Modeling long-range cross-correlations in two-component ARFIMA and FIARCH processes
By Boris Podobnik, Davor Horvatic, A. Ng, H. Eugene Stanley, Plamen Ch. Ivanov. 2007
146 citations recorded by the source corpus.
We investigate how simultaneously recorded long-range power-law correlated multivariate signals cross-correlate. To this end we introduce a two-component ARFIMA stochastic process and a two-component FIARCH process to generate coupled fractal signals with long-range power-law correlations which are at the same time long-range cross-correlated. We study how the degree of cross-correlations between these signals depends on the scaling exponents characterizing the fractal correlations in each signal and on the coupling between the signals. Our findings have relevance when studying parallel outputs of multiple component of physical, physiological and social systems.
Subfields: Economics and Econometrics, Molecular Biology, Signal Processing
View Paper: https://www.semanticscholar.org/paper/21d884b2fdf194d4f2b3fdeed0b32cc93f5080aa
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In Economics and Econometrics
- Ladislav Kristoufek
- Elie Bouri
- Eduard Baumöhl
- Ayoub Ammy-Driss
- Matthieu Garcin
- Miloslav S. Vosvrda
- Rangan Gupta
- David Roubaud
In Molecular Biology
In Signal Processing
Citations in the corpus
- Measuring correlations between non-stationary series with DCCA coefficient (2013, background): “Several processes mimicking the power-law cross-correlations have been proposed as well [25,23,26].”
- Mixed-correlated ARFIMA processes for power-law cross-correlations (2013, methodology): “[42] introduce two-component ARFIMA processes, which are based on ARFIMA-like mixing of two processes.”