Artículo
An evolutionary algorithm to discover quantitative association rules in multidimensional time series
Autor/es | Martínez Ballesteros, María del Mar
Martínez Álvarez, Francisco Troncoso Lora, Alicia Riquelme Santos, José Cristóbal |
Departamento | Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos |
Fecha de publicación | 2011 |
Fecha de depósito | 2016-07-08 |
Publicado en |
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Resumen | An evolutionary approach for finding existing
relationships among several variables of a multidimensional
time series is presented in this work. The proposed model to
discover these relationships is based on quantitative ... An evolutionary approach for finding existing relationships among several variables of a multidimensional time series is presented in this work. The proposed model to discover these relationships is based on quantitative association rules. This algorithm, called QARGA (Quantitative Association Rules by Genetic Algorithm), uses a particular codification of the individuals that allows solving two basic problems. First, it does not perform a previous attribute discretization and, second, it is not necessary to set which variables belong to the antecedent or consequent. Therefore, it may discover all underlying dependencies among different variables. To evaluate the proposed algorithm three experiments have been carried out. As initial step, several public datasets have been analyzed with the purpose of comparing with other existing evolutionary approaches. Also, the algorithm has been applied to synthetic time series (where the relationships are known) to analyze its potential for discovering rules in time series. Finally, a real-world multidimensional time series composed by several climatological variables has been considered. All the results show a remarkable performance of QARGA. |
Agencias financiadoras | Ministerio de Ciencia y Tecnología (MCYT). España Junta de Andalucía |
Identificador del proyecto | TIN2007- 68084-C02-02
P07-TIC- 02611 |
Cita | Martínez Ballesteros, M.d.M., Martínez Álvarez, F., Troncoso Lora, A. y Riquelme Santos, J.C. (2011). An evolutionary algorithm to discover quantitative association rules in multidimensional time series. Soft Computing, 15 (10), 2065-2084. |
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