@inproceedings{441564d738454866b740092977e2ab71,
title = "A rough set approach to events prediction in multiple time series",
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",
author = "Fatma-Ezzahra Gmati and Salem Chakhar and {Lejouad Chaari}, Wided and Huijing Chen",
year = "2018",
month = jun,
doi = "10.1007/978-3-319-92058-0_77",
language = "English",
isbn = "978-3-319-92057-3",
series = "Lecture Notes in Computer Science",
publisher = "Springer International Publishing",
pages = "796--807",
editor = "{ Mouhoub}, Malek and Samira Sadaoui and { Ait Mohamed}, Otmane and Moonis Ali",
booktitle = "Recent Trends and Future Technology in Applied Intelligence - 31st International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2018, Proceedings",
note = "31st International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems : IEA/AIE 2018 ; Conference date: 25-06-2018 Through 28-06-2018",
}