English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
 
 
DownloadE-Mail
 Local TagsRelease HistoryDetailsSummary
  Assessing Nonlinear Granger Causality from Multivariate Time Series

Sun, X. (2008). Assessing Nonlinear Granger Causality from Multivariate Time Series. In W. Daelemans, B. Goethals, & K. Morik (Eds.), Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2008, Antwerp, Belgium, September 15-19, 2008 (pp. 440-455). Berlin, Germany: Springer.

Item is

 

show all hide all

Basic

show hide
Item Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-C72B-B Version Permalink: https://hdl.handle.net/21.11116/0000-0003-37FE-2
Genre: Conference Paper

Files

show Files

Locators

show
hide
Locator:
https://link.springer.com/content/pdf/10.1007%2F978-3-540-87481-2_29.pdf (Publisher version)
Description:
-
OA-Status:

Creators

show
hide
 Creators:
Sun, X1, 2, Author           
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              

Content

show
hide
Free keywords: -
 Abstract: A straightforward nonlinear extension of Granger’s concept of causality in the kernel framework is suggested. The kernel-based approach to assessing nonlinear Granger causality in multivariate time series enables us to determine, in a model-free way, whether the causal relation between two time series is present or not and whether it is direct or mediated by other processes. The trace norm of the so-called covariance operator in feature space is used to measure the prediction error. Relying on this measure, we test the improvement of predictability between time series by subsampling-based multiple testing. The distributional properties of the resulting p-values reveal the direction of Granger causality. Experiments with simulated and real-world data show that our method provides encouraging results.

Details

show
hide
Language(s):
 Dates: 2008-09
 Publication Status: Issued
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1007/978-3-540-87481-2_29
BibTex Citekey: 5254
 Degree: -

Event

show
hide
Title: European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2008)
Place of Event: Antwerpen, Belgium
Start-/End Date: 2008-09-15 - 2008-09-19

Legal Case

show

Project information

show

Source 1

show
hide
Title: Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2008, Antwerp, Belgium, September 15-19, 2008
Source Genre: Proceedings
 Creator(s):
Daelemans, W, Editor
Goethals, B, Editor
Morik, K, Editor
Affiliations:
-
Publ. Info: Berlin, Germany : Springer
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 440 - 455 Identifier: ISBN: 978-3-540-87480-5

Source 2

show
hide
Title: Lecture Notes in Computer Science
Source Genre: Series
 Creator(s):
Affiliations:
Publ. Info: -
Pages: - Volume / Issue: 5212 Sequence Number: - Start / End Page: - Identifier: -