Financial prediction using modified probabilistic learning network with embedded local linear models

Tony Jan, Ting Yu, John Debenham, Simeon Simoff

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-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 shown to provide improved regularization with reduced computation utilizing semiparametric model approach and efficient vector quantization of data space. In this paper, the proposed model is shown to generalize better with reduced variance and model complexity in short-term financial prediction application.

Original languageEnglish
Title of host publication2004 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications, CIMSA
Pages81-84
Number of pages4
Publication statusPublished - 2004
Externally publishedYes
Event2004 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications, CIMSA - Boston, MA, United States
Duration: 14 Jul 200416 Jul 2004

Publication series

Name2004 IEEE International Conference on Computational Intelligence for Measurements Systems and Applications, CIMSA

Conference

Conference2004 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications, CIMSA
Country/TerritoryUnited States
CityBoston, MA
Period14/07/0416/07/04

Keywords

  • Financial prediction
  • Piecewise linear models
  • Probabilistic neural networks

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