Integrating recurrent SOM with wavelet-based kernel partial least square regressions for financial forecasting

研究成果: Article同行評審

20 引文 斯高帕斯(Scopus)

摘要

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.

原文English
頁(從 - 到)5698-5705
頁數8
期刊Expert Systems with Applications
37
發行號8
DOIs
出版狀態Published - 2010 八月 1

All Science Journal Classification (ASJC) codes

  • Engineering(all)
  • Computer Science Applications
  • Artificial Intelligence

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