Detecting and quantifying causal associations in large nonlinear time series datasets

Runge, Jakob and Nowack, Peer and Kretschmer, Marlene and Flaxman, Seth and Sejdinovic, Dino (2019) Detecting and quantifying causal associations in large nonlinear time series datasets. Science Advances, eaau49 (5). American Association for the Advancement of Science (AAAS). doi: 10.1126/sciadv.aau4996. ISSN 2375-2548.

PDF - Published version
1MB

Item URL in elib:

https://elib.dlr.de/131630/

Document Type:

Article

Title:

Detecting and quantifying causal associations in large nonlinear time series datasets

Authors:

Authors	Institution or Email of Authors	Author's ORCID iD	ORCID Put Code
Runge, Jakob	UNSPECIFIED	UNSPECIFIED	UNSPECIFIED
Nowack, Peer	UNSPECIFIED	UNSPECIFIED	UNSPECIFIED
Kretschmer, Marlene	UNSPECIFIED	UNSPECIFIED	UNSPECIFIED
Flaxman, Seth	UNSPECIFIED	UNSPECIFIED	UNSPECIFIED
Sejdinovic, Dino	UNSPECIFIED	UNSPECIFIED	UNSPECIFIED

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

DOI:

10.1126/sciadv.aau4996

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 (old)

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 Jul 2023 13:08

Repository Staff Only: item control page