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)


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.


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


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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

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