Building a Decision Support System for Crude Oil Price Prediction using Bayesian Networks

Decision Support Systems are computer based systems that are aimed at assisting decision-makers in taking productive, agile, innovative and reputable decisions. This work presents a Decision Support System using Bayesian Network to predict crude oil price .Bayesian Network technology and its application in predicting crude oil price is presented. Price data obtained from the Central Bank of Nigeria was classed into High and Low cases to denote the upward and downward price movement in which information was revealed. The input data were used in this model to train the network and to validate its generalization ability in other to deliver the best prediction forecast. A linguistic prediction model which utilized the Bayesian Network whose aim was to integrate linguistic information into a quantitative prediction model was established. The results obtained from the linguistic model demonstrate that linguistic information adds value to oil price prediction

Orthogonal wavelet support vector machine for predicting crude oil prices

Previous studies mainly used radial basis, sigmoid, polynomial, linear, and hyperbolic functions as the kernel function for computation in the neurons of conventional support vector machine (CSVM) whereas orthogonal wavelet requires less number of iterations to converge than these listed kernel functions. We proposed an orthogonal wavelet support vector machine (OSVM) model for predicting the monthly prices of West Texas Intermediate crude oil prices. For evaluation purposes, we compared the performance of our results with that of the CSVM, and multilayer perceptron neural network (MLPNN). It was found to perform better than the CSVM, and the MLPNN. Moreover, the number of iterations, and time computational complexity of the OSVM model is less than that of CSVM, and MLPNN. Experimental results suggest that the OSVM is effective, robust, and can efficiently be used for crude oil price prediction. Our proposal has the potentials of advancing the prediction accuracy of crude oil prices, which makes it suitable for building intelligent decision support systems