Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
386064 | Expert Systems with Applications | 2010 | 8 Pages |
Abstract
This study implements a novel expert system for financial forecasting. In the first stage, wavelet analysis transforms the input space of raw data to a time-scale feature space suitable for financial forecasting, and then a Recurrent Self-Organizing Map (RSOM) algorithm is used for partitioning and storing temporal context of the feature space. In the second stage, multiple kernel partial least square regressors (as local models) that best fit partitioned regions are constructed for final forecasting. Compared with neural networks, pure SVMs or traditional GARCH models, the proposed model performs best. The root-mean-squared forecasting errors are significantly reduced.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Shian-Chang Huang, Tung-Kuang Wu,