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

DOI10.1016/j.neunet.2014.03.004zbMath1308.68095arXiv1411.2515OpenAlexW3125014870WikidataQ39209801 ScholiaQ39209801MaRDI QIDQ2339418

Lyudmila Grigoryeva, Juan-Pablo Ortega, Laurent Larger, Julie Henriques

Publication date: 1 April 2015

Published in: Neural Networks (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1411.2515

zbMATH Keywords

universalitytime series forecastingrealized volatilityvolatility forecastingneural computingreservoir computingecho state networksparallel reservoir computingtime-delay reservoirVEC-GARCH model

Mathematics Subject Classification ID

Inference from stochastic processes and prediction (62M20) Learning and adaptive systems in artificial intelligence (68T05)

Related Items (6)

Fading memory echo state networks are universal ⋮ Learning Theory for Dynamical Systems ⋮ Nonlinear Memory Capacity of Parallel Time-Delay Reservoir Computers in the Processing of Multidimensional Signals ⋮ A new echo state network with variable memory length ⋮ Dimension reduction in recurrent networks by canonicalization ⋮ Memory and forecasting capacities of nonlinear recurrent networks

Uses Software

• itsmr

Cites Work

• Reservoir computing approaches to recurrent neural network training
• Bootstrap prediction for returns and volatilities in GARCH models
• ARCH models and financial applications
• Generalized autoregressive conditional heteroscedasticity
• Optimization and applications of echo state networks with leaky- integrator neurons
• An experimental unification of reservoir computing methods
• Bootstrap prediction intervals in state-space models
• Fading memory and the problem of approximating nonlinear operators with Volterra series
• Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation
• Real-Time Computing Without Stable States: A New Framework for Neural Computation Based on Perturbations
• Introduction to Time Series and Forecasting
• Bootstrap predictive inference for ARIMA processes
• Modeling and Forecasting Realized Volatility
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