Predicting global temperature anomaly : a definitive investigation using an ensemble of twelve competing forecasting models

dc.contributor.authorHassani, Hossein
dc.contributor.authorSilva, Emmanuel Sirimal
dc.contributor.authorGupta, Rangan
dc.contributor.authorDas, Sonali
dc.contributor.emailrangan.gupta@up.ac.zaen_ZA
dc.date.accessioned2018-07-09T06:28:52Z
dc.date.issued2018-11
dc.description.departmentEconomicsen_ZA
dc.description.embargo2019-11-01
dc.description.librarianhj2018en_ZA
dc.description.urihttp://www.elsevier.com/locate/physaen_ZA
dc.identifier.citationHassani, H., Silva, E.S., Gupta, R. & Das, S.2018, 'Predicting global temperature anomaly : a definitive investigation using an ensemble of twelve competing forecasting models', 'Physica A : Statistical Mechanics and its Applications, vol. 509, pp. 121-139.en_ZA
dc.identifier.issn0378-4371 (print)
dc.identifier.issn1873-2119 (online)
dc.identifier.other10.1016/j.physa.2018.05.147
dc.identifier.urihttp://hdl.handle.net/2263/65318
dc.language.isoenen_ZA
dc.publisherElsevieren_ZA
dc.rights© 2018 Elsevier B.V. All rights reserved. Notice : this is the author’s version of a work that was accepted for publication in Physica A: Statistical Mechanics and its Applications. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. A definitive version was subsequently published in Physica A: Statistical Mechanics and its Applications, vol. 509, pp. 121-139, 2018. doi : 10.1016/j.physa.2018.05.147.en_ZA
dc.subjectForecastingen_ZA
dc.subjectStatistical processen_ZA
dc.subjectNonparametric approachen_ZA
dc.subjectMultivariate modelsen_ZA
dc.subjectForecasting modelsen_ZA
dc.subjectCompeting modelsen_ZA
dc.subjectCO2 emissionsen_ZA
dc.subjectGlobal temperature anomalyen_ZA
dc.subjectUnivariate modelsen_ZA
dc.subjectSpectrum analysisen_ZA
dc.subjectCarbon dioxide (CO2)en_ZA
dc.titlePredicting global temperature anomaly : a definitive investigation using an ensemble of twelve competing forecasting modelsen_ZA
dc.typePostprint Articleen_ZA

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