bibtype J - Journal Article
ARLID 0473066
utime 20240103213846.6
mtime 20170316235959.9
SCOPUS 85014923760
WOS 000399513200015
DOI 10.1016/j.cnsns.2017.02.018
title (primary) (eng) Fractal approach towards power-law coherency to measure cross-correlations between time series
specification
page_count 8 s.
media_type P
serial
ARLID cav_un_epca*0314933
ISSN 1007-5704
title Communications in Nonlinear Science and Numerical Simulation
volume_id 50
volume 1 (2017)
page_num 193-200
publisher
name Elsevier
keyword power-law coherency
keyword power-law cross-correlations
keyword correlations
author (primary)
ARLID cav_un_auth*0256902
name1 Krištoufek
name2 Ladislav
full_dept (cz) Ekonometrie
full_dept (eng) Department of Econometrics
department (cz) E
department (eng) E
institution UTIA-B
full_dept Department of Econometrics
country CZ
fullinstit Ústav teorie informace a automatizace AV ČR, v. v. i.
source
url http://library.utia.cas.cz/separaty/2017/E/kristoufek-0473066.pdf
cas_special
project
ARLID cav_un_auth*0303546
project_id GP14-11402P
agency GA ČR
country CZ
abstract (eng) We focus on power-law coherency as an alternative approach towards studying power law cross-correlations between simultaneously recorded time series. To be able to study empirical data, we introduce three estimators of the power-law coherency parameter Hp based on popular techniques usually utilized for studying power-law cross-correlations detrended cross-correlation analysis (DCCA), detrending moving-average cross-correlation analysis (DMCA) and height cross-correlation analysis (HXA). In the finite sample properties study, we focus on the bias, variance and mean squared error of the estimators. We find that the DMCA-based method is the safest choice among the three. The HXA method is reasonable for long time series with at least 104 observations, which can be easily attainable in some disciplines but problematic in others. The DCCA-based method does not provide favorable properties which even deteriorate with an increasing time series length. The paper opens a new venue towards studying cross-correlations between time series.
RIV AH
FORD0 50000
FORD1 50200
FORD2 50202
reportyear 2018
num_of_auth 1
inst_support RVO:67985556
permalink http://hdl.handle.net/11104/0271360
confidential S
mrcbC86 1 Article Mathematics Applied|Mathematics Interdisciplinary Applications|Mechanics|Physics Fluids Plasmas|Physics Mathematical
mrcbC86 1 Article Mathematics Applied|Mathematics Interdisciplinary Applications|Mechanics|Physics Fluids Plasmas|Physics Mathematical
mrcbC86 1 Article Mathematics Applied|Mathematics Interdisciplinary Applications|Mechanics|Physics Fluids Plasmas|Physics Mathematical
mrcbT16-e MATHEMATICSAPPLIED|MATHEMATICSINTERDISCIPLINARYAPPLICATIONS|MECHANICS|PHYSICSFLUIDSPLASMAS|PHYSICSMATHEMATICAL
mrcbT16-j 0.859
mrcbT16-s 1.372
mrcbT16-B 71.568
mrcbT16-D Q2
mrcbT16-E Q1
arlyear 2017
mrcbU14 85014923760 SCOPUS
mrcbU24 PUBMED
mrcbU34 000399513200015 WOS
mrcbU63 cav_un_epca*0314933 Communications in Nonlinear Science and Numerical Simulation 1007-5704 1878-7274 Roč. 50 č. 1 2017 193 200 Elsevier