@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",
}