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Quantitative Association Rules Applied to Climatological Time Series Forecasting

 

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Opened Access Quantitative Association Rules Applied to Climatological Time Series Forecasting
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Author: Martínez Ballesteros, María del Mar
Martínez Álvarez, Francisco
Troncoso Lora, Alicia
Riquelme Santos, José Cristóbal
Department: Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos
Date: 2009
Published in: Intelligent Data Engineering and Automated Learning - IDEAL 2009, Lecture Notes in Computer Science, Volume 5788, pp 284-291
Document type: Chapter of Book
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.
Size: 160.1Kb
Format: PDF

URI: http://hdl.handle.net/11441/40508

DOI: http://dx.doi.org/10.1007/978-3-642-04394-9_35

This work is under a Creative Commons License: 
Attribution-NonCommercial-NoDerivatives 4.0 Internacional

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