ARTIFICIAL NEURAL NETWORK BASED UNIVERAL TIME SERIES

A neural network based universal time series prediction system for financial securities includes a pipelined recurrent ANN architecutre having a plurality of identical modules to first adjust internal weights and biases in response to a first training set representing a nonlinear financial time series of samples of a financial quantity and a target value, and then determine and store an estimated prediction error of the ANN in order to adjust short time stock price predictions in accordance with the stored prediction error. The prediction system is also designed to output upper and lower prediction bounds within a confidence region.