dCovTS: Distance Covariance/Correlation for Time Series

The distance covariance function is a new measure of dependence between random vectors. We drop the assumption of iid data to introduce distance covariance for time series. The R package dCovTS provides functions that compute and plot distance covariance and correlation functions for both univariate and multivariate time series. Additionally it includes functions for testing serial independence based on distance covariance. This paper describes the theoretical background of distance covariance methodology in time series and discusses in detail the implementation of these methods with the R package dCovTS.

Maria Pitsillou , Konstantinos Fokianos

CRAN packages used

energy, doParallel, portes, MTS

CRAN Task Views implied by cited packages

TimeSeries, Multivariate


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For attribution, please cite this work as

Pitsillou & Fokianos, "The R Journal: dCovTS: Distance Covariance/Correlation for Time Series", The R Journal, 2016

BibTeX citation

  author = {Pitsillou, Maria and Fokianos, Konstantinos},
  title = {The R Journal: dCovTS: Distance Covariance/Correlation for Time Series},
  journal = {The R Journal},
  year = {2016},
  note = {https://doi.org/10.32614/RJ-2016-049},
  doi = {10.32614/RJ-2016-049},
  volume = {8},
  issue = {2},
  issn = {2073-4859},
  pages = {324-340}