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

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F18%3A00489765" target="_blank" >RIV/67985807:_____/18:00489765 - isvavai.cz</a>
Result on the web
<a href="https://journals.aps.org/pre/abstract/10.1103/PhysRevE.97.042207" target="_blank" >https://journals.aps.org/pre/abstract/10.1103/PhysRevE.97.042207</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1103/PhysRevE.97.042207" target="_blank" >10.1103/PhysRevE.97.042207</a>

Result language
angličtina
Original language name
Comparison of Six Methods for the Detection of Causality in a Bivariate Time Series
Original language description
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.
Czech name
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Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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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/NV15-33250A" target="_blank" >NV15-33250A: Prediction of therapeutic response in patients with depressive disorder by means of new methods of EEG analysis.</a><br>
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2018
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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