The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Ladislav Kristoufek - One of the best experts on this subject based on the ideXlab platform.
-
detrending moving average cross correlation coefficient measuring cross correlations between non stationary series
2013Co-Authors: Ladislav KristoufekAbstract:In the paper, we introduce a new measure of correlation between possibly non-stationary series. As the measure is based on the detrending moving-average Cross-Correlation analysis (DMCA), we label it as the DMCA coefficient $\rho_{DMCA}(\lambda)$ with a moving average window length $\lambda$. We analytically show that the coefficient ranges between -1 and 1 as a standard correlation does. In the simulation study, we show that the values of $\rho_{DMCA}(\lambda)$ very well correspond to the true correlation between the analyzed series regardless the (non-)stationarity level. Dependence of the newly proposed measure on other parameters -- correlation level, moving average window length and time series length -- is discussed as well.
-
multifractal height cross correlation analysis a new method for analyzing long range cross correlations
2012Co-Authors: Ladislav KristoufekAbstract:We introduce a new method for detection of long-range Cross-Correlations and multifractality - multifractal height Cross-Correlation analysis (MF-HXA) - based on scaling of qth order covariances. MF-HXA is a bivariate generalization of the height-height correlation analysis of Barabasi & Vicsek [Barabasi, A.L., Vicsek, T.: Multifractality of self-affine fractals, Physical Review A 44(4), 1991]. The method can be used to analyze long-range Cross-Correlations and multifractality between two simultaneously recorded series. We illustrate a power of the method on both simulated and real-world time series.
-
multifractal height cross correlation analysis a new method for analyzing long range cross correlations
EPL, 2011Co-Authors: Ladislav KristoufekAbstract:We introduce a new method for the detection of long-range Cross-Correlations and multifractality —multifractal height Cross-Correlation analysis (MF-HXA)— based on scaling of q-th order covariances. MF-HXA is a bivariate generalization of the height-height correlation analysis of Barabasi and Vicsek (Barabasi A. L. and Vicsek T., Phys. Rev. A, 44 (1991) 2730). The method can be used to analyze long-range Cross-Correlations and multifractality between two simultaneously recorded series. We illustrate the utility of the method on both simulated and real-world time series.
Michael C. Fehler - One of the best experts on this subject based on the ideXlab platform.
-
p waves from cross correlation of seismic noise
Geophysical Research Letters, 2005Co-Authors: Philippe Roux, Karim G. Sabra, William A. Kuperman, Peter Gerstoft, Michael C. FehlerAbstract:Received 13 June 2005; revised 19 August 2005; accepted 31 August 2005; published 6 October 2005. [1] We present results from the Cross-Correlations of seismic noise recordings among pairs of stations in the Parkfield network, California. When performed on many station pairs at short ranges, the noise correlation function (NCF) is the passive analog to a shot gather made with active sources. We demonstrate the presence of both a P-wave and a Rayleigh wave in the NCF. A time-frequency analysis allows us to separate the two wave packets that are further identified through their polarization. Arrival times were estimated from the NCF and they compared favorably with predictions using ray tracing in a regional velocity model and with the velocity gradient across the San Andreas Fault. Citation: Roux, P., K. G. Sabra, P. Gerstoft, W. A. Kuperman, and M. C. Fehler (2005), P-waves from crosscorrelation of seismic noise, Geophys. Res. Lett., 32, L19303,
-
extracting time domain green s function estimates from ambient seismic noise
Geophysical Research Letters, 2005Co-Authors: Karim G. Sabra, Philippe Roux, Peter Gerstoft, W A Kuperman, Michael C. FehlerAbstract:[1] It has been demonstrated experimentally and theoretically that an estimate of the Green's function between two seismic stations can be obtained from the time-derivative of the long-time average cross correlation of ambient noise between these two stations. This TDGF estimate from just the noise field includes all tensor components of the Green's function and these Green's function estimates can be used to infer Earth structure. We have computed cross correlations using 1 to 30 continuous days of ambient noise recorded by over 150 broadband seismic stations located in Southern California. The data processing yielded thousands of Cross-Correlation pairs, for receiver separations from 4–500 km, which clearly exhibit coherent broadband propagating dispersive wavetrains across frequency band 0.1–2 Hz.
Weixing Zhou - One of the best experts on this subject based on the ideXlab platform.
-
multifractal detrending moving average cross correlation analysis
Physical Review E, 2011Co-Authors: Zhiqiang Jiang, Weixing ZhouAbstract:There are a number of situations in which several signals are simultaneously recorded in complex systems, which exhibit long-term power-law Cross-Correlations. The multifractal detrended Cross-Correlation analysis (MF-DCCA) approaches can be used to quantify such Cross-Correlations, such as the MF-DCCA based on detrended fluctuation analysis (MF-X-DFA) method. We develop in this work a class of MF-DCCA algorithms based on the detrending moving average analysis, called MF-X-DMA. The performances of the MF-X-DMA algorithms are compared with the MF-X-DFA method by extensive numerical experiments on pairs of time series generated from bivariate fractional Brownian motions, two-component autoregressive fractionally integrated moving average processes and binomial measures, which have theoretical expressions of the multifractal nature. In all cases, the scaling exponents $h_{xy}$ extracted from the MF-X-DMA and MF-X-DFA algorithms are very close to the theoretical values. For bivariate fractional Brownian motions, the scaling exponent of the Cross-Correlation is independent of the Cross-Correlation coefficient between two time series and the MF-X-DFA and centered MF-X-DMA algorithms have comparative performance, which outperform the forward and backward MF-X-DMA algorithms. We apply these algorithms to the return time series of two stock market indexes and to their volatilities. For the returns, the centered MF-X-DMA algorithm gives the best estimates of $h_{xy}(q)$ since its $h_{xy}(2)$ is closest to 0.5 as expected, and the MF-X-DFA algorithm has the second best performance. For the volatilities, the forward and backward MF-X-DMA algorithms give similar results, while the centered MF-X-DMA and the MF-X-DFA algorithms fails to extract rational multifractal nature.
Klausrobert Muller - One of the best experts on this subject based on the ideXlab platform.
-
robust statistical detection of power law cross correlation
Scientific Reports, 2016Co-Authors: Duncan A J Blythe, Vadim V Nikulin, Klausrobert MullerAbstract:We show that widely used approaches in statistical physics incorrectly indicate the existence of power-law Cross-Correlations between financial stock market fluctuations measured over several years and the neuronal activity of the human brain lasting for only a few minutes. While such Cross-Correlations are nonsensical, no current methodology allows them to be reliably discarded, leaving researchers at greater risk when the spurious nature of Cross-Correlations is not clear from the unrelated origin of the time series and rather requires careful statistical estimation. Here we propose a theory and method (PLCC-test) which allows us to rigorously and robustly test for power-law Cross-Correlations, correctly detecting genuine and discarding spurious Cross-Correlations, thus establishing meaningful relationships between processes in complex physical systems. Our method reveals for the first time the presence of power-law Cross-Correlations between amplitudes of the alpha and beta frequency ranges of the human electroencephalogram.
Vadim V Nikulin - One of the best experts on this subject based on the ideXlab platform.
-
robust statistical detection of power law cross correlation
Scientific Reports, 2016Co-Authors: Duncan A J Blythe, Vadim V Nikulin, Klausrobert MullerAbstract:We show that widely used approaches in statistical physics incorrectly indicate the existence of power-law Cross-Correlations between financial stock market fluctuations measured over several years and the neuronal activity of the human brain lasting for only a few minutes. While such Cross-Correlations are nonsensical, no current methodology allows them to be reliably discarded, leaving researchers at greater risk when the spurious nature of Cross-Correlations is not clear from the unrelated origin of the time series and rather requires careful statistical estimation. Here we propose a theory and method (PLCC-test) which allows us to rigorously and robustly test for power-law Cross-Correlations, correctly detecting genuine and discarding spurious Cross-Correlations, thus establishing meaningful relationships between processes in complex physical systems. Our method reveals for the first time the presence of power-law Cross-Correlations between amplitudes of the alpha and beta frequency ranges of the human electroencephalogram.