Machine learning approach for crude oil price prediction with Artificial Neural Networks-Quantitative (ANN-Q) model

S. N. Abdullah, X. Zeng

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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    Abstract

    The volatility of crude oil market and its chain effects to the world economy augmented the interest and fear of individuals, public and private sectors. Previous statistical and econometric techniques used for prediction, offer good results when dealing with linear data. Nevertheless, crude oil price series deal with high nonlinearity and irregular events. The continuous usage of statistical and econometric techniques for crude oil price prediction might demonstrate demotions to the prediction performance. Machine Learning and Computational Intelligence approach through combination of historical quantitative data with qualitative data from experts' view and news is a remedy proposed to predict this. This paper will discuss the first part of the research, focusing on to (i) the development of Hierarchical Conceptual (HC) model and (ii) the development of Artificial Neural Networks-Quantitative (ANN-Q) model. © 2010 IEEE.
    Original languageEnglish
    Title of host publicationProceedings of the International Joint Conference on Neural Networks|Proc Int Jt Conf Neural Networks
    PublisherIEEE
    ISBN (Print)9781424469178
    DOIs
    Publication statusPublished - 2010
    Event2010 6th IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 International Joint Conference on Neural Networks, IJCNN 2010 - Barcelona
    Duration: 1 Jul 2010 → …

    Conference

    Conference2010 6th IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 International Joint Conference on Neural Networks, IJCNN 2010
    CityBarcelona
    Period1/07/10 → …

    Keywords

    • machine learning, artificial neural network, text-mining, prediction, forecasting, crude oil, oil

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