Advertisement

A Rough Set Approach to Events Prediction in Multiple Time Series

  • Fatma Ezzahra Gmati
  • Salem Chakhar
  • Wided Lejouad Chaari
  • Huijing Chen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10868)

Abstract

This paper introduces and illustrates a rough-set based approach to event prediction in multiple time series. The proposed approach uses two different versions of rough set theory to predict events occurrences and intensities. First, classical Indiscernibility relation-based Rough Set Approach (IRSA) is used to predict event classes and occurrences. Then, the Dominance-based Rough Set Approach (DRSA) is employed to predict the intensity of events. This paper presents the fundamental of the proposed approach and the conceptual architecture of a framework implementing this approach.

Keywords

Event prediction Multiple time series Rough sets Dominance-based Rough Set Approach 

References

  1. 1.
    Batal, I., Cooper, G.F., Fradkin, D., Harrison, J., Moerchen, F., Hauskrecht, M.: An efficient pattern mining approach for event detection in multivariate temporal data. Knowl. Inf. Syst. 46, 115–150 (2015)CrossRefGoogle Scholar
  2. 2.
    Damle, C., Yalcin, A.: Flood prediction using time series data mining. J. Hydrol. 333, 305–316 (2006)CrossRefGoogle Scholar
  3. 3.
    Dembczyński, K., Greco, S., Kotłowski, W., Słowiński, R.: Statistical model for rough set approach to multicriteria classification. In: Kok, J.N., Koronacki, J., Lopez de Mantaras, R., Matwin, S., Mladenič, D., Skowron, A. (eds.) PKDD 2007. LNCS (LNAI), vol. 4702, pp. 164–175. Springer, Heidelberg (2007).  https://doi.org/10.1007/978-3-540-74976-9_18CrossRefGoogle Scholar
  4. 4.
    Erechtchoukova, M.G., Khaiter, P.A., Saffarpour, S.: Short-term predictions of hydrological events on an urbanized watershed using supervised classification. Water Resour. Manage. 30, 4329–4343 (2016)CrossRefGoogle Scholar
  5. 5.
    Fox, E.B., Sudderth, E.B., Jordan, M.I., Willsky, A.S.: An HDP-HMM for systems with state persistence. In: Proceedings of the 25th International Conference on Machine Learning, pp. 312–319. ACM, New York (2008)Google Scholar
  6. 6.
    Greco, S., Matarazzo, B., Slowiński, R.: The use of rough sets and fuzzy sets in MCDM. In: Gal, T., Hanne, T., Stewart, T. (eds.) Advances in Multiple Criteria Decision Making, pp. 14.1–14.59. Kluwer Academic Publishers, Dordrecht, Boston (1999)Google Scholar
  7. 7.
    Greco, S., Matarazzo, B., Slowiński, R.: Rough sets theory for multicriteria decision analysis. Euro. J. Oper. Res. 129(1), 1–47 (2001)CrossRefGoogle Scholar
  8. 8.
    Greco, S., Matarazzo, B., Slowinski, R., Stefanowski, J.: Variable consistency model of dominance-based rough sets approach. In: Ziarko, W., Yao, Y. (eds.) RSCTC 2000. LNCS (LNAI), vol. 2005, pp. 170–181. Springer, Heidelberg (2001).  https://doi.org/10.1007/3-540-45554-X_20CrossRefzbMATHGoogle Scholar
  9. 9.
    Harguess, J., Aggarwal, J.K.: Semantic labeling of track events using time series segmentation and shape analysis. In: The 16th IEEE International Conference on Image Processing (ICIP 2009), Cairo, Egypt, pp. 4317–4320. IEEE (2009)Google Scholar
  10. 10.
    Keogh, E., Chu, S., Hart, D., Pazzani, M.: Segmenting time series: a survey and novel approach. In: Machine Perception and Artificial Intelligence, vol. 57, pp. 1–21. World Scientific (2011)Google Scholar
  11. 11.
    Morales-Esteban, A., Martinez-Alvarez, F., Troncoso, A., Justo, J.L., Rubio-Escudero, C.: Pattern recognition to forecast seismic time series. Expert Syst. Appl. 37, 8333–8342 (2010)CrossRefGoogle Scholar
  12. 12.
    Mörchen, F., Ultsch, A.: Discovering temporal knowledge in multivariate time series. In: Weihs, C., Gaul, W. (eds.) Classification – the Ubiquitous Challenge. Studies in Classification, Data Analysis, and Knowledge Organization, pp. 272–279. Springer, Heidelberg (2005).  https://doi.org/10.1007/3-540-28084-7_30
  13. 13.
    Pawlak, Z.: Rough sets. Int. J. Inf. Comput. Sci. 11, 341–356 (1982)CrossRefGoogle Scholar
  14. 14.
    Pawlak, Z.: Rough Set: Theoretical Aspects of Reasoning about Data. Kluwer Academic Publishers, Dordrecht (1991)CrossRefGoogle Scholar
  15. 15.
    Povinelli, J.R., Feng, X.: A new temporal pattern identification method for characterization and prediction of complex time series events. IEEE Trans. Knowl. Data Eng. 15(2), 339–352 (2003)CrossRefGoogle Scholar
  16. 16.
    Povinelli, R.J.: Time Series Data Mining: Identifying Temporal Patterns for Characterization and Prediction of Time Series Events. Ph.D. thesis, Marquette University, Milwaukee, WI (1999)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Fatma Ezzahra Gmati
    • 1
  • Salem Chakhar
    • 2
  • Wided Lejouad Chaari
    • 1
  • Huijing Chen
    • 2
  1. 1.COSMOS, National School of Computer ScienceUniversity of ManoubaManoubaTunisia
  2. 2.Portsmouth Business School and Centre for Operational Research and LogisticsUniversity of PortsmouthPortsmouthUK

Personalised recommendations