English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
  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.

Item is

Files

show Files

Locators

show
hide
Description:
-

Creators

show
hide
 Creators:
Peters, J1, 2, Author              
Janzing, D1, 2, Author              
Gretton, A1, 2, Author              
Schölkopf, B1, 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: 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.

Details

show
hide
Language(s):
 Dates: 2009-06
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1145/1553374.1553477
BibTex Citekey: 5902
 Degree: -

Event

show
hide
Title: 26th International Conference on Machine Learning
Place of Event: Montreal, Canada
Start-/End Date: 2009-06-14 - 2009-06-18

Legal Case

show

Project information

show

Source 1

show
hide
Title: ICML '09: Proceedings of the 26th Annual International Conference on Machine Learning
Source Genre: Proceedings
 Creator(s):
Danyluk, A, Editor
Bottou, L, Editor
Danyluk, M, Editor
Affiliations:
-
Publ. Info: New York, NY, USA : ACM Press
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 801 - 808 Identifier: ISBN: 978-1-60558-516-1

Source 2

show
hide
Title: ACM International Conference Proceeding Series
Source Genre: Series
 Creator(s):
Affiliations:
Publ. Info: -
Pages: - Volume / Issue: 382 Sequence Number: - Start / End Page: - Identifier: -