Publikation: Stochastic nonlinear time series forecasting using time-delay reservoir computers : performance and universality
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2014
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Neural networks. 2014, 55, pp. 59-71. ISSN 0893-6080. eISSN 1879-2782. Available under: doi: 10.1016/j.neunet.2014.03.004
Zusammenfassung
Reservoir computing is a recently introduced machine learning paradigm that has already shown excellent performances in the processing of empirical data. We study a particular kind of reservoir computers called time-delay reservoirs that are constructed out of the sampling of the solution of a time-delay differential equation and show their good performance in the forecasting of the conditional covariances associated to multivariate discrete-time nonlinear stochastic processes of VEC-GARCH type as well as in the prediction of factual daily market realized volatilities computed with intraday quotes, using as training input daily log-return series of moderate size. We tackle some problems associated to the lack of task-universality for individually operating reservoirs and propose a solution based on the use of parallel arrays of time-delay reservoirs.
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Fachgebiet (DDC)
510 Mathematik
Schlagwörter
Reservoir computing, echo state networks, neural computing, time-delay reservoir, time series forecasting, universality, VEC-GARCH model, volatility forecasting, realized volatility, parallel reservoir computing
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GRIGORYEVA, Lyudmila, Julie HENRIQUES, Laurent LARGER, Juan-Pablo ORTEGA, 2014. Stochastic nonlinear time series forecasting using time-delay reservoir computers : performance and universality. In: Neural networks. 2014, 55, pp. 59-71. ISSN 0893-6080. eISSN 1879-2782. Available under: doi: 10.1016/j.neunet.2014.03.004BibTex
@article{Grigoryeva2014-07Stoch-40580,
year={2014},
doi={10.1016/j.neunet.2014.03.004},
title={Stochastic nonlinear time series forecasting using time-delay reservoir computers : performance and universality},
volume={55},
issn={0893-6080},
journal={Neural networks},
pages={59--71},
author={Grigoryeva, Lyudmila and Henriques, Julie and Larger, Laurent and Ortega, Juan-Pablo}
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<dcterms:abstract xml:lang="eng">Reservoir computing is a recently introduced machine learning paradigm that has already shown excellent performances in the processing of empirical data. We study a particular kind of reservoir computers called time-delay reservoirs that are constructed out of the sampling of the solution of a time-delay differential equation and show their good performance in the forecasting of the conditional covariances associated to multivariate discrete-time nonlinear stochastic processes of VEC-GARCH type as well as in the prediction of factual daily market realized volatilities computed with intraday quotes, using as training input daily log-return series of moderate size. We tackle some problems associated to the lack of task-universality for individually operating reservoirs and propose a solution based on the use of parallel arrays of time-delay reservoirs.</dcterms:abstract>
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