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 :
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