Neural network and interval type-2 fuzzy system for stock price forecasting

Nguyen, T., Khosravi, A., Nahavandi, S. and Creighton, D. 2013, Neural network and interval type-2 fuzzy system for stock price forecasting, in FUZZ-IEEE 2013 : Proceedings of the IEEE International Conference on Fuzzy Systems, IEEE, Piscataway, N.J., pp. 1-8.

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Title Neural network and interval type-2 fuzzy system for stock price forecasting
Author(s) Nguyen, T.ORCID iD for Nguyen, T. orcid.org/0000-0001-9709-1663
Khosravi, A.ORCID iD for Khosravi, A. orcid.org/0000-0001-6927-0744
Nahavandi, S.ORCID iD for Nahavandi, S. orcid.org/0000-0002-0360-5270
Creighton, D.ORCID iD for Creighton, D. orcid.org/0000-0002-9217-1231
Conference name Fuzzy Systems. IEEE International Conference (2013 : Hyderabad, India)
Conference location Hyderabad, India
Conference dates 7-10 Jul. 2013
Title of proceedings FUZZ-IEEE 2013 : Proceedings of the IEEE International Conference on Fuzzy Systems
Editor(s) [Unknown]
Publication date 2013
Conference series IEEE International Conference on Fuzzy Systems
Start page 1
End page 8
Total pages 8
Publisher IEEE
Place of publication Piscataway, N.J.
Keyword(s) interval type-2 fuzzy system
feedforward neural network
k-means clustering
genetic algorithm
input selection
stock price forecasting
Summary Stock price forecast has long been received special attention of investors and financial institutions. As stock prices are changeable over time and increasingly uncertain in modern financial markets, their forecasting becomes more important than ever before. A hybrid approach consisting of two components, a neural network and a fuzzy logic system, is proposed in this paper for stock price prediction. The first component of the hybrid, i.e. a feedforward neural network (FFNN), is used to select inputs that are highly relevant to the dependent variables. An interval type-2 fuzzy logic system (IT2 FLS) is employed as the second component of the hybrid forecasting method. The IT2 FLS’s parameters are initialized through deployment of the k-means clustering method and they are adjusted by the genetic algorithm. Experimental results demonstrate the efficiency of the FFNN input selection approach as it reduces the complexity and increase the accuracy of the forecasting models. In addition, IT2 FLS outperforms the widely used type-1 FLS and FFNN models in stock price forecasting. The combination of the FFNN and the IT2 FLS produces dominant forecasting accuracy compared to employing only the IT2 FLSs without the FFNN input selection.
Language eng
Field of Research 080108 Neural, Evolutionary and Fuzzy Computation
Socio Economic Objective 910299 Microeconomics not elsewhere classified
HERDC Research category E1 Full written paper - refereed
Copyright notice ©2013, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30057151

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