TIME SERIES PREDICTION USING ROUGH SETS AND NEURAL NETWORKS HYBRID APPROACH

摘要

This paper presents a novel approach to financial time series analysis and prediction. It incorporates a two-stage hybrid mechanism for selection of prediction-relevant features and for forecasting based on this selected sub-space of attributes. The first module of the methodology is based upon the theory of Rough Sets (RS) whilst the second part employs Artificial Neural Networks (ANN).