Comparison of Six Methods for the Detection of Causality in a Bivariate Time Series

0489765 - ÚI 2019 RIV US eng J - Článek v odborném periodiku
Krakovská, A. - Jakubík, J. - Chvosteková, M. - Coufal, David - Jajcay, Nikola - Paluš, Milan
Comparison of Six Methods for the Detection of Causality in a Bivariate Time Series.
Physical Review E. Roč. 97, č. 4 (2018), č. článku 042207. ISSN 2470-0045. E-ISSN 2470-0053
Grant CEP: GA MZd(CZ) NV15-33250A
Institucionální podpora: RVO:67985807
Klíčová slova: comparative study * causality detection * bivariate models * Granger causality * transfer entropy * convergent cross mappings
Obor OECD: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Impakt faktor: 2.353, rok: 2018
https://journals.aps.org/pre/abstract/10.1103/PhysRevE.97.042207

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.
Trvalý link: http://hdl.handle.net/11104/0284129