Spectral fluctuation characterization of random matrix ensembles through wavelets
arXiv:nlin/0605019 · doi:10.1088/0305-4470/39/42/L02
Abstract
A recently developed wavelet based approach is employed to characterize the scaling behavior of spectral fluctuations of random matrix ensembles, as well as complex atomic systems. Our study clearly reveals anti-persistent behavior and supports the Fourier power spectral analysis. It also finds evidence for multi-fractal nature in the atomic spectra. The multi-resolution and localization nature of the discrete wavelets ideally characterizes the fluctuations in these time series, some of which are not stationary.
7 pages, 2 eps figures
References in corpus (11)
- Effect of Trends on Detrended Fluctuation Analysis
- Effect of nonstationarities on detrended fluctuation analysis
- Scaling of the distribution of fluctuations of financial market indices
- Scaling of the distribution of price fluctuations of individual companies
- Multifractal Properties of Price Fluctuations of Stocks and Commodities
- Quantifying signals with power-law correlations: A comparative study of detrended fluctuation analysis and detrended moving average techniques
- Misleading signatures of quantum chaos
- Theoretical derivation of 1/f noise in quantum chaos
- Wavelet analysis and scaling properties of time series
- Quantum spectrum as a time series : Fluctuation measures
- Chaos and localization in the wavefunctions of complex atoms NdI, PmI and SmI
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