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 language | English |
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| Pages | 1199-1204 |
| Number of pages | 6 |
| Publication status | Published - 2013 Jan 1 |
| Event | 2013 52nd Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2013 - Nagoya, Japan Duration: 2013 Sept 14 → 2013 Sept 17 |
Other
| Other | 2013 52nd Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2013 |
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| Country/Territory | Japan |
| City | Nagoya |
| Period | 13/9/14 → 13/9/17 |
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