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

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17 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)5698-5705
Number of pages8
JournalExpert Systems with Applications
Volume37
Issue number8
DOIs
Publication statusPublished - 2010 Aug 1

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All Science Journal Classification (ASJC) codes

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

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