Comparison of Soft Computing Approaches for Prediction of Crude Oil Price

Authors

  • Lubna A.Gabralla
  • Ajith Abraham

Keywords:

machine learning, direct prediction models, predicting crude oil prices, Isotonic regression, SMOreg, Kstar, IBK ,ExtraTree, REPTree

Abstract

In this paper, we studied a vast array of machine learning approaches (ML). All are sound, robust techniques that are extremely applicable to practical prediction problems. To guarantee build successful machine learning model in predicting crude oil prices, we must apply practical steps for selecting best learning algorithm by running it over our data. We modeled the prediction process and analyzing the direct prediction models, which includes isotonic regression, SMOreg, Kstar, IBK ,ExtraTree, REPTree and several types of NNs includes FFN, RCN and RBF in previous articles . The purpose of this paper is to construct comparison among the previous direct models. Furthermore, the comparison of these algorithms is presented based on a root mean squared error (RMSE) and mean absolute error (MAE) to find out the best suitable approaches. We are confident that this study will be useful to researchers for the problem of predicting oil prices and similar problem.

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Published

2014-10-01

How to Cite

Lubna A.Gabralla, & Ajith Abraham. (2014). Comparison of Soft Computing Approaches for Prediction of Crude Oil Price. Journal of Network and Innovative Computing, 2, 13. Retrieved from https://cspub-jnic.org/index.php/jnic/article/view/82

Issue

Vol. 2 (2014)

Section

Original Article

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