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Carbon price forecasting with a novel hybrid ARIMA and least squares support vector machines methodology


  • Zhu, Bangzhu
  • Wei, Yiming


In general, due to inherently high complexity, carbon prices simultaneously contain linear and nonlinear patterns. Although the traditional autoregressive integrated moving average (ARIMA) model has been one of the most popular linear models in time series forecasting, the ARIMA model cannot capture nonlinear patterns. The least squares support vector machine (LSSVM), a novel neural network technique, has been successfully applied in solving nonlinear regression estimation problems. Therefore, we propose a novel hybrid methodology that exploits the unique strength of the ARIMA and LSSVM models in forecasting carbon prices. Additionally, particle swarm optimization (PSO) is used to find the optimal parameters of LSSVM in order to improve the prediction accuracy. For verification and testing, two main future carbon prices under the EU ETS were used to examine the forecasting ability of the proposed hybrid methodology. The empirical results obtained demonstrate the appeal of the proposed hybrid methodology for carbon price forecasting.

Suggested Citation

  • Zhu, Bangzhu & Wei, Yiming, 2013. "Carbon price forecasting with a novel hybrid ARIMA and least squares support vector machines methodology," Omega, Elsevier, vol. 41(3), pages 517-524.
  • Handle: RePEc:eee:jomega:v:41:y:2013:i:3:p:517-524 DOI: 10.1016/

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    References listed on IDEAS

    1. Hintermann, Beat, 2010. "Allowance price drivers in the first phase of the EU ETS," Journal of Environmental Economics and Management, Elsevier, vol. 59(1), pages 43-56, January.
    2. Zhang, Yue-Jun & Wei, Yi-Ming, 2010. "An overview of current research on EU ETS: Evidence from its operating mechanism and economic effect," Applied Energy, Elsevier, vol. 87(6), pages 1804-1814, June.
    3. Keppler, Jan Horst & Mansanet-Bataller, Maria, 2010. "Causalities between CO2, electricity, and other energy variables during phase I and phase II of the EU ETS," Energy Policy, Elsevier, vol. 38(7), pages 3329-3341, July.
    4. 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.
    5. Maria Mansanet-Bataller & Angel Pardo & Enric Valor, 2007. "CO2 Prices, Energy and Weather," The Energy Journal, International Association for Energy Economics, vol. 0(Number 3), pages 73-92.
    6. repec:dau:papers:123456789/5269 is not listed on IDEAS
    7. Pai, Ping-Feng & Lin, Chih-Sheng, 2005. "A hybrid ARIMA and support vector machines model in stock price forecasting," Omega, Elsevier, vol. 33(6), pages 497-505, December.
    8. Koutroumanidis, Theodoros & Ioannou, Konstantinos & Arabatzis, Garyfallos, 2009. "Predicting fuelwood prices in Greece with the use of ARIMA models, artificial neural networks and a hybrid ARIMA-ANN model," Energy Policy, Elsevier, vol. 37(9), pages 3627-3634, September.
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    Cited by:

    1. Wei, Yi-Ming & Mi, Zhi-Fu & Huang, Zhimin, 2015. "Climate policy modeling: An online SCI-E and SSCI based literature review," Omega, Elsevier, vol. 57(PA), pages 70-84.
    2. Ping Wang & Bangzhu Zhu, 2016. "Estimating the Contribution of Industry Structure Adjustment to the Carbon Intensity Target: A Case of Guangdong," Sustainability, MDPI, Open Access Journal, vol. 8(4), pages 1-11, April.
    3. Bangzhu Zhu & Ping Wang & Julien Chevallier & Yiming Wei, 2015. "Carbon Price Analysis Using Empirical Mode Decomposition," Computational Economics, Springer;Society for Computational Economics, vol. 45(2), pages 195-206, February.
    4. Naber, S.K. & de Ree, D.A. & Spliet, R. & van den Heuvel, W., 2015. "Allocating CO2 emission to customers on a distribution route," Omega, Elsevier, vol. 54(C), pages 191-199.
    5. Wang, Ke & Wei, Yi-Ming & Huang, Zhimin, 2016. "Potential gains from carbon emissions trading in China: A DEA based estimation on abatement cost savings," Omega, Elsevier, vol. 63(C), pages 48-59.
    6. Chen, Xu & Wang, Xiaojun, 2016. "Effects of carbon emission reduction policies on transportation mode selections with stochastic demand," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 90(C), pages 196-205.
    7. Xiong, Tao & Li, Chongguang & Bao, Yukun, 2017. "Interval-valued time series forecasting using a novel hybrid HoltI and MSVR model," Economic Modelling, Elsevier, vol. 60(C), pages 11-23.
    8. Heng-Li Yang & Han-Chou Lin, 2017. "Applying the Hybrid Model of EMD, PSR, and ELM to Exchange Rates Forecasting," Computational Economics, Springer;Society for Computational Economics, vol. 49(1), pages 99-116, January.
    9. Zhao, Weigang & Wang, Jianzhou & Lu, Haiyan, 2014. "Combining forecasts of electricity consumption in China with time-varying weights updated by a high-order Markov chain model," Omega, Elsevier, vol. 45(C), pages 80-91.
    10. Wesseh, Presley K. & Lin, Boqiang, 2016. "Modeling environmental policy with and without abatement substitution: A tradeoff between economics and environment?," Applied Energy, Elsevier, vol. 167(C), pages 34-43.
    11. repec:spr:annopr:v:255:y:2017:i:1:d:10.1007_s10479-015-1967-5 is not listed on IDEAS
    12. Fan, Xinghua & Wang, Li & Li, Shasha, 2016. "Predicting chaotic coal prices using a multi-layer perceptron network model," Resources Policy, Elsevier, vol. 50(C), pages 86-92.
    13. Fan, Liwei & Pan, Sijia & Li, Zimin & Li, Huiping, 2016. "An ICA-based support vector regression scheme for forecasting crude oil prices," Technological Forecasting and Social Change, Elsevier, vol. 112(C), pages 245-253.


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