An effective rolling decomposition-ensemble model for gasoline consumption forecasting

Lean Yu (), Yueming Ma and Mengyao Ma

Energy, 2021, vol. 222, issue C

Abstract: In this paper, an effective rolling decomposition-ensemble model is proposed for quarterly gasoline consumption forecasting in China. In this model, three steps, data decomposition, component prediction and ensemble output, are involved. In the data decomposition, wavelet decomposition and ensemble empirical mode decomposition are used due to few assumptions and excellent performance. In the component prediction, support vector regression is adopted due to the global approximation capability for data scarcity issue. In the ensemble output, the simple addition strategy is used for final aggregation. In order to solve the illusion of high prediction accuracy caused by the decomposition of the test dataset, the rolling decomposition and forecasting mechanism are adopted in this methodology. For illustration and verification purpose, 30 provincially quarterly gasoline consumption data in China are used. The experimental results demonstrate the effectiveness and robustness of the proposed rolling decomposition-ensemble model for gasoline consumption forecasting in terms of the accuracy of level and directional prediction.

Keywords: Gasoline consumption forecasting; Decomposition-ensemble model; Ensemble empirical mode decomposition; Wavelet decomposition; Support vector regression; Rolling mechanism (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (15)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:222:y:2021:i:c:s0360544221001183

DOI: 10.1016/j.energy.2021.119869

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