An adaptive neural network in wavelet space for time-series prediction

An adaptive recurrent neural network (ARNN) in wavelet coefficient space computed from the discrete wavelet transform (DWT) is presented in this paper for generating an adaptive, long-term, coarse resolution prediction of a time series. The weights inside the ARNN are updated by the incoming data, i.e., the network modifies itself with time. With the aid of the newly developed DWT of Cai-Wang, this ARNN is efficient and takes less time to train than a NN in data space since it deals only with wavelet coefficients instead of raw data. Results are demonstrated by applying this method to the long-term prediction of intracranial pressure (ICP) data recorded from head-trauma patients