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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

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• TITLE: Forecasting on Crude Palm Oil Prices Using Artificial Intelligence Approaches

  AUTHORS: Abdul Aziz Karia, Imbarine Bujang, Ismail Ahmad

  KEYWORDS: Crude Palm Oil Prices; Neuro Fuzzy; Neural Networks; Fractionally Integrated; Forecast

  JOURNAL NAME: American Journal of Operations Research, Vol.3 No.2, March 28, 2013

  ABSTRACT: 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.