@inproceedings{653a7617f55a4deda1f67ecd0dbe5cc2,
title = "A taxonomy of event prediction methods",
abstract = "An event is a timestamped element in a temporal sequence. Event prediction problem concerns the prediction of one or several elements constituting the future characteristics of the event. A large number of event prediction approaches have been proposed in the literature but most of existing works consider event prediction problem within a specific application domain while event prediction is naturally a cross-disciplinary problem. This paper introduces a generic taxonomy of event prediction approaches that go beyond the problem considered and application domain. This generic view enables a better understanding of event prediction problems and allows conceiving and developing advanced and context-independent event prediction techniques.",
keywords = "Time series, Event prediction, Taxonomy, Data Mining",
author = "Fatma-Ezzahra Gmati and Salem Chakhar and {Lejouad Chaari}, Wided and Mark Xu",
note = "12 month embargo The final authenticated version is available online at: http://dx.doi.org/[insert DOI].; 32nd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2019 ; Conference date: 09-07-2019 Through 11-07-2019",
year = "2019",
month = jul,
doi = "10.1007/978-3-030-22999-3_2",
language = "English",
isbn = "978-3-030-22998-6",
series = "Lecture Notes in Computer Science",
publisher = "Springer International Publishing",
pages = "12--26",
editor = "Franz Wotawa and Gerhard Friedrich and Ingo Pill and Roxane Koitz-Hristov and Ali Moonis",
booktitle = "Advances and Trends in Artificial Intelligence. From Theory to Practice",
}