Volume 94, Number 1, April 2011
|Number of page(s)||6|
|Section||Interdisciplinary Physics and Related Areas of Science and Technology|
|Published online||08 April 2011|
Detrended cross-correlation analysis for non-stationary time series with periodic trends
Department of Physics, Faculty of Natural Sciences, University of Zagreb - 10000 Zagreb, Croatia
2 Center for Polymer Studies and Department of Physics, Boston University - Boston, MA 02215, USA
3 Department of Physics, Faculty of Civil Engineering, University of Rijeka - 51000 Rijeka, Croatia
4 Zagreb School of Economics and Management - 10000 Zagreb, Croatia
Accepted: 7 March 2011
Noisy signals in many real-world systems display long-range autocorrelations and long-range cross-correlations. Due to periodic trends, these correlations are difficult to quantify. We demonstrate that one can accurately quantify power-law cross-correlations between different simultaneously recorded time series in the presence of highly non-stationary sinusoidal and polynomial overlying trends by using the new technique of detrended cross-correlation analysis with varying order ℓ of the polynomial. To demonstrate the utility of this new method —which we call DCCA-ℓ(n), where n denotes the scale— we apply it to meteorological data.
PACS: 89.20.-a – Interdisciplinary applications of physics / 05.40.-a – Fluctuation phenomena, random processes, noise, and Brownian motion / 02.50.-r – Probability theory, stochastic processes, and statistics
© EPLA, 2011
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