• DocumentCode
    1927770
  • Title

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

  • Author

    Jan, Tony

  • Author_Institution
    Fac. of Inf. Technol., Univ. of Technol., Sydney, NSW, Australia
  • Volume
    4
  • fYear
    2003
  • fDate
    20-24 July 2003
  • Firstpage
    2478
  • 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.
  • Keywords
    extrapolation; financial data processing; neural nets; probability; artificial neural network; extrapolation; modified probabilistic neural network model; piecewise linear regression model; robust short term prediction; Artificial neural networks; Extrapolation; Linear regression; Multilayer perceptrons; Neural networks; Portfolios; Predictive models; Robustness; Training data; Vectors;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks, 2003. Proceedings of the International Joint Conference on
  • ISSN
    1098-7576
  • Print_ISBN
    0-7803-7898-9
  • Type

    conf

  • DOI
    10.1109/IJCNN.2003.1223953
  • Filename
    1223953