On causality of extreme events
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 on...
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| Format: | Article |
| Language: | English |
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PeerJ Inc.
2016-06-01
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| Series: | PeerJ |
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| Online Access: | https://peerj.com/articles/2111.pdf |
| _version_ | 1797419480314281984 |
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| author | Massimiliano Zanin |
| author_facet | Massimiliano Zanin |
| author_sort | Massimiliano Zanin |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T06:48:56Z |
| format | Article |
| id | doaj.art-178856c8021d4121b85e7c64dd48f7c4 |
| institution | Directory Open Access Journal |
| issn | 2167-8359 |
| language | English |
| last_indexed | 2024-03-09T06:48:56Z |
| publishDate | 2016-06-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ |
| spelling | doaj.art-178856c8021d4121b85e7c64dd48f7c42023-12-03T10:31:14ZengPeerJ Inc.PeerJ2167-83592016-06-014e211110.7717/peerj.2111On causality of extreme eventsMassimiliano ZaninMultiple 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.https://peerj.com/articles/2111.pdfCausalityTime seriesData analysisData mining |
| spellingShingle | Massimiliano Zanin On causality of extreme events PeerJ Causality Time series Data analysis Data mining |
| title | On causality of extreme events |
| title_full | On causality of extreme events |
| title_fullStr | On causality of extreme events |
| title_full_unstemmed | On causality of extreme events |
| title_short | On causality of extreme events |
| title_sort | on causality of extreme events |
| topic | Causality Time series Data analysis Data mining |
| url | https://peerj.com/articles/2111.pdf |
| work_keys_str_mv | AT massimilianozanin oncausalityofextremeevents |