Time Series Prediction using Rough Sets and Neural Networks Hybrid Approach

T.G. Smolinski, D.L. Chenoweth, and J.M. Zurada (USA)


Time series prediction, rough sets, neural networks,applications.


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).

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