On causality of extreme events

PeerJ

article

Massimiliano Zanin¹ 

[1] Department of Life Sciences, Innaxis Foundation & Research Institute

关键词: Causality;    Time series;    Data analysis;    Data mining;   

DOI  :  10.7717/peerj.2111

学科分类：社会科学、人文和艺术（综合）

来源: Inra

【 摘 要 】

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 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. We further show how the proposed metric is able to outperform classical causality metrics, provided non-linear relationships are present and large enough data sets are available.

【 授权许可】

CC BY