This 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.
Supplementary materials are available in addition to this article. It can be downloaded at RJ-2019-035.zip
DifferentialEquations, NumericalMathematics, TimeSeries
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For attribution, please cite this work as
Polanco-Martinez, et al., "The R Journal: BINCOR: An R package for Estimating the Correlation between Two Unevenly Spaced Time Series", The R Journal, 2019
BibTeX citation
@article{RJ-2019-035,
author = {Polanco-Martinez, Josue M. and Medina-Elizalde, Martin A. and Goni, Maria Fernanda Sanchez and Mudelsee, Manfred},
title = {The R Journal: BINCOR: An R package for Estimating the Correlation between Two Unevenly Spaced Time Series},
journal = {The R Journal},
year = {2019},
note = {https://doi.org/10.32614/RJ-2019-035},
doi = {10.32614/RJ-2019-035},
volume = {11},
issue = {1},
issn = {2073-4859},
pages = {170-184}
}