IDEAS home Printed from https://ideas.repec.org/a/eee/finana/v98y2025ics1057521924008111.html
   My bibliography  Save this article

An integrated CEEMDAN and TCN-LSTM deep learning framework for forecasting

Author

Listed:
  • Cai, Xiaotong
  • Yuan, Bo
  • Wu, Chao

Abstract

Carbon trading serves as an effective mechanism and a cost-effective tool for countries to reduce carbon emissions. This study develops a hybrid forecasting model using the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and temporal convolutional network-long short-term memory network (TCN-LSTM) methods to address the nonlinear and time-variant nature of carbon prices. The closing prices of carbon emission rights in Guangdong and Shanghai are used for analysis. The CEEMDAN method decomposes the intricate and irregular carbon price series into several low-frequency and regular components. Subsequently, the TCN-LSTM method extracts time-series features from these components to predict future carbon trading prices precisely. The experimental outcomes indicate that this integrated deep learning framework achieves the highest prediction accuracy, with a lag of 15 and 18 days for the Guangdong and Shanghai carbon trading markets, respectively.

Suggested Citation

  • Cai, Xiaotong & Yuan, Bo & Wu, Chao, 2025. "An integrated CEEMDAN and TCN-LSTM deep learning framework for forecasting," International Review of Financial Analysis, Elsevier, vol. 98(C).
    • Handle: RePEc:eee:finana:v:98:y:2025:i:c:s1057521924008111
    DOI: 10.1016/j.irfa.2024.103879
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1057521924008111
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.irfa.2024.103879?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Han, Meng & Ding, Lili & Zhao, Xin & Kang, Wanglin, 2019. "Forecasting carbon prices in the Shenzhen market, China: The role of mixed-frequency factors," Energy, Elsevier, vol. 171(C), pages 69-76.
    2. Dong, Kangyin & Ni, Guohua & Taghizadeh-Hesary, Farhad & Zhao, Congyu, 2023. "Does smart transportation matter in inhibiting carbon inequality?," Energy Economics, Elsevier, vol. 126(C).
    3. Byun, Suk Joon & Cho, Hangjun, 2013. "Forecasting carbon futures volatility using GARCH models with energy volatilities," Energy Economics, Elsevier, vol. 40(C), pages 207-221.
    4. Yue-Jun Zhang & Yi-Ming Wei, 2011. "The dynamic influence of advanced stock market risk on international crude oil returns: an empirical analysis," Quantitative Finance, Taylor & Francis Journals, vol. 11(7), pages 967-978.
    5. Yinpeng Zhang & Ying Chen & You Wu & Panpan Zhu, 2022. "Investor attention and carbon return: evidence from the EU-ETS," Economic Research-Ekonomska Istraživanja, Taylor & Francis Journals, vol. 35(1), pages 709-727, December.
    6. Frank Convery, 2009. "Origins and Development of the EU ETS," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 43(3), pages 391-412, July.
    7. Zhong, Wenli & Liu, Yang & Dong, Kangyin & Ni, Guohua, 2024. "Assessing the synergistic effects of artificial intelligence on pollutant and carbon emission mitigation in China," Energy Economics, Elsevier, vol. 138(C).
    8. 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.
    9. Yan Wu & Chunlai Chen & Cong Hu, 2023. "The impacts of trade intensity with China on carbon emissions in belt and road countries," Journal of the Asia Pacific Economy, Taylor & Francis Journals, vol. 28(2), pages 558-577, April.
    10. Seifert, Jan & Uhrig-Homburg, Marliese & Wagner, Michael, 2008. "Dynamic behavior of CO2 spot prices," Journal of Environmental Economics and Management, Elsevier, vol. 56(2), pages 180-194, September.
    11. Yan Wu & Chunlai Chen & Cong Hu, 2021. "Does the Belt and Road Initiative Increase the Carbon Emission Intensity of Participating Countries?," China & World Economy, Institute of World Economics and Politics, Chinese Academy of Social Sciences, vol. 29(3), pages 1-25, May.
    12. Zhi Yu & Yali Cao & Mo Liu, 2022. "Does carbon emission trading policy affect bank loans of firms? Evidence from China," Applied Economics Letters, Taylor & Francis Journals, vol. 29(18), pages 1709-1714, October.
    13. Hou, Xiang & Hu, Qianlin & Liang, Xin & Xu, Jingxuan, 2023. "How do low-carbon city pilots affect carbon emissions? Staggered difference in difference evidence from Chinese firms," Economic Analysis and Policy, Elsevier, vol. 79(C), pages 664-686.
    14. Sun, Wei & Zhang, Chongchong, 2018. "Analysis and forecasting of the carbon price using multi—resolution singular value decomposition and extreme learning machine optimized by adaptive whale optimization algorithm," Applied Energy, Elsevier, vol. 231(C), pages 1354-1371.
    15. Xu, Hao & Xu, Jingxuan & Wang, Jie & Hou, Xiang, 2023. "Reduce production or increase efficiency? Hazardous air pollutants regulation, energy use, and the synergistic effect on industrial enterprises' carbon emission," Energy Economics, Elsevier, vol. 126(C).
    16. Zhang, Kefei & Cao, Hua & Thé, Jesse & Yu, Hesheng, 2022. "A hybrid model for multi-step coal price forecasting using decomposition technique and deep learning algorithms," Applied Energy, Elsevier, vol. 306(PA).
    17. Xu, Hua & Wang, Minggang & Jiang, Shumin & Yang, Weiguo, 2020. "Carbon price forecasting with complex network and extreme learning machine," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 545(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. Dai, Jiapeng, 2025. "Is policy pilot a viable path to sustainable development? Attention allocation perspective," International Review of Financial Analysis, Elsevier, vol. 98(C).
    2. Zhou, Feite & Huang, Zhehao & Zhang, Changhong, 2022. "Carbon price forecasting based on CEEMDAN and LSTM," Applied Energy, Elsevier, vol. 311(C).
    3. Peng, Shengnan & Liu, Chan & Wang, Ze & Ye, Zihan & Sun, Xialing & Tan, Zhanglu, 2024. "The impact of the carbon reduction policy effectiveness on energy companies' ESG performance," International Review of Financial Analysis, Elsevier, vol. 96(PB).
    4. Wan, Min & Wei, Dedai & Yu, Chenming, 2025. "Low-carbon pilot cities and financialization of listed companies: Inhibitory effects and policy analysis," Finance Research Letters, Elsevier, vol. 71(C).
    5. Wang, Jingru & Liu, Tinghua & Aziz, Noshaba & Sui, Hongguang, 2024. "Exploring the role of trade credit in facilitating low-carbon development: Insights from Chinese enterprises," International Review of Financial Analysis, Elsevier, vol. 96(PB).
    6. Wang, Juling & Liu, Lihua & Ou, Yangchao, 2024. "Low-carbon city pilot policy and corporate environmental violations: Evidence from heavily polluting firms in China," Finance Research Letters, Elsevier, vol. 65(C).
    7. 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.
    8. Wen Zhang & Zhibin Wu, 2022. "Optimal hybrid framework for carbon price forecasting using time series analysis and least squares support vector machine," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(3), pages 615-632, April.
    9. Huang, Yuting & Wen, Wen & Bao, Rui, 2024. "Carbon emission right circulation and corporate green innovation: Evidence from a quasi-natural experiment in China," Finance Research Letters, Elsevier, vol. 69(PA).
    10. Huang, Wenyang & Zhao, Jianyu & Wang, Xiaokang, 2024. "Model-driven multimodal LSTM-CNN for unbiased structural forecasting of European Union allowances open-high-low-close price," Energy Economics, Elsevier, vol. 132(C).
    11. Goodell, John W. & Gurdgiev, Constantin & Karim, Sitara & Palma, Alessia, 2024. "Carbon emissions and liquidity management," International Review of Financial Analysis, Elsevier, vol. 95(PA).
    12. Chang, Lulu & Fang, Senhui, 2025. "Bringing carbon emission reduction to fruition: Insights from city’s low-carbon policy intensity," Finance Research Letters, Elsevier, vol. 72(C).
    13. Qi, Shaozhou & Cheng, Shihan & Tan, Xiujie & Feng, Shenghao & Zhou, Qi, 2022. "Predicting China's carbon price based on a multi-scale integrated model," Applied Energy, Elsevier, vol. 324(C).
    14. Po Yun & Chen Zhang & Yaqi Wu & Yu Yang, 2022. "Forecasting Carbon Dioxide Price Using a Time-Varying High-Order Moment Hybrid Model of NAGARCHSK and Gated Recurrent Unit Network," IJERPH, MDPI, vol. 19(2), pages 1-19, January.
    15. 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).
    16. Gong, Zhenting & Chen, Yanbei & Zhang, He & Chen, Fan, 2024. "Tail risk connectedness in the Carbon-Finance nexus: Evidence from a quantile spillover approach in China," Finance Research Letters, Elsevier, vol. 67(PB).
    17. Li, Yu & Yaacob, Mohd Hasimi & Xie, Tao, 2024. "Effects of China's low carbon pilot city policy on corporate green innovation: Considering the mediating role of public environmental concern," Finance Research Letters, Elsevier, vol. 65(C).
    18. Wang, Minggang & Zhu, Mengrui & Tian, Lixin, 2022. "A novel framework for carbon price forecasting with uncertainties," Energy Economics, Elsevier, vol. 112(C).
    19. Zhu, Mengrui & Xu, Hua & Wang, Minggang & Tian, Lixin, 2024. "Carbon price interval prediction method based on probability density recurrence network and interval multi-layer perceptron," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 636(C).
    20. Ren, Xiaohang & Lu, Qian & Gozgor, Giray & Fu, Haiqin, 2025. "Natural gas and the battle of carbon emissions: Interpreting the spatial effects of provincial carbon emissions in China," International Review of Economics & Finance, Elsevier, vol. 97(C).

    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:eee:finana:v:98:y:2025:i:c:s1057521924008111. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/inca/620166 .

    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.