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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