Title :
Unsupervised learning for financial forecasting
Author :
Fyfe, Colin ; Lees, Brandon
Abstract :
An unsupervised neural based approach to financial forecasting is presented; its performance is compared with that from a statistical technique and two other standard neural network techniques. The authors show that the unsupervised network outperforms multilayer perceptrons, radial basis function network and a standard ARIMA model
Keywords :
autoregressive moving average processes; feedforward neural nets; financial data processing; multilayer perceptrons; unsupervised learning; financial forecasting; multilayer perceptrons; neural network techniques; performance; radial basis function network; standard ARIMA model; statistical technique; unsupervised learning; unsupervised neural based approach; Artificial neural networks; Computational intelligence; Differential equations; Milling machines; Multilayer perceptrons; Negative feedback; Neural networks; Predictive models; Radial basis function networks; Unsupervised learning;
Conference_Titel :
Computational Intelligence for Financial Engineering (CIFEr), 1998. Proceedings of the IEEE/IAFE/INFORMS 1998 Conference on
Conference_Location :
New York, NY
Print_ISBN :
0-7803-4930-X
DOI :
10.1109/CIFER.1998.690316
