Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/101547
Citations | ||
Scopus | Web of Science® | Altmetric |
---|---|---|
?
|
?
|
Type: | Journal article |
Title: | The relationship between model complexity and forecasting performance for computer intelligence optimization in finance |
Author: | Ghandar, A. Michalewicz, Z. Zurbruegg, R. |
Citation: | International Journal of Forecasting, 2016; 32(3):598-613 |
Publisher: | Elsevier |
Issue Date: | 2016 |
ISSN: | 0169-2070 1872-8200 |
Statement of Responsibility: | Adam Ghandar, Zbigniew Michalewicz, Ralf Zurbruegg |
Abstract: | Abstract not available |
Keywords: | Financial forecasting; computer intelligence optimization; evolutionary algorithms |
Rights: | © 2016 International Institute of Forecasters. Published by Elsevier B.V. All rights reserved. |
DOI: | 10.1016/j.ijforecast.2015.10.003 |
Grant ID: | http://purl.org/au-research/grants/arc/DP1096053 |
Published version: | http://dx.doi.org/10.1016/j.ijforecast.2015.10.003 |
Appears in Collections: | Aurora harvest 3 Computer Science publications |
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.