The mvLSW package provides an implementation of the multivariate locally stationary time series modelling approach proposed by Park, Eckley and Ombao (2014).
The approach extends the locally stationary wavelet time series work of
Nason, von Sachs and Kroisandt (2000) to a multivariate setting, introducing
wavelet-based measures of local coherence and local partial coherence. The
package implements the estimation scheme by Park et al. (2014) for such
processes. Note that mvLSW should be used in conjunction with the
wavethresh package developed by Nason (2016).
Package: mvLSW
Type: Package
Version: 1.2.3
Date: 2019-08-05
License: GPL(>=3)
Taylor, S.A.C., Park, T.A. and Eckley, I. (2019) Multivariate locally stationary wavelet analysis with the mvLSW R package. Journal of statistical software 90(11) pp. 1--16, doi: 10.18637/jss.v090.i11.
Park, T.A., Eckley, I. and Ombao, H.C. (2014) Estimating time-evolving partial coherence between signals via multivariate locally stationary wavelet processes IEEE Transactions on Signal Processing 62(20), pp. 5240--5250.
Nason, G.P., von Sachs, R. and Kroisandt, G. (2000) Wavelet processes and adaptive estimation of the evolutionary wavelet spectrum Journal of the Royal Statistical Society B 62, pp. 271--292.
Nason, G. (2016) wavethresh: Wavelets Statistics and Transforms. R package version 4.6.8.
https://CRAN.R-project.org/package=wavethresh
# NOT RUN {
#
# See examples in individual help pages
#
# }
Run the code above in your browser using DataLab