Vector quantized radial basis function neural network with embedded multiple local linear models for financial prediction

Jan, T; Kim, M

Vector quantized radial basis function neural network with embedded multiple local linear models for financial prediction

Jan, T Kim, M

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Publication Type:: Conference Proceeding
Citation:: Proceedings of the International Joint Conference on Neural Networks, 2005, 4 pp. 2538 - 2543
Issue Date:: 2005-12-01

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Field	Value	Language
dc.contributor.author	Jan, T	en_US
dc.contributor.author	Kim, M	en_US
dc.date.issued	2005-12-01	en_US
dc.identifier.citation	Proceedings of the International Joint Conference on Neural Networks, 2005, 4 pp. 2538 - 2543	en_US
dc.identifier.isbn	0780390482	en_US
dc.identifier.isbn	9780780390485	en_US
dc.identifier.uri	http://hdl.handle.net/10453/10811
dc.description.abstract	In this paper, a model is proposed which combines multiple local linear models with a novel modified probabilistic neural network (MPNN). The proposed model is developed to approximate multiple nonlinear model with reduced computational requirement The proposed model shows to provide both low bias and variance with reduced computations by utilizing semi parametric local linear approximation and efficient vector quantization of data space. The proposed model is shown to provide comparable performance to other state-of-the-art models in terms of bias, variance and computational requirement in short-term financial prediction. © 2005 IEEE.	en_US
dc.relation.ispartof	Proceedings of the International Joint Conference on Neural Networks	en_US
dc.relation.isbasedon	10.1109/IJCNN.2005.1556302	en_US
dc.title	Vector quantized radial basis function neural network with embedded multiple local linear models for financial prediction	en_US
dc.type	Conference Proceeding
utslib.citation.volume	4	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	Montreal, Canada	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.publication-status	Published	en_US
pubs.volume	4	en_US

Abstract:

In this paper, a model is proposed which combines multiple local linear models with a novel modified probabilistic neural network (MPNN). The proposed model is developed to approximate multiple nonlinear model with reduced computational requirement The proposed model shows to provide both low bias and variance with reduced computations by utilizing semi parametric local linear approximation and efficient vector quantization of data space. The proposed model is shown to provide comparable performance to other state-of-the-art models in terms of bias, variance and computational requirement in short-term financial prediction. © 2005 IEEE.

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http://hdl.handle.net/10453/10811