Physical time-series prediction using second-order pipelined recurrent neural network

This paper presents a novel type of higher-order pipelined recurrent neural network called the second-order pipelined recurrent neural network. The aim of the network is to improve the performance of the pipelined recurrent neural network by accommodating second order terms in the inputs. The network is tested for the prediction of non-linear and non-stationary signals. Two physical time-series, which are the mean value of the AE index and the sunspot signals are used in the simulation. The simulation results showed an average improvement in the signal to noise ratio, of 6.09 dB when compared to the pipelined recurrent neural networks.