Causality in Time Series: Its Detection and Quantification by Means of Information Theory
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F08%3A00320325" target="_blank" >RIV/67985556:_____/08:00320325 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Causality in Time Series: Its Detection and Quantification by Means of Information Theory
Original language description
While studying complex systems, one of the fundamental questions is to identify causal relationships (i.e., which system drives which) between relevant subsystems. In this paper, we focus on information-theoretic approaches for causality detection by means of directionality index based on mutual information estimation. We briefly review the current methods for mutual information estimation from the point of view of their consistency. We also present some arguments from recent literature, supporting theusefulness of the information-theoretic tools for causality detection.
Czech name
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Czech description
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Classification
Type
C - Chapter in a specialist book
CEP classification
BD - Information theory
OECD FORD branch
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Result continuities
Project
<a href="/en/project/2C06001" target="_blank" >2C06001: Fully probabilistic design of adaptive decision-making strategies suitable under informationally demanding conditions</a><br>
Continuities
Z - Vyzkumny zamer (s odkazem do CEZ)
Others
Publication year
2008
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Book/collection name
Information Theory and Statistical Learning
ISBN
978-0-387-84815-0
Number of pages of the result
24
Pages from-to
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Number of pages of the book
389
Publisher name
Springer
Place of publication
New York
UT code for WoS chapter
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