Using genetic algorithms for improved discrete sequence prediction

Anikó Ekárt

Using genetic algorithms for improved discrete sequence prediction

Computer Science Research Group

Research output: Chapter in Book/Published conference output › Conference publication

Abstract

A statistics-based method using genetic algorithms for predicting discrete sequences is presented. The prediction of the next value is based upon a fixed number of previous values and the statistics offered by the training data. According to the statistics, in similar past cases different values occurred next. If these values are considered with the appropriate weights, the forecast is successful. Weights are generated by genetic algorithms.

Original language	English
Title of host publication	Proceedings of the International Conference on Artificial Intelligence, IC-AI '03, June 23 - 26, 2003, Las Vegas, Nevada, USA
Editors	Hamid R. Arabnia, Rose Joshua, Youngsong Mun
Publisher	CSREA
Pages	475-481
Number of pages	10
Volume	1
ISBN (Print)	1-932415-12-2
Publication status	Published - 2003
Event	2003 International Conference on Artificial Intelligence - Las Vegas, NV, United States Duration: 23 Jun 2003 → 26 Jun 2003

Conference

Conference	2003 International Conference on Artificial Intelligence
Abbreviated title	IC-AI 2003
Country/Territory	United States
City	Las Vegas, NV
Period	23/06/03 → 26/06/03

Keywords

artificial intelligence

Cite this

@inproceedings{0e8f577b5c3e45c6a56c67626b09b028,

title = "Using genetic algorithms for improved discrete sequence prediction",

abstract = "A statistics-based method using genetic algorithms for predicting discrete sequences is presented. The prediction of the next value is based upon a fixed number of previous values and the statistics offered by the training data. According to the statistics, in similar past cases different values occurred next. If these values are considered with the appropriate weights, the forecast is successful. Weights are generated by genetic algorithms.",

keywords = "artificial intelligence",

author = "Anik{\'o} Ek{\'a}rt",

year = "2003",

language = "English",

isbn = "1-932415-12-2",

volume = "1",

pages = "475--481",

editor = "Arabnia, {Hamid R.} and Rose Joshua and Youngsong Mun",

booktitle = "Proceedings of the International Conference on Artificial Intelligence, IC-AI '03, June 23 - 26, 2003, Las Vegas, Nevada, USA",

publisher = "CSREA",

note = "2003 International Conference on Artificial Intelligence, IC-AI 2003 ; Conference date: 23-06-2003 Through 26-06-2003",

}

Ekárt, A 2003, Using genetic algorithms for improved discrete sequence prediction. in HR Arabnia, R Joshua & Y Mun (eds), Proceedings of the International Conference on Artificial Intelligence, IC-AI '03, June 23 - 26, 2003, Las Vegas, Nevada, USA. vol. 1, CSREA, pp. 475-481, 2003 International Conference on Artificial Intelligence, Las Vegas, NV, United States, 23/06/03.

Using genetic algorithms for improved discrete sequence prediction. / Ekárt, Anikó.
Proceedings of the International Conference on Artificial Intelligence, IC-AI '03, June 23 - 26, 2003, Las Vegas, Nevada, USA. ed. / Hamid R. Arabnia; Rose Joshua; Youngsong Mun. Vol. 1 CSREA, 2003. p. 475-481.

Research output: Chapter in Book/Published conference output › Conference publication

TY - GEN

T1 - Using genetic algorithms for improved discrete sequence prediction

AU - Ekárt, Anikó

PY - 2003

Y1 - 2003

N2 - A statistics-based method using genetic algorithms for predicting discrete sequences is presented. The prediction of the next value is based upon a fixed number of previous values and the statistics offered by the training data. According to the statistics, in similar past cases different values occurred next. If these values are considered with the appropriate weights, the forecast is successful. Weights are generated by genetic algorithms.

AB - A statistics-based method using genetic algorithms for predicting discrete sequences is presented. The prediction of the next value is based upon a fixed number of previous values and the statistics offered by the training data. According to the statistics, in similar past cases different values occurred next. If these values are considered with the appropriate weights, the forecast is successful. Weights are generated by genetic algorithms.

KW - artificial intelligence

UR - http://www.scopus.com/inward/record.url?scp=1642300422&partnerID=8YFLogxK

M3 - Conference publication

AN - SCOPUS:1642300422

SN - 1-932415-12-2

VL - 1

SP - 475

EP - 481

BT - Proceedings of the International Conference on Artificial Intelligence, IC-AI '03, June 23 - 26, 2003, Las Vegas, Nevada, USA

A2 - Arabnia, Hamid R.

A2 - Joshua, Rose

A2 - Mun, Youngsong

PB - CSREA

T2 - 2003 International Conference on Artificial Intelligence

Y2 - 23 June 2003 through 26 June 2003

ER -

Using genetic algorithms for improved discrete sequence prediction

Abstract

Conference

Keywords

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