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Multiresolution dynamic predictor based on neural networks

We present a multiresolution dynamic predictor (MDP) based on neural networks for multi- step prediction of a time series. The MDP utilizes the discrete biorthogonal wavelet transform to compute wavelet coefficients at several scale levels and recurrent neural networks (RNNs) to form a set of dynamic nonlinear models for prediction of the time series. By employing RNNs in wavelet coefficient space, the MDP is capable of predicting a time series for both the long-term (with coarse resolution) and short-term (with fine resolution). Experimental results have demonstrated the effectiveness of the MDP for multi-step prediction of intracranial pressure (ICP) recorded from head-trauma patients. This approach has applicability to quasi- stationary signals and is suitable for on-line computation.