A Hybrid Model for Carbon Price Forecasting Based on Improved Feature Extraction and Non-Linear Integration

Yingjie Zhu (), Yongfa Chen (), Qiuling Hua (), Jie Wang, Yinghui Guo, Zhijuan Li, Jiageng Ma and Qi Wei
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Yingjie Zhu: School of Mathematics and Statistics, Changchun University, Changchun 130022, China
Yongfa Chen: School of Mathematics and Statistics, Changchun University, Changchun 130022, China
Qiuling Hua: Economics School, Jilin University, Changchun 130012, China
Jie Wang: School of Mathematics and Statistics, Changchun University, Changchun 130022, China
Yinghui Guo: School of Mathematics and Statistics, Changchun University, Changchun 130022, China
Zhijuan Li: School of Mathematics and Statistics, Changchun University, Changchun 130022, China
Jiageng Ma: School of Mathematics and Statistics, Changchun University, Changchun 130022, China
Qi Wei: Graduate School, Changchun University, Changchun 130022, China

Mathematics, 2024, vol. 12, issue 10, 1-26

Abstract: Accurately predicting the price of carbon is an effective way of ensuring the stability of the carbon trading market and reducing carbon emissions. Aiming at the non-smooth and non-linear characteristics of carbon price, this paper proposes a novel hybrid prediction model based on improved feature extraction and non-linear integration, which is built on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), fuzzy entropy (FuzzyEn), improved random forest using particle swarm optimisation (PSORF), extreme learning machine (ELM), long short-term memory (LSTM), non-linear integration based on multiple linear regression (MLR) and random forest (MLRRF), and error correction with the autoregressive integrated moving average model (ARIMA), named CEEMDAN-FuzzyEn-PSORF-ELM-LSTM-MLRRF-ARIMA. Firstly, CEEMDAN is combined with FuzzyEn in the feature selection process to improve extraction efficiency and reliability. Secondly, at the critical prediction stage, PSORF, ELM, and LSTM are selected to predict high, medium, and low complexity sequences, respectively. Thirdly, the reconstructed sequences are assembled by applying MLRRF, which can effectively improve the prediction accuracy and generalisation ability. Finally, error correction is conducted using ARIMA to obtain the final forecasting results, and the Diebold–Mariano test (DM test) is introduced for a comprehensive evaluation of the models. With respect to carbon prices in the pilot regions of Shenzhen and Hubei, the results indicate that the proposed model has higher prediction accuracy and robustness. The main contributions of this paper are the improved feature extraction and the innovative combination of multiple linear regression and random forests into a non-linear integrated framework for carbon price forecasting. However, further optimisation is still a work in progress.

Keywords: carbon price prediction; hybrid model; feature extraction; non-linear integration; error correction (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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