Multi-step carbon price forecasting based on a new quadratic decomposition ensemble learning approach

Frontiers in Energy Research

Energy Research

Tingting Zhang¹  Zhenpeng Tang² 

[1] School of Economics and Management, Fujian Agriculture and Forestry University, Fuzhou, China;null;

关键词: carbon price;    quadratic decomposition technique;    VMD-CEEMDAN;    LSSVM-LSTM;    multi-step ahead forecasting;   

DOI  :  10.3389/fenrg.2022.991570

 received in 2022-07-11, accepted in 2022-09-14,  发布年份 2023

来源: Frontiers

【 摘 要 】

Numerous studies show that it is reasonable and effective to apply decomposition technology to deal with the complex carbon price series. However, the existing research ignores the residual term containing complex information after applying single decomposition technique. Considering the demand for higher accuracy of the carbon price series prediction and following the existing research path, this paper proposes a new hybrid prediction model VMD-CEEMDAN-LSSVM-LSTM, which combines a new quadratic decomposition technique with the optimized long short term memory (LSTM). In the decomposition part of the hybrid model, the original carbon price series is processed by variational mode decomposition (VMD), and then the residual term obtained by decomposition is further decomposed by complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN). In the prediction part of the hybrid model, least squares support vector machine (LSSVM) is introduced, and LSSVM-LSTM model is constructed to predict the components obtained by decomposition. The empirical research of this paper selects two different case data from the European Union emissions trading system (EU ETS) as samples. Taking the results of Case Ⅰ in the 1-step ahead forecasting scenario as an example, the prediction evaluation indexes eMAPE, eRMSE and R2 of the VMD-CEEMDAN-LSSVM-LSTM hybrid model constructed in this paper are 0.3087, 0.0921 and 0.9987 respectively, which are significantly better than other benchmark models. The empirical results confirm the superiority and robustness of the hybrid model proposed in this paper for carbon price forecasting.

【 授权许可】

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Copyright © 2023 Zhang and Tang.