Time-delay recurrent neural network for temporal correlations and prediction

Abstract

This paper presents a time-delay recurrent neural network (TDRNN) for temporal correlations and prediction. The TDRNN employs adaptive time delays and recurrences where the adaptive time delays make the network choose the optimal values of time delays for the temporal location of the important information in the input sequence and the recurrences enable the network to encode and integrate temporal context information of sequences. The TDRNN and multiple recurrent neural network(MRNN) described in this paper, adaptive time-delay neural network (ATNN) proposed by Lin, and time-delay neural network (TDNN) introduced by Waibel were simulated and applied to the chaotic time series prediction of Mackey–Glass delay-differential equation and the Korean stock market index prediction. The simulation results suggest that employing time delayed recurrences in the layered network is more effective for temporal correlations and prediction than putting multiple time delays into the neurons or their connections. The best performance is attained by the TDRNN. The TDRNN will be well applicable for temporal signal recognition, prediction and identification.