Quantitative Association Rules Applied to Climatological Time Series Forecasting
- 1.5k Downloads
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.
KeywordsTime series forecasting quantitative association rules
Unable to display preview. Download preview PDF.
- 3.Kotsiantis, S., Kanellopoulos, D.: Association rules mining: A recent overview. GESTS International Transactions on Computer Science and Engineering 32(1), 71–82 (2006)Google Scholar
- 4.Orriols-Puig, A., Casillas, J., Bernadó-Mansilla, E.: First approach toward on-line evolution of association rules with learning classifier systems. In: Proceedings of the 2008 GECCO Genetic and Evolutionary Computation Conference, pp. 2031–2038 (2008)Google Scholar
- 6.Vannucci, M., Colla, V.: Meaningful discretization of continuous features for association rules mining by means of a som. In: Proceedings of the European Symposium on Artificial Neural Networks, pp. 489–494 (2004)Google Scholar
- 9.Yin, Y., Zhong, Z., Wang, Y.: Mining quantitative association rules by interval clustering. Journal of Computational Information Systems 4(2), 609–616 (2008)Google Scholar