Title :
Financial prediction using modified probabilistic learning network with embedded local linear models
Author :
Jan, Tony ; Yu, Ting ; Debenham, John ; Simoff, Simeon
Author_Institution :
Fac. of Inf. Technol., Univ. of Technol., Sydney, NSW, Australia
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.
Keywords :
finance; learning (artificial intelligence); multilayer perceptrons; prediction theory; probability; radial basis function networks; vector quantisation; embedded local linear models; financial prediction; model complexity; neural network; piecewise linear model; probabilistic learning network; semiparametric model; vector quantization; Australia; Covariance matrix; Electronic mail; Information technology; Kernel; Mathematical model; Neural networks; Piecewise linear techniques; Predictive models; Smoothing methods;
Conference_Titel :
Computational Intelligence for Measurement Systems and Applications, 2004. CIMSA. 2004 IEEE International Conference on
Print_ISBN :
0-7803-8341-9
DOI :
10.1109/CIMSA.2004.1397236
