Number of the records: 1  

Causality in extremes of time series

  1. 1.
    0578518 - ÚI 2025 DE eng J - Journal Article
    Bodík, Juraj - Paluš, Milan - Pawlas, Z.
    Causality in extremes of time series.
    Extremes. Roč. 27, č. 1 (2024), s. 67-121. ISSN 1386-1999. E-ISSN 1572-915X
    R&D Projects: GA ČR(CZ) GA19-16066S
    Grant - others:AV ČR(CZ) AP1901
    Program: Akademická prémie - Praemium Academiae
    Institutional support: RVO:67985807
    Keywords : Granger causality * Causal inference * Nonlinear time series * Causality-in-tail * Extreme value theory * Heavy tails
    Impact factor: 1.3, year: 2022
    Method of publishing: Open access
    https://doi.org/10.1007/s10687-023-00479-5

    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.
    Permanent Link: https://hdl.handle.net/11104/0347507

     
     
Number of the records: 1  

  This site uses cookies to make them easier to browse. Learn more about how we use cookies.