Open Access
Procedia Computer Science, volume 156, pages 357-366
Pattern Recognition in Non-Stationary Environmental Time Series Using Sparse Regression
Publication type: Journal Article
Publication date: 2019-09-26
Journal:
Procedia Computer Science
Quartile SCImago
— Quartile WOS
—
Impact factor: —
ISSN: 18770509
General Medicine
Abstract
The various real-world tasks of environmental management make it necessary to obtain the hindcasts and forecasts of natural events (wind, ocean waves and currents, sea ice, etc.) using data-driven techniques for metocean processes simulation. The models can be fitted to specific fragments of the non-stationary multivariate time series individually to reproduce metocean environment with desired characteristics. In the paper, the approach based on the LASSO regularised regression is proposed for the environmental time series clustering. It allows the identify the situations with specific interaction between variables, that can be interpreted by the values regression coefficients. The weather generator was used to produce both synthetic time series similar to the general dataset and the identified clusters. The obtained results can be used to increase the quality of the computationally lightweight environmental models’ identification and interpretation.
Citations by journals
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Applied Sciences (Switzerland)
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Applied Sciences (Switzerland)
1 publication, 50%
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Neurocomputing
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Neurocomputing
1 publication, 50%
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1
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Citations by publishers
1
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Multidisciplinary Digital Publishing Institute (MDPI)
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Multidisciplinary Digital Publishing Institute (MDPI)
1 publication, 50%
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Elsevier
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Elsevier
1 publication, 50%
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1
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Deeva I., Nikitin N. O., Kaluyzhnaya A. V. Pattern Recognition in Non-Stationary Environmental Time Series Using Sparse Regression // Procedia Computer Science. 2019. Vol. 156. pp. 357-366.
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Deeva I., Nikitin N. O., Kaluyzhnaya A. V. Pattern Recognition in Non-Stationary Environmental Time Series Using Sparse Regression // Procedia Computer Science. 2019. Vol. 156. pp. 357-366.
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TY - JOUR
DO - 10.1016/j.procs.2019.08.212
UR - https://doi.org/10.1016%2Fj.procs.2019.08.212
TI - Pattern Recognition in Non-Stationary Environmental Time Series Using Sparse Regression
T2 - Procedia Computer Science
AU - Deeva, Irina
AU - Nikitin, Nikolay O
AU - Kaluyzhnaya, Anna V
PY - 2019
DA - 2019/09/26 00:00:00
PB - Elsevier
SP - 357-366
VL - 156
SN - 1877-0509
ER -
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@article{2019_Deeva
author = {Irina Deeva and Nikolay O Nikitin and Anna V Kaluyzhnaya},
title = {Pattern Recognition in Non-Stationary Environmental Time Series Using Sparse Regression},
journal = {Procedia Computer Science},
year = {2019},
volume = {156},
publisher = {Elsevier},
month = {sep},
url = {https://doi.org/10.1016%2Fj.procs.2019.08.212},
pages = {357--366},
doi = {10.1016/j.procs.2019.08.212}
}