{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T12:11:12Z","timestamp":1776082272245,"version":"3.50.1"},"reference-count":64,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,11,9]],"date-time":"2024-11-09T00:00:00Z","timestamp":1731110400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2024,11,9]],"date-time":"2024-11-09T00:00:00Z","timestamp":1731110400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Complex Intell. Syst."],"published-print":{"date-parts":[[2025,1]]},"DOI":"10.1007\/s40747-024-01609-7","type":"journal-article","created":{"date-parts":[[2024,11,9]],"date-time":"2024-11-09T07:02:29Z","timestamp":1731135749000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Integrating fast iterative filtering and ensemble neural network structure with attention mechanism for carbon price forecasting"],"prefix":"10.1007","volume":"11","author":[{"given":"Wang","family":"Zhong","sequence":"first","affiliation":[]},{"given":"Wang","family":"Yue","sequence":"additional","affiliation":[]},{"given":"Wang","family":"Haoran","sequence":"additional","affiliation":[]},{"given":"Tang","family":"Nan","sequence":"additional","affiliation":[]},{"given":"Wang","family":"Shuyue","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,11,9]]},"reference":[{"key":"1609_CR1","unstructured":"Protocol K (1997) Kyoto protocol. UNFCCC Website http:\/\/unfccc.int\/kyoto_protocol\/items\/2830.php. Accessed 1 Jan 2011"},{"key":"1609_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/j.enpol.2019.07.007","volume":"133","author":"KF Forbes","year":"2019","unstructured":"Forbes KF, Zampelli EM (2019) Wind energy, the price of carbon allowances, and CO2 emissions: evidence from Ireland. Energy Policy 133:110871. https:\/\/doi.org\/10.1016\/j.enpol.2019.07.007","journal-title":"Energy Policy"},{"key":"1609_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.jclepro.2019.118556","volume":"244","author":"Y Hao","year":"2020","unstructured":"Hao Y, Tian C, Wu C (2020) Modelling of carbon price in two real carbon trading markets. J Clean Prod 244:118556. https:\/\/doi.org\/10.1016\/j.jclepro.2019.118556","journal-title":"J Clean Prod"},{"key":"1609_CR4","doi-asserted-by":"publisher","first-page":"140","DOI":"10.1016\/j.physa.2018.12.017","volume":"519","author":"J Zhu","year":"2019","unstructured":"Zhu J, Wu P, Chen H et al (2019) Carbon price forecasting with variational mode decomposition and optimal combined model. Phys A 519:140\u2013158. https:\/\/doi.org\/10.1016\/j.physa.2018.12.017","journal-title":"Phys A"},{"key":"1609_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.jclepro.2019.119386","volume":"249","author":"H Lu","year":"2020","unstructured":"Lu H, Ma X, Huang K, Azimi M (2020) Carbon trading volume and price forecasting in China using multiple machine learning models. J Clean Prod 249:119386. https:\/\/doi.org\/10.1016\/j.jclepro.2019.119386","journal-title":"J Clean Prod"},{"key":"1609_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.jclepro.2023.137791","volume":"415","author":"X Zhang","year":"2023","unstructured":"Zhang X, Wang J (2023) An enhanced decomposition integration model for deterministic and probabilistic carbon price prediction based on two-stage feature extraction and intelligent weight optimization. J Clean Prod 415:137791. https:\/\/doi.org\/10.1016\/j.jclepro.2023.137791","journal-title":"J Clean Prod"},{"key":"1609_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.physa.2019.123401","volume":"542","author":"Y Fu","year":"2020","unstructured":"Fu Y, Zheng Z (2020) Volatility modeling and the asymmetric effect for China\u2019s carbon trading pilot market. Phys A 542:123401. https:\/\/doi.org\/10.1016\/j.physa.2019.123401","journal-title":"Phys A"},{"key":"1609_CR8","doi-asserted-by":"publisher","first-page":"1267","DOI":"10.1016\/j.eneco.2011.03.003","volume":"33","author":"J Chevallier","year":"2011","unstructured":"Chevallier J (2011) Nonparametric modeling of carbon prices. Energy Econ 33:1267\u20131282. https:\/\/doi.org\/10.1016\/j.eneco.2011.03.003","journal-title":"Energy Econ"},{"key":"1609_CR9","doi-asserted-by":"publisher","first-page":"306","DOI":"10.1016\/j.apenergy.2016.12.116","volume":"190","author":"X-P Tan","year":"2017","unstructured":"Tan X-P, Wang X-Y (2017) Dependence changes between the carbon price and its fundamentals: a quantile regression approach. Appl Energy 190:306\u2013325. https:\/\/doi.org\/10.1016\/j.apenergy.2016.12.116","journal-title":"Appl Energy"},{"key":"1609_CR10","unstructured":"Bai S, Kolter JZ, Koltun V (2018) An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. arXiv preprint. arXiv:1803.01271"},{"key":"1609_CR11","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6501\/ad3ea4","volume":"35","author":"Z Zhao","year":"2024","unstructured":"Zhao Z, Shen S, Wang Z (2024) Performance degradation trend prediction of proton exchange membrane fuel cell based on GA-TCN. Meas Sci Technol 35:076007. https:\/\/doi.org\/10.1088\/1361-6501\/ad3ea4","journal-title":"Meas Sci Technol"},{"key":"1609_CR12","doi-asserted-by":"publisher","first-page":"2837","DOI":"10.1007\/s11663-024-03145-8","volume":"55","author":"Y Ren","year":"2024","unstructured":"Ren Y, Xing X, Wang B et al (2024) Prediction model for silicon content of hot metal based on PSO-TCN. Metall Mater Trans B 55:2837\u20132846. https:\/\/doi.org\/10.1007\/s11663-024-03145-8","journal-title":"Metall Mater Trans B"},{"key":"1609_CR13","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9:1735\u20131780. https:\/\/doi.org\/10.1162\/neco.1997.9.8.1735","journal-title":"Neural Comput"},{"key":"1609_CR14","doi-asserted-by":"publisher","first-page":"2286188","DOI":"10.1080\/09540091.2023.2286188","volume":"36","author":"X Chen","year":"2024","unstructured":"Chen X, Cao L, Cao Z, Zhang H (2024) A multi-feature stock price prediction model based on multi-feature calculation, LASSO feature selection, and Ca-LSTM network. Connect Sci 36:2286188. https:\/\/doi.org\/10.1080\/09540091.2023.2286188","journal-title":"Connect Sci"},{"key":"1609_CR15","doi-asserted-by":"publisher","first-page":"213","DOI":"10.1007\/s40747-021-00365-2","volume":"8","author":"YO Ouma","year":"2022","unstructured":"Ouma YO, Cheruyot R, Wachera AN (2022) Rainfall and runoff time-series trend analysis using LSTM recurrent neural network and wavelet neural network with satellite-based meteorological data: case study of Nzoia hydrologic basin. Complex Intell Syst 8:213\u2013236. https:\/\/doi.org\/10.1007\/s40747-021-00365-2","journal-title":"Complex Intell Syst"},{"key":"1609_CR16","doi-asserted-by":"publisher","first-page":"478","DOI":"10.1007\/978-3-031-37940-6_39","volume-title":"Advances in computing and data sciences","author":"AK Dastgerdi","year":"2023","unstructured":"Dastgerdi AK, Mercorelli P (2023) Employing a novel metaheuristic algorithm to optimize an LSTM model: a case study of stock market prediction. In: Singh M, Tyagi V, Gupta PK et al (eds) Advances in computing and data sciences. Springer Nature, Cham, pp 478\u2013489"},{"key":"1609_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.120346","volume":"227","author":"B G\u00fclmez","year":"2023","unstructured":"G\u00fclmez B (2023) Stock price prediction with optimized deep LSTM network with artificial rabbits optimization algorithm. Expert Syst Appl 227:120346. https:\/\/doi.org\/10.1016\/j.eswa.2023.120346","journal-title":"Expert Syst Appl"},{"key":"1609_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2024.112026","volume":"299","author":"V Mizdrakovic","year":"2024","unstructured":"Mizdrakovic V, Kljajic M, Zivkovic M et al (2024) Forecasting bitcoin: decomposition aided long short-term memory based time series modeling and its explanation with Shapley values. Knowl Based Syst 299:112026. https:\/\/doi.org\/10.1016\/j.knosys.2024.112026","journal-title":"Knowl Based Syst"},{"key":"1609_CR19","doi-asserted-by":"publisher","first-page":"3792","DOI":"10.3390\/en16093792","volume":"16","author":"HKM Nguyen","year":"2023","unstructured":"Nguyen HKM, Phan Q-D, Wu Y-K, Phan Q-T (2023) Multi-step wind power forecasting with stacked temporal convolutional network (S-TCN). Energies 16:3792. https:\/\/doi.org\/10.3390\/en16093792","journal-title":"Energies"},{"key":"1609_CR20","doi-asserted-by":"publisher","first-page":"424","DOI":"10.1016\/j.egyr.2020.11.219","volume":"6","author":"R Zhu","year":"2020","unstructured":"Zhu R, Liao W, Wang Y (2020) Short-term prediction for wind power based on temporal convolutional network. Energy Rep 6:424\u2013429. https:\/\/doi.org\/10.1016\/j.egyr.2020.11.219","journal-title":"Energy Rep"},{"key":"1609_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2020.118294","volume":"207","author":"W Sun","year":"2020","unstructured":"Sun W, Huang C (2020) A novel carbon price prediction model combines the secondary decomposition algorithm and the long short-term memory network. Energy 207:118294. https:\/\/doi.org\/10.1016\/j.energy.2020.118294","journal-title":"Energy"},{"key":"1609_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2022.118601","volume":"311","author":"F Zhou","year":"2022","unstructured":"Zhou F, Huang Z, Zhang C (2022) Carbon price forecasting based on CEEMDAN and LSTM. Appl Energy 311:118601. https:\/\/doi.org\/10.1016\/j.apenergy.2022.118601","journal-title":"Appl Energy"},{"key":"1609_CR23","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2024.132320","volume":"305","author":"X He","year":"2024","unstructured":"He X, He B, Qin T et al (2024) Ultra-short-term wind power forecasting based on a dual-channel deep learning model with improved coot optimization algorithm. Energy 305:132320. https:\/\/doi.org\/10.1016\/j.energy.2024.132320","journal-title":"Energy"},{"key":"1609_CR24","doi-asserted-by":"publisher","DOI":"10.1007\/s40747-024-01490-4","author":"X Wang","year":"2024","unstructured":"Wang X, Wu Z, Jin B et al (2024) MDSTF: a multi-dimensional spatio-temporal feature fusion trajectory prediction model for autonomous driving. Complex Intell Syst. https:\/\/doi.org\/10.1007\/s40747-024-01490-4","journal-title":"Complex Intell Syst"},{"key":"1609_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/j.engstruct.2024.117869","volume":"306","author":"Q Zhang","year":"2024","unstructured":"Zhang Q, Cai X, Zhong Y et al (2024) Dynamic response prediction of high-speed train on cable-stayed bridge based on genetic algorithm and fused neural networks. Eng Struct 306:117869. https:\/\/doi.org\/10.1016\/j.engstruct.2024.117869","journal-title":"Eng Struct"},{"key":"1609_CR26","doi-asserted-by":"publisher","first-page":"590","DOI":"10.1016\/j.apenergy.2010.06.017","volume":"88","author":"Z-H Feng","year":"2011","unstructured":"Feng Z-H, Zou L-L, Wei Y-M (2011) Carbon price volatility: evidence from EU ETS. Appl Energy 88:590\u2013598. https:\/\/doi.org\/10.1016\/j.apenergy.2010.06.017","journal-title":"Appl Energy"},{"key":"1609_CR27","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2021.107291","volume":"105","author":"J Li","year":"2021","unstructured":"Li J, Hao J, Sun X, Feng Q (2021) Forecasting China\u2019s sovereign CDS with a decomposition reconstruction strategy. Appl Soft Comput 105:107291. https:\/\/doi.org\/10.1016\/j.asoc.2021.107291","journal-title":"Appl Soft Comput"},{"key":"1609_CR28","doi-asserted-by":"publisher","first-page":"4896","DOI":"10.3390\/su13094896","volume":"13","author":"J Zhou","year":"2021","unstructured":"Zhou J, Chen D (2021) Carbon price forecasting based on improved CEEMDAN and extreme learning machine optimized by Sparrow Search Algorithm. Sustainability 13:4896. https:\/\/doi.org\/10.3390\/su13094896","journal-title":"Sustainability"},{"key":"1609_CR29","doi-asserted-by":"publisher","first-page":"4094","DOI":"10.1002\/ese3.799","volume":"8","author":"W Sun","year":"2020","unstructured":"Sun W, Li Z (2020) An ensemble-driven long short-term memory model based on mode decomposition for carbon price forecasting of all eight carbon trading pilots in China. Energy Sci Eng 8:4094\u20134115. https:\/\/doi.org\/10.1002\/ese3.799","journal-title":"Energy Sci Eng"},{"key":"1609_CR30","doi-asserted-by":"publisher","DOI":"10.1016\/j.scitotenv.2020.138350","volume":"725","author":"Y Huang","year":"2020","unstructured":"Huang Y, He Z (2020) Carbon price forecasting with optimization prediction method based on unstructured combination. Sci Total Environ 725:138350. https:\/\/doi.org\/10.1016\/j.scitotenv.2020.138350","journal-title":"Sci Total Environ"},{"key":"1609_CR31","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2020.106686","volume":"214","author":"H Li","year":"2021","unstructured":"Li H, Jin F, Sun S, Li Y (2021) A new secondary decomposition ensemble learning approach for carbon price forecasting. Knowl Based Syst 214:106686. https:\/\/doi.org\/10.1016\/j.knosys.2020.106686","journal-title":"Knowl Based Syst"},{"key":"1609_CR32","doi-asserted-by":"publisher","first-page":"277","DOI":"10.1142\/S1793536910000549","volume":"02","author":"G Wang","year":"2010","unstructured":"Wang G, Chen X-Y, Qiao F-L et al (2010) On intrinsic mode function. Adv Adapt Data Anal 02:277\u2013293. https:\/\/doi.org\/10.1142\/S1793536910000549","journal-title":"Adv Adapt Data Anal"},{"key":"1609_CR33","doi-asserted-by":"publisher","first-page":"4329","DOI":"10.3390\/s18124329","volume":"18","author":"H Wang","year":"2018","unstructured":"Wang H, Ji Y (2018) A revised Hilbert-Huang transform and its application to fault diagnosis in a rotor system. Sensors 18:4329. https:\/\/doi.org\/10.3390\/s18124329","journal-title":"Sensors"},{"key":"1609_CR34","doi-asserted-by":"publisher","first-page":"531","DOI":"10.1109\/TSP.2013.2288675","volume":"62","author":"K Dragomiretskiy","year":"2014","unstructured":"Dragomiretskiy K, Zosso D (2014) Variational mode decomposition. IEEE Trans Signal Process 62:531\u2013544. https:\/\/doi.org\/10.1109\/TSP.2013.2288675","journal-title":"IEEE Trans Signal Process"},{"key":"1609_CR35","doi-asserted-by":"publisher","DOI":"10.1016\/j.jclepro.2020.123469","volume":"278","author":"C-J Ji","year":"2021","unstructured":"Ji C-J, Hu Y-J, Tang B-J, Qu S (2021) Price drivers in the carbon emissions trading scheme: evidence from Chinese emissions trading scheme pilots. J Clean Prod 278:123469. https:\/\/doi.org\/10.1016\/j.jclepro.2020.123469","journal-title":"J Clean Prod"},{"key":"1609_CR36","doi-asserted-by":"crossref","unstructured":"Zito F, Cutello V, Pavone M (2023) Deep learning and metaheuristic for multivariate time-series forecasting. In: Garc\u00eda Bringas P, P\u00e9rez Garc\u00eda H, Mart\u00ednez De Pis\u00f3n FJ, et al (eds) 18th International conference on soft computing models in industrial and environmental applications (SOCO 2023). Springer Nature Switzerland, Cham, pp 249\u2013258","DOI":"10.1007\/978-3-031-42529-5_24"},{"key":"1609_CR37","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1016\/j.neucom.2020.12.086","volume":"434","author":"E Jianwei","year":"2021","unstructured":"Jianwei E, Ye J, He L, Jin H (2021) A denoising carbon price forecasting method based on the integration of kernel independent component analysis and least squares support vector regression. Neurocomputing 434:67\u201379. https:\/\/doi.org\/10.1016\/j.neucom.2020.12.086","journal-title":"Neurocomputing"},{"key":"1609_CR38","doi-asserted-by":"publisher","first-page":"315","DOI":"10.1080\/17583004.2020.1755597","volume":"11","author":"W Sun","year":"2020","unstructured":"Sun W, Wang Y (2020) Factor analysis and carbon price prediction based on empirical mode decomposition and least squares support vector machine optimized by improved particle swarm optimization. Carbon Manag 11:315\u2013329. https:\/\/doi.org\/10.1080\/17583004.2020.1755597","journal-title":"Carbon Manag"},{"key":"1609_CR39","doi-asserted-by":"publisher","DOI":"10.7717\/peerj-cs.1795","volume":"10","author":"R Dama\u0161evi\u010dius","year":"2024","unstructured":"Dama\u0161evi\u010dius R, Jovanovic L, Petrovic A et al (2024) Decomposition aided attention-based recurrent neural networks for multistep ahead time-series forecasting of renewable power generation. PeerJ Comput Sci 10:e1795. https:\/\/doi.org\/10.7717\/peerj-cs.1795","journal-title":"PeerJ Comput Sci"},{"key":"1609_CR40","doi-asserted-by":"publisher","first-page":"45762","DOI":"10.1109\/ACCESS.2024.3380816","volume":"12","author":"MS Alshehri","year":"2024","unstructured":"Alshehri MS, Saidani O, Alrayes FS et al (2024) A self-attention-based deep convolutional neural networks for IIoT networks intrusion detection. IEEE Access 12:45762\u201345772. https:\/\/doi.org\/10.1109\/ACCESS.2024.3380816","journal-title":"IEEE Access"},{"key":"1609_CR41","doi-asserted-by":"publisher","first-page":"811","DOI":"10.1007\/s11075-019-00838-z","volume":"85","author":"A Cicone","year":"2020","unstructured":"Cicone A (2020) Iterative filtering as a direct method for the decomposition of nonstationary signals. Numer Algor 85:811\u2013827. https:\/\/doi.org\/10.1007\/s11075-019-00838-z","journal-title":"Numer Algor"},{"key":"1609_CR42","doi-asserted-by":"publisher","first-page":"903","DOI":"10.1098\/rspa.1998.0193","volume":"454","author":"NE Huang","year":"1998","unstructured":"Huang NE, Shen Z, Long SR et al (1998) The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc R Soc Lond A 454:903\u2013995. https:\/\/doi.org\/10.1098\/rspa.1998.0193","journal-title":"Proc R Soc Lond A"},{"key":"1609_CR43","doi-asserted-by":"publisher","first-page":"A35","DOI":"10.1051\/swsc\/2019032","volume":"9","author":"L Spogli","year":"2019","unstructured":"Spogli L, Piersanti M, Cesaroni C et al (2019) Role of the external drivers in the occurrence of low-latitude ionospheric scintillation revealed by multi-scale analysis. J Space Weather Space Clim 9:A35. https:\/\/doi.org\/10.1051\/swsc\/2019032","journal-title":"J Space Weather Space Clim"},{"key":"1609_CR44","doi-asserted-by":"publisher","first-page":"20150196","DOI":"10.1098\/rsta.2015.0196","volume":"374","author":"A Cicone","year":"2016","unstructured":"Cicone A, Liu J, Zhou H (2016) Hyperspectral chemical plume detection algorithms based on multidimensional iterative filtering decomposition. Philos Trans R Soc A 374:20150196. https:\/\/doi.org\/10.1098\/rsta.2015.0196","journal-title":"Philos Trans R Soc A"},{"key":"1609_CR45","doi-asserted-by":"publisher","first-page":"10","DOI":"10.4401\/ag-7801","volume":"61","author":"M Materassi","year":"2019","unstructured":"Materassi M, Piersanti M, Consolini G et al (2019) Stepping into the equatorward boundary of the Auroral oval: preliminary results of multi scale statistical analysis. Ann Geophys 61:10. https:\/\/doi.org\/10.4401\/ag-7801","journal-title":"Ann Geophys"},{"key":"1609_CR46","doi-asserted-by":"publisher","first-page":"159","DOI":"10.1016\/j.enbuild.2018.10.017","volume":"182","author":"S Sfarra","year":"2019","unstructured":"Sfarra S, Cicone A, Yousefi B et al (2019) Improving the detection of thermal bridges in buildings via on-site infrared thermography: the potentialities of innovative mathematical tools. Energy Build 182:159\u2013171. https:\/\/doi.org\/10.1016\/j.enbuild.2018.10.017","journal-title":"Energy Build"},{"key":"1609_CR47","doi-asserted-by":"publisher","first-page":"543","DOI":"10.1142\/S179353690900028X","volume":"01","author":"L Lin","year":"2009","unstructured":"Lin L, Wang Y, Zhou H (2009) Iterative filtering as an alternative algorithm for empirical mode decomposition. Adv Adapt Data Anal 01:543\u2013560. https:\/\/doi.org\/10.1142\/S179353690900028X","journal-title":"Adv Adapt Data Anal"},{"key":"1609_CR48","doi-asserted-by":"publisher","first-page":"384","DOI":"10.1016\/j.acha.2016.03.001","volume":"41","author":"A Cicone","year":"2016","unstructured":"Cicone A, Liu J, Zhou H (2016) Adaptive local iterative filtering for signal decomposition and instantaneous frequency analysis. Appl Comput Harmon Anal 41:384\u2013411. https:\/\/doi.org\/10.1016\/j.acha.2016.03.001","journal-title":"Appl Comput Harmon Anal"},{"key":"1609_CR49","doi-asserted-by":"publisher","first-page":"15161","DOI":"10.1038\/s41598-020-72193-2","volume":"10","author":"A Stallone","year":"2020","unstructured":"Stallone A, Cicone A, Materassi M (2020) New insights and best practices for the successful use of empirical mode decomposition, iterative filtering and derived algorithms. Sci Rep 10:15161. https:\/\/doi.org\/10.1038\/s41598-020-72193-2","journal-title":"Sci Rep"},{"key":"1609_CR50","doi-asserted-by":"publisher","first-page":"48","DOI":"10.1016\/j.neucom.2021.03.091","volume":"452","author":"Z Niu","year":"2021","unstructured":"Niu Z, Zhong G, Yu H (2021) A review on the attention mechanism of deep learning. Neurocomputing 452:48\u201362. https:\/\/doi.org\/10.1016\/j.neucom.2021.03.091","journal-title":"Neurocomputing"},{"key":"1609_CR51","doi-asserted-by":"publisher","DOI":"10.1088\/1742-6596\/1213\/4\/042050","volume":"1213","author":"Y He","year":"2019","unstructured":"He Y, Zhao J (2019) Temporal convolutional networks for anomaly detection in time series. J Phys Conf Ser 1213:042050. https:\/\/doi.org\/10.1088\/1742-6596\/1213\/4\/042050","journal-title":"J Phys Conf Ser"},{"key":"1609_CR52","doi-asserted-by":"publisher","first-page":"1658","DOI":"10.1109\/TII.2020.2991796","volume":"17","author":"M Ma","year":"2021","unstructured":"Ma M, Mao Z (2021) Deep-convolution-based LSTM network for remaining useful life prediction. IEEE Trans Ind Inf 17:1658\u20131667. https:\/\/doi.org\/10.1109\/TII.2020.2991796","journal-title":"IEEE Trans Ind Inf"},{"key":"1609_CR53","doi-asserted-by":"publisher","first-page":"239","DOI":"10.1016\/j.atmosenv.2016.10.046","volume":"148","author":"Y Xu","year":"2017","unstructured":"Xu Y, Yang W, Wang J (2017) Air quality early-warning system for cities in China. Atmos Environ 148:239\u2013257. https:\/\/doi.org\/10.1016\/j.atmosenv.2016.10.046","journal-title":"Atmos Environ"},{"key":"1609_CR54","doi-asserted-by":"publisher","first-page":"783","DOI":"10.1016\/j.enconman.2019.02.018","volume":"185","author":"Y Chen","year":"2019","unstructured":"Chen Y, Zhang S, Zhang W et al (2019) Multifactor spatio-temporal correlation model based on a combination of convolutional neural network and long short-term memory neural network for wind speed forecasting. Energy Convers Manag 185:783\u2013799. https:\/\/doi.org\/10.1016\/j.enconman.2019.02.018","journal-title":"Energy Convers Manag"},{"key":"1609_CR55","doi-asserted-by":"publisher","DOI":"10.1007\/s10479-022-04858-2","author":"W Xu","year":"2022","unstructured":"Xu W, Wang J, Zhang Y et al (2022) An optimized decomposition integration framework for carbon price prediction based on multi-factor two-stage feature dimension reduction. Ann Oper Res. https:\/\/doi.org\/10.1007\/s10479-022-04858-2","journal-title":"Ann Oper Res"},{"key":"1609_CR56","doi-asserted-by":"publisher","first-page":"182","DOI":"10.1016\/j.apm.2021.03.020","volume":"97","author":"S Sun","year":"2021","unstructured":"Sun S (2021) A new hybrid optimization ensemble learning approach for carbon price forecasting. Appl Math Model 97:182\u2013205","journal-title":"Appl Math Model"},{"key":"1609_CR57","doi-asserted-by":"publisher","first-page":"277","DOI":"10.3390\/en12020277","volume":"12","author":"W Sun","year":"2019","unstructured":"Sun W, Duan M (2019) Analysis and forecasting of the carbon price in China\u2019s regional carbon markets based on fast ensemble empirical mode decomposition, phase space reconstruction, and an improved extreme learning machine. Energies 12:277. https:\/\/doi.org\/10.3390\/en12020277","journal-title":"Energies"},{"key":"1609_CR58","doi-asserted-by":"publisher","DOI":"10.1016\/j.jclepro.2020.120378","volume":"262","author":"Y Hao","year":"2020","unstructured":"Hao Y, Tian C (2020) A hybrid framework for carbon trading price forecasting: the role of multiple influence factor. J Clean Prod 262:120378. https:\/\/doi.org\/10.1016\/j.jclepro.2020.120378","journal-title":"J Clean Prod"},{"key":"1609_CR59","doi-asserted-by":"publisher","DOI":"10.1016\/j.scitotenv.2021.149110","volume":"796","author":"L-T Zhao","year":"2021","unstructured":"Zhao L-T, Miao J, Qu S, Chen X-H (2021) A multi-factor integrated model for carbon price forecasting: market interaction promoting carbon emission reduction. Sci Total Environ 796:149110. https:\/\/doi.org\/10.1016\/j.scitotenv.2021.149110","journal-title":"Sci Total Environ"},{"key":"1609_CR60","doi-asserted-by":"publisher","DOI":"10.1016\/j.scitotenv.2020.143099","volume":"762","author":"J Wang","year":"2021","unstructured":"Wang J, Sun X, Cheng Q, Cui Q (2021) An innovative random forest-based nonlinear ensemble paradigm of improved feature extraction and deep learning for carbon price forecasting. Sci Total Environ 762:143099. https:\/\/doi.org\/10.1016\/j.scitotenv.2020.143099","journal-title":"Sci Total Environ"},{"key":"1609_CR61","doi-asserted-by":"publisher","first-page":"905","DOI":"10.1016\/j.eneco.2007.02.012","volume":"30","author":"X Zhang","year":"2008","unstructured":"Zhang X, Lai KK, Wang S-Y (2008) A new approach for crude oil price analysis based on empirical mode decomposition. Energy Econ 30:905\u2013918. https:\/\/doi.org\/10.1016\/j.eneco.2007.02.012","journal-title":"Energy Econ"},{"key":"1609_CR62","doi-asserted-by":"publisher","first-page":"72","DOI":"10.1016\/j.energy.2019.05.230","volume":"182","author":"T-Y Kim","year":"2019","unstructured":"Kim T-Y, Cho S-B (2019) Predicting residential energy consumption using CNN-LSTM neural networks. Energy 182:72\u201381. https:\/\/doi.org\/10.1016\/j.energy.2019.05.230","journal-title":"Energy"},{"key":"1609_CR63","doi-asserted-by":"publisher","DOI":"10.1016\/j.jenvman.2024.120131","volume":"352","author":"H Shi","year":"2024","unstructured":"Shi H, Wei A, Xu X et al (2024) 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 352:120131. https:\/\/doi.org\/10.1016\/j.jenvman.2024.120131","journal-title":"J Environ Manag"},{"key":"1609_CR64","doi-asserted-by":"publisher","first-page":"1046","DOI":"10.1016\/j.egyr.2020.11.078","volume":"6","author":"X Guo","year":"2020","unstructured":"Guo X, Zhao Q, Zheng D et al (2020) A short-term load forecasting model of multi-scale CNN-LSTM hybrid neural network considering the real-time electricity price. Energy Rep 6:1046\u20131053. https:\/\/doi.org\/10.1016\/j.egyr.2020.11.078","journal-title":"Energy Rep"}],"container-title":["Complex & Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-024-01609-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s40747-024-01609-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-024-01609-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,30]],"date-time":"2025-01-30T20:21:18Z","timestamp":1738268478000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s40747-024-01609-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,9]]},"references-count":64,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,1]]}},"alternative-id":["1609"],"URL":"https:\/\/doi.org\/10.1007\/s40747-024-01609-7","relation":{},"ISSN":["2199-4536","2198-6053"],"issn-type":[{"value":"2199-4536","type":"print"},{"value":"2198-6053","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,9]]},"assertion":[{"value":"23 October 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 September 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 November 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"6"}}