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Quantitative Association Rules Applied to Climatological Time Series Forecasting


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dc.creator Martínez Ballesteros, María del Mar es
dc.creator Martínez Álvarez, Francisco es
dc.creator Troncoso Lora, Alicia es
dc.creator Riquelme Santos, José Cristóbal es 2016-04-27T09:53:49Z 2016-04-27T09:53:49Z 2009
dc.description.abstract 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. es
dc.format application/pdf es
dc.language.iso eng es
dc.relation.ispartof Intelligent Data Engineering and Automated Learning - IDEAL 2009, Lecture Notes in Computer Science, Volume 5788, pp 284-291 es
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 Internacional *
dc.rights.uri *
dc.subject Time series es
dc.subject Forecasting es
dc.subject Quantitative association rules es
dc.title Quantitative Association Rules Applied to Climatological Time Series Forecasting es
dc.type info:eu-repo/semantics/bookPart es
dc.type.version info:eu-repo/semantics/publishedVersion es
dc.contributor.affiliation Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos es
dc.identifier.doi es
idus.format.extent 7 es
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