Sign patterns symbolization and its use in improved dependence test for complex network inference

0576550 - ÚI 2024 RIV US eng J - Journal Article
Yamashita Rios de Sousa, Arthur Matsuo - Hlinka, Jaroslav
Sign patterns symbolization and its use in improved dependence test for complex network inference.
Chaos. Roč. 33, č. 8 (2023), č. článku 083131. ISSN 1054-1500. E-ISSN 1089-7682
R&D Projects: GA ČR(CZ) GA21-17211S
Grant - others:GA AV ČR(CZ) L100302001
Institutional support: RVO:67985807
Keywords : Non linear dynamics * Data analysis * Signal processing * Network theory * Probability theory * Covariance and correlation * Random walks * Statistical analysis * Stochastic processes * Time series analysis
OECD category: Statistics and probability
Impact factor: 2.9, year: 2022
Method of publishing: Limited access
https://doi.org/10.1063/5.0160868

Inferring the dependence structure of complex networks from the observation of the non-linear dynamics of its components is among the common, yet far from resolved challenges faced when studying real-world complex systems. While a range of methods using the ordinal patterns framework has been proposed to particularly tackle the problem of dependence inference in the presence of non-linearity, they come with important restrictions in the scope of their application. Hereby, we introduce the sign patterns as an extension of the ordinal patterns, arising from a more flexible symbolization which is able to encode longer sequences with lower number of symbols. After transforming time series into sequences of sign patterns, we derive improved estimates for statistical quantities by considering necessary constraints on the probabilities of occurrence of combinations of symbols in a symbolic process with prohibited transitions. We utilize these to design an asymptotic chi-squared test to evaluate dependence between two time series and then apply it to the construction of climate networks, illustrating that the developed method can capture both linear and non-linear dependences, while avoiding bias present in the naive application of the often used Pearson correlation coefficient or mutual information.
Permanent Link: https://hdl.handle.net/11104/0346108