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.
energy, doParallel, portes, MTS
Text and figures are licensed under Creative Commons Attribution CC BY 4.0. The figures that have been reused from other sources don't fall under this license and can be recognized by a note in their caption: "Figure from ...".
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
@article{RJ-2016-049,
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}
}