ISSN: 2225-8329
Open access
The implementation of Artificial Intelligence (AI) towards the price prediction process is undoubtedly crucial as the importance of accurate price prediction has increased over the last few decades. This paper aims to predict the price of Crude Palm Oil Futures (FCPO) in Malaysia by using a single-layer feed-forward neural network called Extreme Learning Machine (ELM). Using daily time series data that consists of the opening, the high, low and closing price of FCPO ranging from the first of January 2010 to the end of December 2019. The data is trained and tested against the various number of hidden neurons and different activation functions such as sine, sigmoidal, triangular basis, and radial basis. It is found that the ideal number of hidden neurons is at 2 to 4. Besides that, the main result shows that the radial basis activation function is more suitable in the prediction of FCPO price as compared to other functions such as the sine, sigmoidal and triangular basis functions. The price predicted using the radial basis function has a higher testing and training accuracy as compared with other functions. Not only that, the time taken to test and train the ELM using the radial basis function is also lower as compared to others. The predictability of FCPO prices also disputes the weak form of the Efficient Market Hypothesis (EMH) which indicates that it is nearly impossible to predict future prices of any security using historical data.
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