IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v12y2024i10p1428-d1389739.html
   My bibliography  Save this article

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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Yingjie Zhu & Yongfa Chen & Qiuling Hua & Jie Wang & Yinghui Guo & Zhijuan Li & Jiageng Ma & Qi Wei, 2024. "A Hybrid Model for Carbon Price Forecasting Based on Improved Feature Extraction and Non-Linear Integration," Mathematics, MDPI, vol. 12(10), pages 1-26, May.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:10:p:1428-:d:1389739
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/12/10/1428/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/12/10/1428/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Haoran Zhao & Sen Guo, 2023. "Carbon Trading Price Prediction of Three Carbon Trading Markets in China Based on a Hybrid Model Combining CEEMDAN, SE, ISSA, and MKELM," Mathematics, MDPI, vol. 11(10), pages 1-21, May.
    2. Liu, Haiying & Pata, Ugur Korkut & Zafar, Muhammad Wasif & Kartal, Mustafa Tevfik & Karlilar, Selin & Caglar, Abdullah Emre, 2023. "Do oil and natural gas prices affect carbon efficiency? Daily evidence from China by wavelet transform-based approaches," Resources Policy, Elsevier, vol. 85(PB).
    3. Easwaran Narassimhan & Kelly S. Gallagher & Stefan Koester & Julio Rivera Alejo, 2018. "Carbon pricing in practice: a review of existing emissions trading systems," Climate Policy, Taylor & Francis Journals, vol. 18(8), pages 967-991, September.
    4. Boyce, James K., 2018. "Carbon Pricing: Effectiveness and Equity," Ecological Economics, Elsevier, vol. 150(C), pages 52-61.
    5. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    6. Li, Jingmiao & Liu, Dehong, 2023. "Carbon price forecasting based on secondary decomposition and feature screening," Energy, Elsevier, vol. 278(PA).
    7. Zhao, Xin & Han, Meng & Ding, Lili & Kang, Wanglin, 2018. "Usefulness of economic and energy data at different frequencies for carbon price forecasting in the EU ETS," Applied Energy, Elsevier, vol. 216(C), pages 132-141.
    8. Wang, Jujie & Cui, Quan & He, Maolin, 2022. "Hybrid intelligent framework for carbon price prediction using improved variational mode decomposition and optimal extreme learning machine," Chaos, Solitons & Fractals, Elsevier, vol. 156(C).
    9. Lin, Boqiang & Jia, Zhijie, 2019. "Impacts of carbon price level in carbon emission trading market," Applied Energy, Elsevier, vol. 239(C), pages 157-170.
    10. Liu, Weiping & Wang, Chengzhu & Li, Yonggang & Liu, Yishun & Huang, Keke, 2021. "Ensemble forecasting for product futures prices using variational mode decomposition and artificial neural networks," Chaos, Solitons & Fractals, Elsevier, vol. 146(C).
    11. Zhou, Feite & Huang, Zhehao & Zhang, Changhong, 2022. "Carbon price forecasting based on CEEMDAN and LSTM," Applied Energy, Elsevier, vol. 311(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Li, Dan & Li, Yijun & Wang, Chaoqun & Chen, Min & Wu, Qi, 2023. "Forecasting carbon prices based on real-time decomposition and causal temporal convolutional networks," Applied Energy, Elsevier, vol. 331(C).
    2. Guo, Honggang & Wang, Jianzhou & Li, Zhiwu & Lu, Haiyan & Zhang, Linyue, 2022. "A non-ferrous metal price ensemble prediction system based on innovative combined kernel extreme learning machine and chaos theory," Resources Policy, Elsevier, vol. 79(C).
    3. Huang, Yumeng & Dai, Xingyu & Wang, Qunwei & Zhou, Dequn, 2021. "A hybrid model for carbon price forecastingusing GARCH and long short-term memory network," Applied Energy, Elsevier, vol. 285(C).
    4. Qin Lu & Jingwen Liao & Kechi Chen & Yanhui Liang & Yu Lin, 2024. "Predicting Natural Gas Prices Based on a Novel Hybrid Model with Variational Mode Decomposition," Computational Economics, Springer;Society for Computational Economics, vol. 63(2), pages 639-678, February.
    5. Zhikai Zhang & Yaojie Zhang & Yudong Wang & Qunwei Wang, 2024. "The predictability of carbon futures volatility: New evidence from the spillovers of fossil energy futures returns," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 44(4), pages 557-584, April.
    6. Zeyu Zhang & Xiaoqian Liu & Xiling Zhang & Zhishan Yang & Jian Yao, 2024. "Carbon Price Forecasting Using Optimized Sliding Window Empirical Wavelet Transform and Gated Recurrent Unit Network to Mitigate Data Leakage," Energies, MDPI, vol. 17(17), pages 1-22, August.
    7. Lin, Yu & Lu, Qin & Tan, Bin & Yu, Yuanyuan, 2022. "Forecasting energy prices using a novel hybrid model with variational mode decomposition," Energy, Elsevier, vol. 246(C).
    8. Wang, Jujie & Zhuang, Zhenzhen & Gao, Dongming, 2023. "An enhanced hybrid model based on multiple influencing factors and divide-conquer strategy for carbon price prediction," Omega, Elsevier, vol. 120(C).
    9. Song, Yazhi & Liu, Tiansen & Li, Yin & Zhu, Yue & Ye, Bin, 2022. "Paths and policy adjustments for improving carbon-market liquidity in China," Energy Economics, Elsevier, vol. 115(C).
    10. E, Jianwei & Ye, Jimin & He, Lulu & Jin, Haihong, 2019. "Energy price prediction based on independent component analysis and gated recurrent unit neural network," Energy, Elsevier, vol. 189(C).
    11. Yang, Cai & Zhang, Hongwei & Weng, Futian, 2024. "Effects of COVID-19 vaccination programs on EU carbon price forecasts: Evidence from explainable machine learning," International Review of Financial Analysis, Elsevier, vol. 91(C).
    12. Song, Chao & Wang, Tao & Chen, Xiaohong & Shao, Quanxi & Zhang, Xianqi, 2023. "Ensemble framework for daily carbon dioxide emissions forecasting based on the signal decomposition–reconstruction model," Applied Energy, Elsevier, vol. 345(C).
    13. Ding, Lili & Zhang, Rui & Zhao, Xin, 2024. "Forecasting carbon price in China unified carbon market using a novel hybrid method with three-stage algorithm and long short-term memory neural networks," Energy, Elsevier, vol. 288(C).
    14. Peng, Lijuan & Pan, Zhigang & Liang, Chao & Umar, Muhammad, 2023. "Exchange rate volatility predictability: A new insight from climate policy uncertainty," Economic Analysis and Policy, Elsevier, vol. 80(C), pages 688-700.
    15. Li, Jieyi & Qian, Shuangyue & Li, Ling & Guo, Yuanxuan & Wu, Jun & Tang, Ling, 2024. "A novel secondary decomposition method for forecasting crude oil price with twitter sentiment," Energy, Elsevier, vol. 290(C).
    16. Na Fu & Liyan Geng & Junhai Ma & Xue Ding, 2023. "Price, Complexity, and Mathematical Model," Mathematics, MDPI, vol. 11(13), pages 1-30, June.
    17. Zhou, Feite & Huang, Zhehao & Zhang, Changhong, 2022. "Carbon price forecasting based on CEEMDAN and LSTM," Applied Energy, Elsevier, vol. 311(C).
    18. Jesús Molina‐Muñoz & Andrés Mora‐Valencia & Javier Perote, 2024. "Predicting carbon and oil price returns using hybrid models based on machine and deep learning," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 31(2), June.
    19. Raymond, Leigh, 2019. "Policy perspective:Building political support for carbon pricing—Lessons from cap-and-trade policies," Energy Policy, Elsevier, vol. 134(C).
    20. Yumin Li & Ruiqi Yang & Xiaoman Wang & Jiaming Zhu & Nan Song, 2023. "Carbon Price Combination Forecasting Model Based on Lasso Regression and Optimal Integration," Sustainability, MDPI, vol. 15(12), pages 1-26, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:12:y:2024:i:10:p:1428-:d:1389739. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.