Recurrent neural networks and discrete wavelet transform for time series modeling and prediction

A new approach is presented for time-series modeling and prediction using recurrent neural networks (RRNs) and a discrete wavelet transform (DWT). A specific DWT, based on the cubic spline wavelet, produces a set of wavelet coefficients from coarse to fine scale levels. The RNN has its current output fed back to its input nodes, forming a nonlinear autoregressive model for predicting future wavelet coefficients. A predicted trend signal is obtained by constructing the interpolation function from the predicted wavelet coefficients at the coarsest scale level, V₀. This method has been applied to intracranial pressure data collected from head trauma patients in the intensive care unit. The method has been shown to be more efficient than one which uses raw data to train the RNN