Adaptive rank filtering based on error minimization

A method for adaptive (online) pruning and constructing a (layered) computational network is introduced. The dimensions of the network are updated for every new available sample, which makes this technique highly suitable for tracking nonstationary sources. This method extends work on predictive least squares by Rissanen (1986) and Wax (1988) to an adaptive updating scheme. The algorithm is demonstrated by an application to adaptive prediction of exchange rates.