{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T09:37:24Z","timestamp":1771493844782,"version":"3.50.1"},"reference-count":48,"publisher":"Springer Science and Business Media LLC","issue":"11-12","license":[{"start":{"date-parts":[[2024,1,24]],"date-time":"2024-01-24T00:00:00Z","timestamp":1706054400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,24]],"date-time":"2024-01-24T00:00:00Z","timestamp":1706054400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Soft Comput"],"published-print":{"date-parts":[[2024,6]]},"DOI":"10.1007\/s00500-023-09499-6","type":"journal-article","created":{"date-parts":[[2024,1,24]],"date-time":"2024-01-24T14:52:11Z","timestamp":1706107931000},"page":"7093-7124","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["A hybrid forecasting system using convolutional-based extreme learning with extended elephant herd optimization for time-series prediction"],"prefix":"10.1007","volume":"28","author":[{"given":"Gaurav","family":"Dubey","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Harivans Pratap","family":"Singh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rajesh Kumar","family":"Maurya","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kavita","family":"Sheoran","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Geetika","family":"Dhand","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,1,24]]},"reference":[{"key":"9499_CR1","doi-asserted-by":"crossref","unstructured":"Abayomi-Alli A, Odusami MO, Abayomi-Alli O, Misra S, Ibeh GF (2019) Long short-term memory model for time-series prediction and forecast of solar radiation and other weather parameters. In: 2019 19th International Conference on Computational Science and Its Applications (ICCSA) IEEE, pp 82\u201392","DOI":"10.1109\/ICCSA.2019.00004"},{"issue":"2","key":"9499_CR2","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1007\/s11869-019-00779-5","volume":"13","author":"J Amanollahi","year":"2020","unstructured":"Amanollahi J, Ausati S (2020) PM 2.5 concentration forecasting using ANFIS, EEMD-GRNN, MLP, and MLR models: a case study of Tehran Iran. Air Qual Atmos Health 13(2):161\u2013171","journal-title":"Air Qual Atmos Health"},{"key":"9499_CR3","doi-asserted-by":"crossref","first-page":"286","DOI":"10.1016\/j.chemosphere.2019.01.121","volume":"222","author":"Y Bai","year":"2019","unstructured":"Bai Y, Zeng B, Li C, Zhang J (2019) An ensemble long short-term memory neural network for hourly PM2. 5 concentration forecasting. Chemosphere 222:286\u2013294","journal-title":"Chemosphere"},{"key":"9499_CR4","doi-asserted-by":"crossref","DOI":"10.1016\/j.energy.2019.07.134","volume":"187","author":"Z Chang","year":"2019","unstructured":"Chang Z, Zhang Y, Chen W (2019) Electricity price prediction based on hybrid model of adam optimized LSTM neural network and wavelet transform. Energy 187:115804","journal-title":"Energy"},{"key":"9499_CR5","doi-asserted-by":"crossref","first-page":"681","DOI":"10.1016\/j.enconman.2018.03.098","volume":"165","author":"J Chen","year":"2018","unstructured":"Chen J, Zeng GQ, Zhou W, Du W, Lu KD (2018) Wind speed forecasting using nonlinear-learning ensemble of deep learning time-series prediction and extremal optimization. Energy Convers Manag 165:681\u2013695","journal-title":"Energy Convers Manag"},{"key":"9499_CR6","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1016\/j.techfore.2019.05.015","volume":"146","author":"S Chen","year":"2019","unstructured":"Chen S, Wang JQ, Zhang HY (2019) A hybrid PSO-SVM model based on clustering algorithm for short-term atmospheric pollutant concentration forecasting. Technol Forecast Soc Chang 146:41\u201354","journal-title":"Technol Forecast Soc Chang"},{"issue":"2","key":"9499_CR7","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1109\/4235.996017","volume":"6","author":"K Deb","year":"2002","unstructured":"Deb K, Pratap A, Agarwal S, Meyarivan TA (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evol Comput 6(2):182\u2013197","journal-title":"IEEE Trans Evol Comput"},{"issue":"1","key":"9499_CR8","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1007\/s10845-015-1087-8","volume":"29","author":"Q Fan","year":"2018","unstructured":"Fan Q, Yan X (2018) Multi-objective modified differential evolution algorithm with archive-base mutation for solving multi-objective p-xylene oxidation process. J Intell Manuf 29(1):35\u201349","journal-title":"J Intell Manuf"},{"key":"9499_CR9","volume":"583","author":"ZK Feng","year":"2020","unstructured":"Feng ZK, Niu WJ, Tang ZY, Jiang ZQ, Xu Y, Liu Y, Zhang HR (2020) Monthly runoff time-series prediction by variational mode decomposition and support vector machine based on quantum-behaved particle swarm optimization. J Hydrol 583:124627","journal-title":"J Hydrol"},{"key":"9499_CR10","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1016\/j.engappai.2019.08.018","volume":"86","author":"Z Hajirahimi","year":"2019","unstructured":"Hajirahimi Z, Khashei M (2019) Hybrid structures in time-series modeling and forecasting: a review. Eng Appl Artif Intell 86:83\u2013106","journal-title":"Eng Appl Artif Intell"},{"issue":"6","key":"9499_CR11","doi-asserted-by":"crossref","first-page":"7833","DOI":"10.1109\/JSEN.2019.2923982","volume":"21","author":"Z Han","year":"2019","unstructured":"Han Z, Zhao J, Leung H, Ma KF, Wang W (2019) A review of deep learning models for time-series prediction. IEEE Sens J 21(6):7833\u20137848","journal-title":"IEEE Sens J"},{"key":"9499_CR12","doi-asserted-by":"crossref","first-page":"368","DOI":"10.1016\/j.apenergy.2019.01.063","volume":"238","author":"Y Hao","year":"2019","unstructured":"Hao Y, Tian C (2019) A novel two-stage forecasting model based on error factor and ensemble method for multi-step wind power forecasting. Appl Energy 238:368\u2013383","journal-title":"Appl Energy"},{"key":"9499_CR13","doi-asserted-by":"crossref","first-page":"530","DOI":"10.1016\/j.apenergy.2019.05.044","volume":"250","author":"YY Hong","year":"2019","unstructured":"Hong YY, Rioflorido CL (2019) A hybrid deep learning-based neural network for 24-h ahead wind power forecasting. Appl Energy 250:530\u2013539","journal-title":"Appl Energy"},{"issue":"6","key":"9499_CR14","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1109\/MCOM.2019.1800155","volume":"57","author":"Y Hua","year":"2019","unstructured":"Hua Y, Zhao Z, Li R, Chen X, Liu Z, Zhang H (2019) Deep learning with long short-term memory for time-series prediction. IEEE Commun Mag 57(6):114\u2013119","journal-title":"IEEE Commun Mag"},{"key":"9499_CR15","doi-asserted-by":"crossref","DOI":"10.1016\/j.apenergy.2020.114977","volume":"268","author":"MA Jallal","year":"2020","unstructured":"Jallal MA, Gonzalez-Vidal A, Skarmeta AF, Chabaa S, Zeroual A (2020) A hybrid neuro-fuzzy inference system-based algorithm for time-series forecasting applied to energy consumption prediction. Appl Energy 268:114977","journal-title":"Appl Energy"},{"key":"9499_CR16","doi-asserted-by":"crossref","first-page":"561","DOI":"10.1016\/j.energy.2016.10.040","volume":"119","author":"P Jiang","year":"2017","unstructured":"Jiang P, Wang Y, Wang J (2017) Short-term wind speed forecasting using a hybrid model. Energy 119:561\u2013577","journal-title":"Energy"},{"key":"9499_CR17","doi-asserted-by":"crossref","first-page":"786","DOI":"10.1016\/j.apenergy.2018.11.012","volume":"235","author":"P Jiang","year":"2019","unstructured":"Jiang P, Yang H, Heng J (2019) A hybrid forecasting system based on fuzzy time-series and multi-objective optimization for wind speed forecasting. Appl Energy 235:786\u2013801","journal-title":"Appl Energy"},{"key":"9499_CR18","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1016\/j.procs.2015.04.167","volume":"48","author":"I Khandelwal","year":"2015","unstructured":"Khandelwal I, Adhikari R, Verma G (2015) Time series forecasting using hybrid ARIMA and ANN models based on DWT decomposition. Proced Comput Sci 48:173\u2013179","journal-title":"Proced Comput Sci"},{"key":"9499_CR19","doi-asserted-by":"crossref","first-page":"121285","DOI":"10.1016\/j.jclepro.2020.121285","volume":"261","author":"PY Kow","year":"2020","unstructured":"Kow PY, Wang YS, Zhou Y, Kao IF, Issermann M, Chang LC, Chang FJ (2020) Seamless integration of convolutional and back-propagation neural networks for regional multi-step-ahead PM25 forecasting. J Clean Prod 261:121285","journal-title":"J Clean Prod"},{"issue":"24","key":"9499_CR20","doi-asserted-by":"crossref","first-page":"34595","DOI":"10.1007\/s11042-021-11029-1","volume":"81","author":"R Kumar","year":"2022","unstructured":"Kumar R, Kumar P, Kumar Y (2022) Integrating big data driven sentiments polarity and ABC-optimized LSTM for time-series forecasting. Multimed Tools Appl 81(24):34595\u201334614","journal-title":"Multimed Tools Appl"},{"issue":"4","key":"9499_CR21","doi-asserted-by":"crossref","first-page":"1280","DOI":"10.3390\/su10041280","volume":"10","author":"PH Kuo","year":"2018","unstructured":"Kuo PH, Huang CJ (2018) An electricity price forecasting model by hybrid structured deep neural networks. Sustainability 10(4):1280","journal-title":"Sustainability"},{"key":"9499_CR22","doi-asserted-by":"crossref","first-page":"306","DOI":"10.1016\/j.enconman.2019.02.045","volume":"186","author":"F Li","year":"2019","unstructured":"Li F, Ren G, Lee J (2019) Multi-step wind speed prediction based on turbulence intensity and hybrid deep neural networks. Energy Convers Manag 186:306\u2013322","journal-title":"Energy Convers Manag"},{"key":"9499_CR23","doi-asserted-by":"crossref","first-page":"498","DOI":"10.1016\/j.enconman.2017.11.053","volume":"156","author":"H Liu","year":"2018","unstructured":"Liu H, Mi XW, Li YF (2018) Wind speed forecasting method based on deep learning strategy using empirical wavelet transform, long short-term memory neural network and Elman neural network. Energy Convers Manag 156:498\u2013514","journal-title":"Energy Convers Manag"},{"key":"9499_CR24","volume":"259","author":"Z Liu","year":"2020","unstructured":"Liu Z, Jiang P, Zhang L, Niu X (2020) A combined forecasting model for time-series: application to short-term wind speed forecasting. Appl Energy 259:114137","journal-title":"Appl Energy"},{"key":"9499_CR25","volume":"205","author":"Z Ma","year":"2020","unstructured":"Ma Z, Chen H, Wang J, Yang X, Yan R, Jia J, Xu W (2020) Application of hybrid model based on double decomposition, error correction and deep learning in short-term wind speed prediction. Energy Convers Manag 205:112345","journal-title":"Energy Convers Manag"},{"issue":"2","key":"9499_CR26","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1007\/s41066-021-00265-3","volume":"7","author":"M Pant","year":"2022","unstructured":"Pant M, Kumar S (2022) Particle swarm optimization and intuitionistic fuzzy set-based novel method for fuzzy time series forecasting. Granul Comput 7(2):285\u2013303","journal-title":"Granul Comput"},{"key":"9499_CR27","unstructured":"Petneh\u00e1zi G (2019) Recurrent neural networks for time-series forecasting. arXiv preprint arXiv:1901.00069"},{"key":"9499_CR28","doi-asserted-by":"crossref","first-page":"77674","DOI":"10.1109\/ACCESS.2019.2922420","volume":"7","author":"A Pourdaryaei","year":"2019","unstructured":"Pourdaryaei A, Mokhlis H, Illias HA, Kaboli SH, Ahmad S (2019) Short-term electricity price forecasting via hybrid backtracking search algorithm and ANFIS approach. IEEE Access 7:77674\u201377691","journal-title":"IEEE Access"},{"key":"9499_CR29","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.scitotenv.2019.01.333","volume":"664","author":"Y Qi","year":"2019","unstructured":"Qi Y, Li Q, Karimian H, Liu D (2019) A hybrid model for spatiotemporal forecasting of PM2.5 based on graph convolutional neural network and long short-term memory. Sci Total Environ 664:1","journal-title":"Sci Total Environ"},{"key":"9499_CR30","doi-asserted-by":"crossref","first-page":"20050","DOI":"10.1109\/ACCESS.2019.2897028","volume":"7","author":"D Qin","year":"2019","unstructured":"Qin D, Yu J, Zou G, Yong R, Zhao Q, Zhang B (2019) A novel combined prediction scheme based on CNN and LSTM for urban PM 2.5 concentration. IEEE Access 7:20050\u201320059","journal-title":"IEEE Access"},{"key":"9499_CR31","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1016\/j.neucom.2018.09.082","volume":"323","author":"A Sagheer","year":"2019","unstructured":"Sagheer A, Kotb M (2019) Time series forecasting of petroleum production using deep LSTM recurrent networks. Neurocomputing 323:203\u2013213","journal-title":"Neurocomputing"},{"key":"9499_CR32","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2020.106181","volume":"90","author":"OB Sezer","year":"2020","unstructured":"Sezer OB, Gudelek MU, Ozbayoglu AM (2020) Financial time-series forecasting with deep learning: a systematic literature review: 2005\u20132019. Appl Soft Comput 90:106181","journal-title":"Appl Soft Comput"},{"key":"9499_CR33","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1016\/j.neucom.2022.01.039","volume":"480","author":"L Shen","year":"2022","unstructured":"Shen L, Wang Y (2022) TCCT: Tightly-coupled convolutional transformer on time-series forecasting. Neurocomputing 480:131\u2013145","journal-title":"Neurocomputing"},{"key":"9499_CR34","doi-asserted-by":"crossref","first-page":"370","DOI":"10.1016\/j.jocs.2018.05.008","volume":"27","author":"P Singh","year":"2018","unstructured":"Singh P, Dhiman G (2018) A hybrid fuzzy time-series forecasting model based on granular computing and bio-inspired optimization approaches. J Comput Sci 27:370\u2013385","journal-title":"J Comput Sci"},{"key":"9499_CR35","doi-asserted-by":"crossref","first-page":"121442","DOI":"10.1016\/j.jclepro.2020.121442","volume":"263","author":"W Sun","year":"2020","unstructured":"Sun W, Li Z (2020) Hourly PM2.5 concentration forecasting based on mode decomposition-recombination technique and ensemble learning approach in severe haze episodes of China. J Cleaner Prod 263:121442","journal-title":"J Cleaner Prod"},{"key":"9499_CR36","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1016\/j.neucom.2019.05.023","volume":"360","author":"K Wang","year":"2019","unstructured":"Wang K, Li K, Zhou L, Hu Y, Cheng Z, Liu J, Chen C (2019) Multiple convolutional neural networks for multivariate time-series prediction. Neurocomputing 360:107\u2013119","journal-title":"Neurocomputing"},{"issue":"6","key":"9499_CR37","doi-asserted-by":"crossref","first-page":"2839","DOI":"10.1007\/s13762-018-1999-x","volume":"16","author":"V Yadav","year":"2019","unstructured":"Yadav V, Nath S (2019) Novel hybrid model for daily prediction of PM 10 using principal component analysis and artificial neural network. Int J Environ Sci Technol 16(6):2839\u20132848","journal-title":"Int J Environ Sci Technol"},{"key":"9499_CR38","doi-asserted-by":"crossref","first-page":"128","DOI":"10.1016\/j.eswa.2018.11.019","volume":"120","author":"HF Yang","year":"2019","unstructured":"Yang HF, Chen YP (2019) Hybrid deep learning and empirical mode decomposition model for time-series applications. Expert Syst Appl 120:128\u2013138","journal-title":"Expert Syst Appl"},{"issue":"2","key":"9499_CR39","doi-asserted-by":"crossref","first-page":"2212","DOI":"10.1007\/s10489-021-02442-y","volume":"52","author":"Y Yang","year":"2022","unstructured":"Yang Y, Fan C, Xiong H (2022) A novel general-purpose hybrid model for time-series forecasting. Appl Intell 52(2):2212\u20132223","journal-title":"Appl Intell"},{"issue":"6","key":"9499_CR40","doi-asserted-by":"crossref","first-page":"712","DOI":"10.1109\/TEVC.2007.892759","volume":"11","author":"Q Zhang","year":"2007","unstructured":"Zhang Q, Li H (2007) MOEA\/D: a multiobjective evolutionary algorithm based on decomposition. IEEE Trans Evol Comput 11(6):712\u2013731","journal-title":"IEEE Trans Evol Comput"},{"issue":"4","key":"9499_CR41","doi-asserted-by":"crossref","first-page":"479","DOI":"10.26599\/TST.2018.9010045","volume":"23","author":"L Zhang","year":"2018","unstructured":"Zhang L, Alharbe NR, Luo G, Yao Z, Li Y (2018) A hybrid forecasting framework based on support vector regression with a modified genetic algorithm and a random forest for traffic flow prediction. Tsinghua Sci Technol 23(4):479\u2013492","journal-title":"Tsinghua Sci Technol"},{"key":"9499_CR42","doi-asserted-by":"crossref","first-page":"143423","DOI":"10.1109\/ACCESS.2020.3014241","volume":"8","author":"R Zhang","year":"2020","unstructured":"Zhang R, Li G, Ma Z (2020a) A deep learning based hybrid framework for day-ahead electricity price forecasting. IEEE Access 8:143423\u2013143436","journal-title":"IEEE Access"},{"key":"9499_CR43","volume":"164","author":"W Zhang","year":"2020","unstructured":"Zhang W, Li X, Li X (2020b) Deep learning-based prognostic approach for lithium-ion batteries with adaptive time-series prediction and online validation. Measurement 164:108052","journal-title":"Measurement"},{"key":"9499_CR44","doi-asserted-by":"crossref","first-page":"427","DOI":"10.1016\/j.ins.2020.08.053","volume":"544","author":"S Zhang","year":"2021","unstructured":"Zhang S, Chen Y, Zhang W, Feng R (2021) A novel ensemble deep learning model with dynamic error correction and multi-objective ensemble pruning for time-series forecasting. Inf Sci 544:427\u2013445","journal-title":"Inf Sci"},{"key":"9499_CR45","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2021.3137178","author":"W Zheng","year":"2022","unstructured":"Zheng W, Hu J (2022) Multivariate time-series prediction based on temporal change information learning method. IEEE Trans Neural Netw Learn Syst. https:\/\/doi.org\/10.1109\/TNNLS.2021.3137178","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"9499_CR46","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1016\/j.jclepro.2018.10.243","volume":"209","author":"Y Zhou","year":"2019","unstructured":"Zhou Y, Chang FJ, Chang LC, Kao IF, Wang YS (2019) Explore a deep learning multi-output neural network for regional multi-step-ahead air quality forecasts. J Clean Prod 209:134\u2013145","journal-title":"J Clean Prod"},{"issue":"9","key":"9499_CR47","doi-asserted-by":"crossref","first-page":"1941","DOI":"10.3390\/ijerph15091941","volume":"15","author":"J Zhu","year":"2018","unstructured":"Zhu J, Wu P, Chen H, Zhou L, Tao Z (2018) A hybrid forecasting approach to air quality time-series based on endpoint condition and combined forecasting model. Int J Environ Res Public Health 15(9):1941","journal-title":"Int J Environ Res Public Health"},{"key":"9499_CR48","unstructured":"Zitzler E, Marco L, Lothar T (2001) SPEA2: improving the strength Pareto evolutionary algorithm. TIK-report 103"}],"container-title":["Soft Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00500-023-09499-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00500-023-09499-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00500-023-09499-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,19]],"date-time":"2024-07-19T13:07:47Z","timestamp":1721394467000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00500-023-09499-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,24]]},"references-count":48,"journal-issue":{"issue":"11-12","published-print":{"date-parts":[[2024,6]]}},"alternative-id":["9499"],"URL":"https:\/\/doi.org\/10.1007\/s00500-023-09499-6","relation":{},"ISSN":["1432-7643","1433-7479"],"issn-type":[{"value":"1432-7643","type":"print"},{"value":"1433-7479","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,24]]},"assertion":[{"value":"22 November 2023","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 January 2024","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Authors declares that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This article does not contain any studies with human participants\u00a0and\/or animals performed by any of the authors.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"There is no informed consent for this study.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}}]}}