Robust short term prediction using combination of linear regression and modified probabilistic neural network model

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dc.contributor.author Jan Tony en_US
dc.contributor.editor Liu, j; Cheung, Y; Yin, H en_US
dc.date.accessioned 2009-11-09T05:39:14Z
dc.date.available 2009-11-09T05:39:14Z
dc.date.issued 2003 en_US
dc.identifier 2003002147 en_US
dc.identifier.citation Jan Tony 2003, 'Robust short term prediction using combination of linear regression and modified probabalistic neural network model', IEEE press, Canada, pp. 1-4. en_US
dc.identifier.issn 0-7803-7898-9 en_US
dc.identifier.other E1 en_US
dc.identifier.uri http://hdl.handle.net/10453/3165
dc.description.abstract In many business applications, accurate short term prediction is vital for survival. Many different techniques have been applied to model business data in order to produce accurate prediction. Artificial neural network (ANN) have shown excellent potential however it requires better extrapolation capacity in order to provide reliable prediction. In this paper, a combination of piecewise linear regression model in parallel with general regression neural network is introduced for short term financial prediction. The experiment shows that the propwed hybrid model achieves superior prediction performance compared lo the conventional prediction techniqufs such as the MultiLayer Perceptron (MLP) or Volterra series based prediction. en_US
dc.publisher The Institute of Electrical and Electronic Engineers Inc (IEEE) en_US
dc.relation.isbasedon http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=1223953&isnumber=27487 en_US
dc.title Robust short term prediction using combination of linear regression and modified probabilistic neural network model en_US
dc.parent IEEE International Joint Conference on Neural Networks (IJCNN 2003) en_US
dc.journal.volume 4 en_US
dc.journal.number en_US
dc.publocation Piscataway, USA en_US
dc.identifier.startpage 2478 en_US
dc.identifier.endpage 2481 en_US
dc.cauo.name Computer Systems en_US
dc.conference IEEE Joint Conference on Neural Networks en_US
dc.conference.location Portland, USA en_US


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