An evolutionary algorithm to discover quantitative association rules in multidimensional time series

@article{MartnezBallesteros2011AnEA,
  title={An evolutionary algorithm to discover quantitative association rules in multidimensional time series},
  author={Mar{\'i}a Mart{\'i}nez-Ballesteros and Francisco Mart{\'i}nez-{\'A}lvarez and Alicia Troncoso Lora and Jos{\'e} Crist{\'o}bal Riquelme Santos},
  journal={Soft Comput.},
  year={2011},
  volume={15},
  pages={2065-2084}
}
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… CONTINUE READING
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