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

conference contribution
posted on 2013-01-01, 00:00 authored by Thanh Thi NguyenThanh Thi Nguyen, Abbas KhosraviAbbas Khosravi, Saeid Nahavandi, Douglas CreightonDouglas Creighton
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.



Fuzzy Systems. IEEE International Conference (2013 : Hyderabad, India)


1 - 8




Hyderabad, India

Place of publication

Piscataway, N.J.

Start date


End date




Publication classification

E1 Full written paper - refereed

Copyright notice

2013, IEEE

Title of proceedings

FUZZ-IEEE 2013 : Proceedings of the IEEE International Conference on Fuzzy Systems