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A Taxonomy of Event Prediction Methods

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Advances and Trends in Artificial Intelligence. From Theory to Practice (IEA/AIE 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11606))

Abstract

Most of existing event prediction approaches consider event prediction problems within a specific application domain while event prediction is naturally a cross-disciplinary problem. This paper introduces a generic taxonomy of event prediction approaches. The proposed taxonomy, which oversteps the application domain, enables a better understanding of event prediction problems and allows conceiving and developing advanced and context-independent event prediction techniques.

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Authors and Affiliations

  1. COSMOS, National School of Computer Sciences, University of Manouba, Manouba, Tunisia

    Fatma Ezzahra Gmati & Wided Lejouad Chaari

  2. Portsmouth Business School, University of Portsmouth, Portsmouth, UK

    Salem Chakhar & Mark Xu

  3. CORL, University of Portsmouth, Portsmouth, UK

    Salem Chakhar

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  1. Fatma Ezzahra Gmati
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  2. Salem Chakhar
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  3. Wided Lejouad Chaari
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  4. Mark Xu
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Corresponding author

Correspondence to Salem Chakhar .

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Editors and Affiliations

  1. Institute for Software Technology, Graz University of Technology, Graz, Austria

    Franz Wotawa

  2. Department of Applied Informatics, University of Klagenfurt, Klagenfurt, Austria

    Gerhard Friedrich

  3. Institute for Software Technology, Graz University of Technology, Graz, Austria

    Ingo Pill

  4. Institute for Software Technology, Graz University of Technology, Graz, Austria

    Roxane Koitz-Hristov

  5. Department of Computer Science, Texas State University, San Marcos, TX, USA

    Moonis Ali

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Gmati, F.E., Chakhar, S., Chaari, W.L., Xu, M. (2019). A Taxonomy of Event Prediction Methods. In: Wotawa, F., Friedrich, G., Pill, I., Koitz-Hristov, R., Ali, M. (eds) Advances and Trends in Artificial Intelligence. From Theory to Practice. IEA/AIE 2019. Lecture Notes in Computer Science(), vol 11606. Springer, Cham. https://doi.org/10.1007/978-3-030-22999-3_2

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  • DOI: https://doi.org/10.1007/978-3-030-22999-3_2

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