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A novel method based on numerical fitting for oil price trend forecasting

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  • Zhao, Lu-Tao
  • Wang, Yi
  • Guo, Shi-Qiu
  • Zeng, Guan-Rong

Abstract

Crude oil plays an important role in various production processes throughout the world. Changes in oil prices affect economic development, social stability and the residents in a country. Based on a full consideration of the fluctuations in oil prices and discovering the future dynamic trend of oil prices from historical trend features, a vector trend forecasting method that defines the vector trend over a specified length of time and predicts future price trends of crude oil based on the vector trend series of historical crude oil prices is proposed. The core idea behind vector trend forecasting method is to construct the vector trend by using the parameters of a fitting function within a specified interval. Based on the previous linear regression, a variety of non-linear morphological features were selected for numerical fitting, avoiding unity in the price trend and stochastic factors that are difficult to solve in forecast price trends. Combined with an econometric model composed of simultaneous equations, making full use of the characteristic information of the historical vector trend makes the definition of the trend more reasonable and the prediction more accurate. The empirical results show that the percentage error of the fitted real oil price in the vector trend is less than 4%. At the same time, it is found that the numerical fitting result using exponential and quadratic functions are better than that with general linear regression. The forecasting error of the trend is no more than 5%, which is lower than the traditional forecasting accuracy of econometrics and statistical learning models. This study can provide suggestions for oil market investors to understand trends in oil prices and for their investment decision-making, and provide reference for policy makers to stabilize economic markets and people’s life.

Suggested Citation

  • Zhao, Lu-Tao & Wang, Yi & Guo, Shi-Qiu & Zeng, Guan-Rong, 2018. "A novel method based on numerical fitting for oil price trend forecasting," Applied Energy, Elsevier, vol. 220(C), pages 154-163.
    • Handle: RePEc:eee:appene:v:220:y:2018:i:c:p:154-163
    DOI: 10.1016/j.apenergy.2018.03.060
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    8. Abdollahi, Hooman & Ebrahimi, Seyed Babak, 2020. "A new hybrid model for forecasting Brent crude oil price," Energy, Elsevier, vol. 200(C).
    9. Lu-Tao Zhao & Guan-Rong Zeng & Ling-Yun He & Ya Meng, 2020. "Forecasting Short-Term Oil Price with a Generalised Pattern Matching Model Based on Empirical Genetic Algorithm," Computational Economics, Springer;Society for Computational Economics, vol. 55(4), pages 1151-1169, April.
    10. He, Huizi & Sun, Mei & Li, Xiuming & Mensah, Isaac Adjei, 2022. "A novel crude oil price trend prediction method: Machine learning classification algorithm based on multi-modal data features," Energy, Elsevier, vol. 244(PA).
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