Autoregressive causal relation: Digital filtering approach to causality measures in frequency domain

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F13%3A00208240" target="_blank" >RIV/68407700:21230/13:00208240 - isvavai.cz</a>

Výsledek na webu

<a href="http://www.sciencedirect.com/science/article/pii/S1051200413000857" target="_blank" >http://www.sciencedirect.com/science/article/pii/S1051200413000857</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.dsp.2013.04.006" target="_blank" >10.1016/j.dsp.2013.04.006</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Autoregressive causal relation: Digital filtering approach to causality measures in frequency domain

Popis výsledku v původním jazyce

A novel measure of the Autoregressive Causal Relation based on a multivariate autoregressive model is proposed. It reveals the strength of the connections among a simultaneous time series and also the direction of the information flow. It is defined in the frequency domain, similar to the formerly published methods such as: Directed Transfer Function, Direct Directed Transfer Function, Partial Directed Coherence, and Generalized Partial Directed Coherence. Compared to the Granger causality concept, frequency decomposition extends the possibility to reveal the frequency rhythms participating on the information flow in causal relations. The Autoregressive Causal Relation decomposes diagonal elements of a spectral matrix and enables a user to distinguishbetween direct and indirect causal relations. The main advantage lies in its definition using power spectral densities, thus allowing for a clear interpretation of strength of causal relation in meaningful physical terms. The causal measu

Název v anglickém jazyce

Autoregressive causal relation: Digital filtering approach to causality measures in frequency domain

Popis výsledku anglicky

A novel measure of the Autoregressive Causal Relation based on a multivariate autoregressive model is proposed. It reveals the strength of the connections among a simultaneous time series and also the direction of the information flow. It is defined in the frequency domain, similar to the formerly published methods such as: Directed Transfer Function, Direct Directed Transfer Function, Partial Directed Coherence, and Generalized Partial Directed Coherence. Compared to the Granger causality concept, frequency decomposition extends the possibility to reveal the frequency rhythms participating on the information flow in causal relations. The Autoregressive Causal Relation decomposes diagonal elements of a spectral matrix and enables a user to distinguishbetween direct and indirect causal relations. The main advantage lies in its definition using power spectral densities, thus allowing for a clear interpretation of strength of causal relation in meaningful physical terms. The causal measu

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

JA - Elektronika a optoelektronika, elektrotechnika

OECD FORD obor

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Projekt

<a href="/cs/project/GD102%2F08%2FH008" target="_blank" >GD102/08/H008: Analýza a modelování biomedicínských a řečových signálů</a><br>

Návaznosti

Z - Vyzkumny zamer (s odkazem do CEZ)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2013

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Digital Signal Processing

ISSN

1051-2004

e-ISSN

—

Svazek periodika

23

Číslo periodika v rámci svazku

5

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

11

Strana od-do

1756-1766

Kód UT WoS článku

000323855500039

EID výsledku v databázi Scopus

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