Advertisement

Mining Frequent Distributions in Time Series

  • José Carlos CoutinhoEmail author
  • João Mendes Moreira
  • Cláudio Rebelo de Sá
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11872)

Abstract

Time series data is composed of observations of one or more variables along a time period. By analyzing the variability of the variables we can reveal patterns that repeat or that are correlated, which helps to understand the behaviour of the variables over time. Our method finds frequent distributions of a target variable in time series data and discovers relationships between frequent distributions in consecutive time intervals. The frequent distributions are found using a new method, and relationships between them are found using association rules mining.

Notes

Acknowledgements

This work is financed by National Funds through the Portuguese funding agency, FCT - Fundação para a Ciência e a Tecnologia within project : UID/EEA/50014/2019

References

  1. 1.
    Agrawal, R., Imielinski, T., Swami, A.N.: Mining association rules between sets of items in large databases. In: Buneman, P., Jajodia, S. (eds.) Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, Washington, DC, USA, May 26–28, 1993. pp. 207–216. ACM Press (1993). https://doi.org/10.1145/170035.170072
  2. 2.
    Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: Bocca, J.B., Jarke, M., Zaniolo, C. (eds.) VLDB 1994, Proceedings of 20th International Conference on Very Large Data Bases, September 12–15, 1994, Santiago de Chile, Chile, pp. 487–499. Morgan Kaufmann (1994)Google Scholar
  3. 3.
    Ayres, J., Flannick, J., Gehrke, J., Yiu, T.: Sequential pattern mining using a bitmap representation. In: Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, July 23–26, 2002, Edmonton, Alberta, Canada, pp. 429–435. ACM (2002). https://doi.org/10.1145/775047.775109
  4. 4.
    Bishop, C.M.: Pattern Recognition and Machine Learning, 5th edn. Springer, Information science and statistics (2007)Google Scholar
  5. 5.
    Brockwell, P., Davis, R.: Introduction to Time Series and Forecasting. Springer Texts in Statistics. Springer, New York (2013)zbMATHGoogle Scholar
  6. 6.
    Henderson, K., Gallagher, B., Eliassi-Rad, T.: EP-MEANS: an efficient nonparametric clustering of empirical probability distributions. In: Wainwright, R.L., Corchado, J.M., Bechini, A., Hong, J. (eds.) Proceedings of the 30th Annual ACM Symposium on Applied Computing, Salamanca, Spain, April 13–17, 2015, pp. 893–900. ACM (2015). https://doi.org/10.1145/2695664.2695860
  7. 7.
    Jorge, A.M., Azevedo, P.J., Pereira, F.: Distribution rules with numeric attributes of interest. In: Fürnkranz, J., Scheffer, T., Spiliopoulou, M. (eds.) PKDD 2006. LNCS (LNAI), vol. 4213, pp. 247–258. Springer, Heidelberg (2006).  https://doi.org/10.1007/11871637_26CrossRefGoogle Scholar
  8. 8.
    Rubner, Y., Tomasi, C., Guibas, L.J.: A metric for distributions with applications to image databases. In: Proceedings of the Sixth International Conference on Computer Vision (ICCV-1998), Bombay, India, January 4–7, 1998, pp. 59–66. IEEE Computer Society (1998). https://doi.org/10.1109/ICCV.1998.710701

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • José Carlos Coutinho
    • 1
    • 2
    Email author
  • João Mendes Moreira
    • 2
  • Cláudio Rebelo de Sá
    • 1
  1. 1.University of TwenteEnschedeThe Netherlands
  2. 2.University of PortoPortoPortugal

Personalised recommendations