scholarly journals Time-irreversibility test for random-length time series: The matching-time approach applied to DNA

2021 ◽  
Vol 31 (12) ◽  
pp. 123126
Author(s):  
R. Salgado-García
Keyword(s):  
Time Series ◽  
Time Irreversibility

scholarly journals Detecting Determinism in Time Series with Complex Networks Constructed Using a Compression Algorithm

2018 ◽  
Vol 28 (13) ◽  
pp. 1850165
Author(s):  
Débora C. Corrêa ◽  
David M. Walker ◽  
Michael Small
Keyword(s):  
Time Series ◽  
Complex Networks ◽  
Information Content ◽  
Compression Algorithm ◽  
Connectivity Structure ◽  
Cycle Basis ◽  
Minimal Cycle ◽  
Time Irreversibility ◽  
Basis Structure

The properties of complex networks derived from applying a compression algorithm to time series subject to symbolic ordinal-based encoding is explored. The information content of compression codewords can be used to detect forbidden symbolic patterns indicative of nonlinear determinism. The connectivity structure of ordinal-based compression networks summarized by their minimal cycle basis structure can also be used in tests for nonlinear determinism, in particular, detection of time irreversibility in a signal.

scholarly journals The lack of reversibility during financial crisis and its identification

2021 ◽  
Vol 107 ◽  
pp. 03002
Author(s):  
Andrii Bielinskyi ◽  
Serhii Hushko ◽  
Andriy Matviychuk ◽  
Oleksandr Serdyuk ◽  
Serhiy Semerikov ◽  
...  
Keyword(s):  
Time Series ◽  
Stock Markets ◽  
Permutation Patterns ◽  
Financial Systems ◽  
Fundamental Idea ◽  
Nonlinear Properties ◽  
Time Irreversibility ◽  
Non Gaussian ◽  
Multiscale Time Irreversibility ◽  
Over Time

The focus of this study to measure the varying irreversibility of stock markets. A fundamental idea of this study is that financial systems are complex and nonlinear systems that are presented to be non-Gaussian fractal and chaotic. Their complexity and different aspects of nonlinear properties, such as time irreversibility, vary over time and for a long-range of scales. Therefore, our work presents approaches to measure the complexity and irreversibility of the time series. To the presented methods we include Guzik’s index, Porta’s index, Costa’s index, based on complex networks measures, Multiscale time irreversibility index and based on permutation patterns measures. Our study presents that the corresponding measures can be used as indicators or indicator-precursors of crisis states in stock markets.

scholarly journals Assessing Time Series Reversibility through Permutation Patterns

2018 ◽  
Author(s):  
Massimiliano Zanin ◽  
Alejandro Rodríguez González ◽  
Ernestina Menasalvas Ruiz ◽  
David Papo
Keyword(s):  
Time Series ◽  
Time Reversal ◽  
Financial Time Series ◽  
Statistical Tests ◽  
Fundamental Property ◽  
Permutation Entropy ◽  
Permutation Patterns ◽  
Visibility Graphs ◽  
Time Irreversibility ◽  
Financial Data Analysis

Time irreversibility, i.e. the lack of invariance of the statistical properties of a system under time reversal, is a fundamental property of all systems operating out of equilibrium. Time reversal symmetry is associated with important statistical and physical properties and is related to the predictability of the system generating the time series. Over the past fifteen years, various methods to quantify time irreversibility in time series have been proposed, but these can be computationally expensive. Here we propose a new method, based on permutation entropy, which is essentially parameter-free, temporally local, yields straightforward statistical tests, and has fast convergence properties. We apply this method to the study of financial time series, showing that stocks and indices present a rich irreversibility dynamics. We illustrate the comparative methodological advantages of our method with respect to a recently proposed method based on visibility graphs, and discuss the implications of our results for financial data analysis and interpretation.

Multiscale Analysis of Time Irreversibility Based on Phase-Space Reconstruction and Horizontal Visibility Graph Approach

2018 ◽  
Vol 17 (01) ◽  
pp. 1850006 ◽  
Author(s):  
Yongping Zhang ◽  
Pengjian Shang ◽  
Hui Xiong ◽  
Jianan Xia
Keyword(s):  
Time Series ◽  
Phase Space ◽  
Financial Time Series ◽  
Real Data ◽  
Accurate Estimate ◽  
Phase Space Reconstruction ◽  
Visibility Graph ◽  
Reconstruction Technique ◽  
Nonequilibrium Dynamic ◽  
Time Irreversibility

Time irreversibility is an important property of nonequilibrium dynamic systems. A visibility graph approach was recently proposed, and this approach is generally effective to measure time irreversibility of time series. However, its result may be unreliable when dealing with high-dimensional systems. In this work, we consider the joint concept of time irreversibility and adopt the phase-space reconstruction technique to improve this visibility graph approach. Compared with the previous approach, the improved approach gives a more accurate estimate for the irreversibility of time series, and is more effective to distinguish irreversible and reversible stochastic processes. We also use this approach to extract the multiscale irreversibility to account for the multiple inherent dynamics of time series. Finally, we apply the approach to detect the multiscale irreversibility of financial time series, and succeed to distinguish the time of financial crisis and the plateau. In addition, Asian stock indexes away from other indexes are clearly visible in higher time scales. Simulations and real data support the effectiveness of the improved approach when detecting time irreversibility.

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