Application of Rough Sets and Neural Networks to Forecasting University Facility and Administrative Cost Recovery

摘要

This paper presents a novel approach to financial time series analysis and prediction. It is mainly devoted to the problem of forecasting university facility and administrative cost recovery. However, it can also be used in other areas of a similar nature. The methodology 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- employs the theory of rough sets (RS) while the second part is based upon artificial neural networks (ANN).