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dc.contributor.authorPolanco Martínez, Josué Moisés
dc.contributor.authorMedina-Elizalde, M.A.
dc.contributor.authorSanchez Goni, M.F.
dc.contributor.authorMudelsee, M.
dc.date.accessioned2020-11-12T14:19:43Z
dc.date.available2020-11-12T14:19:43Z
dc.date.issued2019
dc.identifier.citationR Journal 11(1) : 1-14 (2019)
dc.identifier.issn2073-4859
dc.identifier.urihttp://hdl.handle.net/10810/47903
dc.description.abstractThis paper presents a computational program named BINCOR (BINned CORrelation) for estimating the correlation between two unevenly spaced time series. This program is also applicable to the situation of two evenly spaced time series not on the same time grid. BINCOR is based on a novel estimation approach proposed by Mudelsee (2010) for estimating the correlation between two climate time series with different timescales. The idea is that autocorrelation (e.g. an AR1 process) means that memory enables values obtained on different time points to be correlated. Binned correlation is performed by resampling the time series under study into time bins on a regular grid, assigning the mean values of the variable under scrutiny within those bins. We present two examples of our BINCOR package with real data: instrumental and paleoclimatic time series. In both applications BINCOR works properly in detecting well-established relationships between the climate records compared. © Technische Universitaet Wien.
dc.language.isoeng
dc.publisherThe R Foundation
dc.relation.urihttps://dx.doi.org/10.32614/rj-2019-035
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.titleBINCOR: An r package for estimating the correlation between two unevenly spaced time series
dc.typeinfo:eu-repo/semantics/article
dc.rights.holder(cc) by This article is licensed under a Creative Commons Attribution 4.0 International license.
dc.identifier.doi10.32614/rj-2019-035


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