Modeling long-range cross-correlations in two-component ARFIMA and FIARCH processes
Modeling long-range cross-correlations in two-component ARFIMA and FIARCH processes
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DOI：
10.1016/j.physa.2008.01.062
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发表时间：
2008-06-15
影响因子：
3.3
通讯作者：
Ivanov, Plamen Ch.
中科院分区：
文献类型：
--
作者：
Podobnik, Boris;Horvatic, Davor;Ivanov, Plamen Ch.
我们研究如何同时记录长程幂律相关的多元信号互相关。为此，我们引入了一个两个组件ARFIMA随机过程和两个组件MARCH过程产生耦合的分形信号与长程幂律相关，这是在同一时间长程互相关。我们研究了这些信号之间的互相关程度如何取决于标度指数表征每个信号中的分形相关性和信号之间的耦合。我们的研究结果在研究物理，生理和社会系统的多个组件的并行输出时具有相关性。(C)2008 Elsevier B.V.保留所有权利。
We investigate how simultaneously recorded long-range power-law con-elated multivariate signals cross-correlate. To this end we introduce a two-component ARFIMA stochastic process and a two-component MARCH 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. (C) 2008 Elsevier B.V. All rights reserved.