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Correlated Trends: A New Representation for Imperfect and Large Dataseries

  • Miguel Delgado
  • Waldo Fajardo
  • Miguel Molina-Solana
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8132)

Abstract

The computational representation of dataseries is a task of growing interest in our days. However, as these data are often imperfect, new representation models are required to effectively handle them. This work presents Frequent Correlated Trends, our proposal for representing uncertain and imprecise multivariate dataseries. Such a model can be applied to any domain where dataseries contain patterns that recur in similar —but not identical— shape. We describe here the model representation and an associated learning algorithm.

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References

  1. 1.
    Kriegler, E., Held, H.: Utilizing belief functions for the estimation of future climate change. Int. Journal of Approximate Reasoning 39(2-3), 185–209 (2005)MathSciNetzbMATHCrossRefGoogle Scholar
  2. 2.
    Molina-Solana, M., Arcos, J.L., Gómez, E.: Identifying Violin Performers by their Expressive Trends. Intelligent Data Analysis 14(5), 555–571 (2010)Google Scholar
  3. 3.
    Delgado, M., Ros, M., Vila, M.A.: Correct behavior identification system in a Tagged World. Expert Systems with Applications 36(6), 9899–9906 (2009)CrossRefGoogle Scholar
  4. 4.
    Zhang, Y.Q., Wan, X.: Statistical fuzzy interval neural networks for currency exchange rate time series prediction. Applied Soft Computing 7(4), 1149–1156 (2007)CrossRefGoogle Scholar
  5. 5.
    Motro, A.: Sources of Uncertainty, Imprecision, and Inconsistency in Information Systems. In: Motro, A., Smets, P. (eds.) Uncertainty Management in Information Systems: From Needs to Solutions, pp. 9–34. Kluwer Academic Publishers (1996)Google Scholar
  6. 6.
    Liao, S.S., Tang, T.H., Liu, W.Y.: Finding relevant sequences in time series containing crisp, interval, and fuzzy interval data. IEEE Transactions on Systems, Man, and Cybernetics 34(5), 2071–2079 (2004)CrossRefGoogle Scholar
  7. 7.
    Herbst, G., Bocklisch, S.F.: Short-Time Prediction Based on Recognition of Fuzzy Time Series Patterns. In: Hüllermeier, E., Kruse, R., Hoffmann, F. (eds.) IPMU 2010. LNCS, vol. 6178, pp. 320–329. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  8. 8.
    Saleh, B., Masseglia, F.: Discovering frequent behaviors: time is an essential element of the context. Knowledge and Information Systems 28(2), 311–331 (2010)CrossRefGoogle Scholar
  9. 9.
    Xu, W., Kuhnert, L., Foster, K., Bronlund, J., Potgieter, J., Diegel, O.: Object-oriented knowledge representation and discovery of human chewing behaviours. Engineering Applications of Artificial Intelligence 20(7), 1000–1012 (2007)CrossRefGoogle Scholar
  10. 10.
    Zadeh, L.A.: Outline of a new approach to the analysis of complex systems and decision processes. IEEE Trans. on Systems, Man and Cybernetics 3(1), 28–44 (1973)MathSciNetzbMATHCrossRefGoogle Scholar
  11. 11.
    Zadeh, L.A.: Probability measures of fuzzy events. Journal of Mathematical Analysis and Applications 23(2), 421–427 (1968)MathSciNetzbMATHCrossRefGoogle Scholar
  12. 12.
    Yager, R.: A note on probabilities of fuzzy events. Information Sciences 18(2), 113–129 (1979)MathSciNetzbMATHCrossRefGoogle Scholar
  13. 13.
    Trillas, E., Nakama, T., García-Honrado, I.: Fuzzy Probabilities: Tentative Discussions on the Mathematical Concepts. In: Hüllermeier, E., Kruse, R., Hoffmann, F. (eds.) IPMU 2010. LNCS, vol. 6178, pp. 139–148. Springer, Heidelberg (2010)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Miguel Delgado
    • 1
  • Waldo Fajardo
    • 1
  • Miguel Molina-Solana
    • 1
  1. 1.Department Computer Science and Artificial IntelligenceUniversidad de GranadaGranadaSpain

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