{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,20]],"date-time":"2026-02-20T04:44:00Z","timestamp":1771562640227,"version":"3.50.1"},"reference-count":70,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,6,6]],"date-time":"2025-06-06T00:00:00Z","timestamp":1749168000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,6,6]],"date-time":"2025-06-06T00:00:00Z","timestamp":1749168000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"name":"Anhui Province young and middle-aged teachers training action project","award":["YQYB2023089"],"award-info":[{"award-number":["YQYB2023089"]}]},{"name":"Research Projects of Higher Education Institutions in Anhui Province","award":["2023AH052906"],"award-info":[{"award-number":["2023AH052906"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J Comput Intell Syst"],"DOI":"10.1007\/s44196-025-00881-w","type":"journal-article","created":{"date-parts":[[2025,6,6]],"date-time":"2025-06-06T10:23:52Z","timestamp":1749205432000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["A Multi-granularity Heterogeneous Ensemble Model for Point and Interval Forecasting of Carbon Prices"],"prefix":"10.1007","volume":"18","author":[{"given":"Di","family":"Sha","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xianyi","family":"Zeng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kim-Phuc","family":"Tran","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lin","family":"Xia","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ruolin","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,6,6]]},"reference":[{"key":"881_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.119539","author":"J Wang","year":"2023","unstructured":"Wang, J., Zhou, Y., Jiang, H.: A novel interval forecasting system based on multi-objective optimization and hybrid data reconstruct strategy. Expert Syst. Appl. (2023). https:\/\/doi.org\/10.1016\/j.eswa.2023.119539","journal-title":"Expert Syst. Appl."},{"key":"881_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eneco.2022.105906","author":"W Wang","year":"2022","unstructured":"Wang, W., Zhang, Y.J.: Does China\u2019s carbon emissions trading scheme affect the market power of high-carbon enterprises? Energy Econ. (2022). https:\/\/doi.org\/10.1016\/j.eneco.2022.105906","journal-title":"Energy Econ."},{"key":"881_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.120647","author":"S Mao","year":"2023","unstructured":"Mao, S., Zeng, X.J.: SimVGNets: similarity-based visibility graph networks for carbon price forecasting. Expert Syst. Appl. (2023). https:\/\/doi.org\/10.1016\/j.eswa.2023.120647","journal-title":"Expert Syst. Appl."},{"key":"881_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2023.129761","author":"L Ding","year":"2024","unstructured":"Ding, L., Zhang, R., Zhao, X.: 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 (2024). https:\/\/doi.org\/10.1016\/j.energy.2023.129761","journal-title":"Energy"},{"key":"881_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.jclepro.2023.136959","author":"K Zhang","year":"2023","unstructured":"Zhang, K., Yang, X., Wang, T., Th\u00e9, J., Tan, Z., Yu, H.: Multi-step carbon price forecasting using a hybrid model based on multivariate decomposition strategy and deep learning algorithms. J. Clean. Prod. (2023). https:\/\/doi.org\/10.1016\/j.jclepro.2023.136959","journal-title":"J. Clean. Prod."},{"key":"881_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2022.105172","author":"P Wang","year":"2022","unstructured":"Wang, P., Liu, J., Tao, Z., Chen, H.: A novel carbon price combination forecasting approach based on multi-source information fusion and hybrid multi-scale decomposition. Eng. Appl. Artif. Intell. (2022). https:\/\/doi.org\/10.1016\/j.engappai.2022.105172","journal-title":"Eng. Appl. Artif. Intell."},{"key":"881_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.jclepro.2019.119386","author":"H Lu","year":"2020","unstructured":"Lu, H., Ma, X., Huang, K., Azimi, M.: Carbon trading volume and price forecasting in China using multiple machine learning models. J. Clean. Prod. (2020). https:\/\/doi.org\/10.1016\/j.jclepro.2019.119386","journal-title":"J. Clean. Prod."},{"key":"881_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2024.109510","author":"Y Xu","year":"2025","unstructured":"Xu, Y., Liu, T., Fang, Q., Du, P., Wang, J.: Crude oil price forecasting with multivariate selection, machine learning, and a nonlinear combination strategy. Eng. Appl. Artif. Intell. (2025). https:\/\/doi.org\/10.1016\/j.engappai.2024.109510","journal-title":"Eng. Appl. Artif. Intell."},{"key":"881_CR9","doi-asserted-by":"publisher","DOI":"10.3390\/su12187317","author":"C Sheng","year":"2020","unstructured":"Sheng, C., Wang, G., Geng, Y., Chen, L.: The correlation analysis of futures pricing mechanism in China\u2019s carbon financial market. Sustainability (2020). https:\/\/doi.org\/10.3390\/su12187317","journal-title":"Sustainability"},{"key":"881_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.jenvman.2024.120131","author":"H Shi","year":"2024","unstructured":"Shi, H., Wei, A., Xu, X., Zhu, Y., Hu, H., Tang, S.: A CNN-LSTM based deep learning model with high accuracy and robustness for carbon price forecasting: a case of Shenzhen\u2019s carbon market in China. J. Environ. Manag. (2024). https:\/\/doi.org\/10.1016\/j.jenvman.2024.120131","journal-title":"J. Environ. Manag."},{"key":"881_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2025.127832","author":"X Chen","year":"2025","unstructured":"Chen, X., Liu, J., Wu, C.: Multi-class financial distress prediction based on hybrid feature selection and improved stacking ensemble model. Expert Syst. Appl. (2025). https:\/\/doi.org\/10.1016\/j.eswa.2025.127832","journal-title":"Expert Syst. Appl."},{"key":"881_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2024.131352","author":"L Liu","year":"2024","unstructured":"Liu, L., Zhou, S., Jie, Q., Du, P., Xu, Y., Wang, J.: A robust time-varying weight combined model for crude oil price forecasting. Energy (2024). https:\/\/doi.org\/10.1016\/j.energy.2024.131352","journal-title":"Energy"},{"key":"881_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2024.126361","author":"P Du","year":"2025","unstructured":"Du, P., Ye, Y., Wu, H., Wang, J.: Study on deterministic and interval forecasting of electricity load based on multi-objective whale optimization algorithm and transformer model. Expert Syst. Appl. (2025). https:\/\/doi.org\/10.1016\/j.eswa.2024.126361","journal-title":"Expert Syst. Appl."},{"key":"881_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2024.132895","author":"R Cheng","year":"2024","unstructured":"Cheng, R., Yang, D., Liu, D., Zhang, G.: A reconstruction-based secondary decomposition-ensemble framework for wind power forecasting. Energy (2024). https:\/\/doi.org\/10.1016\/j.energy.2024.132895","journal-title":"Energy"},{"key":"881_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.jhydrol.2024.132373","author":"M Poursaeid","year":"2025","unstructured":"Poursaeid, M.: Optimizing transient monitoring of river streamflow by a highly predictive model utilizing ensemble learning models and multi algorithms. J. Hydrol. (2025). https:\/\/doi.org\/10.1016\/j.jhydrol.2024.132373","journal-title":"J. Hydrol."},{"key":"881_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.cie.2022.108563","author":"R Afshari","year":"2022","unstructured":"Afshari, R., Nadi, A.A., Johannssen, A., Chukhrova, N., Tran, K.P.: The effects of measurement errors on estimating and assessing the multivariate process capability with imprecise characteristic. Comput. Ind. Eng. (2022). https:\/\/doi.org\/10.1016\/j.cie.2022.108563","journal-title":"Comput. Ind. Eng."},{"key":"881_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/j.jclepro.2023.137853","author":"D Li","year":"2023","unstructured":"Li, D., Ren, X.: Carbon price prediction based on LsOALEO feature selection and time-delay least angle regression. J. Clean. Prod. (2023). https:\/\/doi.org\/10.1016\/j.jclepro.2023.137853","journal-title":"J. Clean. Prod."},{"key":"881_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.jenvman.2024.120967","author":"Z Huang","year":"2024","unstructured":"Huang, Z., Zhang, W.: Forecasting carbon prices in China\u2019s pilot carbon market: a multi-source information approach with conditional generative adversarial networks. J. Environ. Manag. (2024). https:\/\/doi.org\/10.1016\/j.jenvman.2024.120967","journal-title":"J. Environ. Manag."},{"key":"881_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.enconman.2017.07.065","author":"P Du","year":"2017","unstructured":"Du, P., Wang, J., Guo, Z., Yang, W.: Research and application of a novel hybrid forecasting system based on multi-objective optimization for wind speed forecasting. Energy Convers. Manag. (2017). https:\/\/doi.org\/10.1016\/j.enconman.2017.07.065","journal-title":"Energy Convers. Manag."},{"key":"881_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.eneco.2020.104790","author":"J Wang","year":"2020","unstructured":"Wang, J., Zhou, H., Hong, T., Li, X., Wang, S.: A multi-granularity heterogeneous combination approach to crude oil price forecasting. Energy Econ. (2020). https:\/\/doi.org\/10.1016\/j.eneco.2020.104790","journal-title":"Energy Econ."},{"key":"881_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.eneco.2022.106361","author":"B Zhu","year":"2022","unstructured":"Zhu, B., Wan, C., Wang, P.: Interval forecasting of carbon price: a novel multiscale ensemble forecasting approach. Energy Econ. (2022). https:\/\/doi.org\/10.1016\/j.eneco.2022.106361","journal-title":"Energy Econ."},{"key":"881_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2018.10.043","author":"LG Mar\u00edn","year":"2019","unstructured":"Mar\u00edn, L.G., Cruz, N., S\u00e1ez, D., Sumner, M., N\u00fa\u00f1ez, A.: Prediction interval methodology based on fuzzy numbers and its extension to fuzzy systems and neural networks. Expert Syst. Appl. (2019). https:\/\/doi.org\/10.1016\/j.eswa.2018.10.043","journal-title":"Expert Syst. Appl."},{"key":"881_CR23","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2025.125951","author":"M Ji","year":"2025","unstructured":"Ji, M., Du, J., Du, P., Niu, T., Wang, J.: A novel probabilistic carbon price prediction model: integrating the transformer framework with mixed-frequency modeling at different quartiles. Appl. Energy (2025). https:\/\/doi.org\/10.1016\/j.apenergy.2025.125951","journal-title":"Appl. Energy"},{"key":"881_CR24","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.122502","author":"S Deng","year":"2024","unstructured":"Deng, S., Su, J., Zhu, Y., Yu, Y., Xiao, C.: Forecasting carbon price trends based on an interpretable light gradient boosting machine and Bayesian optimization. Expert Syst. Appl. (2024). https:\/\/doi.org\/10.1016\/j.eswa.2023.122502","journal-title":"Expert Syst. Appl."},{"key":"881_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2020.12.086","author":"E Jianwei","year":"2021","unstructured":"Jianwei, E., Ye, J., He, L., Jin, H.: A denoising carbon price forecasting method based on the integration of kernel independent component analysis and least squares support vector regression. Neurocomputing (2021). https:\/\/doi.org\/10.1016\/j.neucom.2020.12.086","journal-title":"Neurocomputing"},{"key":"881_CR26","doi-asserted-by":"publisher","DOI":"10.1016\/j.jenvman.2024.122275","author":"X Zhang","year":"2024","unstructured":"Zhang, X., Zong, Y., Du, P., Wang, S., Wang, J.: Framework for multivariate carbon price forecasting: a novel hybrid model. J. Environ. Manag. (2024). https:\/\/doi.org\/10.1016\/j.jenvman.2024.122275","journal-title":"J. Environ. Manag."},{"key":"881_CR27","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2019.01.009","author":"M Han","year":"2019","unstructured":"Han, M., Ding, L., Zhao, X., Kang, W.: Forecasting carbon prices in the Shenzhen market, China: the role of mixed-frequency factors. Energy (2019). https:\/\/doi.org\/10.1016\/j.energy.2019.01.009","journal-title":"Energy"},{"key":"881_CR28","doi-asserted-by":"publisher","DOI":"10.1016\/j.scitotenv.2020.138350","author":"Y Huang","year":"2020","unstructured":"Huang, Y., He, Z.: Carbon price forecasting with optimization prediction method based on unstructured combination. Sci. Total. Environ. (2020). https:\/\/doi.org\/10.1016\/j.scitotenv.2020.138350","journal-title":"Sci. Total. Environ."},{"key":"881_CR29","doi-asserted-by":"publisher","DOI":"10.1186\/s40854-023-00567-2","author":"M Li","year":"2024","unstructured":"Li, M., Yang, K., Lin, W., Wei, Y., Wang, S.: An interval constraint-based trading strategy with social sentiment for the stock market. Financ. Innov. (2024). https:\/\/doi.org\/10.1186\/s40854-023-00567-2","journal-title":"Financ. Innov."},{"key":"881_CR30","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.122884","author":"Z Sun","year":"2024","unstructured":"Sun, Z., Xie, H., Liu, J., Yu, Y.: Multi-label feature selection via adaptive dual-graph optimization. Expert Syst. Appl. (2024). https:\/\/doi.org\/10.1016\/j.eswa.2023.122884","journal-title":"Expert Syst. Appl."},{"key":"881_CR31","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2017.10.028","author":"E Hancer","year":"2018","unstructured":"Hancer, E., Xue, B., Zhang, M.: Differential evolution for filter feature selection based on information theory and feature ranking. Knowl. Based Syst. (2018). https:\/\/doi.org\/10.1016\/j.knosys.2017.10.028","journal-title":"Knowl. Based Syst."},{"key":"881_CR32","doi-asserted-by":"publisher","unstructured":"Jovi\u0107, A., Brki\u0107, K., Bogunovi\u0107, N.: A review of feature selection methods with applications. In: In 2015 38th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO) (2015). https:\/\/doi.org\/10.1109\/MIPRO.2015.7160458","DOI":"10.1109\/MIPRO.2015.7160458"},{"key":"881_CR33","doi-asserted-by":"publisher","DOI":"10.1016\/j.jobe.2023.108044","author":"L Liu","year":"2023","unstructured":"Liu, L., Fu, Q., Lu, Y., Wang, Y., Wu, H., Chen, J.: CorrDQN-FS: a two-stage feature selection method for energy consumption prediction via deep reinforcement learning. J. Build. Eng. (2023). https:\/\/doi.org\/10.1016\/j.jobe.2023.108044","journal-title":"J. Build. Eng."},{"key":"881_CR34","doi-asserted-by":"publisher","DOI":"10.1016\/j.najef.2023.102022","author":"J Zhang","year":"2024","unstructured":"Zhang, J., Liu, H., Bai, W., Li, X.: A hybrid approach of wavelet transform, ARIMA and LSTM model for the share price index futures forecasting. North Am. J. Econ. Financ. (2024). https:\/\/doi.org\/10.1016\/j.najef.2023.102022","journal-title":"North Am. J. Econ. Financ."},{"key":"881_CR35","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2022.11.250","author":"F Wang","year":"2022","unstructured":"Wang, F., Jiang, J., Shu, J.: Carbon trading price forecasting: based on improved deep learning method. Procedia Comput. Sci. (2022). https:\/\/doi.org\/10.1016\/j.procs.2022.11.250","journal-title":"Procedia Comput. Sci."},{"key":"881_CR36","doi-asserted-by":"publisher","DOI":"10.1016\/j.jclepro.2023.139131","author":"O Nadirgil","year":"2023","unstructured":"Nadirgil, O.: The relationship between the contaminating industries and the European carbon price, machine learning approach. J. Clean. Prod. (2023). https:\/\/doi.org\/10.1016\/j.jclepro.2023.139131","journal-title":"J. Clean. Prod."},{"key":"881_CR37","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2022.118601","author":"F Zhou","year":"2022","unstructured":"Zhou, F., Huang, Z., Zhang, C.: Carbon price forecasting based on CEEMDAN and LSTM. Appl. Energy (2022). https:\/\/doi.org\/10.1016\/j.apenergy.2022.118601","journal-title":"Appl. Energy"},{"key":"881_CR38","doi-asserted-by":"publisher","DOI":"10.1016\/j.jenvman.2023.118962","author":"X Zhang","year":"2023","unstructured":"Zhang, X., Yang, K., Lu, Q., Wu, J., Yu, L., Lin, Y.: Predicting carbon futures prices based on a new hybrid machine learning: comparative study of carbon prices in different periods. J. Environ. Manag. (2023). https:\/\/doi.org\/10.1016\/j.jenvman.2023.118962","journal-title":"J. Environ. Manag."},{"key":"881_CR39","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2024.131321","author":"K Yang","year":"2024","unstructured":"Yang, K., Zhang, X., Luo, H., Hou, X., Lin, Y., Wu, J., Yu, L.: Predicting energy prices based on a novel hybrid machine learning: comprehensive study of multi-step price forecasting. Energy (2024). https:\/\/doi.org\/10.1016\/j.energy.2024.131321","journal-title":"Energy"},{"key":"881_CR40","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2024.132483","author":"Q Sun","year":"2024","unstructured":"Sun, Q., Chen, H., Long, R., Chen, J.: Integrated prediction of carbon price in China based on heterogeneous structural information and wall-value constraints. Energy (2024). https:\/\/doi.org\/10.1016\/j.energy.2024.132483","journal-title":"Energy"},{"key":"881_CR41","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2024.124825","author":"Y Tian","year":"2024","unstructured":"Tian, Y., Wen, H., Fu, S.: Multi-step ahead prediction of carbon price movement using time-series privileged information. Expert Syst. Appl. (2024). https:\/\/doi.org\/10.1016\/j.eswa.2024.124825","journal-title":"Expert Syst. Appl."},{"key":"881_CR42","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2025.110106","author":"AE Bilali","year":"2025","unstructured":"Bilali, A.E., Hadri, A., Taleb, A., Tanarhte, M., Khalki, E.M.E., Kharrou, M.H.: A novel hybrid modeling approach based on empirical methods, PSO, XGBoost, and multiple GCMs for forecasting long-term reference evapotranspiration in a data scarce-area. Comput. Electron. Agric. (2025). https:\/\/doi.org\/10.1016\/j.compag.2025.110106","journal-title":"Comput. Electron. Agric."},{"key":"881_CR43","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2025.113012","author":"S Perla","year":"2025","unstructured":"Perla, S., Bisoi, R., Dash, P.K., Rout, A.K.: Short-term forecasting of electricity price using ensemble deep kernel based random vector functional link network. Appl. Soft Comput. (2025). https:\/\/doi.org\/10.1016\/j.asoc.2025.113012","journal-title":"Appl. Soft Comput."},{"key":"881_CR44","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijepes.2023.109269","author":"LO Seman","year":"2023","unstructured":"Seman, L.O., Stefenon, S.F., Mariani, V.C., dos Santos Coelho, L.: Ensemble learning methods using the Hodrick-Prescott filter for fault forecasting in insulators of the electrical power grids. Int. J. Electr. Power Energy Syst. (2023). https:\/\/doi.org\/10.1016\/j.ijepes.2023.109269","journal-title":"Int. J. Electr. Power Energy Syst."},{"key":"881_CR45","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2019.12.140","author":"M Larrea","year":"2021","unstructured":"Larrea, M., Porto, A., Irigoyen, E., Barrag\u00e1n, A.J., And\u00fajar, J.M.: Extreme learning machine ensemble model for time series forecasting boosted by PSO: application to an electric consumption problem. Neurocomputing (2021). https:\/\/doi.org\/10.1016\/j.neucom.2019.12.140","journal-title":"Neurocomputing"},{"key":"881_CR46","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2022.109690","author":"H Xian","year":"2022","unstructured":"Xian, H., Che, J.: Unified whale optimization algorithm based multi-kernel SVR ensemble learning for wind speed forecasting. Appl. Soft Comput. (2022). https:\/\/doi.org\/10.1016\/j.asoc.2022.109690","journal-title":"Appl. Soft Comput."},{"key":"881_CR47","doi-asserted-by":"publisher","DOI":"10.1016\/j.renene.2022.12.120","author":"Z Zhang","year":"2023","unstructured":"Zhang, Z., Wang, J., Wei, D., Luo, T., Xia, Y.: A novel ensemble system for short-term wind speed forecasting based on two-stage attention-based recurrent neural network. Renew. Energy (2023). https:\/\/doi.org\/10.1016\/j.renene.2022.12.120","journal-title":"Renew. Energy"},{"key":"881_CR48","doi-asserted-by":"publisher","DOI":"10.1016\/j.enconman.2018.02.006","author":"H Liu","year":"2018","unstructured":"Liu, H., Wu, H., Li, Y.: Smart wind speed forecasting using EWT decomposition, GWO evolutionary optimization, RELM learning and IEWT reconstruction. Energy Convers. Manag. (2018). https:\/\/doi.org\/10.1016\/j.enconman.2018.02.006","journal-title":"Energy Convers. Manag."},{"key":"881_CR49","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2022.118938","author":"K Wang","year":"2022","unstructured":"Wang, K., Wang, J., Zeng, B., Lu, H.: An integrated power load point-interval forecasting system based on information entropy and multi-objective optimization. Appl. Energy (2022). https:\/\/doi.org\/10.1016\/j.apenergy.2022.118938","journal-title":"Appl. Energy"},{"key":"881_CR50","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2020.115600","author":"S Zhang","year":"2020","unstructured":"Zhang, S., Wang, Y., Zhang, Y., Wang, D., Zhang, N.: Load probability density forecasting by transforming and combining quantile forecasts. Appl. Energy (2020). https:\/\/doi.org\/10.1016\/j.apenergy.2020.115600","journal-title":"Appl. Energy"},{"key":"881_CR51","doi-asserted-by":"publisher","DOI":"10.1016\/j.eneco.2022.105862","author":"X Ren","year":"2022","unstructured":"Ren, X., Duan, K., Tao, L., Shi, Y., Yan, C.: Carbon prices forecasting in quantiles. Energy Econ. (2022). https:\/\/doi.org\/10.1016\/j.eneco.2022.105862","journal-title":"Energy Econ."},{"key":"881_CR52","doi-asserted-by":"publisher","DOI":"10.1016\/j.jclepro.2023.139508","author":"N Jiang","year":"2023","unstructured":"Jiang, N., Yu, X., Alam, M.: A hybrid carbon price prediction model based-combinational estimation strategies of quantile regression and long short-term memory. J. Clean. Prod. (2023). https:\/\/doi.org\/10.1016\/j.jclepro.2023.139508","journal-title":"J. Clean. Prod."},{"key":"881_CR53","doi-asserted-by":"publisher","DOI":"10.1016\/j.ecoinf.2021.101489","author":"X Zhou","year":"2022","unstructured":"Zhou, X., Wang, J., Wang, H., Lin, J.: Panel semiparametric quantile regression neural network for electricity consumption forecasting. Eco. Inform. (2022). https:\/\/doi.org\/10.1016\/j.ecoinf.2021.101489","journal-title":"Eco. Inform."},{"key":"881_CR54","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2015.09.040","author":"W Deng","year":"2016","unstructured":"Deng, W., Wang, G., Zhang, X., Xu, J., Li, G.: A multi-granularity combined prediction model based on fuzzy trend forecasting and particle swarm techniques. Neurocomputing (2016). https:\/\/doi.org\/10.1016\/j.neucom.2015.09.040","journal-title":"Neurocomputing"},{"key":"881_CR55","doi-asserted-by":"publisher","DOI":"10.1016\/j.cam.2017.05.038","author":"S Wang","year":"2018","unstructured":"Wang, S., Zhao, Y., Shu, Y., Yuan, H., Geng, J., Wang, S.: Fast search local extremum for maximal information coefficient (MIC). J. Comput. Appl. Math. (2018). https:\/\/doi.org\/10.1016\/j.cam.2017.05.038","journal-title":"J. Comput. Appl. Math."},{"key":"881_CR56","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiolchem.2022.107747","author":"L Li","year":"2022","unstructured":"Li, L., Ching, W.K., Liu, Z.P.: Robust biomarker screening from gene expression data by stable machine learning-recursive feature elimination methods. Comput. Biol. Chem. (2022). https:\/\/doi.org\/10.1016\/j.compbiolchem.2022.107747","journal-title":"Comput. Biol. Chem."},{"key":"881_CR57","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2017.11.077","author":"J Cai","year":"2018","unstructured":"Cai, J., Luo, J., Wang, S., Yang, S.: Feature selection in machine learning: a new perspective. Neurocomputing (2018). https:\/\/doi.org\/10.1016\/j.neucom.2017.11.077","journal-title":"Neurocomputing"},{"key":"881_CR58","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2017.02.057","author":"A Mirzaei","year":"2017","unstructured":"Mirzaei, A., Mohsenzadeh, Y., Sheikhzadeh, H.: Variational relevant sample-feature machine: a fully Bayesian approach for embedded feature selection. Neurocomputing (2017). https:\/\/doi.org\/10.1016\/j.neucom.2017.02.057","journal-title":"Neurocomputing"},{"key":"881_CR59","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2023.01.037","author":"X Zhang","year":"2023","unstructured":"Zhang, X., Zhong, C., Zhang, J., Wang, T., Ng, W.W.: Robust recurrent neural networks for time series forecasting. Neurocomputing (2023). https:\/\/doi.org\/10.1016\/j.neucom.2023.01.037","journal-title":"Neurocomputing"},{"key":"881_CR60","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2023.106440","author":"I Kumar","year":"2023","unstructured":"Kumar, I., Tripathi, B.K., Singh, A.: Attention-based LSTM network-assisted time series forecasting models for petroleum production. Eng. Appl. Artif. Intell. (2023). https:\/\/doi.org\/10.1016\/j.engappai.2023.106440","journal-title":"Eng. Appl. Artif. Intell."},{"key":"881_CR61","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2023.127865","author":"YM Zhang","year":"2023","unstructured":"Zhang, Y.M., Wang, H.: Multi-head attention-based probabilistic CNN-BiLSTM for day-ahead wind speed forecasting. Energy (2023). https:\/\/doi.org\/10.1016\/j.energy.2023.127865","journal-title":"Energy"},{"key":"881_CR62","doi-asserted-by":"publisher","DOI":"10.1016\/j.renene.2023.01.118","author":"T Limouni","year":"2023","unstructured":"Limouni, T., Yaagoubi, R., Bouziane, K., Guissi, K., Baali, E.H.: Accurate one step and multistep forecasting of very short-term PV power using LSTM-TCN model. Renew. Energy (2023). https:\/\/doi.org\/10.1016\/j.renene.2023.01.118","journal-title":"Renew. Energy"},{"key":"881_CR63","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2022.103962","author":"S Hu","year":"2023","unstructured":"Hu, S., Xiong, C.: High-dimensional population inflow time series forecasting via an interpretable hierarchical transformer. Transp. Res. Part C Emerg. Technol. (2023). https:\/\/doi.org\/10.1016\/j.trc.2022.103962","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"881_CR64","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.119617","author":"S Zhang","year":"2023","unstructured":"Zhang, S., Luo, J., Wang, S., Liu, F.: Oil price forecasting: a hybrid GRU neural network based on decomposition-reconstruction methods. Expert Syst. Appl. (2023). https:\/\/doi.org\/10.1016\/j.eswa.2023.119617","journal-title":"Expert Syst. Appl."},{"key":"881_CR65","doi-asserted-by":"publisher","DOI":"10.1016\/j.advengsoft.2013.12.007","author":"S Mirjalili","year":"2014","unstructured":"Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Adv. Eng. Softw. (2014). https:\/\/doi.org\/10.1016\/j.advengsoft.2013.12.007","journal-title":"Adv. Eng. Softw."},{"key":"881_CR66","doi-asserted-by":"publisher","DOI":"10.1007\/s12206-018-1128-2","author":"YJ Kang","year":"2018","unstructured":"Kang, Y.J., Noh, Y., Lim, O.K.: Development of a kernel density estimation with hybrid estimated bounded data. J. Mech. Sci. Technol. (2018). https:\/\/doi.org\/10.1007\/s12206-018-1128-2","journal-title":"J. Mech. Sci. Technol."},{"key":"881_CR67","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2022.118936","author":"TC Carneiro","year":"2022","unstructured":"Carneiro, T.C., Rocha, P.A., Carvalho, P.C., Fern\u00e1ndez-Ram\u00edrez, L.M.: Ridge regression ensemble of machine learning models applied to solar and wind forecasting in Brazil and Spain. Appl. Energy (2022). https:\/\/doi.org\/10.1016\/j.apenergy.2022.118936","journal-title":"Appl. Energy"},{"key":"881_CR68","doi-asserted-by":"publisher","DOI":"10.1198\/073500102753410444","author":"FX Diebold","year":"2002","unstructured":"Diebold, F.X., Mariano, R.S.: Comparing predictive accuracy. J. Bus. Econ. Stat. (2002). https:\/\/doi.org\/10.1198\/073500102753410444","journal-title":"J. Bus. Econ. Stat."},{"key":"881_CR69","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2021.108204","author":"Z Ji","year":"2022","unstructured":"Ji, Z., Niu, D., Li, M., Li, W., Sun, L., Zhu, Y.: A three-stage framework for vertical carbon price interval forecast based on decomposition-integration method. Appl. Soft Comput. (2022). https:\/\/doi.org\/10.1016\/j.asoc.2021.108204","journal-title":"Appl. Soft Comput."},{"key":"881_CR70","doi-asserted-by":"publisher","DOI":"10.1016\/j.apm.2019.10.022","author":"C Tian","year":"2020","unstructured":"Tian, C., Hao, Y.: Point and interval forecasting for carbon price based on an improved analysis-forecast system. Appl. Math. Model. (2020). https:\/\/doi.org\/10.1016\/j.apm.2019.10.022","journal-title":"Appl. Math. Model."}],"container-title":["International Journal of Computational Intelligence Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44196-025-00881-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s44196-025-00881-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44196-025-00881-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,6]],"date-time":"2025-06-06T10:23:53Z","timestamp":1749205433000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s44196-025-00881-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,6]]},"references-count":70,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["881"],"URL":"https:\/\/doi.org\/10.1007\/s44196-025-00881-w","relation":{},"ISSN":["1875-6883"],"issn-type":[{"value":"1875-6883","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,6,6]]},"assertion":[{"value":"6 March 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 May 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 May 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 June 2025","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"142"}}