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

Research output: Contribution to journalArticle

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

Fingerprint

Wavelet analysis
Self organizing maps
Expert systems
Neural networks

All Science Journal Classification (ASJC) codes

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

Cite this

@article{fdcab96f403e4e0c8160304da21cdc3f,
title = "Integrating recurrent SOM with wavelet-based kernel partial least square regressions for financial forecasting",
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.",
author = "Shian-Chang Huang and Tung-Kuang Wu",
year = "2010",
month = "8",
day = "1",
doi = "10.1016/j.eswa.2010.02.040",
language = "English",
volume = "37",
pages = "5698--5705",
journal = "Expert Systems with Applications",
issn = "0957-4174",
publisher = "Elsevier Limited",
number = "8",

}

TY - JOUR

T1 - Integrating recurrent SOM with wavelet-based kernel partial least square regressions for financial forecasting

AU - Huang, Shian-Chang

AU - Wu, Tung-Kuang

PY - 2010/8/1

Y1 - 2010/8/1

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=77951204216&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=77951204216&partnerID=8YFLogxK

U2 - 10.1016/j.eswa.2010.02.040

DO - 10.1016/j.eswa.2010.02.040

M3 - Article

AN - SCOPUS:77951204216

VL - 37

SP - 5698

EP - 5705

JO - Expert Systems with Applications

JF - Expert Systems with Applications

SN - 0957-4174

IS - 8

ER -