Detrended cross-correlation analysis: a new method for analyzing two nonstationary time series.
By Boris Podobnik, H. Eugene Stanley. Physical Review Letters, 2007
1,309 citations recorded by the source corpus.
Here we propose a new method, detrended cross-correlation analysis, which is a generalization of detrended fluctuation analysis and is based on detrended covariance. This method is designed to investigate power-law cross correlations between different simultaneously recorded time series in the presence of nonstationarity. We illustrate the method by selected examples from physics, physiology, and finance.
Subfields: Economics and Econometrics, Molecular Biology, Statistical and Nonlinear Physics
View Paper: https://www.semanticscholar.org/paper/255a6bf261e33a345d193c0a616e74dea0b3eb3f
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Citations in the corpus
- Exact results of the limited penetrable horizontal visibility graph associated to random time series and its application (2017, methodology): “Several methodologies for understanding the complicated behavior of nonlinear time series have been recently developed, including chaos analysis [1], fractal analysis [2], and complexity measurement [3].”
- Detrending moving-average cross-correlation coefficient: Measuring cross-correlations between non-stationary series (2013, background): “As the ρDMCA(λ) coefficient can be seen as both an alternative and a complement to the ρDCCA(s) coefficient [17], precision of these coefficients should be compared.”
- Multifractal Detrended Fluctuation Analysis of the Chinese Stock Index Futures Market (2013, background): “Scholars have also studied the cross-correlations between two non-stationary time series [10-13] by generalising DFA and MF-DFA analyses with an emphasis on detrended covariance.”