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Analyzing categorical time series with the R package ctsfeatures
dc.contributor.author | López-Oriona, Ángel | |
dc.contributor.author | Vilar, José | |
dc.date.accessioned | 2024-04-15T09:46:05Z | |
dc.date.available | 2024-04-15T09:46:05Z | |
dc.date.issued | 2024-03 | |
dc.identifier.citation | Á. López-Oriona y J. A. Vilar, «Analyzing categorical time series with the package ctsfeatures», Journal of Computational Science, vol. 76, p. 102233, mar. 2024, doi: 10.1016/j.jocs.2024.102233. | es_ES |
dc.identifier.issn | 1877-7511 | |
dc.identifier.issn | 1877-7503 | |
dc.identifier.uri | http://hdl.handle.net/2183/36186 | |
dc.description.abstract | [Absctract]: Time series data are ubiquitous nowadays. Whereas most of the literature on the topic deals with real-valued time series, categorical time series have received much less attention. However, the development of data mining techniques for this kind of data has substantially increased in recent years. The R package ctsfeatures offers users a set of useful tools for analyzing categorical time series. In particular, several functions allowing the extraction of well-known statistical features and the construction of illustrative graphs describing underlying temporal patterns are provided in the package. The output of some functions can be employed to perform traditional machine learning tasks including clustering, classification and outlier detection. The package also includes two datasets of biological sequences introduced in the literature for clustering purposes, one dataset of sleep stages, and three interesting synthetic databases. In this work, the main characteristics of the package are described and its use is illustrated through various examples. Practitioners from a wide variety of fields could benefit from the valuable tools provided by ctsfeatures. | es_ES |
dc.description.sponsorship | The authors are grateful to the anonymous referees for their comments and suggestions. This research has been supported by the Ministerio de Economía y Competitividad (MINECO) grants MTM2017-82724-R and PID2020-113578RB-100, the Xunta de Galicia (Grupos de Referencia Competitiva ED431C-2020-14), and the Centro de Investigación del Sistema Universitario de Galicia, “CITIC” grant ED431G 2019/01; all of them through the European Regional Development Fund (ERDF) . This work has received funding for open access charge by Universidade da Coruña/CISUG. | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431C-2020-14 | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431G 2019/01 | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Elsevier B.V. | es_ES |
dc.relation | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/MTM2017-82724-R/ES/INFERENCIA ESTADISTICA FLEXIBLE PARA DATOS COMPLEJOS DE GRAN VOLUMEN Y DE ALTA DIMENSION | es_ES |
dc.relation | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-113578RB-I00/ES/METODOS ESTADISTICOS FLEXIBLES EN CIENCIA DE DATOS PARA DATOS COMPLEJOS Y DE GRAN VOLUMEN: TEORIA Y APLICACIONES | es_ES |
dc.relation.uri | https://doi.org/10.1016/j.jocs.2024.102233 | es_ES |
dc.rights | Atribución 3.0 España | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by/3.0/es/ | * |
dc.subject | ctsfeatures | es_ES |
dc.subject | R package | es_ES |
dc.subject | Categorical time series | es_ES |
dc.subject | Feature extraction | es_ES |
dc.subject | Association measures | es_ES |
dc.title | Analyzing categorical time series with the R package ctsfeatures | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.access | info:eu-repo/semantics/openAccess | es_ES |
UDC.volume | 76 | es_ES |
UDC.startPage | 102233 | es_ES |
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