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Comparison of Six Methods for the Detection of Causality in a Bivariate Time Series
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SYSNO ASEP 0489765 Document Type J - Journal Article R&D Document Type Journal Article Subsidiary J Článek ve WOS Title Comparison of Six Methods for the Detection of Causality in a Bivariate Time Series Author(s) Krakovská, A. (SK)
Jakubík, J. (SK)
Chvosteková, M. (SK)
Coufal, David (UIVT-O) RID, SAI, ORCID
Jajcay, Nikola (UIVT-O) RID, ORCID, SAI
Paluš, Milan (UIVT-O) RID, SAI, ORCIDArticle number 042207 Source Title Physical Review E. - : American Physical Society - ISSN 2470-0045
Roč. 97, č. 4 (2018)Number of pages 14 s. Language eng - English Country US - United States Keywords comparative study ; causality detection ; bivariate models ; Granger causality ; transfer entropy ; convergent cross mappings Subject RIV IN - Informatics, Computer Science OECD category Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8) R&D Projects NV15-33250A GA MZd - Ministry of Health (MZ) Institutional support UIVT-O - RVO:67985807 UT WOS 000429526600003 EID SCOPUS 85045395761 DOI 10.1103/PhysRevE.97.042207 Annotation In this comparative study, six causality detection methods were compared, namely, the Granger vector autoregressive test, the extended Granger test, the kernel version of the Granger test, the conditional mutual information (transfer entropy), the evaluation of cross mappings between state spaces, and an assessment of predictability improvement due to the use of mixed predictions. Seven test data sets were analyzed: linear coupling of autoregressive models, a unidirectional connection of two Hénon systems, a unidirectional connection of chaotic systems of Rössler and Lorenz type and of two different Rössler systems, an example of bidirectionally connected two-species systems, a fishery model as an example of two correlated observables without a causal relationship, and an example of mediated causality. We tested not only 20 000 points long clean time series but also noisy and short variants of the data. The standard and the extended Granger tests worked only for the autoregressive models. The remaining methods were more successful with the more complex test examples, although they differed considerably in their capability to reveal the presence and the direction of coupling and to distinguish causality from mere correlation. Workplace Institute of Computer Science Contact Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800 Year of Publishing 2019 Electronic address https://journals.aps.org/pre/abstract/10.1103/PhysRevE.97.042207
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