Forecasting carbon futures price: a hybrid method incorporating fuzzy entropy and extreme learning machine
In this paper, we propose a novel hybrid model that extends prior work involving ensemble empirical mode decomposition (EEMD) by using fuzzy entropy and extreme learning machine (ELM) methods. We demonstrate this 3-stage model by applying it to forecast carbon futures prices which are characterized...
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| Vydáno v: | Annals of operations research Ročník 313; číslo 1; s. 559 - 601 |
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01.06.2022
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| ISSN: | 0254-5330, 1572-9338 |
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| Abstract | In this paper, we propose a novel hybrid model that extends prior work involving ensemble empirical mode decomposition (EEMD) by using fuzzy entropy and extreme learning machine (ELM) methods. We demonstrate this 3-stage model by applying it to forecast carbon futures prices which are characterized by chaos and complexity. First, we employ the EEMD method to decompose carbon futures prices into a couple of intrinsic mode functions (IMFs) and one residue. Second, the fuzzy entropy and K-means clustering methods are used to reconstruct the IMFs and the residue to obtain three reconstructed components, specifically a high frequency series, a low frequency series, and a trend series. Third, the ARMA model is implemented for the stationary high and low frequency series, while the extreme learning machine (ELM) model is utilized for the non-stationary trend series. Finally, all the component forecasts are aggregated to form final forecasts of the carbon price for each model. The empirical results show that the proposed reconstruction algorithm can bring more than 40% improvement in prediction accuracy compared to the traditional fine-to-coarse reconstruction algorithm under the same forecasting framework. The hybrid forecasting model proposed in this paper also well captures the direction of the price changes, with strong and robust forecasting ability, which is significantly better than the single forecasting models and the other hybrid forecasting models. |
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| AbstractList | In this paper, we propose a novel hybrid model that extends prior work involving ensemble empirical mode decomposition (EEMD) by using fuzzy entropy and extreme learning machine (ELM) methods. We demonstrate this 3-stage model by applying it to forecast carbon futures prices which are characterized by chaos and complexity. First, we employ the EEMD method to decompose carbon futures prices into a couple of intrinsic mode functions (IMFs) and one residue. Second, the fuzzy entropy and K-means clustering methods are used to reconstruct the IMFs and the residue to obtain three reconstructed components, specifically a high frequency series, a low frequency series, and a trend series. Third, the ARMA model is implemented for the stationary high and low frequency series, while the extreme learning machine (ELM) model is utilized for the non-stationary trend series. Finally, all the component forecasts are aggregated to form final forecasts of the carbon price for each model. The empirical results show that the proposed reconstruction algorithm can bring more than 40% improvement in prediction accuracy compared to the traditional fine-to-coarse reconstruction algorithm under the same forecasting framework. The hybrid forecasting model proposed in this paper also well captures the direction of the price changes, with strong and robust forecasting ability, which is significantly better than the single forecasting models and the other hybrid forecasting models. In this paper, we propose a novel hybrid model that extends prior work involving ensemble empirical mode decomposition (EEMD) by using fuzzy entropy and extreme learning machine (ELM) methods. We demonstrate this 3-stage model by applying it to forecast carbon futures prices which are characterized by chaos and complexity. First, we employ the EEMD method to decompose carbon futures prices into a couple of intrinsic mode functions (IMFs) and one residue. Second, the fuzzy entropy and K-means clustering methods are used to reconstruct the IMFs and the residue to obtain three reconstructed components, specifically a high frequency series, a low frequency series, and a trend series. Third, the ARMA model is implemented for the stationary high and low frequency series, while the extreme learning machine (ELM) model is utilized for the non-stationary trend series. Finally, all the component forecasts are aggregated to form final forecasts of the carbon price for each model. The empirical results show that the proposed reconstruction algorithm can bring more than 40% improvement in prediction accuracy compared to the traditional fine-to-coarse reconstruction algorithm under the same forecasting framework. The hybrid forecasting model proposed in this paper also well captures the direction of the price changes, with strong and robust forecasting ability, which is significantly better than the single forecasting models and the other hybrid forecasting models.In this paper, we propose a novel hybrid model that extends prior work involving ensemble empirical mode decomposition (EEMD) by using fuzzy entropy and extreme learning machine (ELM) methods. We demonstrate this 3-stage model by applying it to forecast carbon futures prices which are characterized by chaos and complexity. First, we employ the EEMD method to decompose carbon futures prices into a couple of intrinsic mode functions (IMFs) and one residue. Second, the fuzzy entropy and K-means clustering methods are used to reconstruct the IMFs and the residue to obtain three reconstructed components, specifically a high frequency series, a low frequency series, and a trend series. Third, the ARMA model is implemented for the stationary high and low frequency series, while the extreme learning machine (ELM) model is utilized for the non-stationary trend series. Finally, all the component forecasts are aggregated to form final forecasts of the carbon price for each model. The empirical results show that the proposed reconstruction algorithm can bring more than 40% improvement in prediction accuracy compared to the traditional fine-to-coarse reconstruction algorithm under the same forecasting framework. The hybrid forecasting model proposed in this paper also well captures the direction of the price changes, with strong and robust forecasting ability, which is significantly better than the single forecasting models and the other hybrid forecasting models. |
| Audience | Academic |
| Author | Vivian, Andrew Ye, Cheng Chen, Peng |
| Author_xml | – sequence: 1 givenname: Peng surname: Chen fullname: Chen, Peng organization: Department of Finance and Institute of Finance, School of Economics, Jinan University – sequence: 2 givenname: Andrew orcidid: 0000-0002-6036-4477 surname: Vivian fullname: Vivian, Andrew email: A.J.Vivian@lboro.ac.uk organization: School of Business and Economics, Loughborough University – sequence: 3 givenname: Cheng surname: Ye fullname: Ye, Cheng organization: Department of Finance, School of Economics, Jinan University |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/35002000$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1016/j.omega.2012.06.005 10.1016/j.enpol.2016.09.021 10.1007/s10479-020-03690-w 10.1007/s10479-018-2982-0 10.1016/j.apenergy.2012.04.001 10.1016/j.rser.2016.11.060 10.1016/j.apenergy.2009.12.019 10.1007/s10479-015-1864-y 10.1016/j.eneco.2011.03.003 10.1016/j.ecolecon.2018.10.001 10.1016/j.eneco.2007.02.012 10.1016/j.apenergy.2012.01.070 10.7763/IJESD.2013.V4.327 10.1016/j.renene.2016.03.103 10.1016/j.apenergy.2010.06.017 10.1016/j.enpol.2015.02.024 10.1109/TSTE.2014.2365580 10.1142/S1793536909000047 10.1016/j.apenergy.2017.01.076 10.1016/j.eneco.2017.12.030 10.1016/j.enconman.2016.02.013 10.1016/j.eneco.2020.104870 10.1016/j.eneco.2018.05.008 10.1016/j.renene.2011.06.023 10.1016/j.physa.2017.02.072 10.1080/07350015.1995.10524599 10.1016/j.eneco.2013.02.006 10.1016/j.eneco.2015.12.002 10.1016/j.eswa.2014.12.047 10.1016/j.eneco.2008.07.003 10.1016/j.apenergy.2021.116485 10.1016/j.eneco.2013.06.017 10.1007/s11269-015-0962-6 10.1098/rspa.1998.0193 10.1016/j.apenergy.2015.11.082 10.1016/j.enpol.2011.10.057 10.1016/j.medengphy.2008.04.005 10.1109/TNSRE.2007.897025 10.1016/j.eswa.2013.06.071 10.1016/j.econmod.2011.11.003 10.1016/j.apenergy.2015.07.025 10.1016/j.eneco.2010.04.001 10.1016/j.jhydrol.2015.08.022 10.1016/j.jclepro.2017.06.016 10.3390/en5020355 10.2991/emeeit-15.2015.61 10.1016/S0169-2070(96)00719-4 10.1007/978-1-4757-2440-0 10.3982/ECTA5771 10.1016/j.neucom.2005.12.126 |
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| Keywords | Carbon futures price Fuzzy entropy ARMA Extreme learning machine K-means clustering method EEMD |
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| References | ChenWZhuangJYuWMeasuring complexity using FuzzyEn, ApEn, and SampEnMedical Engineering & Physics20093116168 SongYLiuTLiangDLiYSongXA Fuzzy Stochastic Model for Carbon Price Prediction Under the Effect of Demand-related Policy in China's Carbon MarketEcological Economics2019157253265 ChenWWangZXieHCharacterization of surface EMG signal based on fuzzy entropyIEEE Transactions on Neural Systems and Rehabilitation Engineering2007152266272 Jiang, M., Jia, L., Chen, Z., & Chen, W. (2020). The two-stage machine learning ensemble models for stock price prediction by combining mode decomposition, extreme learning machine and improved harmony search algorithm. Annals of Operations Research, 1–33. https://doi.org/10.1007/s10479-020-03690-w TanXSirichandKVivianAWangXHow connected is the carbon market to energy and financial markets? A systematic analysis of spillovers and dynamicsEnergy Economics202090104870 WuZHuangNEEnsemble empirical mode decomposition: A noise-assisted data analysis methodAdvances in Adaptive Data Analysis200911141 SegnonMLuxTGuptaRModeling and forecasting the volatility of carbon dioxide emission allowance prices: A review and comparison of modern volatility modelsRenewable and Sustainable Energy Reviews201769692704 TangBJGongPQShenCFactors of carbon price volatility in a comparative analysis of the EUA and sCERAnnals of Operations Research20172551157168 Huang, Y., Dai, X., Wang, Q., & Zhou, D. (2021). A hybrid model for carbon price forecasting using GARCH and long short-term memory network. Applied Energy, 285, 116485. ZhangYJWeiYMAn overview of current research on EU ETS: Evidence from its operating mechanism and economic effectApplied Energy201087618041814 BenzETrückSModeling the price dynamics of CO 2 emission allowancesEnergy Economics2009311415 Arouri, M. E. H., Jawadi, F., & Nguyen, D. K. (2012). Nonlinearities in carbon spot-futures price relationships during Phase II of the EU ETS. Economic Modelling, 29(3), 884–892. ZhuBHanDWangPWuZCForecasting carbon price using empirical mode decomposition and evolutionary least squares support vector regressionApplied Energy2017191521530 Yu, L., Wang, Z., & Tang, L. (2015). A decomposition–ensemble model with data-characteristic-driven reconstruction for crude oil price forecasting. Applied Energy, 156, 251–267. ZhangNLinAShangPMultidimensional k-nearest neighbor model based on EEMD for financial time series forecastingPhysica a: Statistical Mechanics and Its Applications2017477161173 SaninMEViolanteFMansanet-BatallerMUnderstanding volatility dynamics in the EU-ETS marketEnergy Policy201582321331 ZhuBYeSHeKChevallierJXieRMeasuring the risk of European carbon market: An empirical mode decomposition-based value at risk approachAnnals of Operations Research20192811373395 ZhangXKarplusVJQiTCarbon emissions in China: How far can new efforts bend the curve?Energy Economics201654388395 ZhangXLaiKKWangSYA new approach for crude oil price analysis based on empirical mode decompositionEnergy Economics2008303905918 VapnikVNThe nature of statistical learning theory1995Springer ChevallierJNonparametric modeling of carbon pricesEnergy Economics201133612671282 HuangGBZhuQYSiewCKExtreme learning machine: Theory and applicationsNeurocomputing2006701489501 DieboldFXMarianoRSComparing Predictive AccuracyJournal of Business & Economic Statistics1995133253263 MengAGeJYinHWind speed forecasting based on wavelet packet decomposition and artificial neural networks trained by crisscross optimization algorithmEnergy Conversion and Management20161147588 FengZHWeiYMWangKEstimating risk for the carbon market via extreme value theory: An empirical analysis of the EU ETSApplied Energy20129997108 WangWChauKXuDImproving forecasting accuracy of annual runoff time series using ARIMA based on EEMD decompositionWater Resources Management201529826552675 HuangNEShenZLongSRThe empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysisProceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences19984541971903995 LiuHShiJApplying ARMA–GARCH approaches to forecasting short-term electricity pricesEnergy Economics201337152166 LiuHTianHLiYComparison of two new ARIMA-ANN and ARIMA-Kalman hybrid methods for wind speed predictionApplied Energy201298415424 HarveyDLeybourneSNewboldPTesting the equality of prediction mean squared errorsInternational Journal of Forecasting1997132281291 ZhuBA novel multiscale ensemble carbon price prediction model integrating empirical mode decomposition, genetic algorithm and artificial neural networkEnergies201252355370 de Oliveira, F. A., Nobre, C. N., & Zárate, L. E. (2013). Applying artificial neural networks to prediction of stock price and improvement of the directional prediction index–Case study of PETR4, Petrobras, Brazil. Expert Systems with Applications, 40(18), 7596–7606. Zhu, B., Ye, S., Wang, P., He, K., Zhang, T., & Wei, Y. M. (2018). A novel multiscale nonlinear ensemble leaning paradigm for carbon price forecasting. Energy Economics, 70, 143–157. FengZHZouLLWeiYMCarbon price volatility: Evidence from EU ETSApplied Energy2011883590598 MontagnoliADe VriesFPCarbon trading thickness and market efficiencyEnergy Economics201032613311336 ByunSJChoHForecasting carbon futures volatility using GARCH models with energy volatilitiesEnergy Economics201340207221 TaorminaRChauKWData-driven input variable selection for rainfall–runoff modeling using binary-coded particle swarm optimization and Extreme Learning MachinesJournal of Hydrology201552916171632 Hansen, P. R., Lunde, A., & Nason, J. M. (2011). The model confidence set. Econometrica, 79(2), 453–497. Ortiz-CruzARodriguezEIbarra-ValdezCEfficiency of crude oil markets: Evidences from informational entropy analysisEnergy Policy201241365373 WangSZhangNWuLWind speed forecasting based on the hybrid ensemble empirical mode decomposition and GA-BP neural network methodRenewable Energy201694629636 RootzénJJohnssonFPaying the full price of steel–Perspectives on the cost of reducing carbon dioxide emissions from the steel industryEnergy Policy201698459469 SunWWangCFZhangCFactor analysis and forecasting of CO2 emissions in Hebei, using extreme learning machine based on particle swarm optimizationJournal of Cleaner Production201716210951101 Tsai, M. T., & Kuo, Y. T. (2013). A forecasting system of carbon price in the carbon trading markets using artificial neural network. International Journal of Environmental Science and Development, 4(2), 163. FanXLiSTianLChaotic characteristic identification for carbon price and a multi-layer perceptron network prediction modelExpert Systems with Applications201542839453952 JiaoLLiaoYZhouQPredicting carbon market risk using information from macroeconomic fundamentalsEnergy Economics201873212227 ZhuBWeiYCarbon price forecasting with a novel hybrid ARIMA and least squares support vector machines methodologyOmega2013413517524 LiMJHeYLTaoWQModeling a hybrid methodology for evaluating and forecasting regional energy efficiency in ChinaApplied Energy201718517691777 Jiang, L., & Wu, P. (2015). International carbon market price forecasting using an integration model based on SVR. In International Conference on Engineering Management, Engineering Education and Information Technology. GuoZZhaoWLuHMulti-step forecasting for wind speed using a modified EMD-based artificial neural network modelRenewable Energy2012371241249 RenYSuganthanPNSrikanthNA comparative study of empirical mode decomposition-based short-term wind speed forecasting methodsIEEE Transactions on Sustainable Energy201561236244 Z Wu (4406_CR37) 2009; 1 X Zhang (4406_CR40) 2008; 30 X Fan (4406_CR9) 2015; 42 A Ortiz-Cruz (4406_CR24) 2012; 41 4406_CR85 H Liu (4406_CR21) 2012; 98 4406_CR83 4406_CR80 4406_CR81 B Zhu (4406_CR45) 2013; 41 ZH Feng (4406_CR10) 2012; 99 M Segnon (4406_CR28) 2017; 69 B Zhu (4406_CR44) 2017; 191 4406_CR17 SJ Byun (4406_CR3) 2013; 40 X Zhang (4406_CR39) 2016; 54 W Chen (4406_CR6) 2009; 31 MJ Li (4406_CR19) 2017; 185 Y Ren (4406_CR25) 2015; 6 4406_CR91 A Meng (4406_CR22) 2016; 114 W Chen (4406_CR5) 2007; 15 X Tan (4406_CR32) 2020; 90 S Wang (4406_CR36) 2016; 94 J Rootzén (4406_CR26) 2016; 98 D Harvey (4406_CR13) 1997; 13 E Benz (4406_CR2) 2009; 31 B Zhu (4406_CR43) 2012; 5 ZH Feng (4406_CR11) 2011; 88 Z Guo (4406_CR12) 2012; 37 L Jiao (4406_CR16) 2018; 73 R Taormina (4406_CR31) 2015; 529 W Sun (4406_CR30) 2017; 162 ME Sanin (4406_CR27) 2015; 82 4406_CR444 H Liu (4406_CR20) 2013; 37 Y Song (4406_CR29) 2019; 157 4406_CR79 4406_CR78 GB Huang (4406_CR14) 2006; 70 B Zhu (4406_CR46) 2019; 281 NE Huang (4406_CR15) 1998; 454 YJ Zhang (4406_CR41) 2010; 87 A Montagnoli (4406_CR23) 2010; 32 N Zhang (4406_CR38) 2017; 477 J Chevallier (4406_CR7) 2011; 33 BJ Tang (4406_CR33) 2017; 255 W Wang (4406_CR35) 2015; 29 VN Vapnik (4406_CR34) 1995 FX Diebold (4406_CR8) 1995; 13 |
| References_xml | – reference: RootzénJJohnssonFPaying the full price of steel–Perspectives on the cost of reducing carbon dioxide emissions from the steel industryEnergy Policy201698459469 – reference: TangBJGongPQShenCFactors of carbon price volatility in a comparative analysis of the EUA and sCERAnnals of Operations Research20172551157168 – reference: ByunSJChoHForecasting carbon futures volatility using GARCH models with energy volatilitiesEnergy Economics201340207221 – reference: HuangNEShenZLongSRThe empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysisProceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences19984541971903995 – reference: ZhuBYeSHeKChevallierJXieRMeasuring the risk of European carbon market: An empirical mode decomposition-based value at risk approachAnnals of Operations Research20192811373395 – reference: ZhangNLinAShangPMultidimensional k-nearest neighbor model based on EEMD for financial time series forecastingPhysica a: Statistical Mechanics and Its Applications2017477161173 – reference: ChenWWangZXieHCharacterization of surface EMG signal based on fuzzy entropyIEEE Transactions on Neural Systems and Rehabilitation Engineering2007152266272 – reference: ZhangXKarplusVJQiTCarbon emissions in China: How far can new efforts bend the curve?Energy Economics201654388395 – reference: WuZHuangNEEnsemble empirical mode decomposition: A noise-assisted data analysis methodAdvances in Adaptive Data Analysis200911141 – reference: MontagnoliADe VriesFPCarbon trading thickness and market efficiencyEnergy Economics201032613311336 – reference: ZhuBWeiYCarbon price forecasting with a novel hybrid ARIMA and least squares support vector machines methodologyOmega2013413517524 – reference: TaorminaRChauKWData-driven input variable selection for rainfall–runoff modeling using binary-coded particle swarm optimization and Extreme Learning MachinesJournal of Hydrology201552916171632 – reference: Ortiz-CruzARodriguezEIbarra-ValdezCEfficiency of crude oil markets: Evidences from informational entropy analysisEnergy Policy201241365373 – reference: ZhangXLaiKKWangSYA new approach for crude oil price analysis based on empirical mode decompositionEnergy Economics2008303905918 – reference: SegnonMLuxTGuptaRModeling and forecasting the volatility of carbon dioxide emission allowance prices: A review and comparison of modern volatility modelsRenewable and Sustainable Energy Reviews201769692704 – reference: ZhangYJWeiYMAn overview of current research on EU ETS: Evidence from its operating mechanism and economic effectApplied Energy201087618041814 – reference: VapnikVNThe nature of statistical learning theory1995Springer – reference: FengZHWeiYMWangKEstimating risk for the carbon market via extreme value theory: An empirical analysis of the EU ETSApplied Energy20129997108 – reference: LiuHTianHLiYComparison of two new ARIMA-ANN and ARIMA-Kalman hybrid methods for wind speed predictionApplied Energy201298415424 – reference: ZhuBHanDWangPWuZCForecasting carbon price using empirical mode decomposition and evolutionary least squares support vector regressionApplied Energy2017191521530 – reference: de Oliveira, F. A., Nobre, C. N., & Zárate, L. E. (2013). Applying artificial neural networks to prediction of stock price and improvement of the directional prediction index–Case study of PETR4, Petrobras, Brazil. Expert Systems with Applications, 40(18), 7596–7606. – reference: FengZHZouLLWeiYMCarbon price volatility: Evidence from EU ETSApplied Energy2011883590598 – reference: ChenWZhuangJYuWMeasuring complexity using FuzzyEn, ApEn, and SampEnMedical Engineering & Physics20093116168 – reference: Yu, L., Wang, Z., & Tang, L. (2015). A decomposition–ensemble model with data-characteristic-driven reconstruction for crude oil price forecasting. Applied Energy, 156, 251–267. – reference: MengAGeJYinHWind speed forecasting based on wavelet packet decomposition and artificial neural networks trained by crisscross optimization algorithmEnergy Conversion and Management20161147588 – reference: SaninMEViolanteFMansanet-BatallerMUnderstanding volatility dynamics in the EU-ETS marketEnergy Policy201582321331 – reference: HarveyDLeybourneSNewboldPTesting the equality of prediction mean squared errorsInternational Journal of Forecasting1997132281291 – reference: LiMJHeYLTaoWQModeling a hybrid methodology for evaluating and forecasting regional energy efficiency in ChinaApplied Energy201718517691777 – reference: DieboldFXMarianoRSComparing Predictive AccuracyJournal of Business & Economic Statistics1995133253263 – reference: Zhu, B., Ye, S., Wang, P., He, K., Zhang, T., & Wei, Y. M. (2018). A novel multiscale nonlinear ensemble leaning paradigm for carbon price forecasting. Energy Economics, 70, 143–157. – reference: LiuHShiJApplying ARMA–GARCH approaches to forecasting short-term electricity pricesEnergy Economics201337152166 – reference: GuoZZhaoWLuHMulti-step forecasting for wind speed using a modified EMD-based artificial neural network modelRenewable Energy2012371241249 – reference: ZhuBA novel multiscale ensemble carbon price prediction model integrating empirical mode decomposition, genetic algorithm and artificial neural networkEnergies201252355370 – reference: Hansen, P. R., Lunde, A., & Nason, J. M. (2011). The model confidence set. Econometrica, 79(2), 453–497. – reference: Tsai, M. T., & Kuo, Y. T. (2013). A forecasting system of carbon price in the carbon trading markets using artificial neural network. International Journal of Environmental Science and Development, 4(2), 163. – reference: JiaoLLiaoYZhouQPredicting carbon market risk using information from macroeconomic fundamentalsEnergy Economics201873212227 – reference: Jiang, L., & Wu, P. (2015). International carbon market price forecasting using an integration model based on SVR. In International Conference on Engineering Management, Engineering Education and Information Technology. – reference: WangWChauKXuDImproving forecasting accuracy of annual runoff time series using ARIMA based on EEMD decompositionWater Resources Management201529826552675 – reference: SongYLiuTLiangDLiYSongXA Fuzzy Stochastic Model for Carbon Price Prediction Under the Effect of Demand-related Policy in China's Carbon MarketEcological Economics2019157253265 – reference: BenzETrückSModeling the price dynamics of CO 2 emission allowancesEnergy Economics2009311415 – reference: HuangGBZhuQYSiewCKExtreme learning machine: Theory and applicationsNeurocomputing2006701489501 – reference: ChevallierJNonparametric modeling of carbon pricesEnergy Economics201133612671282 – reference: FanXLiSTianLChaotic characteristic identification for carbon price and a multi-layer perceptron network prediction modelExpert Systems with Applications201542839453952 – reference: Arouri, M. E. H., Jawadi, F., & Nguyen, D. K. (2012). Nonlinearities in carbon spot-futures price relationships during Phase II of the EU ETS. Economic Modelling, 29(3), 884–892. – reference: WangSZhangNWuLWind speed forecasting based on the hybrid ensemble empirical mode decomposition and GA-BP neural network methodRenewable Energy201694629636 – reference: TanXSirichandKVivianAWangXHow connected is the carbon market to energy and financial markets? A systematic analysis of spillovers and dynamicsEnergy Economics202090104870 – reference: RenYSuganthanPNSrikanthNA comparative study of empirical mode decomposition-based short-term wind speed forecasting methodsIEEE Transactions on Sustainable Energy201561236244 – reference: SunWWangCFZhangCFactor analysis and forecasting of CO2 emissions in Hebei, using extreme learning machine based on particle swarm optimizationJournal of Cleaner Production201716210951101 – reference: Huang, Y., Dai, X., Wang, Q., & Zhou, D. (2021). A hybrid model for carbon price forecasting using GARCH and long short-term memory network. Applied Energy, 285, 116485. – reference: Jiang, M., Jia, L., Chen, Z., & Chen, W. (2020). The two-stage machine learning ensemble models for stock price prediction by combining mode decomposition, extreme learning machine and improved harmony search algorithm. Annals of Operations Research, 1–33. https://doi.org/10.1007/s10479-020-03690-w – volume: 41 start-page: 517 issue: 3 year: 2013 ident: 4406_CR45 publication-title: Omega doi: 10.1016/j.omega.2012.06.005 – volume: 98 start-page: 459 year: 2016 ident: 4406_CR26 publication-title: Energy Policy doi: 10.1016/j.enpol.2016.09.021 – ident: 4406_CR17 doi: 10.1007/s10479-020-03690-w – volume: 281 start-page: 373 issue: 1 year: 2019 ident: 4406_CR46 publication-title: Annals of Operations Research doi: 10.1007/s10479-018-2982-0 – volume: 98 start-page: 415 year: 2012 ident: 4406_CR21 publication-title: Applied Energy doi: 10.1016/j.apenergy.2012.04.001 – volume: 69 start-page: 692 year: 2017 ident: 4406_CR28 publication-title: Renewable and Sustainable Energy Reviews doi: 10.1016/j.rser.2016.11.060 – volume: 87 start-page: 1804 issue: 6 year: 2010 ident: 4406_CR41 publication-title: Applied Energy doi: 10.1016/j.apenergy.2009.12.019 – volume: 255 start-page: 157 issue: 1 year: 2017 ident: 4406_CR33 publication-title: Annals of Operations Research doi: 10.1007/s10479-015-1864-y – volume: 33 start-page: 1267 issue: 6 year: 2011 ident: 4406_CR7 publication-title: Energy Economics doi: 10.1016/j.eneco.2011.03.003 – volume: 157 start-page: 253 year: 2019 ident: 4406_CR29 publication-title: Ecological Economics doi: 10.1016/j.ecolecon.2018.10.001 – volume: 30 start-page: 905 issue: 3 year: 2008 ident: 4406_CR40 publication-title: Energy Economics doi: 10.1016/j.eneco.2007.02.012 – volume: 99 start-page: 97 year: 2012 ident: 4406_CR10 publication-title: Applied Energy doi: 10.1016/j.apenergy.2012.01.070 – ident: 4406_CR80 doi: 10.7763/IJESD.2013.V4.327 – volume: 94 start-page: 629 year: 2016 ident: 4406_CR36 publication-title: Renewable Energy doi: 10.1016/j.renene.2016.03.103 – volume: 88 start-page: 590 issue: 3 year: 2011 ident: 4406_CR11 publication-title: Applied Energy doi: 10.1016/j.apenergy.2010.06.017 – volume: 82 start-page: 321 year: 2015 ident: 4406_CR27 publication-title: Energy Policy doi: 10.1016/j.enpol.2015.02.024 – volume: 6 start-page: 236 issue: 1 year: 2015 ident: 4406_CR25 publication-title: IEEE Transactions on Sustainable Energy doi: 10.1109/TSTE.2014.2365580 – volume: 1 start-page: 1 issue: 1 year: 2009 ident: 4406_CR37 publication-title: Advances in Adaptive Data Analysis doi: 10.1142/S1793536909000047 – volume: 191 start-page: 521 year: 2017 ident: 4406_CR44 publication-title: Applied Energy doi: 10.1016/j.apenergy.2017.01.076 – ident: 4406_CR444 doi: 10.1016/j.eneco.2017.12.030 – volume: 114 start-page: 75 year: 2016 ident: 4406_CR22 publication-title: Energy Conversion and Management doi: 10.1016/j.enconman.2016.02.013 – volume: 90 start-page: 104870 year: 2020 ident: 4406_CR32 publication-title: Energy Economics doi: 10.1016/j.eneco.2020.104870 – volume: 73 start-page: 212 year: 2018 ident: 4406_CR16 publication-title: Energy Economics doi: 10.1016/j.eneco.2018.05.008 – volume: 37 start-page: 241 issue: 1 year: 2012 ident: 4406_CR12 publication-title: Renewable Energy doi: 10.1016/j.renene.2011.06.023 – volume: 477 start-page: 161 year: 2017 ident: 4406_CR38 publication-title: Physica a: Statistical Mechanics and Its Applications doi: 10.1016/j.physa.2017.02.072 – volume: 13 start-page: 253 issue: 3 year: 1995 ident: 4406_CR8 publication-title: Journal of Business & Economic Statistics doi: 10.1080/07350015.1995.10524599 – volume: 37 start-page: 152 year: 2013 ident: 4406_CR20 publication-title: Energy Economics doi: 10.1016/j.eneco.2013.02.006 – volume: 54 start-page: 388 year: 2016 ident: 4406_CR39 publication-title: Energy Economics doi: 10.1016/j.eneco.2015.12.002 – volume: 42 start-page: 3945 issue: 8 year: 2015 ident: 4406_CR9 publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2014.12.047 – volume: 31 start-page: 4 issue: 1 year: 2009 ident: 4406_CR2 publication-title: Energy Economics doi: 10.1016/j.eneco.2008.07.003 – ident: 4406_CR83 doi: 10.1016/j.apenergy.2021.116485 – volume: 40 start-page: 207 year: 2013 ident: 4406_CR3 publication-title: Energy Economics doi: 10.1016/j.eneco.2013.06.017 – volume: 29 start-page: 2655 issue: 8 year: 2015 ident: 4406_CR35 publication-title: Water Resources Management doi: 10.1007/s11269-015-0962-6 – volume: 454 start-page: 903 issue: 1971 year: 1998 ident: 4406_CR15 publication-title: Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences doi: 10.1098/rspa.1998.0193 – volume: 185 start-page: 1769 year: 2017 ident: 4406_CR19 publication-title: Applied Energy doi: 10.1016/j.apenergy.2015.11.082 – volume: 41 start-page: 365 year: 2012 ident: 4406_CR24 publication-title: Energy Policy doi: 10.1016/j.enpol.2011.10.057 – volume: 31 start-page: 61 issue: 1 year: 2009 ident: 4406_CR6 publication-title: Medical Engineering & Physics doi: 10.1016/j.medengphy.2008.04.005 – volume: 15 start-page: 266 issue: 2 year: 2007 ident: 4406_CR5 publication-title: IEEE Transactions on Neural Systems and Rehabilitation Engineering doi: 10.1109/TNSRE.2007.897025 – ident: 4406_CR79 doi: 10.1016/j.eswa.2013.06.071 – ident: 4406_CR78 doi: 10.1016/j.econmod.2011.11.003 – ident: 4406_CR91 doi: 10.1016/j.apenergy.2015.07.025 – volume: 32 start-page: 1331 issue: 6 year: 2010 ident: 4406_CR23 publication-title: Energy Economics doi: 10.1016/j.eneco.2010.04.001 – volume: 529 start-page: 1617 year: 2015 ident: 4406_CR31 publication-title: Journal of Hydrology doi: 10.1016/j.jhydrol.2015.08.022 – volume: 162 start-page: 1095 year: 2017 ident: 4406_CR30 publication-title: Journal of Cleaner Production doi: 10.1016/j.jclepro.2017.06.016 – volume: 5 start-page: 355 issue: 2 year: 2012 ident: 4406_CR43 publication-title: Energies doi: 10.3390/en5020355 – ident: 4406_CR81 doi: 10.2991/emeeit-15.2015.61 – volume: 13 start-page: 281 issue: 2 year: 1997 ident: 4406_CR13 publication-title: International Journal of Forecasting doi: 10.1016/S0169-2070(96)00719-4 – volume-title: The nature of statistical learning theory year: 1995 ident: 4406_CR34 doi: 10.1007/978-1-4757-2440-0 – ident: 4406_CR85 doi: 10.3982/ECTA5771 – volume: 70 start-page: 489 issue: 1 year: 2006 ident: 4406_CR14 publication-title: Neurocomputing doi: 10.1016/j.neucom.2005.12.126 |
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| SubjectTerms | Algorithms Artificial neural networks Business and Management Carbon Cluster analysis Clustering Combinatorics Emissions credit trading Entropy Forecasting Futures Low frequencies Machine learning Mathematical models Operations research Operations Research/Decision Theory Original Research Pricing Reconstruction Residues Theory of Computation Vector quantization |
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| Title | Forecasting carbon futures price: a hybrid method incorporating fuzzy entropy and extreme learning machine |
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