Comparing recurrent networks for time-series forecasting

Abstract

This paper provides a comparison between two methods for time series forecasting. The first method is based on traditional recurrent neural networks (RNNs) while the second method is based in Reservoir Computing (RC). Reservoir Computing is a new paradigm that offers an intuitive methodology for using the temporal processing power of RNNs without the inconvenience of training them. So we decided to compare the advantages / disadvantages of using Reservoir Computing and RNNs in the problem of time series forecasting. The first method uses a Nonlinear Autoregressive Network with exogenous inputs (NARX). Optimization was carried out on the NARX architecture through an optimization procedure focused on the best mean squared error (MSE) metrics in the training set. The second method, called RCDESIGN, combines an evolutionary algorithm with Reservoir Computing and simultaneously looks for the best values of parameters, topology and weight matrices without rescaling the reservoir by the spectral radius. Nevertheless RCDESIGN has yielded fast tracking and excellent performance in some benchmark problems including the Narma and Mackey-Glass time-series.