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.
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Martínez-Ballesteros, M., Martínez-Álvarez, F., Troncoso, A., Riquelme, J.C. (2009). Quantitative Association Rules Applied to Climatological Time Series Forecasting. In: Corchado, E., Yin, H. (eds) Intelligent Data Engineering and Automated Learning - IDEAL 2009. IDEAL 2009. Lecture Notes in Computer Science, vol 5788. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04394-9_35
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DOI: https://doi.org/10.1007/978-3-642-04394-9_35
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