Forecasting Stock Exchange Market Using Hybrid Neuro Fuzzy Model

This paper proposes a hybrid approach based on neuro fuzzy model and emotional learning for prediction of stock exchange market. Neuro fuzzy models are powerful in modeling and forecasting highly nonlinear and complex time series. The emotional Learning, which is successfully used in bounded rational decision making, is introduced as an appropriate method to achieve particular goals in the prediction of real world data. The emotional learning based fuzzy inference system (ELFIS) has the advantages of simplicity and low computational complexity in comparison with other multi-objective optimization methods, that eliminates noisy variations of the original time series and results in a well-behaved series which can be predicted with higher accuracy. The proposed hybrid method is applied to prediction of stock exchange index used in literature. The prediction results and comparison to optimized multi-layer perceptron (MLP) models, revealed the promising performance of the proposed approach for stock exchange indexes prediction and its potential usage for real world applications.