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A taxonomy of event prediction methods

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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A taxonomy of event prediction methods. / Gmati, Fatma-Ezzahra ; Chakhar, Salem; Lejouad Chaari , Wided; Xu, Mark.

Advances and Trends in Artificial Intelligence. From Theory to Practice: 32nd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2019, Graz, Austria, July 9–11, 2019, Proceedings. ed. / Franz Wotawa; Gerhard Friedrich; Ingo Pill; Roxane Koitz-Hristov; Ali Moonis. Switzerland : Springer International Publishing, 2019. p. 12-26 (Lecture Notes in Computer Science; Vol. 11606).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Harvard

Gmati, F-E, Chakhar, S, Lejouad Chaari , W & Xu, M 2019, A taxonomy of event prediction methods. in F Wotawa, G Friedrich, I Pill, R Koitz-Hristov & A Moonis (eds), Advances and Trends in Artificial Intelligence. From Theory to Practice: 32nd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2019, Graz, Austria, July 9–11, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11606, Springer International Publishing, Switzerland, pp. 12-26, 32nd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, Graz, Austria, 9/07/19. https://doi.org/10.1007/978-3-030-22999-3_2

APA

Gmati, F-E., Chakhar, S., Lejouad Chaari , W., & Xu, M. (2019). A taxonomy of event prediction methods. In F. Wotawa, G. Friedrich, I. Pill, R. Koitz-Hristov, & A. Moonis (Eds.), Advances and Trends in Artificial Intelligence. From Theory to Practice: 32nd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2019, Graz, Austria, July 9–11, 2019, Proceedings (pp. 12-26). (Lecture Notes in Computer Science; Vol. 11606). Springer International Publishing. https://doi.org/10.1007/978-3-030-22999-3_2

Vancouver

Gmati F-E, Chakhar S, Lejouad Chaari W, Xu M. A taxonomy of event prediction methods. In Wotawa F, Friedrich G, Pill I, Koitz-Hristov R, Moonis A, editors, Advances and Trends in Artificial Intelligence. From Theory to Practice: 32nd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2019, Graz, Austria, July 9–11, 2019, Proceedings. Switzerland: Springer International Publishing. 2019. p. 12-26. (Lecture Notes in Computer Science). https://doi.org/10.1007/978-3-030-22999-3_2

Author

Gmati, Fatma-Ezzahra ; Chakhar, Salem ; Lejouad Chaari , Wided ; Xu, Mark. / A taxonomy of event prediction methods. Advances and Trends in Artificial Intelligence. From Theory to Practice: 32nd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2019, Graz, Austria, July 9–11, 2019, Proceedings. editor / Franz Wotawa ; Gerhard Friedrich ; Ingo Pill ; Roxane Koitz-Hristov ; Ali Moonis. Switzerland : Springer International Publishing, 2019. pp. 12-26 (Lecture Notes in Computer Science).

Bibtex

@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",

}

RIS

TY - GEN

T1 - A taxonomy of event prediction methods

AU - Gmati, Fatma-Ezzahra

AU - Chakhar, Salem

AU - Lejouad Chaari , Wided

AU - Xu, Mark

N1 - 12 month embargo The final authenticated version is available online at: http://dx.doi.org/[insert DOI].

PY - 2019/7

Y1 - 2019/7

N2 - 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.

AB - 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.

KW - Time series

KW - Event prediction

KW - Taxonomy

KW - Data Mining

U2 - 10.1007/978-3-030-22999-3_2

DO - 10.1007/978-3-030-22999-3_2

M3 - Conference contribution

SN - 978-3-030-22998-6

T3 - Lecture Notes in Computer Science

SP - 12

EP - 26

BT - Advances and Trends in Artificial Intelligence. From Theory to Practice

A2 - Wotawa, Franz

A2 - Friedrich, Gerhard

A2 - Pill, Ingo

A2 - Koitz-Hristov, Roxane

A2 - Moonis, Ali

PB - Springer International Publishing

CY - Switzerland

T2 - 32nd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems

Y2 - 9 July 2019 through 11 July 2019

ER -

ID: 13460744