期刊
NEURAL NETWORKS
卷 55, 期 -, 页码 59-71出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.neunet.2014.03.004
关键词
Reservoir computing; Echo state networks; Neural computing; Time-delay reservoir; Time series forecasting; Universality; VEC-GARCH model; Volatility forecasting; Realized volatility; Parallel reservoir computing
资金
- Region de Franche-Comte [2013C-5493]
- European project PHOCUS [240763]
- Labex ACTION program [ANR-11-LABX-01-01]
- Deployment S.L
- Future Program Award of the Schlumberger Foundation
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 fog-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. (C) 2014 Elsevier Ltd. All rights reserved.
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