W2CWM2C (version 2.2)

WCC: Wavelet cross-correlation (bivariate case).

Description

The WCC function (bivariate case) computes the wavelet cross correlation using the spin.correlation function of the waveslim package for two time series, and presents the result as a plot that reduce the number of plots of the classical function spin.correlation. The heatmap plot is built using the colorspace package and can be displayed in the screen or can be saved as PNG, JPG, PDF or EPS.

Usage

WCC(inputDATA, Wname, J, lmax, device="screen", filename,
    Hfig, WFig, Hpdf, Wpdf)

Arguments

inputDATA

A couple of time series as a ts object (please, check the ts manual to get more information about the ts function in R).

Wname

The wavelet function or filter to use in the decomposition.

J

Specifies the depth of the decomposition.

lmax

The maximum lag.

device

The type of the output device (by default the option is ``screen'', and the other options are ``jpg'', ``png'', ``eps'' and ``pdf'').

filename

The output filename.

Hfig

The height of the 'jpg' or 'png' image.

WFig

The width of the 'jpg' or 'png' image.

Hpdf

The height of the 'eps' or 'pdf'.

Wpdf

The width of the 'eps' or 'pdf'.

Value

Output:

Output plot: screen or 'filename + .png, .jpg, .eps or .pdf'.

returns.cross.cor: a matrix with the WCC values.

Details

The WCC function compute the wavelet cross-correlation between two time series and plot the results in a single heatmap plot (please, look at Figure 5 in Polanco-Martinez and Fernandez-Macho 2014). The WCC code is based on the spin.correlation routine from Brandon Whitcher's waveslim R package Version: 1.7.1, which is based mainly on wavelet methodology developed by Whitcher, B., P. Guttorp and D.B. Percival (2000) and Gencay, Selcuk and Whitcher (2001).

References

Gencay, R., F. Selcuk and B. Whitcher (2001). An Introduction to Wavelets and Other Filtering Methods in Finance and Economics, Academic Press.

Ihaka, R., Murrell, P., Hornik, K., Fisher, J. C. and Zeileis, A. (2012). colorspace: Color Space Manipulation. R package version 1.2.0, The Comprehensive R Archive Network (CRAN), <URL: https://cran.r-project.org/package=colorspace>.

Polanco-Martinez, J. and J. Fernandez-Macho (2014). The package 'W2CWM2C': description, features and applications. Computing in Science & Engineering, 16(6):68--78. 10.1109/MCSE.2014.96.

Polanco-Martinez, J. M. and Abadie, L. M. (2016). Analyzing crude oil spot price dynamics versus long term future prices: A wavelet analysis approach. Energies, 9(12), 1089. 10.3390/en9121089.

Whitcher, B., P. Guttorp, and D.B. Percival (2000). Wavelet analysis of covariance with application to atmospheric time series. Journal of Geophysical Research - Atmospheres, 105(D11):941--962. 10.1029/2000JD900110.

Whitcher, B. (2012). waveslim: Basic wavelet routines for one-, two- and three-dimensional signal processing. R package version 1.7.1, The Comprehensive R Archive Network (CRAN), <URL: https://cran.r-project.org/package=waveslim>.

Examples

# NOT RUN {
## Figure 5 (Polanco-Martinez and Fernandez-Macho 2014)

 library("colorspace")
 library("waveslim")
 library("W2CWM2C")
 data(dataexample)  

 #:: Transforms to log returns using: ln(t + deltat) - ln(t). 
 #:: The application in this example uses stock market 
 #:: indexes (it is common to use log returns instead of
 #:: raw data). Other kinds of pre-processing data are possible. 

 dataexample  <- dataexample[-1] #remove the dates!
 DAXCAC       <- dataexample[,c(3,4)] 
 lrdatex      <- apply(log(DAXCAC), 2, diff)
 inputDATA    <- ts(lrdatex, start=1, frequency=1)

 Wname     <- "la8"
 J         <- 8
 lmax      <- 30
 compWCC   <- WCC(inputDATA, Wname, J, lmax, device="screen", NULL,
               NULL, NULL, NULL, NULL)
# }