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Fractal approach towards power-law coherency to measure cross-correlations between time series
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SYSNO ASEP 0473066 Document Type J - Journal Article R&D Document Type Journal Article Subsidiary J Článek ve WOS Title Fractal approach towards power-law coherency to measure cross-correlations between time series Author(s) Krištoufek, Ladislav (UTIA-B) RID, ORCID Number of authors 1 Source Title Communications in Nonlinear Science and Numerical Simulation. - : Elsevier - ISSN 1007-5704
Roč. 50, č. 1 (2017), s. 193-200Number of pages 8 s. Publication form Print - P Language eng - English Country NL - Netherlands Keywords power-law coherency ; power-law cross-correlations ; correlations Subject RIV AH - Economics OECD category Applied Economics, Econometrics R&D Projects GP14-11402P GA ČR - Czech Science Foundation (CSF) Institutional support UTIA-B - RVO:67985556 UT WOS 000399513200015 EID SCOPUS 85014923760 DOI 10.1016/j.cnsns.2017.02.018 Annotation 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. Workplace Institute of Information Theory and Automation Contact Markéta Votavová, votavova@utia.cas.cz, Tel.: 266 052 201. Year of Publishing 2018
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