Forecasting China’s Short-Term Energy Futures Price Using a Novel Secondary Decomposition-Optimized System

Energy futures price forecasting is challenging due to the nonlinear and fluctuant characteristics. Existing literature mainly uses decomposition and ensemble method which neglects the intrinsic mode function obtained by the first decomposition could be irregular and thus reduces the prediction accuracy. To fill the research gap, a novel secondary decomposition-optimized-KELM-ensemble forecasting system is proposed to perform short-term forecasting in this study, which synthesizes two-stage data decomposition method, Sparrow search optimization algorithm, and extreme learning machine with kernel. We test the method with two energy futures prices in China, demonstrating that both one-day and three-day ahead forecasting results obtained are more accurate and stable compared to existing models in the literature, such as BPNN (improved by 58.42% on one-day ahead and 56.44% on three-day ahead by MAE) and KELM (improved by 56.40% on one-day ahead and 49.04% on three-day ahead by MAE). Therefore, the forecasting system introduced in this paper can provide useful implications for both policy makers and financial practitioners in the energy sector.