Causality in extremes of time series

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F24%3A00578518" target="_blank" >RIV/67985807:_____/24:00578518 - isvavai.cz</a>

Alternative codes found

RIV/00216208:11320/24:10485354

Result on the web

<a href="https://doi.org/10.1007/s10687-023-00479-5" target="_blank" >https://doi.org/10.1007/s10687-023-00479-5</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s10687-023-00479-5" target="_blank" >10.1007/s10687-023-00479-5</a>

Result language

angličtina

Original language name

Causality in extremes of time series

Original language description

Consider two stationary time series with heavy-tailed marginal distributions. We aim to detect whether they have a causal relation, that is, if a change in one causes a change in the other. Usual methods for causal discovery are not well suited if the causal mechanisms only appear during extreme events. We propose a framework to detect a causal structure from the extremes of time series, providing a new tool to extract causal information from extreme events. We introduce the causal tail coefficient for time series, which can identify asymmetrical causal relations between extreme events under certain assumptions. This method can handle nonlinear relations and latent variables. Moreover, we mention how our method can help estimate a typical time difference between extreme events. Our methodology is especially well suited for large sample sizes, and we show the performance on the simulations. Finally, we apply our method to real-world space-weather and hydro-meteorological datasets.

Czech name

—

Czech description

—

Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

<a href="/en/project/GA19-16066S" target="_blank" >GA19-16066S: Nonlinear interactions and information transfer in complex systems with extreme events</a><br>

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2024

Confidentiality

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

Name of the periodical

Extremes

ISSN

1386-1999

e-ISSN

1572-915X

Volume of the periodical

27

Issue of the periodical within the volume

1

Country of publishing house

DE - GERMANY

Number of pages

55

Pages from-to

67-121

UT code for WoS article

001118760400001

EID of the result in the Scopus database

2-s2.0-85175314970