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

题目	Forecasting China's Short-Term Energy Futures Price Using a Novel Secondary Decomposition-Optimized System
作者	Jiang, Zhe Zhang, Zili Zhang, Lin
作者单位	Peking Univ, Guanghua Sch Management, Beijing 100871, Peoples R China Harvest Fund Management Co Ltd, Beijing 100020, Peoples R China City Univ Hong Kong, Sch Energy & Environm, Kowloon, Tat Chee Ave, Hong Kong, Peoples R China
关键词：	EMPIRICAL MODE DECOMPOSITION EXTREME LEARNING-MACHINE CRUDE-OIL PRICE WAVELET TRANSFORM
时间：	2025年1月11日
出版者：	COMPUTATIONAL ECONOMICS
摘要	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.
URL	http://hdl.handle.net/20.500.11897/740370
ISSN	0927-7099
DOI	10.1007/s10614-024-10840-w
收录情况	SCI(E) SSCI