Financial time series analysis using FWNN with robust training algorithm

Yuji Ikutake, Hiromitsu Ohmori

Research output: Contribution to conferencePaper

Abstract

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.

Original languageEnglish
Pages1199-1204
Number of pages6
Publication statusPublished - 2013 Jan 1
Event2013 52nd Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2013 - Nagoya, Japan
Duration: 2013 Sep 142013 Sep 17

Other

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

Keywords

  • Fuzzy wavelet neural networks
  • Robust training algorithm
  • Time-series analysis

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science Applications
  • Electrical and Electronic Engineering

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  • Cite this

    Ikutake, Y., & Ohmori, H. (2013). Financial time series analysis using FWNN with robust training algorithm. 1199-1204. Paper presented at 2013 52nd Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2013, Nagoya, Japan.