  Neural networks are powerful tools for classification and regression in
static environments. This paper describes a technique for creating an ensemble
of neural networks that adapts dynamically to changing conditions. The model
separates the input space into four regions and each network is given a weight
in each region based on its performance on samples from that region. The
ensemble adapts dynamically by constantly adjusting these weights based on the
current performance of the networks. The data set used is a collection of
financial indicators with the goal of predicting the future platinum price. An
ensemble with no weightings does not improve on the naive estimate of no weekly
change; our weighting algorithm gives an average percentage error of 63% for
twenty weeks of prediction.
