Trend- and Periodicity-Trait-Driven Gasoline Demand Forecasting

Jindai Zhang and Jinlou Zhao
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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

Energies, 2022, vol. 15, issue 10, 1-15

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.

Keywords: trend trait; periodicity trait; decomposition-ensemble forecasting; gasoline demand prediction (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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