Skip to main content
Springer Nature Link
Log in

Carbon prices forecasting based on sliding time window and improved support vector regression

  • Regular Paper
  • Published:
Computing Aims and scope Submit manuscript

Abstract

Carbon emission trading system is one of the important means for China to tackle climate warming and achieve the dual-carbon goals. However, due to the characteristics of the carbon emission trading prices, such as nonlinearity, instability, and complexity of predictor variables, the performance of existing prediction models is relatively poor. In order to solve the above problems, a prediction model considering the predictor variables of carbon emission trading prices is proposed in this paper. The proposed method first analyzes 18 predictor variables in six categories and Lasso is used to select predictor variables as inputs for the prediction model. Secondly, sliding time window technique is adopted to analyze the dynamics and trends in the carbon market. Then, differential evolution (DE) algorithm is utilized to improve the performance of support vector regression (SVR), where SVR is used as the prediction model for carbon emission trading prices. Finally, the carbon emission trading price is taken as a sample set in Hubei Province, together with the selected predictor variables, as input to the prediction model. The experimental results show that the proposed model obtains optimal results in three performance evaluation metrics, including root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE). Compared with the basic model, the results decreased by 1.6081, 1.4753 and 5.5418% respectively, which improved the prediction accuracy of the model. And the proposed model has also achieved better performance in predicting the prices of China’s carbon emissions trading market.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Narassimhan E, Gallagher KS, Koester S, Alejo JR (2018) Carbon pricing in practice: a review of existing emissions trading systems. Clim Policy 18(8):967–991

    Article  Google Scholar 

  2. Wu L, Zhu Q (2021) Impacts of the carbon emission trading system on China’s carbon emission peak: a new data-driven approach. Nat Hazards 107(3):2487–2515

    Article  MATH  Google Scholar 

  3. Wang R (2010) Short-term electricity price forecasting based on grey system theory and time series analysis. IEEE, Chengdu, China, pp 1–4. https://doi.org/10.1109/APPEEC.2010.5448651

  4. Liu H, Shi J (2013) Applying ARMA–GARCH approaches to forecasting short-term electricity prices. Energy Econ 37(may):152–166

    Article  MathSciNet  MATH  Google Scholar 

  5. Valipour M, Banihabib ME, Behbahani SMR (2013) Comparison of the ARMA, ARIMA, and the autoregressive artificial neural network models in forecasting the monthly inflow of DEZ dam reservoir. J Hydrol 476:433–441

    Article  Google Scholar 

  6. Zhang H, Zhang S, Wang P, Qin Y, Wang H (2017) Forecasting of particulate matter time series using wavelet analysis and wavelet-ARMA/ARIMA model in Taiyuan, China. J Air Waste Manag Assoc 67(7):776–788

    Article  MATH  Google Scholar 

  7. Zhang YJ, Yao T, He LY (2015) Forecasting crude oil market volatility: can the regime switching GARCH model beat the single-regime GARCH models? Int Rev Econ Finance 59

  8. Yu Z, Sun T, Sun H, Yang F (2015) Research on combinational forecast models for the traffic flow. Math Probl Eng 2015(1):201686

    MATH  Google Scholar 

  9. Thenmozhi M, Sarath Chand G (2016) Forecasting stock returns based on information transmission across global markets using support vector machines. Neural Comput Appl 27(4):805–824

    Article  MATH  Google Scholar 

  10. Zhang J, Teng Y, Chen W (2019) Support vector regression with modified firefly algorithm for stock price forecasting. Appl Intell

  11. Sun H, Yu B, Amman H (2020) Forecasting financial returns volatility: a GARCH-SVR model. Comput Econ 55(2):451–471

    Article  MATH  Google Scholar 

  12. Ge M, Junfeng Z, Jinfei W, Huiting H, Hongye W (2021) ARIMA-FSVR hybrid method for high-speed railway passenger traffic forecasting. Math Probl Eng 2021:1–5

    Google Scholar 

  13. Meng Z, Sun H, Wang X (2022) Forecasting energy consumption based on SVR and Markov model: a case study of china. Front Environ Sci 10:883711

    Article  MATH  Google Scholar 

  14. Zhu B, Chevallier J, Zhu B, Chevallier J (2017) Carbon price forecasting with a hybrid ARIMA and least squares support vector machines methodology. In: Pricing and forecasting carbon markets: models and empirical analyses, pp 87–107

  15. Tsai M-T, Kuo Y-T (2013) A forecasting system of carbon price in the carbon trading markets using artificial neural network. Int J Environ Sci Dev 4(2):163

    Article  MathSciNet  MATH  Google Scholar 

  16. Jiang L, Wu P (2015) International carbon market price forecasting using an integration model based on SVR. In: 2015 international conference on engineering management, engineering education and information technology. Atlantis Press, pp 303–308

  17. Zhang J, Li D, Hao Y, Tan Z (2018) A hybrid model using signal processing technology, econometric models and neural network for carbon spot price forecasting. J Clean Prod 204:958–964

    Article  MATH  Google Scholar 

  18. Sun W, Zhang C (2018) Analysis and forecasting of the carbon price using multi-resolution singular value decomposition and extreme learning machine optimized by adaptive whale optimization algorithm. Appl Energy 231:1354–1371

    Article  MATH  Google Scholar 

  19. Huang Y, Dai X, Wang Q, Zhou D (2021) A hybrid model for carbon price forecasting using GARCH and long short-term memory network. Appl Energy 285:116485

    Article  MATH  Google Scholar 

  20. Zhou J, Yu X, Yuan X (2018) Predicting the carbon price sequence in the Shenzhen emissions exchange using a multiscale ensemble forecasting model based on ensemble empirical mode decomposition. Energies 11(7):1907

    Article  MATH  Google Scholar 

  21. Han M, Ding L, Zhao X, Kang W (2019) Forecasting carbon prices in the Shenzhen market, China: the role of mixed-frequency factors. Energy 171:69–76

    Article  MATH  Google Scholar 

  22. Zhu J, Wu P, Chen H, Liu J, Zhou L (2019) Carbon price forecasting with variational mode decomposition and optimal combined model. Physica A 519:140–158

    Article  MathSciNet  MATH  Google Scholar 

  23. Ye P, Li Y, Siddik AB (2023) Forecasting the return of carbon price in the Chinese market based on an improved stacking ensemble algorithm. Energies 16(11):4520

    Article  MATH  Google Scholar 

  24. Wang J, Zhuang Z (2023) A novel cluster based multi-index nonlinear ensemble framework for carbon price forecasting. Environ Dev Sustain 25(7):6225–6247

    Article  MATH  Google Scholar 

  25. Liu J, Li X, Wang P, Chen H, Zhu J (2023) An interval-valued carbon price forecasting method based on web search data and social media sentiment. Environ Sci Pollut Res 30(42):95840–95859

    Article  Google Scholar 

  26. Ji C-J, Hu Y-J, Tang B-J (2018) Research on carbon market price mechanism and influencing factors: a literature review. Nat Hazards 92:761–782

    Article  MATH  Google Scholar 

  27. Lin B, Jia Z (2019) What are the main factors affecting carbon price in emission trading scheme? A case study in china. Sci Total Environ 654:525–534

    Article  MATH  Google Scholar 

  28. Zhang X, Zhang C, Wei Z (2019) Carbon price forecasting based on multi-resolution singular value decomposition and extreme learning machine optimized by the moth-flame optimization algorithm considering energy and economic factors. Energies 12(22):4283

    Article  MATH  Google Scholar 

  29. Sun W, Sun C, Li Z (2020) A hybrid carbon price forecasting model with external and internal influencing factors considered comprehensively: a case study from china. Pol J Environ Stud 29(5):3305–3316

    Article  MathSciNet  MATH  Google Scholar 

  30. Zeng S, Zhang H, Qu Y, Zeng B (2021) Study on price fluctuation and influencing factors of regional carbon emission trading in China under the background of high-quality economic development. Int. Energy J 21:201–211

    MATH  Google Scholar 

  31. Zhang S, Ji H, Tian M, Wang B (2022) High-dimensional nonlinear dependence and risk spillovers analysis between China’s carbon market and its major influence factors. Ann Oper Res. https://doi.org/10.1007/s10479-022-04770-9

    Article  MATH  Google Scholar 

  32. Song X, Zhang W, Ge Z, Huang S, Huang Y, Xiong S (2022) A study of the influencing factors on the carbon emission trading price in china based on the improved gray relational analysis model. Sustainability 14(13):8002

    Article  MATH  Google Scholar 

  33. Li J, Liu D (2023) Carbon price forecasting based on secondary decomposition and feature screening. Energy 278:127783

    Article  MATH  Google Scholar 

  34. Liu YL, Zhang JJ, Fang Y (2023) The driving factors of China’s carbon prices: evidence from using ICEEMDAN-HC method and quantile regression. Financ Res Lett 54:103756

    Article  MATH  Google Scholar 

  35. Cohen I, Huang Y, Chen J, Benesty J, Benesty J, Chen J, Huang Y, Cohen I (2009) Pearson correlation coefficient. In: Noise reduction in speech processing, Springer, Heidelberg, pp 37–40

  36. Gu D, Huang J (2020) Multifractal detrended cross-correlation analysis of high-frequency stock series based on ensemble empirical mode decomposition. Fractals 28(02):2050035

    Article  MATH  Google Scholar 

  37. Xie Y, Liu S, Fang H, Wang J (2020) Global autocorrelation test based on the Monte Carlo method and impacts of eliminating nonstationary components on the global autocorrelation test. Stoch Environ Res Risk Assess 34:1645–1658

    Article  MATH  Google Scholar 

  38. Narula SC, Wellington JF (2007) Multiple criteria linear regression. Eur J Oper Res 181(2):767–772

    Article  MathSciNet  MATH  Google Scholar 

  39. Duan J, Soussen C, Brie D, Idier J, Wan M, Wang Y-P (2016) Generalized lasso with under-determined regularization matrices. Signal Process 127:239–246

    Article  MATH  Google Scholar 

  40. Awad M, Khanna R, Awad M, Khanna R (2015) Support vector regression. In: Efficient learning machines. Theories, concepts, and applications for engineers and system designers, pp 67–80

  41. Peng C, Che Z, Liao TW, Zhang Z (2023) Prediction using multi-objective slime mould algorithm optimized support vector regression model. Appl Soft Comput 145:110580

    Article  MATH  Google Scholar 

  42. Li C, Yao W, Wang H, Jiang T, Zhang X (2023) Bayesian evolutionary optimization for crafting high-quality adversarial examples with limited query budget. Appl Soft Comput 142:110370

    Article  Google Scholar 

  43. Ji C-J, Li X-Y, Hu Y-J, Wang X-Y, Tang B-J (2019) Research on carbon price in emissions trading scheme: a bibliometric analysis. Nat Hazards 99:1381–1396

    Article  MATH  Google Scholar 

  44. Li H, Lei M (2018) The influencing factors of china carbon price: a study based on carbon trading market in Hubei Province. In: IOP conference series: earth and environmental science. IOP Publishing, vol 121, p 052073

  45. Yin Y, Jiang Z, Liu Y, Yu Z (2019) Factors affecting carbon emission trading price: evidence from china. Emerg Mark Financ Trade 55(15):3433–3451

    Article  MATH  Google Scholar 

  46. Ye J, Xue M (2021) Influences of sentiment from news articles on EU carbon prices. Energy Econ 101:105393

    Article  MATH  Google Scholar 

  47. Ratner B (2009) The correlation coefficient: its values range between \(+1/-1\), or do they? J Target Meas Anal Mark 17(2):139–142

    Article  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Management, China University of Mining and Technology Beijing, Beijing, 100083, China

    Shuting Jia & Zhanglu Tan

  2. School of Automation and Electrical Engineering, Zhongyuan University of Technology, Zhengzhou, 450007, China

    Chao Li

Authors
  1. Shuting Jia
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Zhanglu Tan
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Chao Li
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Zhanglu Tan.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jia, S., Tan, Z. & Li, C. Carbon prices forecasting based on sliding time window and improved support vector regression. Computing 107, 53 (2025). https://doi.org/10.1007/s00607-024-01404-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s00607-024-01404-9

Keywords