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  Detecting the Direction of Causal Time Series

Peters, J., Janzing, D., Gretton, A., & Schölkopf, B. (2009). Detecting the Direction of Causal Time Series. In A. Danyluk, L. Bottou, & M. Danyluk (Eds.), ICML '09: Proceedings of the 26th Annual International Conference on Machine Learning (pp. 801-808). New York, NY, USA: ACM Press.

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Datensatz-Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-C477-C Versions-Permalink: https://hdl.handle.net/21.11116/0000-0002-F922-F
Genre: Konferenzbeitrag

Externe Referenzen

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externe Referenz:
https://dl.acm.org/citation.cfm?doid=1553374.1553477 (Verlagsversion)
Beschreibung:
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Urheber

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 Urheber:
Peters, J1, 2, Autor           
Janzing, D1, 2, Autor           
Gretton, A1, 2, Autor           
Schölkopf, B1, 2, Autor           
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

Inhalt

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Schlagwörter: -
 Zusammenfassung: We propose a method that detects the true
direction of time series, by fitting an autoregressive
moving average model to the data.
Whenever the noise is independent of the previous
samples for one ordering of the observations,
but dependent for the opposite ordering,
we infer the former direction to be the
true one. We prove that our method works
in the population case as long as the noise of
the process is not normally distributed (for
the latter case, the direction is not identificable).
A new and important implication of
our result is that it confirms a fundamental
conjecture in causal reasoning - if after regression
the noise is independent of signal for
one direction and dependent for the other,
then the former represents the true causal
direction - in the case of time series. We
test our approach on two types of data: simulated
data sets conforming to our modeling
assumptions, and real world EEG time series.
Our method makes a decision for a significant
fraction of both data sets, and these
decisions are mostly correct. For real world
data, our approach outperforms alternative
solutions to the problem of time direction recovery.

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Sprache(n):
 Datum: 2009-06
 Publikationsstatus: Erschienen
 Seiten: -
 Ort, Verlag, Ausgabe: -
 Inhaltsverzeichnis: -
 Art der Begutachtung: -
 Identifikatoren: DOI: 10.1145/1553374.1553477
BibTex Citekey: 5902
 Art des Abschluß: -

Veranstaltung

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Titel: 26th International Conference on Machine Learning
Veranstaltungsort: Montreal, Canada
Start-/Enddatum: 2009-06-14 - 2009-06-18

Entscheidung

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Projektinformation

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Quelle 1

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Titel: ICML '09: Proceedings of the 26th Annual International Conference on Machine Learning
Genre der Quelle: Konferenzband
 Urheber:
Danyluk, A, Herausgeber
Bottou, L, Herausgeber
Danyluk, M, Herausgeber
Affiliations:
-
Ort, Verlag, Ausgabe: New York, NY, USA : ACM Press
Seiten: - Band / Heft: - Artikelnummer: - Start- / Endseite: 801 - 808 Identifikator: ISBN: 978-1-60558-516-1

Quelle 2

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Titel: ACM International Conference Proceeding Series
Genre der Quelle: Reihe
 Urheber:
Affiliations:
Ort, Verlag, Ausgabe: -
Seiten: - Band / Heft: 382 Artikelnummer: - Start- / Endseite: - Identifikator: -