A novel hybrid optimization ensemble learning approach for energy futures price forecasting

Article type: Research Article

Authors: Zhan, Linjie^a | Tang, Zhenpeng^{a; b; *}

Affiliations: [a] School of Economics and Management, Fuzhou University, Fuzhou, Fujian, PR China | [b] School of Economics and Management, Fujian Agriculture and Forestry University, Fuzhou, Fujian, PR China

Correspondence: [*] Corresponding author. Zhenpeng Tang. E-mail: [email protected].

Abstract: Effective energy futures price prediction is an important work in the energy market. However, the existing research on the application of “decomposition-prediction” framework still has shortcomings in noise processing and signal reconstruction. In view of this, this paper first uses PSO to optimize VMD to improve the effectiveness of single decomposition, and further uses SGMD to capture the remaining key information after extracting low-frequency modal components by using PSO-VMD technology. Further, combined with LSTM to predict each component, a new PSO-VMD-SGMD-LSTM hybrid model is innovatively constructed. The empirical research results based on the real energy market transaction price show that compared with the benchmark model, the hybrid model proposed in this paper has obvious forecasting advantages in different forecasting scenarios.

Keywords: Energy futures price forecast, secondary decomposition technique, long short term memory

DOI: 10.3233/JIFS-236019

Journal: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6697-6713, 2024