{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T05:29:27Z","timestamp":1768282167080,"version":"3.49.0"},"reference-count":54,"publisher":"Springer Science and Business Media LLC","issue":"34","license":[{"start":{"date-parts":[[2024,9,4]],"date-time":"2024-09-04T00:00:00Z","timestamp":1725408000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,9,4]],"date-time":"2024-09-04T00:00:00Z","timestamp":1725408000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100014266","name":"Shell Brasil","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100014266","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2024,12]]},"DOI":"10.1007\/s00521-024-10305-z","type":"journal-article","created":{"date-parts":[[2024,9,4]],"date-time":"2024-09-04T12:04:06Z","timestamp":1725451446000},"page":"21581-21605","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["A multi-modal approach for mixed-frequency time series forecasting"],"prefix":"10.1007","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8283-3764","authenticated-orcid":false,"given":"Leopoldo Lusquino","family":"Filho","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rafael","family":"de Oliveira Werneck","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Manuel","family":"Castro","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pedro","family":"Ribeiro Mendes J\u00fanior","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Augusto","family":"Lustosa","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marcelo","family":"Zampieri","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Oscar","family":"Linares","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Renato","family":"Moura","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Elayne","family":"Morais","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Murilo","family":"Amaral","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Soroor","family":"Salavati","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ashish","family":"Loomba","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ahmed","family":"Esmin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Maiara","family":"Gon\u00e7alves","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Denis Jos\u00e9","family":"Schiozer","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alexandre","family":"Ferreira","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alessandra","family":"Dav\u00f3lio","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anderson","family":"Rocha","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,9,4]]},"reference":[{"issue":"15","key":"10305_CR1","doi-asserted-by":"publisher","first-page":"2897","DOI":"10.3390\/en12152897","volume":"12","author":"T Ertekin","year":"2019","unstructured":"Ertekin T, Sun Q (2019) Artificial intelligence applications in reservoir engineering: a status check. Energies 12(15):2897","journal-title":"Energies"},{"key":"10305_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/j.petrol.2020.107013","volume":"189","author":"W Liu","year":"2020","unstructured":"Liu W, Liu WD, Gu J (2020) Forecasting oil production using ensemble empirical model decomposition based long short-term memory neural network. J Petrol Sci Eng 189:107013","journal-title":"J Petrol Sci Eng"},{"key":"10305_CR3","doi-asserted-by":"crossref","unstructured":"Sun J, Ma X, Kazi M (2018) Comparison of decline curve analysis DCA with recursive neural networks RNN for production forecast of multiple wells. In: SPE western regional meeting, p 11","DOI":"10.2118\/190104-MS"},{"key":"10305_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.cosrev.2023.100595","volume":"50","author":"C Xu","year":"2023","unstructured":"Xu C, Qu Y, Xiang Y, Gao L (2023) Asynchronous federated learning on heterogeneous devices: a survey. Comput Sci Rev 50:100595","journal-title":"Comput Sci Rev"},{"issue":"5","key":"10305_CR5","doi-asserted-by":"publisher","first-page":"829","DOI":"10.1162\/neco_a_01273","volume":"32","author":"J Gao","year":"2020","unstructured":"Gao J, Li P, Chen Z, Zhang J (2020) A survey on deep learning for multimodal data fusion. Neural Comput 32(5):829\u2013864","journal-title":"Neural Comput"},{"key":"10305_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.petrol.2021.109937","volume":"210","author":"R Oliveira Werneck","year":"2022","unstructured":"Oliveira Werneck R, Prates R, Moura R, Goncalves MM, Castro M, Soriano-Vargas A, J\u00fanior PRM, Hossain MM, Zampieri MF, Ferreira A et al (2022) Data-driven deep-learning forecasting for oil production and pressure. J Petrol Sci Eng 210:109937","journal-title":"J Petrol Sci Eng"},{"key":"10305_CR7","doi-asserted-by":"publisher","first-page":"171","DOI":"10.1007\/s11036-013-0489-0","volume":"19","author":"M Chen","year":"2014","unstructured":"Chen M, Mao S, Liu Y (2014) Big data: a survey. Mobile Netw Appl 19:171\u2013209","journal-title":"Mobile Netw Appl"},{"key":"10305_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.bdr.2020.100178","volume":"23","author":"X Ren","year":"2021","unstructured":"Ren X, Li X, Ren K, Song J, Xu Z, Deng K, Wang X (2021) Deep learning-based weather prediction: a survey. Big Data Res 23:100178","journal-title":"Big Data Res"},{"key":"10305_CR9","volume-title":"Time series analysis: forecasting and control","author":"GE Box","year":"2015","unstructured":"Box GE, Jenkins GM, Reinsel GC, Ljung GM (2015) Time series analysis: forecasting and control. Wiley, Hoboken"},{"key":"10305_CR10","doi-asserted-by":"publisher","first-page":"197","DOI":"10.2307\/2281054","volume":"49","author":"J Gurland","year":"1954","unstructured":"Gurland J (1954) Hypothesis testing in time series analysis. J Am Stat Assoc 49:197","journal-title":"J Am Stat Assoc"},{"issue":"1\u20132","key":"10305_CR11","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1016\/j.jeconom.2005.01.004","volume":"131","author":"E Ghysels","year":"2006","unstructured":"Ghysels E, Santa-Clara P, Valkanov R (2006) Predicting volatility: getting the most out of return data sampled at different frequencies. J Econom 131(1\u20132):59\u201395","journal-title":"J Econom"},{"issue":"3","key":"10305_CR12","doi-asserted-by":"publisher","first-page":"447","DOI":"10.1016\/S0169-2070(03)00067-0","volume":"20","author":"A Baffigi","year":"2004","unstructured":"Baffigi A, Golinelli R, Parigi G (2004) Bridge models to forecast the euro area GDP. Int J Forecast 20(3):447\u2013460","journal-title":"Int J Forecast"},{"issue":"1","key":"10305_CR13","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1111\/j.1468-0084.2009.00567.x","volume":"72","author":"RS Mariano","year":"2010","unstructured":"Mariano RS, Murasawa Y (2010) A coincident index, common factors, and monthly real GDP. Oxford Bull Econ Stat 72(1):27\u201346","journal-title":"Oxford Bull Econ Stat"},{"issue":"1\u20132","key":"10305_CR14","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1002\/for.1142","volume":"29","author":"C Frale","year":"2010","unstructured":"Frale C, Marcellino M, Mazzi GL, Proietti T (2010) Survey data as coincident or leading indicators. J Forecast 29(1\u20132):109\u2013131","journal-title":"J Forecast"},{"issue":"2","key":"10305_CR15","doi-asserted-by":"publisher","first-page":"439","DOI":"10.1111\/j.1467-985X.2010.00675.x","volume":"174","author":"C Frale","year":"2011","unstructured":"Frale C, Marcellino M, Mazzi GL, Proietti T (2011) EUROMIND: a monthly indicator of the euro area economic conditions. J R Stat Soc Ser A Stat Soc 174(2):439\u2013470","journal-title":"J R Stat Soc Ser A Stat Soc"},{"key":"10305_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2024.111537","volume":"157","author":"H Yu","year":"2024","unstructured":"Yu H, Wang Z, Xie Y, Wang G (2024) A multi-granularity hierarchical network for long- and short-term forecasting on multivariate time series data. Appl Soft Comput 157:111537","journal-title":"Appl Soft Comput"},{"issue":"4","key":"10305_CR17","doi-asserted-by":"publisher","first-page":"1470","DOI":"10.1080\/07350015.2021.1933501","volume":"40","author":"A Babii","year":"2022","unstructured":"Babii A (2022) High-dimensional mixed-frequency IV regression. J Bus Econ Stat 40(4):1470\u20131483","journal-title":"J Bus Econ Stat"},{"key":"10305_CR18","doi-asserted-by":"publisher","first-page":"319","DOI":"10.1016\/j.eap.2023.05.022","volume":"79","author":"T Chang","year":"2023","unstructured":"Chang T, Hsu C-M, Chen S-T, Wang M-C, Wu C-F (2023) Revisiting economic growth and CO2 emissions nexus in Taiwan using a mixed-frequency var model. Econ Anal Policy 79:319\u2013342","journal-title":"Econ Anal Policy"},{"key":"10305_CR19","unstructured":"Kamolthip S (2021) Macroeconomic forecasting with LSTM and mixed frequency time series data. arXiv preprint arXiv:2109.13777"},{"key":"10305_CR20","unstructured":"Ghysels E, Santa-Clara P, Valkanov R (2004) The MIDAS touch: mixed data sampling regression models. https:\/\/escholarship.org\/uc\/item\/9mf223rs"},{"issue":"5","key":"10305_CR21","doi-asserted-by":"publisher","first-page":"861","DOI":"10.1002\/for.2743","volume":"40","author":"K Kuck","year":"2021","unstructured":"Kuck K, Schweikert K (2021) Forecasting Baden-W\u00fcrttemberg\u2019s GDP growth: Midas regressions versus dynamic mixed-frequency factor models. J Forecast 40(5):861\u2013882","journal-title":"J Forecast"},{"issue":"7","key":"10305_CR22","doi-asserted-by":"publisher","first-page":"779","DOI":"10.1080\/07474938.2012.690675","volume":"32","author":"J Bai","year":"2013","unstructured":"Bai J, Ghysels E, Wright JH (2013) State space models and MIDAS regressions. Econom Rev 32(7):779\u2013813","journal-title":"Econom Rev"},{"issue":"2","key":"10305_CR23","doi-asserted-by":"publisher","first-page":"529","DOI":"10.1016\/j.ijforecast.2010.02.006","volume":"27","author":"V Kuzin","year":"2011","unstructured":"Kuzin V, Marcellino M, Schumacher C (2011) MIDAS vs. mixed-frequency var: nowcasting GDP in the euro area. Int J Forecast 27(2):529\u2013542","journal-title":"Int J Forecast"},{"key":"10305_CR24","doi-asserted-by":"crossref","unstructured":"Foroni C, Marcellino MG (2013) A survey of econometric methods for mixed-frequency data. Available at SSRN 2268912","DOI":"10.2139\/ssrn.2268912"},{"key":"10305_CR25","unstructured":"Wohlrabe K (2009) Forecasting with mixed-frequency time series models. PhD thesis, LMU"},{"key":"10305_CR26","first-page":"1","volume":"9","author":"E Ghysels","year":"2019","unstructured":"Ghysels E, Qian H (2019) Estimating MIDAS regressions via OLS with polynomial parameter profiling. Econom Stat 9:1\u201316","journal-title":"Econom Stat"},{"issue":"6","key":"10305_CR27","doi-asserted-by":"publisher","first-page":"2575","DOI":"10.1017\/S0022109018001308","volume":"54","author":"F Audrino","year":"2019","unstructured":"Audrino F, Kostrov A, Ortega J-P (2019) Predicting US bank failures with MIDAS logit models. J Financ Quant Anal 54(6):2575\u20132603","journal-title":"J Financ Quant Anal"},{"key":"10305_CR28","unstructured":"Liu Y (2019) Statistical methods for mixed frequency data sampling models. PhD thesis, Michigan Technological University"},{"key":"10305_CR29","volume-title":"Forecasting mixed frequency time series with ECM-MIDAS models","author":"A Hecq","year":"2012","unstructured":"Hecq A, G\u00f6tz T, Urbain J (2012) Forecasting mixed frequency time series with ECM-MIDAS models. METEOR, Maastricht University School of Business and Economics, Maastricht"},{"key":"10305_CR30","doi-asserted-by":"publisher","first-page":"549","DOI":"10.1007\/s11846-014-0130-z","volume":"9","author":"A Hamid","year":"2015","unstructured":"Hamid A (2015) Prediction power of high-frequency based volatility measures: a model based approach. RMS 9:549\u2013576","journal-title":"RMS"},{"issue":"1","key":"10305_CR31","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1080\/07350015.2012.727721","volume":"31","author":"P Gu\u00e9rin","year":"2013","unstructured":"Gu\u00e9rin P, Marcellino M (2013) Markov-switching MIDAS models. J Bus Econ Stat 31(1):45\u201356","journal-title":"J Bus Econ Stat"},{"key":"10305_CR32","doi-asserted-by":"publisher","first-page":"247","DOI":"10.1016\/j.econmod.2020.06.003","volume":"91","author":"Y Qiu","year":"2020","unstructured":"Qiu Y (2020) Forecasting the consumer confidence index with tree-based MIDAS regressions. Econ Model 91:247\u2013256","journal-title":"Econ Model"},{"issue":"10","key":"10305_CR33","doi-asserted-by":"publisher","first-page":"1980","DOI":"10.1080\/00949655.2021.1879082","volume":"91","author":"N Bonino-Gayoso","year":"2021","unstructured":"Bonino-Gayoso N, Garcia-Hiernaux A (2021) TF-MIDAS: a transfer function based mixed-frequency model. J Stat Comput Simul 91(10):1980\u20132017","journal-title":"J Stat Comput Simul"},{"issue":"3","key":"10305_CR34","doi-asserted-by":"publisher","first-page":"776","DOI":"10.1162\/REST_a_00300","volume":"95","author":"RF Engle","year":"2013","unstructured":"Engle RF, Ghysels E, Sohn B (2013) Stock market volatility and macroeconomic fundamentals. Rev Econ Stat 95(3):776\u2013797","journal-title":"Rev Econ Stat"},{"key":"10305_CR35","doi-asserted-by":"crossref","unstructured":"Foroni C, Marcellino MG, Schumacher C (2011) U-MIDAS: MIDAS regressions with unrestricted lag polynomials. Bundesbank Series 1 Discussion. Paper No. 2011,35","DOI":"10.2139\/ssrn.2785452"},{"key":"10305_CR36","doi-asserted-by":"publisher","first-page":"127","DOI":"10.1016\/j.eswa.2018.10.013","volume":"118","author":"Q Xu","year":"2019","unstructured":"Xu Q, Zhuo X, Jiang C, Liu Y (2019) An artificial neural network for mixed frequency data. Expert Syst Appl 118:127\u2013139","journal-title":"Expert Syst Appl"},{"key":"10305_CR37","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1016\/j.neucom.2021.06.006","volume":"457","author":"Q Xu","year":"2021","unstructured":"Xu Q, Liu S, Jiang C, Zhuo X (2021) QRNN-MIDAS: a novel quantile regression neural network for mixed sampling frequency data. Neurocomputing 457:84\u2013105","journal-title":"Neurocomputing"},{"issue":"3","key":"10305_CR38","doi-asserted-by":"publisher","first-page":"301","DOI":"10.1049\/cit2.12013","volume":"6","author":"X Li","year":"2021","unstructured":"Li X, Yu H, Xie Y, Li J (2021) Attention-based novel neural network for mixed frequency data. CAAI Trans Intell Technol 6(3):301\u2013311","journal-title":"CAAI Trans Intell Technol"},{"key":"10305_CR39","doi-asserted-by":"crossref","unstructured":"Challu C, Olivares KG, Oreshkin BN, Ramirez FG, Canseco MM, Dubrawski A (2023) N-HiTS: Neural hierarchical interpolation for time series forecasting. In: Proceedings of the AAAI conference on artificial intelligence, vol 37, pp 6989\u20136997","DOI":"10.1609\/aaai.v37i6.25854"},{"key":"10305_CR40","unstructured":"Oreshkin BN, Carpov D, Chapados N, Bengio Y (2019) N-beats: neural basis expansion analysis for interpretable time series forecasting. arXiv preprint arXiv:1905.10437"},{"issue":"2","key":"10305_CR41","doi-asserted-by":"publisher","first-page":"423","DOI":"10.1109\/TPAMI.2018.2798607","volume":"41","author":"T Baltru\u0161aitis","year":"2018","unstructured":"Baltru\u0161aitis T, Ahuja C, Morency L-P (2018) Multimodal machine learning: a survey and taxonomy. IEEE Trans Pattern Anal Mach Intell 41(2):423\u2013443","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"3","key":"10305_CR42","doi-asserted-by":"publisher","first-page":"478","DOI":"10.1109\/JSTSP.2020.2987728","volume":"14","author":"C Zhang","year":"2020","unstructured":"Zhang C, Yang Z, He X, Deng L (2020) Multimodal intelligence: representation learning, information fusion, and applications. IEEE J Sel Top Signal Process 14(3):478\u2013493","journal-title":"IEEE J Sel Top Signal Process"},{"issue":"6","key":"10305_CR43","doi-asserted-by":"publisher","first-page":"96","DOI":"10.1109\/MSP.2017.2738401","volume":"34","author":"D Ramachandram","year":"2017","unstructured":"Ramachandram D, Taylor GW (2017) Deep multimodal learning: a survey on recent advances and trends. IEEE Signal Process Mag 34(6):96\u2013108","journal-title":"IEEE Signal Process Mag"},{"key":"10305_CR44","doi-asserted-by":"crossref","unstructured":"Morvant E, Habrard A, Ayache S (2014) Majority vote of diverse classifiers for late fusion. In: Structural, syntactic, and statistical pattern recognition: joint IAPR international workshop, S+ SSPR 2014, Joensuu, Finland, August 20\u201322, 2014. Proceedings, pp. 153\u2013162. Springer","DOI":"10.1007\/978-3-662-44415-3_16"},{"key":"10305_CR45","doi-asserted-by":"crossref","unstructured":"Shutova E, Kiela D, Maillard J (2016) Black holes and white rabbits: Metaphor identification with visual features. In: Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp 160\u2013170","DOI":"10.18653\/v1\/N16-1020"},{"key":"10305_CR46","doi-asserted-by":"crossref","unstructured":"Glodek M, Tschechne S, Layher G, Schels M, Brosch T, Scherer S, K\u00e4chele M, Schmidt M, Neumann H, Palm G et al (2011) Multiple classifier systems for the classification of audio-visual emotional states. In: Affective computing and intelligent interaction: fourth international conference, ACII 2011, Memphis, October 9\u201312, 2011, Proceedings, Part II. Springer, pp 359\u2013368","DOI":"10.1007\/978-3-642-24571-8_47"},{"key":"10305_CR47","doi-asserted-by":"crossref","unstructured":"Chen L, Li Z, Xu T, Wu H, Wang Z, Yuan NJ, Chen E (2022) Multi-modal siamese network for entity alignment. In: Proceedings of the 28th ACM SIGKDD conference on knowledge discovery and data mining, pp 118\u2013126","DOI":"10.1145\/3534678.3539244"},{"issue":"1","key":"10305_CR48","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2020.102437","volume":"58","author":"C Song","year":"2021","unstructured":"Song C, Ning N, Zhang Y, Wu B (2021) A multimodal fake news detection model based on crossmodal attention residual and multichannel convolutional neural networks. Inf Process Manag 58(1):102437","journal-title":"Inf Process Manag"},{"key":"10305_CR49","doi-asserted-by":"publisher","first-page":"296","DOI":"10.1016\/j.patcog.2019.06.013","volume":"95","author":"M Angelou","year":"2019","unstructured":"Angelou M, Solachidis V, Vretos N, Daras P (2019) Graph-based multimodal fusion with metric learning for multimodal classification. Pattern Recogn 95:296\u2013307","journal-title":"Pattern Recogn"},{"key":"10305_CR50","doi-asserted-by":"crossref","unstructured":"Lusquino\u00a0Filho LAD, Werneck RDO, Mendes\u00a0J\u00fanior PR, Castro M, Santos Pereira E, Moura R, Sousa\u00a0Ferreira VH, Ferreira AM, Gomes AD, Rocha A (2022) Oil production and pressure multimodal forecasting integrating high-frequency production data. In: Rio oil & gas expo and conference. IBP, pp 308\u2013309","DOI":"10.48072\/2525-7579.rog.2022.308"},{"issue":"2194","key":"10305_CR51","doi-asserted-by":"publisher","first-page":"20200209","DOI":"10.1098\/rsta.2020.0209","volume":"379","author":"B Lim","year":"2021","unstructured":"Lim B, Zohren S (2021) Time-series forecasting with deep learning: a survey. Philos Trans R Soc A 379(2194):20200209","journal-title":"Philos Trans R Soc A"},{"issue":"1","key":"10305_CR52","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1089\/big.2020.0159","volume":"9","author":"JF Torres","year":"2021","unstructured":"Torres JF, Hadjout D, Sebaa A, Mart\u00ednez-\u00c1lvarez F, Troncoso A (2021) Deep learning for time series forecasting: a survey. Big Data 9(1):3\u201321","journal-title":"Big Data"},{"key":"10305_CR53","doi-asserted-by":"publisher","unstructured":"Reiss A, Indlekofer I, Schmidt P (2019) PPG-DaLiA. UCI Machine Learning Repository. https:\/\/doi.org\/10.24432\/C53890","DOI":"10.24432\/C53890"},{"key":"10305_CR54","unstructured":"Institute MP (2016) Jena Climate Dataset. https:\/\/www.bgc-jena.mpg.de\/wetter\/. Weather time series dataset recorded at the Weather Station of the Max Planck Institute for Biogeochemistry in Jena, Germany"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-10305-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-024-10305-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-10305-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,23]],"date-time":"2024-11-23T09:08:59Z","timestamp":1732352939000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-024-10305-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,4]]},"references-count":54,"journal-issue":{"issue":"34","published-print":{"date-parts":[[2024,12]]}},"alternative-id":["10305"],"URL":"https:\/\/doi.org\/10.1007\/s00521-024-10305-z","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,9,4]]},"assertion":[{"value":"29 April 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 July 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 September 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 declare that they have no conflict of interest","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}