Prediction of Platinum Prices Using Dynamically Weighted Mixture of Experts

TL;DR

This paper introduces a dynamically weighted ensemble of neural networks that adaptively predicts platinum prices by adjusting network weights based on regional performance, improving prediction accuracy over static models.

Contribution

The paper presents a novel dynamic weighting method for neural network ensembles that adapt to changing data conditions in financial time series prediction.

Findings

Dynamic ensemble improves prediction accuracy.

Weighting algorithm reduces average percentage error.

Model outperforms static ensemble approaches.

Abstract

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.