Quantitative Association Rules Applied to Climatological Time Series Forecasting

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5788)


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.


Time series forecasting quantitative association rules 


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  1. 1.
    Alatas, B., Akin, E.: Rough particle swarm optimization and its applications in data mining. Soft Computing 12(12), 1205–1218 (2008)CrossRefzbMATHGoogle Scholar
  2. 2.
    Alatas, B., Akin, E., Karci, A.: MODENAR: Multi-objective differential evolution algorithm for mining numeric association rules. Applied Soft Computing 8(1), 646–656 (2008)CrossRefGoogle Scholar
  3. 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. 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
  5. 5.
    Sahua, S.K., Yipc, S., Hollandb, D.M.: Improved space-time forecasting of next day ozone concentrations in the eastern US. Atmospheric Environment 43(3), 494–501 (2009)CrossRefGoogle Scholar
  6. 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
  7. 7.
    Venturini, G.: SIA: a Supervised Inductive Algorithm with genetic search for learning attribute based concepts. In: Brazdil, P.B. (ed.) ECML 1993. LNCS, vol. 667, pp. 280–296. Springer, Heidelberg (1993)CrossRefGoogle Scholar
  8. 8.
    Yan, X., Zhang, C., Zhang, S.: Genetic algorithm-based strategy for identifying association rules without specifying actual minimum support. Expert Systems with Applications: An International Journal 36(2), 3066–3076 (2009)CrossRefGoogle Scholar
  9. 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

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of SevilleSpain
  2. 2.Area of Computer SciencePablo de Olavide University of SevilleSpain

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