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Neural networks are powerful tools for classification and regression instatic environments. This paper describes a technique for creating an ensembleof neural networks that adapts dynamically to changing conditions. The modelseparates the input space into four regions and each network is given a weightin each region based on its performance on samples from that region. Theensemble adapts dynamically by constantly adjusting these weights based on thecurrent performance of the networks. The data set used is a collection offinancial indicators with the goal of predicting the future platinum price. Anensemble with no weightings does not improve on the naive estimate of no weeklychange; our weighting algorithm gives an average percentage error of 63% fortwenty weeks of prediction.

Prediction of Platinum Prices Using Dynamically Weighted Mixture of Experts