Multifractal temporally weighted detrended cross-correlation analysis of multivariate time series
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AbstractFractal and multifractal properties of various systems have been studied extensively. In this paper, first, the multivariate multifractal detrend cross-correlation analysis (MMXDFA) is proposed to investigate the multifractal features in multivariate time series. MMXDFA may produce oscillations in the fluctuation function and spurious cross correlations. In order to overcome these problems, we then propose the multivariate multifractal temporally weighted detrended cross-correlation analysis (MMTWXDFA). In relation to the multivariate detrended cross-correlation analysis and multifractal temporally weighted detrended cross-correlation analysis, an innovation of MMTWXDFA is the application of the signed Manhattan distance to calculate the local detrended covariance function. To evaluate the performance of the MMXDFA and MMTWXDFA methods, we apply them on some artificially generated multivariate series. Several numerical tests demonstrate that both methods can identify their fractality, but MMTWXDFA can detect long-range cross correlations and simultaneously quantify the levels of cross correlation between two multivariate series more accurately.
Acceptance Date04/02/2020
All Author(s) ListShan Jiang, Bao-Gen Li, Zu-Guo Yu, Fang Wang, Vo Anh, Yu Zhou
Journal nameChaos
Volume Number30
Issue Number2
PublisherAIP Publishing
Article number023134
LanguagesEnglish-United States

Last updated on 2021-18-09 at 00:01