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Article Dans Une Revue Neural Networks Année : 2014

Stochastic nonlinear time series forecasting using time-delay reservoir computers: performance and universality

Résumé

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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Dates et versions

hal-03222045 , version 1 (10-05-2021)

Identifiants

  • HAL Id : hal-03222045 , version 1

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Lyudmila Grigoryeva, Julie Henriques, Laurent Larger, Juan-Pablo Ortega. Stochastic nonlinear time series forecasting using time-delay reservoir computers: performance and universality. Neural Networks, 2014, 55, pp.59 - 71. ⟨hal-03222045⟩
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