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Forecasting the Direction of Short-Term Crude Oil Price Changes with Genetic-Fuzzy Information Distribution

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  • Xinyu Wang
  • Kegui Chen
  • Xueping Tan

Abstract

This paper proposes a novel approach to the directional forecasting problem of short-term oil price changes. In this approach, the short-term oil price series is associated with incomplete fuzzy information, and a new fused genetic-fuzzy information distribution method is developed to process such a fuzzy incomplete information set; then a feasible coding method of multidimensional information controlling points is adopted to fit genetic-fuzzy information distribution to time series forecasting. Using the crude oil spot prices of West Texas Intermediate (WTI) and Brent as sample data, the empirical analysis results demonstrate that the novel fused genetic-fuzzy information distribution method statistically outperforms the benchmark of logistic regression model in prediction accuracy. The results indicate that this new approach is effective in direction accuracy.

Suggested Citation

  • Xinyu Wang & Kegui Chen & Xueping Tan, 2018. "Forecasting the Direction of Short-Term Crude Oil Price Changes with Genetic-Fuzzy Information Distribution," Mathematical Problems in Engineering, Hindawi, vol. 2018, pages 1-12, December.
    • Handle: RePEc:hin:jnlmpe:3868923
    DOI: 10.1155/2018/3868923
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