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

Jan, T

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

Jan, T

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Publisher:: IEEE press
Publication Type:: Conference Proceeding
Citation:: IEEE Joint Conference on Neural Networks (IJCNN), 2003, pp. 1 - 4
Issue Date:: 2003-01

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Field	Value	Language
dc.contributor.author	Jan, T	en_US
dc.contributor.editor	Liu, J	en_US
dc.contributor.editor	Cheung, Y	en_US
dc.contributor.editor	Yin, H	en_US
dc.date	2003-07-20	en_US
dc.date.issued	2003-01	en_US
dc.identifier.citation	IEEE Joint Conference on Neural Networks (IJCNN), 2003, pp. 1 - 4	en_US
dc.identifier.isbn	0-7803-7899-7	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.	en_US
dc.publisher	IEEE press	en_US
dc.relation.ispartof	IEEE Joint Conference on Neural Networks (IJCNN)	en_US
dc.relation.ispartof	IEEE International Joint Conference on Neural Networks	en_US
dc.relation.isbasedon	10.1109/IJCNN.2003.1223953	en_US
dc.rights	© 2003 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
dc.title	Robust short term prediction using combination of linear regression and modified probabalistic neural network model	en_US
dc.type	Conference Proceeding
utslib.location	Canada	en_US
utslib.location.activity	Portland, USA	en_US
utslib.for	080108 Neural, Evolutionary and Fuzzy Computation	en_US
utslib.for	080104 Computer Vision	en_US
utslib.for	080109 Pattern Recognition and Data Mining	en_US
utslib.for	080105 Expert Systems	en_US
dc.location.activity	Portland, USA	en_US
pubs.embargo.period	Not known	en_US
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 Electrical and Data Engineering
utslib.copyright.status	closed_access
pubs.consider-herdc	true	en_US
pubs.place-of-publication	Canada	en_US
pubs.start-date	2003-07-20	en_US

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.

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http://hdl.handle.net/10453/3165