BINCOR: An R package for Estimating the Correlation between Two Unevenly Spaced Time Series

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.

Josue M. Polanco-Martinez (Basque Centre for Climate Change - BC3) , Martin A. Medina-Elizalde (Dept. of Geosciences, Auburn University) , Maria F. Sanchez Goni (Ecole Pratique des Hautes Etudes (EPHE), PSL University) , Manfred Mudelsee (Climate Risk Analysis)

1 Introduction

There are several approaches for quantifying the potential association between two evenly spaced climate time series, e.g. Pearson’s and Spearman’s correlation or the cross-correlation function (CCF). However, these methods should not be directly applied when the time series are unevenly spaced (“irregular”), particularly when two time series under analysis are not sampled at identical points in time, as is usually the case in climate research, especially in paleoclimate studies (Weedon 2003; Mudelsee 2014; Emile-Geay 2016). The most common way of tackling this problem is to interpolate the original unevenly spaced climate time series in the time domain so as to obtain equidistance and the same times. The series can then be analysed using existing conventional correlation analysis techniques. However, experience shows that interpolation has its drawbacks: depending on the features of the method applied, the interpolated time series may show deviations in terms of variability or noise properties, and additional serial dependence may be introduced (Horowitz 1974; Mudelsee 2014; Olafsdottir and Mudelsee 2014). Thus, interpolation should be avoided as far as possible.

Fortunately, there are some algorithms and software available to carry out this task, at least for unevenly spaced climate time series sampled at identical points in time (Mudelsee 2003; Olafsdottir and Mudelsee 2014). However, there are few statistical techniques for estimating the correlation between two time series not sampled at identical points in time and their corresponding computational implementations. One exception is the Gaussian-Kernel-based cross-correlation (gXCF) method and its associated software named NESTOOLBOX (Rehfeld et al. 2011; Rehfeld and Bedartha 2014; Rehfeld and Kurths 2014) and the extended version (Roberts et al. 2017) that includes a confidence interval obtained by a bootstrapping resampling approach; another exception is binned correlation as proposed by (Mudelsee 2010, 2014). However, the software for this method is not freely available on the Internet.

Binned correlation is a statistical technique developed to estimate the correlation between two unevenly spaced time series sampled at different points in time. It is also applicable to two evenly spaced time series that are not on the same time grid (Mudelsee 2014). It is performed by resampling the time series into time bins on a regular grid, and then assigning the mean values of the variable under scrutiny within those bins. (Mudelsee 2010) proposes a novel approach adapting the binned correlation technique (used mainly with astronomical data) to analyse climate time series taking into account their memory (or persistence), which is a genuine property of climate time series. Autocorrelation, persistence, memory or serial dependence is characteristic of weather and climate fluctuations, and is recorded in climate time series (Mudelsee 2002; Wilks 2011). A simple persistence model used to “represent” climate time series is a first-order autoregressive (AR1) process where a fluctuation depends only on its own immediate past plus a random component (Gilman et al. 1963; Mann and Lees 1996; Mudelsee 2002). However, paleoclimate time series are usually unevenly spaced in time, and it is necessary to use an AR1 version for the case of uneven spacing, such as the method proposed by (Robinson 1977). The technique of (Mudelsee 2010) requires the concept of nonzero persistence times, enabling the mixing information (i.e. covariance) to be recovered, even when the two timescales differ. The BINCOR package presented in this paper is based on a method that is not applicable when one or both of the time series under examination have zero persistence. Similarly, this method is not applicable when the time series are sampled with significantly longer spacing than the persistence time, so that the effectively sampled persistence time is zero. A fundamental condition for using this method is that the time spacing should not be much larger than the persistence times. Enough common data points then fall within a time bin, and knowledge can be acquired on the covariance (Mudelsee 2010, 2014).

In this paper we present a computational package named BINCOR (BINned CORrelation), which is based on the approach proposed by (Mudelsee 2010, 2014). The BINCOR package contains (i) a main function named bin_cor, which is used to convert the irregular time series to a binned time series; (ii) two complementary functions (cor_ts and ccf_ts) for computing the correlation between the two binned climate time series obtained with the bin_cor function; and (iii) an additional function (plot_ts) for plotting the “primary” vs. the binned time series. This package is programmed in R language and is available at the CRAN repository (

This paper is divided into four sections. The first outlines the method and the computational program. The second presents a Monte Carlo experiment to study the effect of binning size selection. In the Examples section we apply BINCOR to a couple of unevenly spaced real-world climate data sets: instrumental and paleoclimate. Finally, the Summary section presents our main conclusions.

2 The BINCOR package

The method

In this section we outline the main mathematical ideas behind the binned correlation technique for unevenly spaced sampled at different points in time, following the methodology introduced by (Mudelsee 2010, 2014). The procedure is described as follows:

  1. Input: two unevenly spaced climate time series \(\{X(i), T_X\}_{i=1}^{N_X}\) and \(\{Y(i), T_Y\}_{i=1}^{N_Y}\), where \(T_X\), \(T_Y\) and \(N_Y\), \(N_Y\) are the time domains and the sample sizes of each series, respectively.

  2. Compute the average spacing between samples

    • \(\bar{d}_X = [T_X(N_X) - T_X(1)]/(N_X - 1)\)
    • \(\bar{d}_Y = [T_Y(N_X) - T_Y(1)]/(N_Y - 1)\)
    • \(\bar{d}_{XY} = [\bar{T}_\mathrm{max} - \bar{T}_\mathrm{min}]/(N_X + N_Y - 1)\)

    where \(\bar{T}_\mathrm{max} = \max[T_X(N_X), T_Y(N_Y)]\) and \(\bar{T}_\mathrm{min} = \min[T_X(1), T_Y(1)]\).

  3. Estimate the bin-width (\(\bar{\tau}\)) taking into account the persistence (memory) estimated for each unevenly spaced climate time series, \(X\) and \(Y\) denoted as \(\hat{\tau}_X\) and \(\hat{\tau}_Y\), respectively. To estimate the persistence, an AR1 model (Robinson 1977) is fitted to each unevenly spaced time series (Mudelsee 2002). BINCOR includes three rules for estimating the bin-width (the options are shown in Table 1), but we prefer to use rule number 3 as the default value (FLAGTAU=3) because in terms of the RMSE (Section Monte Carlo experiments) of this rule Monte Carlo simulations are superior to the other rules for estimating the bin-width (Mudelsee 2014).

    • Estimate the bias-corrected equivalent autocorrelation coefficients

      • \(\hat{\bar{a}}'_X = \exp (-\bar{d}_X/\hat{\tau}'_X)\), \(\hat{\bar{a}}'_Y = \exp (-\bar{d}_Y/\hat{\tau}'_Y)\), and \(\hat{\bar{a}}'_{XY} = \sqrt{ \hat{\bar{a}}'_X \cdot \hat{\bar{a}}'_Y }\)
    • Estimate the bin-width as \(\bar{\tau} = -\bar{d}_{XY} / \ln (\hat{\bar{a}}'_{XY})\) (Eq. 7.48 in (Mudelsee 2002)), the default option (FLAGTAU=3) in the BINCOR package, other options are:

      Table 1: The FLAGTAU options and its corresponding methods (rules) to estimate the bin-width.
      \(\bar{\tau}\) rule FLAGTAU option Reference
      \(\tau_x + \tau_y\) 1 Eq. 7.44 in (Mudelsee 2014)
      \(\mathrm{max}(\tau_x, \tau_y)\) 2 Eq. 7.45 in (Mudelsee 2014)
      \(-\bar{d}_{XY} / \ln (\hat{\bar{a}}'_{XY})\) 3 Eq. 7.48 in (Mudelsee 2014)
  4. Determine the number of bins: \(N_b = (\bar{T}_\mathrm{max} - \bar{T}_\mathrm{min}) / \bar{\tau}\)

  5. Set: \(\lim_\mathrm{inf}(n=1) = \bar{T}_\mathrm{min}\). Then, for \(n=1, 2, \dots, N_b\), define (Figure 1):

    1. \(\lim_\mathrm{sup}(n) = \bar{T}_\mathrm{min} + n \cdot \bar{\tau}\)

    2. id\(T_X\) = WHICH \([T_X \geq \lim_\mathrm{inf}(n)\) AND \(T_X \leq \lim_\mathrm{sup}(n)]\)

    3. id\(T_Y\) = WHICH \([T_Y \geq \lim_\mathrm{inf}(n)\) AND \(T_Y \leq \lim_{sup}(n)]\)

    4. L\(T_X\) = LENGTH(id\(T_X\))

    5. L\(T_Y\) = LENGTH(id\(T_Y\))

      if (L\(T_X\) \(>\) 0 AND L\(T_Y\) \(>\) 0)

      1. \(F(n)\) = mean of \(X\)(id\(T_X\))

      2. \(G(n)\) = mean of \(Y\)(id\(T_Y\))

      3. \(T(n)\) = [\(\lim_\mathrm{inf}(n)\) + \(\lim_\mathrm{sup}(n)\)] / 2

    6. \(\lim_\mathrm{inf}(n) = \lim_\mathrm{sup}(n)\)

  6. Output: two binned climate time series \(\{T_n,\, F(n)\}_{n=1}^{N_b}\) and \(\{T_n, G(n)\}_{n=1}^{N_b}\), where \(N_b\) is the number of bins.

  7. Estimate the correlation between the two binned time series. This can be done through the native R functions cor and ccf or by means of the BINCOR functions cor_ts and ccf_ts.

graphic without alt text
Figure 1: Graphical representation for the binned correlation procedure presented in Step 5. Modified from (Mudelsee 2010, 2014).

3 Monte Carlo experiments

We conducted Monte Carlo experiments to study how the specific rules (Table 1) chosen for calculating the bin-width based on persistence reduce the error compared to arbitrarily choosing a bin-width. The parameter configuration for the Monte Carlo experiments is presented in Figure 2. To carry out the Monte Carlo simulations, we used the bivariate Gaussian AR1 process for uneven time spacings (Mudelsee 2014), which is given by

\[\begin{aligned} ~\label{biAR1-1} X(1) = \mu_{N(0,1)}^{X}(1), \nonumber \\ Y(1) = \mu_{N(0,1)}^{Y}(1), \nonumber \\ X(t) = a_X X(t-1) + \mu_{N(0,1-a_X^2)}^{X}(t), \;\; t= 2,...,N,\nonumber \\ Y(t) = a_Y Y(t-1) + \mu_{N(0,1-a_Y^2)}^{Y}(t), \;\; t= 2,...,N, \end{aligned} \tag{1}\]

where \(a_X\) and \(a_Y\), the autoregressive parameters for \(X(t)\) and \(Y(t)\), are defined as (Mudelsee 2014): \(a_X = exp\{-[T_X(t) - T_X(t-1)]/\tau_X\}\) and \(a_Y = exp\{-[T_Y(t) - T_Y(t-1)]/\tau_Y\}\). The correlation (by construction) between \(X(t)\) and \(Y(t)\) is \(\rho_{XY}\) (see Mudelsee 2014 307 for more details about the statistical properties of the bivariate AR1 process for unevenly spaced time series). To generate the uneven timescales for \(X(i)\) and \(Y(j)\), we follow the methodology proposed by (see Mudelsee 2014 299), which consists of producing a number (10 \(N\)) of data pairs on an evenly spaced grid of 1.0, discarding 90% of points and retaining 10% of \(X\) and \(Y\) (\(N_x=N_y=N\)) points. The time points for \(X(i)\) and \(Y(j)\) are subject to the following conditions:

  1. Control case (equal timescales):

    • Condition 1: \(N_X=N_Y\)
    • Condition 2: \(\{T_X(i)\}_{i=1}^{N_X}=\{T_Y(j)\}_{j=1}^{N_Y}\)
  2. “Well” mixed unequal timescales:

    • Condition 1: \(T_X(i) \neq T_Y(j) \; for all \; i \; and \; j\)
    • Condition 2: \(T_X(1) < T_Y(1) < T_X(2) < T_Y(2) < T_X(3) < ... < T_X(N_X) < T_Y(N_Y)\)
  3. “Wildly” mixed unequal timescales:

    • There are not conditions for this case.
graphic without alt text graphic without alt text graphic without alt text
graphic without alt text graphic without alt text graphic without alt text
Figure 2: Monte Carlo experiments to test the impact of the rules (Table ) used to calculate the bin-width and their role in the estimation of the binned correlation. The persistence figures for X and Y are 10 (column 1), 20 (column 2) and 50 (column 3), respectively. The constraints for the resampling timescales are for well mixed (first row) and wildly mixed (second row) cases. The horizontal axis indicates the sample sizes (in log10 scale) and the vertical axis shows the RMSE that is determined via averaging (ρ̂XYρXY)2 over 5,000 simulations. The blue, green and red curves indicate rules 1 (sum), 2 (max) and 3 (the default rule option in BINCOR).

The outcome of the Monte Carlo experiments is as follows: 1) For equal timescales (figures not shown), all three rules behave similarly (as expected) in terms of RMSE, although the RMSE increases slightly as the persistence increases. 2) The well mixed case shows that for RMSE the rules take two different “patterns” with the first two rules (sum and max) on one hand and the third rule (the default rule option) on the other. This difference is most noticeable in the first values of the samples (from 10 to 100) and is most pronounced with high persistence values (\(\tau_x\) and \(\tau_y\)). The rule that shows the smallest RMSE is rule 3 (the default option), though it is important to point out that for \(\tau_x = \tau_y\) = 50 the RMSE figures are practically indistinguishable for sample sizes from 200 to 1000. 3) Finally, RMSE in the wildly mixed case behaves more or less similarly to the well mixed case, though rule 3 yields the smallest RMSE for all three persistence values. Bearing in mind that the wildly mixed case does not impose conditions on generating timescales, and in practice the unevenly spaced climate time series could contain some degree of randomness in the sampling times, the best rule in terms of RMSE for estimating bin-width (\(\bar{\tau}\)) and binned correlation can be said to be number 3, i.e. the default rule used in BINCOR to estimate the bin-width.

The computer program

The BINCOR package developed in R version 3.1.2 to be run from the command line runs on all major operating systems and is available from the CRAN repository ( The BINCOR package contains four functions: 1) bin_cor (the main function for building the binned time series); 2) plot_ts (for plotting and comparing the “primary” and binned time series); 3) cor_ts (for estimating the correlation between the binned time series); and 4) ccf_ts (for estimating the cross-correlation between the binned time series). The graphical outputs can be displayed on the screen or saved as PNG, JPG, or PDF graphics files. BINCOR depends on the dplR (Bunn et al. 2015) and pracma (Borchers 2015) packages. The dplR package is used by the function bin_cor to calculate the persistence for the climate time series under study, whereas the pracma package is used by the functions cor_ts and ccf_ts to remove the linear trend before estimating the correlation.

The first (and main) function, bin_cor, estimates the binned time series taking into account the memory or persistence of the unevenly spaced climate time series to be analysed (Mudelsee 2002). It has the following syntax:

  R> bin_cor(ts1, ts2, FLAGTAU=3, ofilename), 


bin_cor returns a list object containing the following outputs:

 "Binned_time_series", "Auto._cor._coef._ts1", "Persistence_ts1", "Auto._cor._coef._ts2",  
 "Persistence_ts2", "bin width", "Number_of_bins",  "Average spacing", "VAR. ts1", 
 "VAR. bin ts1", "VAR. ts2", "VAR. bin ts2", "VAR. ts1 - VAR bints1", 
 "VAR. ts2 - VAR bints2", "% of VAR. lost ts1", "% of VAR. lost ts2".

The names of the outputs are self-explanatory, but we wish to highlight that Average spacing is the mean value of the times for the binned time series; VAR. ts1, VAR. bin ts1, VAR. ts2 and VAR. bin ts2 are the variances for ts1 and ts2 for their respective binned time series; the next two outputs are the differences between the variances of ts1 and ts2 and their corresponding binned time series; and the last two outputs are the percentages of variance lost for ts1 and ts2 as a result of the binned process.

The second function, called plot_ts, plots the “primary” (unevenly spaced) time series and the binned time series. The plot_ts function contains the following elements:

 R> plot_ts(ts1, ts2, bints1, bints2, varnamets1="", varnamets2="", 
    colts1=1, colts2=1, colbints1=2, colbints2=2, ltyts1=1, 
    ltyts2=1, ltybints1=2, ltybints2=2, device="screen", ofilename),

where the input arguments ts1 and ts2 are the unevenly spaced time series, bints1 and bints2 are the binned time series, varnamets1 and varnamets2 are the names of the variables under study, colts1, colts2 (by default both curves are in black) and colbints1, colbints2 (by default both curves are in red) are the colours for the “primary” and binned times series; ltyts1, ltyts2, ltybints1 and ltybints2 are the types of line to be plotted for the “primary” and binned times series, respectively (1 = solid, 2 = dashed, 3 = dotted, 4 = dot-dashed, 5 = long-dashed, 6 = double-dashed); device is the type of output device (“screen” by default, the other options being “jpg,” “png,” and “pdf”); resfig is the image resolution in “ppi” (by default R does not record a resolution in the image file, except for BMP; 150 ppi could be a suitable value); ofilename is the output filename; and finally, Hfig, WFig and Hpdf, Wpdf are the height and width of the output for the JPG/PNG and PDF formats, respectively.

The third function, cor_ts, calculates three types of correlation coefficient: Pearson’s correlation, Spearman’s and Kendall’s rank correlations. These correlation coefficients are estimated through the native R function cor.test from the R package Stats. The cor_ts function has an option to remove the linear trend of the time series under analysis – other pre-processing methods could be used before the cor_ts function is applied. This function has the following syntax:

 R> cor_ts(bints1, bints2, varnamets1="", varnamets2="", 
    KoCM, rmltrd="N", device="screen", Hfig, Wfig, Hpdf, Wpdf, 
    resfig, ofilename)

where KoCM indicates the correlation estimator: pearson for Pearson (the option by default), spearman for Spearman and kendall for Kendall; rmltrd is the option to remove the linear trend in the time series under study (by default the linear trend is not removed, but the function can be enabled via the option “Y” or “y”). The other parameters are described some lines above. cor_ts has as its output a list object containing the main information for the estimated correlation coefficient (e.g. a 95% confidence interval for Pearson and a p-value for Spearman and Kendall). The cor_ts function also provides a scatterplot for the binned time series, which can be plotted on the screen (by default) or saved in JPG, PNG or PDF formats (the parameter ofilename is available to assign a name to this output).

Finally, the fourth function, ccf_ts, estimates and plots the cross-correlation between two evenly spaced paleoclimate time series. We use the native R function ccf (R Stats package) to estimate the cross-correlation in our ccf_ts function. The ccf_ts function has the following syntax:

  R> ccf_acf <- ccf_ts(bints1, bints2, lagmax=NULL, ylima=-1, ylimb=1, 
            rmltrd="N", RedL=T, device="screen", Hfig, Wfig, 
            Hpdf, Wpdf, resfig, ofilename)

All these elements are already defined above except the parameters lagmax=NULL, ylima=-1, ylimb=1 and RedL. The first parameter indicates the maximum lag for which the cross-correlation is calculated (its value depends on the length of the data set), the next two parameters indicate the extremes of the range in which the CCF will be plotted and the last parameter (the default option is TRUE) plots a straight red line to highlight the correlation coefficient at lag 0. The ccf_ts function generates as its output the acf (auto-correlation function; ACF) R object, which is a list with the following parameters: lag is a three dimensional array containing the lags at which the ACF is estimated; acf is an array with the same dimensions as lag containing the estimated ACF; type is the type of correlation (correlation (the default), covariance and partial); n.used is the number of observations in the time series; and snames provides the names of the time series (bints1 and bints2).

4 Examples

We first examine two evenly-spaced annually-resolved instrumental climate records that cover the time interval from 1850 to 2006 (\(N = 157\) points). To test our BINCOR package we created irregular time series by randomly removing 20% of the data from the evenly spaced time series. We note that the new “sampling” times are not necessarily the same for both irregular series. The new irregular time series (“primary” hereafter) consist of 125 data points and have an average temporal spacing \(\bar{d}\) of 1.24 years. Specifically the two time series used were a record of Northern Hemisphere (NH) sea surface temperature (SST) anomalies (HadCRUT3, (Brohan et al. 2006)) and a record of equatorial Pacific SST anomalies from the El Niño 3 region (2.5\(^\circ\)S to 2.5\(^\circ\)N, 92.5 to 147.5\(^\circ\)W) (Mann et al. 2009), which is a indicator of El Niño-Southern Oscillation (ENSO). Both time series, especially the NH-SST data, show strong autocorrelation (plots not shown) and long-term trends (inspected by Mann-Kendall test; ENSO, z=6.52 and p-value \(<\) 0.001 and NH-SST, z = 10.214 and p-value \(<\) 0.001). To generate the sample data, we fit a linear model to each evenly spaced time series and, after removing the model fitted to the evenly spaced data, we use the residuals (i.e. the difference between the observed data and the model fitted) to build the irregular time series and then create the binned time series.

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