Comparison of Six Methods for the Detection of Causality in a Bivariate Time Series
Identifikátory výsledku
Kód výsledku v 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>
Výsledek na webu
<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>
Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
Comparison of Six Methods for the Detection of Causality in a Bivariate Time Series
Popis výsledku v původním jazyce
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.
Název v anglickém jazyce
Comparison of Six Methods for the Detection of Causality in a Bivariate Time Series
Popis výsledku anglicky
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.
Klasifikace
Druh
J<sub>imp</sub> - Článek v periodiku v databázi Web of Science
CEP obor
—
OECD FORD obor
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Návaznosti výsledku
Projekt
<a href="/cs/project/NV15-33250A" target="_blank" >NV15-33250A: Predikce terapeutické odpovědi u pacientů s depresivním onemocněním pomocí nových metod EEG analýzy.</a><br>
Návaznosti
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Ostatní
Rok uplatnění
2018
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Údaje specifické pro druh výsledku
Název periodika
Physical Review E
ISSN
2470-0045
e-ISSN
—
Svazek periodika
97
Číslo periodika v rámci svazku
4
Stát vydavatele periodika
US - Spojené státy americké
Počet stran výsledku
14
Strana od-do
—
Kód UT WoS článku
000429526600003
EID výsledku v databázi Scopus
2-s2.0-85045395761