A Multivariate Approach to Time Series Forecasting of Copper Prices with the Help of Multiple Imputation by Chained Equations and Multivariate Adaptive Regression Splines

dc.contributor.author	Sánchez Lasheras, Fernando
dc.contributor.author	Gracia Rodríguez, Javier
dc.contributor.author	García Nieto, Paulino José
dc.contributor.author	García Gonzalo, María Esperanza
dc.contributor.author	Fidalgo Valverde, Gregorio
dc.date.accessioned	2021-06-01T09:44:49Z
dc.date.available	2021-06-01T09:44:49Z
dc.date.issued	2021
dc.identifier.isbn	9783030578015
dc.identifier.issn	2194-5357
dc.identifier.uri	http://hdl.handle.net/10651/58961
dc.description	International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO) (15th. 2020. Burgos, Spain)
dc.format.extent	p. 691-701
dc.language.iso	eng
dc.relation.ispartof	15th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2020)
dc.relation.ispartofseries	Advances in Intelligent Systems and Computing; 1268
dc.rights	© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021
dc.source	Scopus
dc.source.uri	https://www.scopus.com/inward/record.uri?eid=2-s2.0-85091277162&doi=10.1007%2f978-3-030-57802-2_66&partnerID=40&md5=74fddb53b9dad7e36a016dbdc6ba6730
dc.title	A Multivariate Approach to Time Series Forecasting of Copper Prices with the Help of Multiple Imputation by Chained Equations and Multivariate Adaptive Regression Splines
dc.type	conference output	spa
dc.identifier.doi	10.1007/978-3-030-57802-2_66
dc.relation.publisherversion	http://dx.doi.org/10.1007/978-3-030-57802-2_66

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