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Trend- and Periodicity-Trait-Driven Gasoline Demand Forecasting

Author

Listed:
  • Jindai Zhang

    (School of Economics and Management, Harbin Engineering University, Harbin 150001, China)

  • Jinlou Zhao

    (School of Economics and Management, Harbin Engineering University, Harbin 150001, China)

Abstract

In order to make reasonable production-sales-stock decision-making for gasoline production enterprises, it is necessary to make an accurate prediction of the gasoline demand. However, gasoline demand is often affected by many factors, which makes it very difficult to predict. Therefore, this paper tries to construct a trend- and periodicity-trait-driven decomposition-ensemble forecasting model in terms of trend and periodicity characteristics of gasoline demand data. In order to verify the effectiveness of the proposed model, the demand data of a typical gasoline product-93# gasoline in China, is used. The empirical results show that the proposed trend- and periodicity-trait-driven decomposition-ensemble forecasting model can achieve better prediction results than the single models, indicating that the proposed methodology can be used as a feasible solution to predict the gasoline demand series with trend and periodicity traits.

Suggested Citation

  • Jindai Zhang & Jinlou Zhao, 2022. "Trend- and Periodicity-Trait-Driven Gasoline Demand Forecasting," Energies, MDPI, vol. 15(10), pages 1-15, May.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:10:p:3553-:d:814143
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    References listed on IDEAS

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