View : 127 Download: 0

Time delay neural networks and genetic algorithms for detecting temporal patterns in stock markets

Title
Time delay neural networks and genetic algorithms for detecting temporal patterns in stock markets
Authors
Kim, HJShin, KSPark, K
Ewha Authors
신경식박경도김현정
SCOPUS Author ID
신경식scopus; 김현정scopus
Issue Date
2005
Journal Title
ADVANCES IN NATURAL COMPUTATION, PT 1, PROCEEDINGS
ISSN
0302-9743JCR Link
Citation
vol. 3610, pp. 1247 - 1255
Publisher
SPRINGER-VERLAG BERLIN
Indexed
SCOPUS WOS scopus
Abstract
This study investigates the effectiveness of a hybrid approach with the time delay neural networks (TDNNs) and the genetic algorithms (GAs) in detecting temporal patterns for stock market prediction tasks. Since TDNN is a multi-layer, feed-forward network whose hidden neurons and output neurons are replicated across time, it has one more estimate of time delays in addition to a number of control variables of the artificial neural network (ANN) design. To estimate these many aspects of the TDNN design, a general method based on trial and error along with various heuristics or statistical techniques is proposed. However, for the reason that determining time delays or network architectural factors in a stand-alone mode doesn't guarantee the illuminating improvement of the performance for building the TDNN models, we apply GAs to support optimization of time delays and network architectural factors simultaneously for the TDNN model. The results show that the accuracy of the integrated approach proposed for this study is higher than that of the standard TDNN and the recurrent neural networks (RNNs).
ISBN
3-540-28323-4
Appears in Collections:
경영대학 > 경영학전공 > Journal papers
Files in This Item:
There are no files associated with this item.
Export
RIS (EndNote)
XLS (Excel)
XML


qrcode

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

BROWSE