Forecasting on Crude Palm Oil Prices Using Artificial Intelligence Approaches

Abdul Aziz Karia · Imbarine Bujang · Ismail Ahmad

29 citations0/0 files storedAmerican Journal of Operations Research

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Stock Market Forecasting Methods Economics of Agriculture and Food Markets Forecasting Techniques and Applications

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.

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1977Time Series Analysis: Forecasting and Control 1994Comparing Predictive Accuracy 1995Comparing Predictive Accuracy 2010An Adaptive Network-Based Fuzzy Inference System (ANFIS) for the prediction of stock market return: The case of the Istanbul Stock Exchange 2001A simulation study of artificial neural networks for nonlinear time-series forecasting 2008Improvement of Auto-Regressive Integrated Moving Average models using Fuzzy logic and Artificial Neural Networks (ANNs)2008Improving artificial neural networks’ performance in seasonal time series forecasting 2007Soft-computing techniques and ARMA model for time series prediction

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