Institute of Information Theory and Automation

Publication details

Fractal approach towards power-law coherency to measure cross-correlations between time series

Journal Article

Krištoufek Ladislav

serial: Communications in Nonlinear Science and Numerical Simulation vol.50, 1 (2017), p. 193-200

project(s): GP14-11402P, GA ČR

keywords: power-law coherency, power-law cross-correlations, correlations

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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 Hρ 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 proper- ties 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 10^4 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.


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Last modification: 21.12.2012
Institute of Information Theory and Automation