Financial time series analysis using FWNN with robust training algorithm

Yuji Ikutake, Hiromitsu Ohmori

研究成果: Paper査読

抄録

Finanicial market is characterized with complex, stochastic, nonstationary process and the development of effective models for prediction of a stock price is one of the important problems in finance. For analyzing nonlinear time- series, the importance of nonlinear models, such as neural networks (NNs) and fuzzy systems (FSs), has been increasing in recent years. Combining NNs, FSs and wavelets, FuzzyWavelet Neural Network (FWNN) ,which has advantages of each systems, was devised. However, when time-series analysis is actually conducted, these time-series data are influenced by disturbance or noise. So in this paper, we introduce FWNN with robust training algorithm which can guarantee the prediction accuracy to some extent even in such a case.

本文言語English
ページ1199-1204
ページ数6
出版ステータスPublished - 2013 1月 1
イベント2013 52nd Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2013 - Nagoya, Japan
継続期間: 2013 9月 142013 9月 17

Other

Other2013 52nd Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2013
国/地域Japan
CityNagoya
Period13/9/1413/9/17

ASJC Scopus subject areas

  • 制御およびシステム工学
  • コンピュータ サイエンスの応用
  • 電子工学および電気工学

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