Quantitative Association Rules Applied to Climatological Time Series Forecasting

This work presents the discovering of association rules based on evolutionary techniques in order to obtain relationships among correlated time series. For this purpose, a genetic algorithm has been proposed to determine the intervals that form the rules without discretizing the attributes and allow...

Descripción completa

Detalles Bibliográficos
Autores: Martínez Ballesteros, María del Mar, Martínez Álvarez, Francisco, Troncoso Lora, Alicia, Riquelme Santos, José Cristóbal
Tipo de recurso: capítulo de libro
Estado:Versión publicada
Fecha de publicación:2009
País:España
Institución:Universidad de Sevilla (US)
Repositorio:idUS. Depósito de Investigación de la Universidad de Sevilla
OAI Identifier:oai:idus.us.es:11441/40508
Acceso en línea:http://hdl.handle.net/11441/40508
https://doi.org/10.1007/978-3-642-04394-9_35
Access Level:acceso abierto
Palabra clave:Time series
Forecasting
Quantitative association rules
id ES_077d3e2f600af71da9eb45581013827d
oai_identifier_str oai:idus.us.es:11441/40508
network_acronym_str ES
network_name_str España
repository_id_str
spelling Quantitative Association Rules Applied to Climatological Time Series ForecastingMartínez Ballesteros, María del MarMartínez Álvarez, FranciscoTroncoso Lora, AliciaRiquelme Santos, José CristóbalTime seriesForecastingQuantitative association rulesThis work presents the discovering of association rules based on evolutionary techniques in order to obtain relationships among correlated time series. For this purpose, a genetic algorithm has been proposed to determine the intervals that form the rules without discretizing the attributes and allowing the overlapping of the regions covered by the rules. In addition, the algorithm has been tested on real-world climatological time series such as temperature, wind and ozone and results are reported and compared to that of the well-known Apriori algorithm.Lenguajes y Sistemas Informáticos2009info:eu-repo/semantics/bookPartinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttp://hdl.handle.net/11441/40508https://doi.org/10.1007/978-3-642-04394-9_35reponame:idUS. Depósito de Investigación de la Universidad de Sevillainstname:Universidad de Sevilla (US)InglésIntelligent Data Engineering and Automated Learning - IDEAL 2009, Lecture Notes in Computer Science, Volume 5788, pp 284-291info:eu-repo/semantics/openAccessoai:idus.us.es:11441/405082026-06-17T12:51:07Z
dc.title.none.fl_str_mv Quantitative Association Rules Applied to Climatological Time Series Forecasting
title Quantitative Association Rules Applied to Climatological Time Series Forecasting
spellingShingle Quantitative Association Rules Applied to Climatological Time Series Forecasting
Martínez Ballesteros, María del Mar
Time series
Forecasting
Quantitative association rules
title_short Quantitative Association Rules Applied to Climatological Time Series Forecasting
title_full Quantitative Association Rules Applied to Climatological Time Series Forecasting
title_fullStr Quantitative Association Rules Applied to Climatological Time Series Forecasting
title_full_unstemmed Quantitative Association Rules Applied to Climatological Time Series Forecasting
title_sort Quantitative Association Rules Applied to Climatological Time Series Forecasting
dc.creator.none.fl_str_mv Martínez Ballesteros, María del Mar
Martínez Álvarez, Francisco
Troncoso Lora, Alicia
Riquelme Santos, José Cristóbal
author Martínez Ballesteros, María del Mar
author_facet Martínez Ballesteros, María del Mar
Martínez Álvarez, Francisco
Troncoso Lora, Alicia
Riquelme Santos, José Cristóbal
author_role author
author2 Martínez Álvarez, Francisco
Troncoso Lora, Alicia
Riquelme Santos, José Cristóbal
author2_role author
author
author
dc.contributor.none.fl_str_mv Lenguajes y Sistemas Informáticos
dc.subject.none.fl_str_mv Time series
Forecasting
Quantitative association rules
topic Time series
Forecasting
Quantitative association rules
description This work presents the discovering of association rules based on evolutionary techniques in order to obtain relationships among correlated time series. For this purpose, a genetic algorithm has been proposed to determine the intervals that form the rules without discretizing the attributes and allowing the overlapping of the regions covered by the rules. In addition, the algorithm has been tested on real-world climatological time series such as temperature, wind and ozone and results are reported and compared to that of the well-known Apriori algorithm.
publishDate 2009
dc.date.none.fl_str_mv 2009
dc.type.none.fl_str_mv info:eu-repo/semantics/bookPart
info:eu-repo/semantics/publishedVersion
format bookPart
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11441/40508
https://doi.org/10.1007/978-3-642-04394-9_35
url http://hdl.handle.net/11441/40508
https://doi.org/10.1007/978-3-642-04394-9_35
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Intelligent Data Engineering and Automated Learning - IDEAL 2009, Lecture Notes in Computer Science, Volume 5788, pp 284-291
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.source.none.fl_str_mv reponame:idUS. Depósito de Investigación de la Universidad de Sevilla
instname:Universidad de Sevilla (US)
instname_str Universidad de Sevilla (US)
reponame_str idUS. Depósito de Investigación de la Universidad de Sevilla
collection idUS. Depósito de Investigación de la Universidad de Sevilla
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869402985708126208
score 15,198674

Similares en LA Referencia