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Procedia Computer Science, volume 156, pages 357-366

Pattern Recognition in Non-Stationary Environmental Time Series Using Sparse Regression

Publication typeJournal Article
Publication date2019-09-26
Quartile SCImago
Quartile WOS
Impact factor
ISSN18770509
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

1
Applied Sciences (Switzerland)
Applied Sciences (Switzerland), 1, 50%
Applied Sciences (Switzerland)
1 publication, 50%
Neurocomputing
Neurocomputing, 1, 50%
Neurocomputing
1 publication, 50%
1

Citations by publishers

1
Multidisciplinary Digital Publishing Institute (MDPI)
Multidisciplinary Digital Publishing Institute (MDPI), 1, 50%
Multidisciplinary Digital Publishing Institute (MDPI)
1 publication, 50%
Elsevier
Elsevier, 1, 50%
Elsevier
1 publication, 50%
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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RIS Copy
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 -
BibTex
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BibTex Copy
@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}
}
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