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Forecasting coking coal prices by means of ARIMA models and neural networks, considering the transgenic time series theory

dc.contributor.authorMatyjaszek, Marta Janyna
dc.contributor.authorRiesgo Fernández, Pedro 
dc.contributor.authorKrzemien, A.
dc.contributor.authorWodarski, K.
dc.contributor.authorFidalgo Valverde, Gregorio 
dc.date.accessioned2019-08-21T07:34:28Z
dc.date.available2019-08-21T07:34:28Z
dc.date.issued2019
dc.identifier.citationResources Policy, 61, p. 283-292 (2019); doi:10.1016/j.resourpol.2019.02.017
dc.identifier.issn0301-4207
dc.identifier.urihttp://hdl.handle.net/10651/52383
dc.description.sponsorshipThe first author greatly appreciates support from the Doctoral Research Fellowship of the “Luis Fernández Velasco” Foundation on Mineral & Mining Economics at the School of Mining, Energy and Material Engineering at the University of Oviedo (Spain).
dc.format.extentp. 283-292
dc.language.isoeng
dc.relation.ispartofResources Policy, 61
dc.rights© 2019 Elsevier
dc.rightsCC Reconocimiento – No Comercial – Sin Obra Derivada 4.0 Internacional
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.sourceScopus
dc.source.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85063026286&doi=10.1016%2fj.resourpol.2019.02.017&partnerID=40&md5=3bdb53e2b922ca4106ff0998ac7598b9
dc.titleForecasting coking coal prices by means of ARIMA models and neural networks, considering the transgenic time series theory
dc.typejournal article
dc.identifier.doi10.1016/j.resourpol.2019.02.017
dc.relation.publisherversionhttp://dx.doi.org/10.1016/j.resourpol.2019.02.017
dc.rights.accessRightsopen access
dc.type.hasVersionAM


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© 2019 Elsevier
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