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modwtcorr

Multiscale correlation using the maximal overlap discrete wavelet transform

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Syntax

wcorr = modwtcorr(w1,w2)
wcorr = modwtcorr(w1,w2,wav)
[wcorr,wcorrci] = modwtcorr(___)
[wcorr,wcorrci] = modwtcorr(___,conflevel)
[wcorr,wcorrci,pval] = modwtcorr(___)
[wcorr,wcorrci,pval,nj] = modwtcorr(___)
wcorrtable = modwtcorr(___,'table')
[___] = modwtcorr(___,'reflection')
modwtcorr(___)

Description

wcorr = modwtcorr(w1,w2) returns the wavelet correlation by scale for the maximal overlap discrete wavelet transforms (MODWTs) specified in w1 and w2. wcorr is an M-by-1 vector of correlation coefficients, where M is the number of levels with nonboundary wavelet coefficients. If the final level has enough nonboundary coefficients, modwtcorr returns the scaling correlation in the final row of wcorr.

example

wcorr = modwtcorr(w1,w2,wav) uses the wavelet wav to determine the number of boundary coefficients by level.

example

[wcorr,wcorrci] = modwtcorr(___) returns in wcorrci the lower and upper 95% confidence bounds for the correlation coefficients of wcorr, using any arguments from the previous syntaxes.

example

[wcorr,wcorrci] = modwtcorr(___,conflevel) uses conflevel for the coverage probability of the confidence interval. conflevel is a real scalar strictly greater than 0 and less than 1. If conflevel is unspecified or specified as empty, the coverage probability defaults to 0.95.

example

[wcorr,wcorrci,pval] = modwtcorr(___) returns the p-values for the null hypothesis test that the correlation coefficient in wcorr is equal to zero. pval is an M-by-2 matrix, where M is the number of levels with nonboundary wavelet coefficients. T

example

[wcorr,wcorrci,pval,nj] = modwtcorr(___) returns the number of nonboundary coefficients used in the computation of the correlation estimates by level, nj.

wcorrtable = modwtcorr(___,'table') returns an M-by-6 table with the correlation, confidence bounds, p-value, and adjusted p-value. The table also lists the number of nonboundary coefficients by level. The row names of the table wcorrtable designate the type and level of each estimate. For example, D1 designates that the row corresponds to a wavelet or detail estimate at level 1 and S6 designates that the row corresponds to the scaling estimate at level 6. The scaling correlation is only computed for the final level of the MODWT and only when there are nonboundary scaling coefficients. You can specify the 'table' flag anywhere after the input transforms w1 and w2. You must enter the entire character vector 'table'. If you specify 'table', modwtcorr only outputs one argument.

example

[___] = modwtcorr(___,'reflection') reduces the number of wavelet and scaling coefficients at each scale by half before computing the correlation. Use this option only when you obtain the MODWT of w1 and w2 were obtained using the 'reflection' boundary condition. You must enter the entire character vector 'reflection'. If you added a wavelet named 'reflection' using the wavelet manager, you must rename that wavelet prior to using this option.

modwtcorr supports only unbiased estimates of the wavelet correlation. For these estimates, the algorithm must remove the extra coefficients obtained using the 'reflection' boundary condition. Specifying the 'reflection' option in modwtcorr is identical to first obtaining the MODWT of w1 and w2 using the default 'periodic' boundary handling and then computing the wavelet correlation estimates.

example

modwtcorr(___) with no output arguments plots the wavelet correlations by scale with lower and upper confidence bounds. By default, the coverage probability is 0.95. Scales with NaNs for the confidence bounds and the scaling correlation are excluded.

example

Examples

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Open Live Script

Find the correlation by scale for monthly DM-USD exchange rate returns from 1970 to 1998. The return data are log transformed. Use the Daubechies wavelet with two vanishing moments ('db2') to obtain the MODWT down to level 6. Then obtain the correlation data.

load DM_USD;
load JY_USD;
wdm = modwt(DM_USD,'db2',6);
wjy = modwt(JY_USD,'db2',6);
wcorr = modwtcorr(wdm,wjy,'db2')
wcorr = 7×1

    0.5854
    0.5748
    0.6264
    0.4948
    0.3787
    0.9072
    0.7976

wcorr contains seven elements. The first six elements are the correlation coefficients for the wavelet (detail) levels one to six. The final element is the correlation for the scaling (lowpass) level six.

Open Live Script

Obtain the MODWT of the Southern Oscillation Index and Truk Island daily pressure data sets. Tabulate the correlation between the two data sets by level.

load soi;
load truk;
wsoi = modwt(soi);
wtruk = modwt(truk);
wcorr = modwtcorr(wsoi,wtruk)
wcorr = 10×1

    0.1749
    0.2936
    0.0914
    0.0883
    0.2667
    0.0894
   -0.0415
    0.4825
    0.4394
    0.7433

Show that the number of nonboundary coefficients, in this case, is less than the maximal length of the input. The MODWT is computed down to level thirteen, which is the maximal level for the length of the input. Level thirteen contains thirteen wavelet coefficient vectors and one scaling coefficient vector. 

size(wsoi,1)
ans = 
14

The multiscale correlations are computed only down to level ten because the levels after than do not contain nonboundary coefficients. For unbiased estimates, you must use nonboundary coefficients only.

numel(wcorr)
ans = 
10
Open Live Script

Obtain the MODWT of the monthly US-DM and US-JPY exchange return data from 1970 to 1998. The return data are log transformed. Use the Daubechies wavelet with two vanishing moments ('db2') and obtain the MODWT of each series down to level six. Obtain the correlation estimates by scale and the 95% confidence intervals.

load DM_USD
load JY_USD
wdm = modwt(DM_USD,'db2',6);
wjy = modwt(JY_USD,'db2',6);
[wcorr,wcorrci] = modwtcorr(wdm,wjy,'db2');
[wcorr wcorrci]
ans = 7×3

    0.5854    0.4780    0.6756
    0.5748    0.4133    0.7013
    0.6264    0.4016    0.7800
    0.4948    0.0803    0.7634
    0.3787   -0.3295    0.8142
    0.9072    0.1247    0.9939
    0.7976   -0.2857    0.9860

The width of the confidence interval increases as you go down in level.

Open Live Script

Specify the coverage probability for the confidence intervals. Obtain the 99% confidence intervals for the US-DM and US-JY exchange returns.

load DM_USD;
load JY_USD;
wdm = modwt(DM_USD,'db2',6);
wjy = modwt(JY_USD,'db2',6);
[wcorr,wcorrci] = modwtcorr(wdm,wjy,'db2',0.99);
[wcorr wcorrci]
ans = 7×3

    0.5854    0.4407    0.7005
    0.5748    0.3557    0.7340
    0.6264    0.3169    0.8153
    0.4948   -0.0646    0.8176
    0.3787   -0.5191    0.8792
    0.9072   -0.3006    0.9975
    0.7976   -0.6227    0.9941
Open Live Script

Return p-values for the test of zero correlation by scale. Obtain the MODWT of the DM-USD and JY-USD exchange return data down to level six using the Daubechies wavelet with two vanishing moments ('db2') wavelet. Compute the correlation by scale and return the p-values.

load DM_USD;
load JY_USD;
wdm = modwt(DM_USD,'db2',6);
wjy = modwt(JY_USD,'db2',6);
[wcorr,wcorrci,pval] = modwtcorr(wdm,wjy,'db2');
format longE
pval
pval = 7×2

     2.694174887029554e-17     4.889927419958641e-16
     7.125460513473893e-09     6.466355415977557e-08
     7.012389783536700e-06     4.242495819039703e-05
     2.258540027996925e-02     1.024812537703605e-01
     2.805930327935258e-01     7.275376493146417e-01
     3.348079529469850e-02     1.215352869197555e-01
     1.059217509938030e-01     3.204132967562542e-01


format

The first column contains the p-value and the second column contains the adjusted p-value based on the false discovery rate.

Open Live Script

Output results from modwtcorr in tabular form. Obtain the MODWT of the DM-USD and JY-USD exchange returns down to level six using the Daubechies wavelet with two vanishing moments ('db2'). Output the results in a table.

load DM_USD;
load JY_USD;
wdm = modwt(DM_USD,'db2',6);
wjy = modwt(JY_USD,'db2',6);
corrtable = modwtcorr(wdm,wjy,'db2','table')
corrtable=7×6 table
          NJ      Lower        Rho       Upper       Pvalue      AdjustedPvalue
          ___    ________    _______    _______    __________    ______________

    D1    344     0.47797    0.58542    0.67561    2.6942e-17      4.8899e-16  
    D2    338     0.41329    0.57483    0.70129    7.1255e-09      6.4664e-08  
    D3    326     0.40163    0.62641    0.78001    7.0124e-06      4.2425e-05  
    D4    302    0.080255     0.4948    0.76342      0.022585         0.10248  
    D5    254    -0.32954    0.37865    0.81417       0.28059         0.72754  
    D6    158     0.12469    0.90716    0.99393      0.033481         0.12154  
    S6    158    -0.28573    0.79761    0.98601       0.10592         0.32041
Open Live Script

Obtain multiscale correlation estimates when using 'reflection' boundary handling. Obtain the MODWT of the Southern Oscillation Index and Truk Islands pressure data sets using 'reflection' boundary handling for both data sets.

load soi
load truk
wsoi = modwt(soi,'fk4',6,'reflection');
wtruk = modwt(truk,'fk4',6,'reflection');
corrtable = modwtcorr(wsoi,wtruk,'fk4',0.95,'reflection','table')
corrtable=7×6 table
           NJ        Lower        Rho       Upper       Pvalue      AdjustedPvalue
          _____    _________    _______    _______    __________    ______________

    D1    12995      0.16942    0.19294    0.21624    1.5466e-55      2.8071e-54  
    D2    12989      0.21426    0.24683    0.27885    2.7037e-46      2.4536e-45  
    D3    12977     0.057885    0.10623    0.15407     1.789e-05       6.494e-05  
    D4    12953     0.048034    0.11645    0.18378    0.00088579       0.0026795  
    D5    12905      0.13281     0.2272     0.3175    3.7566e-06      1.7046e-05  
    D6    12809    -0.019835     0.1182    0.25181      0.093044         0.24125  
    S6    12809      0.26664    0.39003    0.50084    8.8066e-09       5.328e-08
Open Live Script

Plot the multiscale correlation of the DM-USD and JY-USD exchange returns down to level six. Use modwtcorr with no output arguments.

load DM_USD;
load JY_USD;
wdm = modwt(DM_USD,'db2',6);
wjy = modwt(JY_USD,'db2',6);
modwtcorr(wdm,wjy,'db2')

Figure contains an axes object. The axes object with title Correlation by Scale -- Wavelet Coefficients, xlabel Log(scale) -- base 2, ylabel Correlation Coefficient contains 2 objects of type errorbar, line.

Input Arguments

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MODWT transform of signal 1, specified as a matrix. w1 is the output of modwt. w1 and w2 must be the same size and both must have been obtained using the same analyzing wavelet.

Data Types: double

MODWT transform of signal 2, specified as a matrix. w2 is the output of modwt. w1 and w2 must be the same size and both must have been obtained using the same analyzing wavelet.

Wavelet, specified as a character vector or string scalar indicating a valid wavelet name, or as a positive even scalar indicating the length of the wavelet and scaling filters. wav must be the same wavelet and length used to obtain the MODWTs of w1 and w2. For a list of valid wavelets, see modwt. If unspecified or specified as an empty, [], wav defaults to the symlet wavelet with four vanishing moments, 'sym4'.

Confidence level, specified as a positive scalar less than 1. conflevel determines the coverage probability of the confidence intervals in wcorrci and in the table, if you specify 'table' as an input. If unspecified, or if specified as empty, [], conflevel defaults to 0.95.

Output Arguments

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Correlation coefficients by scale, returned as a vector. wcorr is an M-by-1 vector of correlation coefficients, where M is the number of levels with nonboundary wavelet coefficients. modwtcorr returns correlation estimates only where there are nonboundary coefficients. This condition is satisfied when the transform level is not greater than floor(log2(N/(L-1)+1)), where N is the length of the original signal and L is the filter length. If the final level has enough nonboundary coefficients, modwtcorr returns the scaling correlation in the final row of wcorr. By default, modwtcorr uses the symlet wavelet with four vanishing moments, 'sym4' to determine the boundary coefficients.

Confidence intervals by scale, returned as a matrix. The matrix is of size M-by-2, where M is the number of levels with nonboundary wavelet coefficients. The first column contains the lower confidence bound and the second column contains the upper confidence bound. The conflevel determines the coverage probability.

Confidence bounds are computed using Fisher's Z-transformation. The standard error of Fisher's Z statistic is the square root of (N – 3). In this case, N is the equivalent number of coefficients in the critically sampled discrete wavelet transform (DWT), floor(size(w1,2)/2^LEV), where LEV is the level of the wavelet transform. modwtcorr returns NaNs for the confidence bounds when (N – 3) is less than or equal to zero.

P-values for null hypothesis test, returned as a matrix. pval is an M-by-2 matrix.

  • The first column of pval is the p-value computed using the standard t-statistic test for a correlation coefficient of zero.

  • The second column of pval contains the adjusted p-value using the false discovery procedure of Benjamini & Yekutieli under arbitrary dependence assumptions.

The degrees of freedom, (N – 2), for the t-statistic are determined by the equivalent number of coefficients N in the critically sampled DWT, floor(size(w1,2)/2^LEV), where LEV is the level of the wavelet transform. modwtcorr returns NaNs when (N – 2) is less than or equal to zero.

Number of nonboundary coefficients by scale, returned as a vector.

Correlation table, returned as a MATLAB® table. The table contains six variables:

  • NJ — Number of nonboundary coefficients by level.

  • Lower — Lower confidence bound for the coverage probability specified by conflevel.

  • Rho — Correlation coefficient.

  • Upper — Upper confidence bound for the coverage probability specified by conflevel.

  • Pvalue — P-value for hypothesis test. The null hypothesis is that the correlation coefficient is equal to zero.

  • AdjustedPvalue — P-value adjusted for multiple comparisons. The p-values are adjusted using false discovery rate under dependency assumptions.

References

[1] Percival, D. B., and A. T. Walden. Wavelet Methods for Time Series Analysis. Cambridge, UK: Cambridge University Press, 2000.

[2] Whitcher, B., P. Guttorp, and D. B. Percival. “Wavelet analysis of covariance with application to atmospheric time series.” Journal of Geophysical Research, Vol. 105, pp. 14941–14962, 2000.

[3] Benjamini, Y., and Yekutieli, D. “The Control of the False Discovery Rate in Multiple Testing Under Dependency.” Annals of Statistics, Vol. 29, Number 4, pp. 1165–1188, 2001.

Version History

Introduced in R2015b

See Also

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