Title :
Financial prediction using higher order trigonometric polynomial neural network group model
Author :
Zhang, Jing Chun ; Zhang, Ming ; Fulcher, John
Author_Institution :
Dept. of Comput. & Inf. Syst., Western Sydney Macarthur Univ., Campbelltown, NSW, Australia
Abstract :
A higher order trigonometric polynomial neural network group model (HTG) which can be used for financial prediction is discussed in this paper. HTG is written in C, incorporates a user-friendly graphical user interface, and runs under XWindows on a Sun workstation. The experimental results show that HTG is able to handle higher frequency, higher order nonlinear and discontinuous data. The accuracy of HTG is around 5% to 10% better than conventional trigonometric polynomial neural network models
Keywords :
financial data processing; forecasting theory; graphical user interfaces; neural nets; statistical analysis; XWindows; financial prediction; graphical user interface; higher order trigonometric polynomial neural network group model; Econometrics; Economic forecasting; Electronic mail; Frequency; Multi-layer neural network; Neural networks; Nonlinear equations; Polynomials; Predictive models; Sampling methods;
Conference_Titel :
Neural Networks,1997., International Conference on
Conference_Location :
Houston, TX
Print_ISBN :
0-7803-4122-8
DOI :
10.1109/ICNN.1997.614373
