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Highly comparative time-series analysis: the empirical structure of time series and their methods

The process of collecting and organizing sets of observations represents a common theme throughout the history of science. However, despite the ubiquity of scientists measuring, recording and analysing the dynamics of different processes, an extensive organization of scientific time-series data and...

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Detalles Bibliográficos
Autores principales: Fulcher, Ben D., Little, Max A., Jones, Nick S.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Royal Society 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3645413/
https://www.ncbi.nlm.nih.gov/pubmed/23554344
http://dx.doi.org/10.1098/rsif.2013.0048
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author Fulcher, Ben D.
Little, Max A.
Jones, Nick S.
author_facet Fulcher, Ben D.
Little, Max A.
Jones, Nick S.
author_sort Fulcher, Ben D.
collection PubMed
description The process of collecting and organizing sets of observations represents a common theme throughout the history of science. However, despite the ubiquity of scientists measuring, recording and analysing the dynamics of different processes, an extensive organization of scientific time-series data and analysis methods has never been performed. Addressing this, annotated collections of over 35 000 real-world and model-generated time series, and over 9000 time-series analysis algorithms are analysed in this work. We introduce reduced representations of both time series, in terms of their properties measured by diverse scientific methods, and of time-series analysis methods, in terms of their behaviour on empirical time series, and use them to organize these interdisciplinary resources. This new approach to comparing across diverse scientific data and methods allows us to organize time-series datasets automatically according to their properties, retrieve alternatives to particular analysis methods developed in other scientific disciplines and automate the selection of useful methods for time-series classification and regression tasks. The broad scientific utility of these tools is demonstrated on datasets of electroencephalograms, self-affine time series, heartbeat intervals, speech signals and others, in each case contributing novel analysis techniques to the existing literature. Highly comparative techniques that compare across an interdisciplinary literature can thus be used to guide more focused research in time-series analysis for applications across the scientific disciplines.
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spelling pubmed-36454132013-06-06 Highly comparative time-series analysis: the empirical structure of time series and their methods Fulcher, Ben D. Little, Max A. Jones, Nick S. J R Soc Interface Research Articles The process of collecting and organizing sets of observations represents a common theme throughout the history of science. However, despite the ubiquity of scientists measuring, recording and analysing the dynamics of different processes, an extensive organization of scientific time-series data and analysis methods has never been performed. Addressing this, annotated collections of over 35 000 real-world and model-generated time series, and over 9000 time-series analysis algorithms are analysed in this work. We introduce reduced representations of both time series, in terms of their properties measured by diverse scientific methods, and of time-series analysis methods, in terms of their behaviour on empirical time series, and use them to organize these interdisciplinary resources. This new approach to comparing across diverse scientific data and methods allows us to organize time-series datasets automatically according to their properties, retrieve alternatives to particular analysis methods developed in other scientific disciplines and automate the selection of useful methods for time-series classification and regression tasks. The broad scientific utility of these tools is demonstrated on datasets of electroencephalograms, self-affine time series, heartbeat intervals, speech signals and others, in each case contributing novel analysis techniques to the existing literature. Highly comparative techniques that compare across an interdisciplinary literature can thus be used to guide more focused research in time-series analysis for applications across the scientific disciplines. The Royal Society 2013-06-06 /pmc/articles/PMC3645413/ /pubmed/23554344 http://dx.doi.org/10.1098/rsif.2013.0048 Text en http://creativecommons.org/licenses/by/3.0/ © 2013 The Authors. Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/3.0/, which permits unrestricted use, provided the original author and source are credited.
spellingShingle Research Articles
Fulcher, Ben D.
Little, Max A.
Jones, Nick S.
Highly comparative time-series analysis: the empirical structure of time series and their methods
title Highly comparative time-series analysis: the empirical structure of time series and their methods
title_full Highly comparative time-series analysis: the empirical structure of time series and their methods
title_fullStr Highly comparative time-series analysis: the empirical structure of time series and their methods
title_full_unstemmed Highly comparative time-series analysis: the empirical structure of time series and their methods
title_short Highly comparative time-series analysis: the empirical structure of time series and their methods
title_sort highly comparative time-series analysis: the empirical structure of time series and their methods
topic Research Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3645413/
https://www.ncbi.nlm.nih.gov/pubmed/23554344
http://dx.doi.org/10.1098/rsif.2013.0048
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