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

Tony Jan; Maria Kim

doi:10.1109/IJCNN.2005.1556302

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

Tony Jan, Maria Kim

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Citation (Scopus)

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.

Original language	English
Title of host publication	Proceedings of the International Joint Conference on Neural Networks, IJCNN 2005
Pages	2538-2543
Number of pages	6
DOIs	https://doi.org/10.1109/IJCNN.2005.1556302
Publication status	Published - 2005
Externally published	Yes
Event	International Joint Conference on Neural Networks, IJCNN 2005 - Montreal, QC, Canada Duration: 31 Jul 2005 → 4 Aug 2005

Publication series

Name	Proceedings of the International Joint Conference on Neural Networks
Volume	4

Conference

Conference	International Joint Conference on Neural Networks, IJCNN 2005
Country/Territory	Canada
City	Montreal, QC
Period	31/07/05 → 4/08/05

Access to Document

10.1109/IJCNN.2005.1556302

Cite this

Jan, T., & Kim, M. (2005). Vector quantized radial basis function neural network with embedded multiple local linear models for financial prediction. In Proceedings of the International Joint Conference on Neural Networks, IJCNN 2005 (pp. 2538-2543). Article 1556302 (Proceedings of the International Joint Conference on Neural Networks; Vol. 4). https://doi.org/10.1109/IJCNN.2005.1556302

@inproceedings{cd9c5b3c5bd842a289ac03bb833cb30e,

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

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.",

author = "Tony Jan and Maria Kim",

year = "2005",

doi = "10.1109/IJCNN.2005.1556302",

language = "English",

isbn = "0780390482",

series = "Proceedings of the International Joint Conference on Neural Networks",

pages = "2538--2543",

booktitle = "Proceedings of the International Joint Conference on Neural Networks, IJCNN 2005",

note = "International Joint Conference on Neural Networks, IJCNN 2005 ; Conference date: 31-07-2005 Through 04-08-2005",

}

Jan, T & Kim, M 2005, Vector quantized radial basis function neural network with embedded multiple local linear models for financial prediction. in Proceedings of the International Joint Conference on Neural Networks, IJCNN 2005., 1556302, Proceedings of the International Joint Conference on Neural Networks, vol. 4, pp. 2538-2543, International Joint Conference on Neural Networks, IJCNN 2005, Montreal, QC, Canada, 31/07/05. https://doi.org/10.1109/IJCNN.2005.1556302

Vector quantized radial basis function neural network with embedded multiple local linear models for financial prediction. / Jan, Tony; Kim, Maria.
Proceedings of the International Joint Conference on Neural Networks, IJCNN 2005. 2005. p. 2538-2543 1556302 (Proceedings of the International Joint Conference on Neural Networks; Vol. 4).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Jan, Tony

AU - Kim, Maria

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N2 - 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.

AB - 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.

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DO - 10.1109/IJCNN.2005.1556302

M3 - Conference contribution

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ER -

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

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