An effective rolling decomposition-ensemble model for gasoline consumption forecasting

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. ? 2021 Elsevier Ltd. All rights reserved.

Automated summary

This paper proposes an effective rolling decomposition-ensemble model for quarterly gasoline consumption forecasting in China, involving data decomposition, component prediction, and ensemble output. By utilizing wavelet decomposition and support vector regression, the model addresses data scarcity issue and improves prediction accuracy.