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Detecting Causality from Nonlinear Dynamics with Short-term Time Series
Quantifying causality between variables from observed time series data is of great importance in various disciplines but also a challenging task, especially when the observed data are short. Unlike the conventional methods, we find it possible to detect causality only with very short time series dat...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5376982/ https://www.ncbi.nlm.nih.gov/pubmed/25501646 http://dx.doi.org/10.1038/srep07464 |
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author | Ma, Huanfei Aihara, Kazuyuki Chen, Luonan |
author_facet | Ma, Huanfei Aihara, Kazuyuki Chen, Luonan |
author_sort | Ma, Huanfei |
collection | PubMed |
description | Quantifying causality between variables from observed time series data is of great importance in various disciplines but also a challenging task, especially when the observed data are short. Unlike the conventional methods, we find it possible to detect causality only with very short time series data, based on embedding theory of an attractor for nonlinear dynamics. Specifically, we first show that measuring the smoothness of a cross map between two observed variables can be used to detect a causal relation. Then, we provide a very effective algorithm to computationally evaluate the smoothness of the cross map, or “Cross Map Smoothness” (CMS), and thus to infer the causality, which can achieve high accuracy even with very short time series data. Analysis of both mathematical models from various benchmarks and real data from biological systems validates our method. |
format | Online Article Text |
id | pubmed-5376982 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | Nature Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-53769822017-04-05 Detecting Causality from Nonlinear Dynamics with Short-term Time Series Ma, Huanfei Aihara, Kazuyuki Chen, Luonan Sci Rep Article Quantifying causality between variables from observed time series data is of great importance in various disciplines but also a challenging task, especially when the observed data are short. Unlike the conventional methods, we find it possible to detect causality only with very short time series data, based on embedding theory of an attractor for nonlinear dynamics. Specifically, we first show that measuring the smoothness of a cross map between two observed variables can be used to detect a causal relation. Then, we provide a very effective algorithm to computationally evaluate the smoothness of the cross map, or “Cross Map Smoothness” (CMS), and thus to infer the causality, which can achieve high accuracy even with very short time series data. Analysis of both mathematical models from various benchmarks and real data from biological systems validates our method. Nature Publishing Group 2014-12-12 /pmc/articles/PMC5376982/ /pubmed/25501646 http://dx.doi.org/10.1038/srep07464 Text en Copyright © 2014, Macmillan Publishers Limited. All rights reserved http://creativecommons.org/licenses/by-nc-sa/4.0/ This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. The images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder in order to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-sa/4.0/ |
spellingShingle | Article Ma, Huanfei Aihara, Kazuyuki Chen, Luonan Detecting Causality from Nonlinear Dynamics with Short-term Time Series |
title | Detecting Causality from Nonlinear Dynamics with Short-term Time Series |
title_full | Detecting Causality from Nonlinear Dynamics with Short-term Time Series |
title_fullStr | Detecting Causality from Nonlinear Dynamics with Short-term Time Series |
title_full_unstemmed | Detecting Causality from Nonlinear Dynamics with Short-term Time Series |
title_short | Detecting Causality from Nonlinear Dynamics with Short-term Time Series |
title_sort | detecting causality from nonlinear dynamics with short-term time series |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5376982/ https://www.ncbi.nlm.nih.gov/pubmed/25501646 http://dx.doi.org/10.1038/srep07464 |
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