首页|Characterizing ordinal network of time series based on complexity-entropy curve
Characterizing ordinal network of time series based on complexity-entropy curve
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NSTL
Elsevier
Characterizing signal dynamics with network approaches have attracted significant attention in nonlinear time series analysis. Among these approaches, ordinal networks have received great interest for their simplicity and computational efficiency. But most studies mainly use the topological structure of ordinal network to characterize time series while the underlying information in the transition probabilities remain insufficiently concerned. In this paper, the authors introduce an ordinal network-based complexity entropy curve to fill this gap. The numerical results show that this curve has a great discriminating power for signals with different dynamics, outperforming the recently proposed global node entropy. In the empirical application on stock indices, these curves distinguish stock market with different market development and further identify the impact of the 2008 global financial crisis on stock market dynamics. In the analysis of geomagnetic activity, these curves detect the dynamical change in Earths magnetic field caused by the geomagnetic storm. (c) 2021 Elsevier Ltd. All rights reserved.
NSTL
Elsevier
