMA Family and Artificial Neural Network Approach,” International Research Journal of Finance and Economics, No. 64, 2011, pp. 66-79.
[7]  S. BuHamra, N. Smaoui and M. Gabr, “The Box-Jenkins Analysis and Neural Networks: Prediction and Time Series Modelling,” Applied Mathematical and Modelling, Vol. 27, No. 10, 2003, pp. 805-815. doi:10.1016/S0307-904X(03)00079-9
[8]  S.-H. Chen, Y.-H. Lin, L.-C. Chang and F.-J. Chang, “The Strategy of Building a Flood Forecast Model by Neuro-Fuzzy Network,” Hydrological Processes, Vol. 20, No. 7, pp. 1525-1540. doi:10.1002/hyp.5942
[9]  C. P. Kurian, V. I. George, J. Bhat and R. S. Aithal, “ANFIS Model for the Time Series Prediction of Interior Daylight Illuminance,” AIML Journal, Vol. 6, No. 3, 2006, pp. 35-40.
[10]  A. Talei, L. H. C. Chua and T. S. W. Wong, “Evaluation of Rainfall and Discharge Inputs Used by Adaptive Network-Based Fuzzy Inference System (ANFIS) in Rainfall-Runoff Modelling,” Journal of Hydrology, Vol. 391, No. 3-4, 2010, pp. 248-262. doi:10.1016/j.jhydrol.2010.07.023
[11]  Z. M. Yunos, S. M. Shamsuddin and R. Sallehuddin, “Data Modeling for Kuala Lumpur Composite Index with ANFIS,” 2nd Asia International Conference on Modelling & Simulation, 2008, pp. 609-614.
[12]  L. Naderloo, R. Alimardani, M. Omid, F. Sarmadian, P. Javadikia, M. Y. Torabi and F. Alimardani, “Application of ANFIS to Predict Crop Yield Based on Different Energy Inputs,” Measurement, Vol. 45, No. 6, 2012, pp. 1406-1413. doi:10.1016/j.measurement.2012.03.025
[13]  R. Hossain, A. M. T. Oo and A. B. M. S. Alia, “Historical Weather Data Supported Hybrid Renewable Energy Forecasting Using Artificial Neural Network (ANN),” Energy Procedia, Vol. 14, 2012, pp. 1035-1040. doi:10.1016/j.egypro.2011.12.1051
[14]  M. Mohandes, S. Rehman and S. M. Rahman, “Estimation of Wind Speed Profile using Adaptive Neuro-Fuzzy Inference System (ANFIS),” Applied Energy, Vol. 88, No. 11, 2011, pp. 4024-4032. doi:10.1016/j.apenergy.2011.04.015
[15]  A. Abraham and B. Nath, “A Neuro-Fuzzy Approach for Modelling Electricity Demand in Victoria,” Applied Soft Computing, Vol. 1, No. 2, 2001, pp. 127-138. doi:10.1016/S1568-4946(01)00013-8
[16]  M. Buragohain and C. Mahanta, “A Novel Approach for ANFIS Modelling Based on Full Factorial Design,” Applied Soft Computing, Vol. 8, No. 1, 2008, pp. 609-625. doi:10.1016/j.asoc.2007.03.010
[17]  T. V. T. Duy, Y. Sato and Y. Inoguchi, “Improving Accuracy of Host Load Predictions on Computational Grids by Artificial Neural Networks,” IEEE International Symposium on Parallel & Distributed Processing (IPDPS 2009), Rome, 23-29 May 2009, pp. 23-29. doi:10.1109/IPDPS.2009.5160878
[18]  C. Hamzacebi, “Improving Artificial Neural Networks’ Performance in Seasonal Time Series Forecasting,” Information Sciences, Vol. 178, No. 23, 2008, pp. 4550- 4559. doi:10.1016/j.ins.2008.07.024
[19]  I. Rojas, O. Valenzuela, F. Rojas, A. Guillen, L. J. Herrera, H. Pomares, L. Marquez and M. Pasadas, “Soft-Computing Techniques and ARMA Model for Time Series Prediction,” Neurocomputing, Vol. 71, No. 4-6, 2008, pp. 519-537. doi:10.1016/j.neucom.2007.07.018
[20]  R. Sallehuddin, S. M. Shamsuddin, S. Z. Hashim and A. Abraham, “Forecasting Time Series Data Using Hybrid Grey Relational Artificial Neural Network and Autoregressive Integrated Moving Average Model,” Neural Network World, Vol. 6, No. 7, 2009, pp. 573-605.
[21]  A. L. S. Maia, F. A. T. Carvalho and T. B. Ludermir, “Forecasting Models for Interval-Valued Time Series,” Neurocomputing, Vol. 71, No. 16-18, 2008, pp. 3344- 3352. doi:10.1016/j.neucom.2008.02.022
[22]  H. R. Maier and G. C. Dandy, “Neural Network Models for Forecasting Univariate Time Series,” Water Resource Research, Vol. 32, No. 4, 1996, pp. 1013-1022. doi:10.1029/96WR03529
[23]  M. Khashei, M. Bijari and G. A. R. Ardali, “Improvement of Auto-Regressive Integrated Moving Average Models using Fuzzy Logic and Artificial Neural Networks (ANNs),” Neurocomputing, Vol. 72, No. 4-6, 2009, pp. 956-967. doi:10.1016/j.neucom.2008.04.017
[24]  G. P. Zhang, B. E. Patuwo and M. Y. Hu, “A Simulation Study of Artificial Neural Networks for Nonlinear Time-Series Forecasting,” Computer & Operations Research, Vol. 28, No. 4, 2001, pp. 381-396. doi:10.1016/S0305-0548(99)00123-9
[25]  T. Y. Kim, K. J. Oh, C. Kim and J. D. Do, “Artificial Neural Networks for Non-Stationary Time Series,” Neurocomputing, Vol. 61, 2004, pp. 439-447. doi:10.1016/j.neucom.2004.04.002
[26]  W. Y. Hsu, “EEG-Based Motor Imagery Classification using Neuro-Fuzzy Prediction and Wavelet Fractal Features,” Journal of Neuroscience Methods, Vol. 189, No. 2, 2010, pp. 295-302. doi:10.1016/j.jneumeth.2010.03.030
[27]  C. Vairappan, H. Tamura, S. Gao and Z. Tang, “Batch Type Local Search-Based Adaptive Neuro-Fuzzy Inference System (ANFIS) with Self-Feedbacks for Time-Series Prediction,” Neurocomputing, Vol. 72, No. 7-9, 2009, pp. 1870-1877. doi:10.1016/j.neucom.2008.05.010
[28]  F. J. Chang and Y. T. Chang, “Adaptive Neuro-Fuzzy Inference System for Prediction of Water Level in Reservoir,” Advances in Water Resources, Vol. 29, No. 1, 2006, pp. 1-10. doi:10.1016/j.advwatres.2005.04.015
[29]  L. C. Chang and F. J. Chang, “Intelligent Control for Modelling of Real-Time Reservoir Operation,” Hydrological Processes, Vol. 15, No. 9, 2001, pp. 1621-1634. doi:10.1002/hyp.226
[30]  J. R. Chang, L. Y. Wei and C. H. Cheng, “A Hybrid ANFIS Model Based on AR and Volatility for TAIEX Forecasting,” Applied Soft Computing, Vol. 11, No. 1, 2011, pp. 1388-1395. doi:10.1016/j.asoc.2010.04.010
[31]  Y. L. Loukas, “Adaptive Neuro-Fuzzy Inference System: An Instant and Architecture-Free Predictor for Improved QSAR Studies,” Journal of Medicinal Chemistry, Vol. 44, No. 17, 2001, pp. 2772-2783. doi:10.1021/jm000226c
[32]  M. A. Boyacioglu and D. Avci, “An Adaptive Network-Based Fuzzy Inference System (ANFIS) for the Prediction of Stock Market Return: The Case of the Istanbul Stock Exchange,” Expert System with Applications, Vol. 37, No. 12, 2010, pp. 7908-7912. doi:10.1016/j.eswa.2010.04.045
[33]  I. Malekmohamadi, M. R. B. Lari, R. Kerachian, M. R. Nikoo and M. Fallahnia, “Evaluating the Efficacy of SVMs, BNs, ANNs, and ANFIS in Wave Height Prediction,” Ocean Engineering, Vol. 38, No. 2-3, 2011, pp. 487-497. doi:10.1016/j.oceaneng.2010.11.020
[34]  M. Wei, B. Bai, A. H. Sung, Q. Liu, J. Wang and M. E. Cather, “Predicting Injection Profiles Using ANFIS,” Information Sciences, Vol. 177, No. 20, 2007, pp. 4445- 4461. doi:10.1016/j.ins.2007.03.021
[35]  C. M. Bishop, “Neural Networks for Pattern Recognition,” Clarendon Press, Oxford, 1995.
[36]  P. Werbos, “Beyond Regression: New Tools for Prediction and Analysis in the Behavioral Sciences,” Ph.D. Dissertation, Harvard University, Cambridge, 1974.
[37]  J. S. R. Jang, “ANFIS: Adaptive-Network Based Fuzzy Inference System,” IEEE Transactions on Systems, Man, and Cybernetics, Vol. 23, No. 3, 1993, pp. 665-685. doi:10.1109/21.256541
[38]  J.A. Doornik and M. Ooms, “Inference and Forecasting for ARFIMA Models with an Application to US and UK Inflation,” Studies in Nonlinear Dynamics & Econometrics, Vol. 8, No. 2, 2004, pp. 1-23. doi:10.2202/1558-3708.1218
[39]  F. X. Diebold and R. S. Mariano, “Comparing Predictive Accuracy,” Journal of Business & Economics Statistics, Vol. 13, No. 3, 1995, pp. 3-25.
[40]  M. E. Keskin, O. Terzi and D. Taylan, “Fuzzy Logic Model Approaches to Daily Pan Evaporation Estimation in Western Turkey,” Hydrological Sciences Journal, Vol. 49, No. 6, 2004, pp. 1001-1010. doi:10.1623/hysj.49.6.1001.55718


comments powered by Disqus