On causality of extreme events
- Published
- Accepted
- Subject Areas
- Bioinformatics, Neuroscience, Statistics
- Keywords
- Causality, Time series, Data analysis, Data mining
- Copyright
- © 2016 Zanin
- Licence
- This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Preprints) and either DOI or URL of the article must be cited.
- Cite this article
- 2016. On causality of extreme events. PeerJ Preprints 4:e1827v1 https://doi.org/10.7287/peerj.preprints.1827v1
Abstract
Multiple metrics have been developed to detect causality relations between data describing the elements constituting complex systems, all of them considering their evolution through time. Here we propose a metric able to detect causality within static data sets, by analysing how extreme events in one element correspond to the appearance of extreme events in a second one. The metric is able to detect both linear and non-linear causalities; to analyse both cross-sectional and longitudinal data sets; and to discriminate between real causalities and correlations caused by confounding factors. We validate the metric through synthetic data, dynamical and chaotic systems, and data representing the human brain activity in a cognitive task.
Author Comment
This is a submission to PeerJ for review.