Procedia Computer Science, volume 178, pages 424-433
Closed-form algebraic expressions discovery using combined evolutionary optimization and sparse regression approach
Publication type: Journal Article
Publication date: 2020-12-07
Journal:
Procedia Computer Science
Quartile SCImago
— Quartile WOS
—
Impact factor: —
ISSN: 18770509
General Medicine
Abstract
In the literature, vast amounts of methods of time-series modeling are described. Most for the methods, either classical or machine learning, left interpretation to the expert. Even though the interpretation is sometimes possible, usually, it is done only in a very narrow range of the applications. In the article approach to the extended time-series model interpretation is proposed. The algorithm of time-series model discovery in the form of the algebraic expression in a closed-form is described. The resulting algorithm utilizes the flexibility of the evolutionary optimization and possibility of the sparse regression to make concise models.
Citations by journals
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2
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Procedia Computer Science
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Procedia Computer Science
2 publications, 40%
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Communications in Computer and Information Science
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Communications in Computer and Information Science
1 publication, 20%
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Advances in Intelligent Systems and Computing
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Advances in Intelligent Systems and Computing
1 publication, 20%
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1
2
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Citations by publishers
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2
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Springer Nature
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Springer Nature
2 publications, 40%
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Elsevier
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Elsevier
2 publications, 40%
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1
2
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Merezhnikov M., Hvatov A. Closed-form algebraic expressions discovery using combined evolutionary optimization and sparse regression approach // Procedia Computer Science. 2020. Vol. 178. pp. 424-433.
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Merezhnikov M., Hvatov A. Closed-form algebraic expressions discovery using combined evolutionary optimization and sparse regression approach // Procedia Computer Science. 2020. Vol. 178. pp. 424-433.
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TY - JOUR
DO - 10.1016/j.procs.2020.11.044
UR - https://doi.org/10.1016%2Fj.procs.2020.11.044
TI - Closed-form algebraic expressions discovery using combined evolutionary optimization and sparse regression approach
T2 - Procedia Computer Science
AU - Merezhnikov, Mark
AU - Hvatov, Alexander
PY - 2020
DA - 2020/12/07 00:00:00
PB - Elsevier
SP - 424-433
VL - 178
SN - 1877-0509
ER -
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@article{2020_Merezhnikov,
author = {Mark Merezhnikov and Alexander Hvatov},
title = {Closed-form algebraic expressions discovery using combined evolutionary optimization and sparse regression approach},
journal = {Procedia Computer Science},
year = {2020},
volume = {178},
publisher = {Elsevier},
month = {dec},
url = {https://doi.org/10.1016%2Fj.procs.2020.11.044},
pages = {424--433},
doi = {10.1016/j.procs.2020.11.044}
}
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