Multifractal temporally weighted detrended partial cross-correlation analysis of two non-stationary time series affected by common external factors
Multifractal temporally weighted detrended partial cross-correlation analysis of two non-stationary time series affected by common external factors
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受共同外部因素影响的两个非平稳时间序列的多重分形时间加权去趋势部分互相关分析
DOI：
10.1016/j.physa.2021.125920
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发表时间：
2021-03
期刊：
影响因子：
--
通讯作者：
Zu-Guo Yu
中科院分区：
文献类型：
--
作者：
Bao-Gen Li;Dian-Yi Ling;Zu-Guo Yu
When common factors strongly influence two cross-correlated time series recorded in complex natural and social systems, the results will be biased if we use multifractal detrended cross-correlation analysis (MF-DXA) without considering these common factors. Based on multifractal temporally weighted detrended cross-correlation analysis (MF-TWXDFA) proposed by our group and multifractal partial cross-correlation analysis (MF-DPXA) proposed by Qian et al., we propose a new method---multifractal temporally weighted detrended partial cross-correlation analysis (MF-TWDPCCA) to quantify intrinsic power-law cross-correlation of two non-stationary time series affected by common external factors in this paper. We use MF-TWDPCCA to characterize the intrinsic cross-correlations between the two simultaneously recorded time series by removing the effects of other potential time series. To test the performance of MF-TWDPCCA, we apply it, MF-TWXDFA and MF-DPXA on simulated series. Numerical tests on artificially simulated series demonstrate that MF-TWDPCCA can accurately detect the intrinsic cross-correlations for two simultaneously recorded series. To further show the utility of MF-TWDPCCA, we apply it on time series from stock markets and find that there exists significantly multifractal power-law cross-correlation between stock returns. A new partial cross-correlation coefficient is defined to quantify the level of intrinsic cross-correlation between two time series.
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DOI：
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发表时间：
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期刊：
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