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

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dc.contributor.author Hassani, Hossein
dc.contributor.author Silva, Emmanuel Sirimal
dc.contributor.author Gupta, Rangan
dc.contributor.author Das, Sonali
dc.date.accessioned 2018-07-09T06:28:52Z
dc.date.issued 2018-11
dc.description.department Economics en_ZA
dc.description.embargo 2019-11-01
dc.description.librarian hj2018 en_ZA
dc.description.uri http://www.elsevier.com/locate/physa en_ZA
dc.identifier.citation Hassani, 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.issn 0378-4371 (print)
dc.identifier.issn 1873-2119 (online)
dc.identifier.other 10.1016/j.physa.2018.05.147
dc.identifier.uri http://hdl.handle.net/2263/65318
dc.language.iso en en_ZA
dc.publisher Elsevier en_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.subject Forecasting en_ZA
dc.subject Statistical process en_ZA
dc.subject Nonparametric approach en_ZA
dc.subject Multivariate models en_ZA
dc.subject Forecasting models en_ZA
dc.subject Competing models en_ZA
dc.subject CO2 emissions en_ZA
dc.subject Global temperature anomaly en_ZA
dc.subject Univariate models en_ZA
dc.subject Spectrum analysis en_ZA
dc.subject Carbon dioxide (CO2) en_ZA
dc.title Predicting global temperature anomaly : a definitive investigation using an ensemble of twelve competing forecasting models en_ZA
dc.type Postprint Article en_ZA


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