Sign patterns symbolization and its use in improved dependence test for complex network inference
The result's identifiers
Result code in 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>
Alternative codes found
RIV/67985807:_____/23:00576550
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
Sign patterns symbolization and its use in improved dependence test for complex network inference
Original language description
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.
Czech name
—
Czech description
—
Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
—
OECD FORD branch
10102 - Applied mathematics
Result continuities
Project
<a href="/en/project/GA21-17211S" target="_blank" >GA21-17211S: Network modelling of complex systems: from correlation graphs to information hypergraphs</a><br>
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Chaos
ISSN
1054-1500
e-ISSN
1089-7682
Volume of the periodical
33
Issue of the periodical within the volume
8
Country of publishing house
US - UNITED STATES
Number of pages
20
Pages from-to
"Article number: 083131"
UT code for WoS article
001051787700001
EID of the result in the Scopus database
2-s2.0-85169610880