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Parametric Construction of Episode Networks from Pseudoperiodic Time Series Based on Mutual Information
Recently, the construction of networks from time series data has gained widespread interest. In this paper, we develop this area further by introducing a network construction procedure for pseudoperiodic time series. We call such networks episode networks, in which an episode corresponds to a tempor...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Public Library of Science
2011
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3245224/ https://www.ncbi.nlm.nih.gov/pubmed/22216086 http://dx.doi.org/10.1371/journal.pone.0027733 |
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author | Emmert-Streib, Frank |
author_facet | Emmert-Streib, Frank |
author_sort | Emmert-Streib, Frank |
collection | PubMed |
description | Recently, the construction of networks from time series data has gained widespread interest. In this paper, we develop this area further by introducing a network construction procedure for pseudoperiodic time series. We call such networks episode networks, in which an episode corresponds to a temporal interval of a time series, and which defines a node in the network. Our model includes a number of features which distinguish it from current methods. First, the proposed construction procedure is a parametric model which allows it to adapt to the characteristics of the data; the length of an episode being the parameter. As a direct consequence, networks of minimal size containing the maximal information about the time series can be obtained. In this paper, we provide an algorithm to determine the optimal value of this parameter. Second, we employ estimates of mutual information values to define the connectivity structure among the nodes in the network to exploit efficiently the nonlinearities in the time series. Finally, we apply our method to data from electroencephalogram (EEG) experiments and demonstrate that the constructed episode networks capture discriminative information from the underlying time series that may be useful for diagnostic purposes. |
format | Online Article Text |
id | pubmed-3245224 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2011 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-32452242012-01-03 Parametric Construction of Episode Networks from Pseudoperiodic Time Series Based on Mutual Information Emmert-Streib, Frank PLoS One Research Article Recently, the construction of networks from time series data has gained widespread interest. In this paper, we develop this area further by introducing a network construction procedure for pseudoperiodic time series. We call such networks episode networks, in which an episode corresponds to a temporal interval of a time series, and which defines a node in the network. Our model includes a number of features which distinguish it from current methods. First, the proposed construction procedure is a parametric model which allows it to adapt to the characteristics of the data; the length of an episode being the parameter. As a direct consequence, networks of minimal size containing the maximal information about the time series can be obtained. In this paper, we provide an algorithm to determine the optimal value of this parameter. Second, we employ estimates of mutual information values to define the connectivity structure among the nodes in the network to exploit efficiently the nonlinearities in the time series. Finally, we apply our method to data from electroencephalogram (EEG) experiments and demonstrate that the constructed episode networks capture discriminative information from the underlying time series that may be useful for diagnostic purposes. Public Library of Science 2011-12-22 /pmc/articles/PMC3245224/ /pubmed/22216086 http://dx.doi.org/10.1371/journal.pone.0027733 Text en Frank Emmert-Streib. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. |
spellingShingle | Research Article Emmert-Streib, Frank Parametric Construction of Episode Networks from Pseudoperiodic Time Series Based on Mutual Information |
title | Parametric Construction of Episode Networks from Pseudoperiodic Time Series Based on Mutual Information |
title_full | Parametric Construction of Episode Networks from Pseudoperiodic Time Series Based on Mutual Information |
title_fullStr | Parametric Construction of Episode Networks from Pseudoperiodic Time Series Based on Mutual Information |
title_full_unstemmed | Parametric Construction of Episode Networks from Pseudoperiodic Time Series Based on Mutual Information |
title_short | Parametric Construction of Episode Networks from Pseudoperiodic Time Series Based on Mutual Information |
title_sort | parametric construction of episode networks from pseudoperiodic time series based on mutual information |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3245224/ https://www.ncbi.nlm.nih.gov/pubmed/22216086 http://dx.doi.org/10.1371/journal.pone.0027733 |
work_keys_str_mv | AT emmertstreibfrank parametricconstructionofepisodenetworksfrompseudoperiodictimeseriesbasedonmutualinformation |