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Ordinal Time Series Analysis with the R Package otsfeatures
dc.contributor.author | López-Oriona, Ángel | |
dc.contributor.author | Vilar, José | |
dc.date.accessioned | 2023-11-02T11:33:11Z | |
dc.date.available | 2023-11-02T11:33:11Z | |
dc.date.issued | 2023-06 | |
dc.identifier.citation | Á. López-Oriona and J. A. Vilar, “Ordinal Time Series Analysis with the R Package otsfeatures,” Mathematics, vol. 11, no. 11, p. 2565, Jun. 2023, doi: 10.3390/math11112565. | es_ES |
dc.identifier.uri | http://hdl.handle.net/2183/34001 | |
dc.description.abstract | [Abstract] The 21st century has witnessed a growing interest in the analysis of time series data. While most of the literature on the topic deals with real-valued time series, ordinal time series have typically received much less attention. However, the development of specific analytical tools for the latter objects has substantially increased in recent years. The R package otsfeatures attempts to provide a set of simple functions for analyzing ordinal time series. In particular, several commands allowing the extraction of well-known statistical features and the execution of inferential tasks are available for the user. The output of several functions can be employed to perform traditional machine learning tasks including clustering, classification, or outlier detection. otsfeatures also incorporates two datasets of financial time series which were used in the literature for clustering purposes, as well as three interesting synthetic databases. The main properties of the package are described and its use is illustrated through several examples. Researchers from a broad variety of disciplines could benefit from the powerful tools provided by otsfeatures. | es_ES |
dc.description.sponsorship | 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 Universitariode Galicia, “CITIC” grant ED431G 2019/01; all of them through the European Regional Development Fund (ERDF). | 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 | MDPI | 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 | 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.uri | https://doi.org/10.3390/math11112565 | es_ES |
dc.rights | Attribution (CC BY) license 4.0 | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by/3.0/es/ | * |
dc.subject | Otsfeatures | es_ES |
dc.subject | Ordinal time series | es_ES |
dc.subject | Feature extraction | es_ES |
dc.subject | Cumulative probabilities | es_ES |
dc.subject | R package | es_ES |
dc.title | Ordinal Time Series Analysis with the R Package otsfeatures | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.access | info:eu-repo/semantics/openAccess | es_ES |
UDC.journalTitle | Mathematics | es_ES |
UDC.volume | 11 | es_ES |
UDC.issue | 11 | es_ES |
UDC.startPage | 2565 | es_ES |
dc.identifier.doi | 10.3390/math11112565 |
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