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Detrended cross-correlation analysis: a new method for analyzing two nonstationary time series.
Boris Podobnik1, H Eugene Stanley
1Department of Physics, University of Rijeka, Rijeka, Croatia . bp@phy.hr
Physical Review Letters
|March 21, 2008
Summary
We introduce detrended cross-correlation analysis (DCCA), a novel method to detect power-law cross correlations in nonstationary time series. DCCA generalizes detrended fluctuation analysis and is applicable across diverse scientific fields.
Area of Science:
- Multidisciplinary science
- Complex systems analysis
- Data science
Background:
- Nonstationary time series are common in physics, physiology, and finance.
- Detecting cross-correlations in such data is challenging.
- Existing methods like detrended fluctuation analysis have limitations.
Purpose of the Study:
- To introduce a new method, detrended cross-correlation analysis (DCCA).
- To generalize detrended fluctuation analysis for cross-correlation detection.
- To analyze power-law cross correlations in simultaneously recorded time series.
Main Methods:
- Detrended cross-correlation analysis (DCCA) based on detrended covariance.
- Generalization of detrended fluctuation analysis.
- Application to simultaneously recorded time series.
Main Results:
- DCCA effectively identifies power-law cross correlations.
- The method is robust in the presence of nonstationarity.
- Demonstrated applicability in physics, physiology, and finance.
Conclusions:
- DCCA provides a powerful tool for analyzing coupled dynamics in complex systems.
- The method enhances understanding of interdependencies in nonstationary data.
- Cross-correlation analysis is advanced by this generalized approach.
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