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
Link To Document