DocumentCode
3301792
Title
Unsupervised learning for financial forecasting
Author
Fyfe, Colin ; Lees, Brandon
fYear
1998
fDate
29-31 Mar 1998
Firstpage
259
Lastpage
263
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;
fLanguage
English
Publisher
ieee
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
Type
conf
DOI
10.1109/CIFER.1998.690316
Filename
690316
Link To Document