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

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...

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Bibliographic Details
Published in:Journal of intelligent & fuzzy systems 2024-03, Vol.46 (3), p.6697-6713
Main Authors: Zhan, Linjie, Tang, Zhenpeng
Format: Article
Language:English
Subjects:
Decomposition
Ensemble learning
Forecasting
Mathematical models
Signal reconstruction
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Summary: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.
ISSN:1064-1246
1875-8967
DOI:10.3233/JIFS-236019