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Time Series Prediction by Reservoir Neural Networks. / Tarkov, Mikhail S.; Chernov, Ivan A.

Advances in Neural Computation, Machine Learning, and Cognitive Research IV - Selected Papers from the 22nd International Conference on Neuroinformatics, 2020. ред. / Boris Kryzhanovsky; Witali Dunin-Barkowski; Vladimir Redko; Yury Tiumentsev. Springer Science and Business Media Deutschland GmbH, 2021. стр. 303-308 (Studies in Computational Intelligence; Том 925 SCI).

Результаты исследований: Публикации в книгах, отчётах, сборниках, трудах конференцийстатья в сборнике материалов конференциинаучнаяРецензирование

Harvard

Tarkov, MS & Chernov, IA 2021, Time Series Prediction by Reservoir Neural Networks. в B Kryzhanovsky, W Dunin-Barkowski, V Redko & Y Tiumentsev (ред.), Advances in Neural Computation, Machine Learning, and Cognitive Research IV - Selected Papers from the 22nd International Conference on Neuroinformatics, 2020. Studies in Computational Intelligence, Том. 925 SCI, Springer Science and Business Media Deutschland GmbH, стр. 303-308, 22nd International Conference on Neuroinformatics, 2020, Moscow, Российская Федерация, 12.10.2020. https://doi.org/10.1007/978-3-030-60577-3_36

APA

Tarkov, M. S., & Chernov, I. A. (2021). Time Series Prediction by Reservoir Neural Networks. в B. Kryzhanovsky, W. Dunin-Barkowski, V. Redko, & Y. Tiumentsev (Ред.), Advances in Neural Computation, Machine Learning, and Cognitive Research IV - Selected Papers from the 22nd International Conference on Neuroinformatics, 2020 (стр. 303-308). (Studies in Computational Intelligence; Том 925 SCI). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-60577-3_36

Vancouver

Tarkov MS, Chernov IA. Time Series Prediction by Reservoir Neural Networks. в Kryzhanovsky B, Dunin-Barkowski W, Redko V, Tiumentsev Y, Редакторы, Advances in Neural Computation, Machine Learning, and Cognitive Research IV - Selected Papers from the 22nd International Conference on Neuroinformatics, 2020. Springer Science and Business Media Deutschland GmbH. 2021. стр. 303-308. (Studies in Computational Intelligence). doi: 10.1007/978-3-030-60577-3_36

Author

Tarkov, Mikhail S. ; Chernov, Ivan A. / Time Series Prediction by Reservoir Neural Networks. Advances in Neural Computation, Machine Learning, and Cognitive Research IV - Selected Papers from the 22nd International Conference on Neuroinformatics, 2020. Редактор / Boris Kryzhanovsky ; Witali Dunin-Barkowski ; Vladimir Redko ; Yury Tiumentsev. Springer Science and Business Media Deutschland GmbH, 2021. стр. 303-308 (Studies in Computational Intelligence).

BibTeX

@inproceedings{de60e2c5c9ec4d5cbb390a5aa243864a,
title = "Time Series Prediction by Reservoir Neural Networks",
abstract = "The tasks of forecasting time series arise in many areas of computer science. Algorithms based on machine learning do a good job of this task. In this work, we performed a comparative analysis of a number of algorithms for predicting time series by reservoir neural networks (echo-state networks) according to the forecast accuracy and the time it takes to build the forecast. To test forecasting algorithms, data sets obtained from the Mackey-Glass equation were used. The experiments showed that the sigmoidal and radial networks with a SOM projector give the most accurate forecast, but they are also the least fast. A new reservoir optimization algorithm is proposed - a direct version of the Infomax method. The functionality of the mutual information of the input and output of the reservoir is maximized. This algorithm requires non-negativity of data values, but it works much faster than the well-known iterative version of Infomax and a radial network with a SOM projector, although it slightly reduces the forecast accuracy.",
keywords = "Forecast, Maximization of mutual information, Reservoir neural networks, Time series",
author = "Tarkov, {Mikhail S.} and Chernov, {Ivan A.}",
year = "2021",
doi = "10.1007/978-3-030-60577-3_36",
language = "English",
isbn = "9783030605766",
series = "Studies in Computational Intelligence",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "303--308",
editor = "Boris Kryzhanovsky and Witali Dunin-Barkowski and Vladimir Redko and Yury Tiumentsev",
booktitle = "Advances in Neural Computation, Machine Learning, and Cognitive Research IV - Selected Papers from the 22nd International Conference on Neuroinformatics, 2020",
address = "Germany",
note = "22nd International Conference on Neuroinformatics, 2020 ; Conference date: 12-10-2020 Through 16-10-2020",

}

RIS

TY - GEN

T1 - Time Series Prediction by Reservoir Neural Networks

AU - Tarkov, Mikhail S.

AU - Chernov, Ivan A.

PY - 2021

Y1 - 2021

N2 - The tasks of forecasting time series arise in many areas of computer science. Algorithms based on machine learning do a good job of this task. In this work, we performed a comparative analysis of a number of algorithms for predicting time series by reservoir neural networks (echo-state networks) according to the forecast accuracy and the time it takes to build the forecast. To test forecasting algorithms, data sets obtained from the Mackey-Glass equation were used. The experiments showed that the sigmoidal and radial networks with a SOM projector give the most accurate forecast, but they are also the least fast. A new reservoir optimization algorithm is proposed - a direct version of the Infomax method. The functionality of the mutual information of the input and output of the reservoir is maximized. This algorithm requires non-negativity of data values, but it works much faster than the well-known iterative version of Infomax and a radial network with a SOM projector, although it slightly reduces the forecast accuracy.

AB - The tasks of forecasting time series arise in many areas of computer science. Algorithms based on machine learning do a good job of this task. In this work, we performed a comparative analysis of a number of algorithms for predicting time series by reservoir neural networks (echo-state networks) according to the forecast accuracy and the time it takes to build the forecast. To test forecasting algorithms, data sets obtained from the Mackey-Glass equation were used. The experiments showed that the sigmoidal and radial networks with a SOM projector give the most accurate forecast, but they are also the least fast. A new reservoir optimization algorithm is proposed - a direct version of the Infomax method. The functionality of the mutual information of the input and output of the reservoir is maximized. This algorithm requires non-negativity of data values, but it works much faster than the well-known iterative version of Infomax and a radial network with a SOM projector, although it slightly reduces the forecast accuracy.

KW - Forecast

KW - Maximization of mutual information

KW - Reservoir neural networks

KW - Time series

UR - http://www.scopus.com/inward/record.url?scp=85093092403&partnerID=8YFLogxK

UR - https://www.mendeley.com/catalogue/42b48358-a880-3f68-9011-c04082457209/

U2 - 10.1007/978-3-030-60577-3_36

DO - 10.1007/978-3-030-60577-3_36

M3 - Conference contribution

AN - SCOPUS:85093092403

SN - 9783030605766

T3 - Studies in Computational Intelligence

SP - 303

EP - 308

BT - Advances in Neural Computation, Machine Learning, and Cognitive Research IV - Selected Papers from the 22nd International Conference on Neuroinformatics, 2020

A2 - Kryzhanovsky, Boris

A2 - Dunin-Barkowski, Witali

A2 - Redko, Vladimir

A2 - Tiumentsev, Yury

PB - Springer Science and Business Media Deutschland GmbH

T2 - 22nd International Conference on Neuroinformatics, 2020

Y2 - 12 October 2020 through 16 October 2020

ER -

ID: 25677048