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Detecting causal associations in large nonlinear time series datasets

Runge, Jakob and Nowack, Peer and Kretschmer, Marlene and Flaxman, Seth and Sejdinovic, Dino (2019) Detecting causal associations in large nonlinear time series datasets. Science Advances, eaau49 (5). American Association for the Advancement of Science (AAAS). ISSN 2375-2548

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Item URL in elib:https://elib.dlr.de/131630/
Document Type:Article
Title:Detecting causal associations in large nonlinear time series datasets
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Runge, JakobUNSPECIFIEDUNSPECIFIED
Nowack, PeerUNSPECIFIEDUNSPECIFIED
Kretschmer, MarleneUNSPECIFIEDUNSPECIFIED
Flaxman, SethUNSPECIFIEDUNSPECIFIED
Sejdinovic, DinoUNSPECIFIEDUNSPECIFIED
Date:2019
Journal or Publication Title:Science Advances
Refereed publication:Yes
Open Access:Yes
Gold Open Access:Yes
In SCOPUS:Yes
In ISI Web of Science:Yes
Volume:eaau49
Publisher:American Association for the Advancement of Science (AAAS)
ISSN:2375-2548
Status:Published
Keywords:causal inference, time series, climate
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Aeronautics
HGF - Program Themes:air traffic management and operations
DLR - Research area:Aeronautics
DLR - Program:L AO - Air Traffic Management and Operation
DLR - Research theme (Project):L - Climate, Weather and Environment
Location: Jena
Institutes and Institutions:Institute of Data Science > Datamangagement and Analysis
Deposited By: Runge, Jakob
Deposited On:07 Jan 2020 12:38
Last Modified:07 Jan 2020 12:38

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