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

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00023752%3A_____%2F23%3A43921139" target="_blank" >RIV/00023752:_____/23:43921139 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/67985807:_____/23:00576550

Výsledek na webu

<a href="https://pubs.aip.org/aip/cha/article/33/8/083131/2906646/Sign-patterns-symbolization-and-its-use-in" target="_blank" >https://pubs.aip.org/aip/cha/article/33/8/083131/2906646/Sign-patterns-symbolization-and-its-use-in</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1063/5.0160868" target="_blank" >10.1063/5.0160868</a>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10102 - Applied mathematics

Projekt

<a href="/cs/project/GA21-17211S" target="_blank" >GA21-17211S: Síťové modely komplexních systémů: od korelačních grafů k informačním hypergrafům</a><br>

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2023

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Chaos

ISSN

1054-1500

e-ISSN

1089-7682

Svazek periodika

33

Číslo periodika v rámci svazku

8

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

20

Strana od-do

"Article number: 083131"

Kód UT WoS článku

001051787700001

EID výsledku v databázi Scopus

2-s2.0-85169610880