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A novel algorithm for prediction of crude oil price variation based on soft computing

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  • Ghaffari, Ali
  • Zare, Samaneh
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    Abstract

    In this paper a method based on soft computing approaches is developed to predict the daily variation of the crude oil price of the West Texas Intermediate (WTI). The predicted daily oil price variation is compared with the actual daily variation of the oil price and the difference is implemented to activate the learning algorithms. In order to reduce the effect of unpredictable short term disturbances, a data filtering algorithm is used. In this paper, the prediction is called "true" if the predicted variation of the oil price has the same sign as the actual variation, otherwise the prediction is "false". It is shown that for several randomly selected durations, the true prediction is considerably higher than the result of most recent published prediction algorithms. To ensure the accuracy and reliability of the algorithm, several on line predictions are executed during one complete month. The on line results indicate that the true predictions are consistently the same percentage for periods of one month.

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    Bibliographic Info

    Article provided by Elsevier in its journal Energy Economics.

    Volume (Year): 31 (2009)
    Issue (Month): 4 (July)
    Pages: 531-536

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    Handle: RePEc:eee:eneeco:v:31:y:2009:i:4:p:531-536

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    Web page: http://www.elsevier.com/locate/eneco

    Related research

    Keywords: Soft computing Forecast Crude oil price;

    References

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    1. Yu, Lean & Wang, Shouyang & Lai, Kin Keung, 2008. "Forecasting crude oil price with an EMD-based neural network ensemble learning paradigm," Energy Economics, Elsevier, vol. 30(5), pages 2623-2635, September.
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    Cited by:
    1. Godarzi, Ali Abbasi & Amiri, Rohollah Madadi & Talaei, Alireza & Jamasb, Tooraj, 2014. "Predicting oil price movements: A dynamic Artificial Neural Network approach," Energy Policy, Elsevier, vol. 68(C), pages 371-382.
    2. Wang, Tao & Yang, Jian, 2010. "Nonlinearity and intraday efficiency tests on energy futures markets," Energy Economics, Elsevier, vol. 32(2), pages 496-503, March.
    3. Alvarez-Ramirez, Jose & Alvarez, Jesus & Solis, Ricardo, 2010. "Crude oil market efficiency and modeling: Insights from the multiscaling autocorrelation pattern," Energy Economics, Elsevier, vol. 32(5), pages 993-1000, September.
    4. Sueyoshi, Toshiyuki, 2010. "An agent-based approach equipped with game theory: Strategic collaboration among learning agents during a dynamic market change in the California electricity crisis," Energy Economics, Elsevier, vol. 32(5), pages 1009-1024, September.
    5. Xiong, Tao & Bao, Yukun & Hu, Zhongyi, 2013. "Beyond one-step-ahead forecasting: Evaluation of alternative multi-step-ahead forecasting models for crude oil prices," Energy Economics, Elsevier, vol. 40(C), pages 405-415.

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