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

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dc.contributor.author Jan, T
dc.contributor.editor Liu, J
dc.contributor.editor Cheung, Y
dc.contributor.editor Yin, H
dc.date.accessioned 2009-11-09T05:39:14Z
dc.date.issued 2003-01
dc.identifier.citation IEEE Joint Conference on Neural Networks (IJCNN), 2003, pp. 1 - 4
dc.identifier.isbn 0-7803-7899-7
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 proposed hybrid model achieves superior prediction performance compared to the conventional prediction techniques such as the multilayer perceptron (MLP) or Volterra series based prediction.
dc.publisher IEEE press
dc.relation.isbasedon 10.1109/IJCNN.2003.1223953
dc.title Robust short term prediction using combination of linear regression and modified probabalistic neural network model
dc.type Conference Proceeding
dc.description.version Published
dc.parent IEEE Joint Conference on Neural Networks (IJCNN)
dc.journal.number en_US
dc.publocation Canada en_US
dc.identifier.startpage 1 en_US
dc.identifier.endpage 4 en_US
dc.cauo.name FEIT.School of Computing and Communications en_US
dc.conference Verified OK en_US
dc.conference IEEE International Joint Conference on Neural Networks
dc.conference.location Portland, USA en_US
dc.for 080108 Neural, Evolutionary and Fuzzy Computation
dc.for 080104 Computer Vision
dc.for 080109 Pattern Recognition and Data Mining
dc.for 080105 Expert Systems
dc.personcode 020524
dc.percentage 40 en_US
dc.classification.name Neural, Evolutionary and Fuzzy Computation en_US
dc.classification.type FOR-08 en_US
dc.custom IEEE International Joint Conference on Neural Networks en_US
dc.date.activity 20030720 en_US
dc.date.activity 2003-07-20
dc.location.activity Portland, USA en_US
dc.description.keywords extrapolation financial data processing neural nets probability en_US
dc.description.keywords Science & Technology
dc.description.keywords Technology
dc.description.keywords Computer Science, Software Engineering
dc.description.keywords Computer Science
dc.description.keywords COMPUTER SCIENCE, SOFTWARE ENGINEERING
dc.description.keywords theory
dc.description.keywords verification
dc.description.keywords human factors
dc.description.keywords requirements
dc.description.keywords default logic
dc.description.keywords natural language
dc.description.keywords inconsistency
dc.description.keywords SOFTWARE-DEVELOPMENT
dc.description.keywords LOGIC
dc.description.keywords SPECIFICATIONS
dc.description.keywords MANAGEMENT
dc.description.keywords STATE
dc.description.keywords extrapolation financial data processing neural nets probability
pubs.embargo.period Not known
pubs.organisational-group /University of Technology Sydney
pubs.organisational-group /University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group /University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computing and Communications
utslib.copyright.status Closed Access
utslib.copyright.date 2015-04-15 12:17:09.805752+10
utslib.collection.history Closed (ID: 3)
utslib.collection.history School of Computing and Communications (ID: 335)


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