5 papers
From Many Models, One: Macroeconomic Forecasting with Reservoir Ensembles
Giovanni Ballarin, Lyudmila Grigoryeva, Yui Ching Li
Model combination is a powerful approach for achieving superior performance compared to selecting a single model. We study both theoretically and empirically the effectiveness of e…
Reservoir kernels and Volterra series
Lukas Gonon, Lyudmila Grigoryeva, Juan-Pablo Ortega
A universal kernel is constructed whose sections approximate any causal and time-invariant filter in the fading memory category with inputs and outputs in a finite-dimensional Eucl…
Memory Capacity of Nonlinear Recurrent Networks: Is it Informative?
Giovanni Ballarin, Lyudmila Grigoryeva, Juan-Pablo Ortega
The total memory capacity (MC) of linear recurrent neural networks (RNNs) has been proven to be equal to the rank of the corresponding Kalman controllability matrix, and it is almo…
Forecasting causal dynamics with universal reservoirs
Lyudmila Grigoryeva, James Louw, Juan-Pablo Ortega
An iterated multistep forecasting scheme based on recurrent neural networks (RNN) is proposed for the time series generated by causal chains with infinite memory. This forecasting…
Infinite-dimensional next-generation reservoir computing
Lyudmila Grigoryeva, Hannah Lim Jing Ting, Juan-Pablo Ortega
Next-generation reservoir computing (NG-RC) has attracted much attention due to its excellent performance in spatio-temporal forecasting of complex systems and its ease of implemen…