Adaptive filter theory (3rd ed.)
Adaptive filter theory (3rd ed.)
Non-linear modelling and forecasting of S&P 500 volatility
Mathematics and Computers in Simulation - Selected papers of the MSSANZ/IMACS 13th biennial conference on modelling and simulation, Hamilton, New Zealand, December 1999
A hybrid genetic-neural architecture for stock indexes forecasting
Information Sciences: an International Journal - Special issue: Computational intelligence in economics and finance
An adaptive recurrent fuzzy system for nonlinear identification
Applied Soft Computing
A steganographic method based upon JPEG and particle swarm optimization algorithm
Information Sciences: an International Journal
A fuzzy logic-based computational recognition-primed decision model
Information Sciences: an International Journal
Adaptive signal processing of asset price dynamics with predictability analysis
Information Sciences: an International Journal
Hybridization of intelligent techniques and ARIMA models for time series prediction
Fuzzy Sets and Systems
Information Sciences: an International Journal
A new point symmetry based fuzzy genetic clustering technique for automatic evolution of clusters
Information Sciences: an International Journal
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Toward a generalized theory of uncertainty (GTU)--an outline
Information Sciences: an International Journal
Free Search-a comparative analysis
Information Sciences: an International Journal
A study of particle swarm optimization particle trajectories
Information Sciences: an International Journal
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
New Chaotic PSO-Based Neural Network Predictive Control for Nonlinear Process
IEEE Transactions on Neural Networks
Information Sciences: an International Journal
Information Sciences: an International Journal
Hybrid Kansei-SOM model using risk management and company assessment for stock trading
Information Sciences: an International Journal
Information Sciences: an International Journal
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Fluctuations in the stock market follow the principle of volatility clustering in which changes are cataloged by similarity; as such, large changes tend to follow large changes, and small changes tend to follow small changes. This clustering is one of the major reasons why many generalized autoregression conditional heteroscedasticity (GARCH) models do not forecast the stock market well. In this paper, an adaptive Fuzzy-GARCH model with particle swarm optimization (PSO) is proposed to solve this problem. The adaptive Fuzzy-GARCH model refers to both GARCH models and the parameters of membership functions, which are determined by the characteristics of market itself. Here, we present an iterative algorithm based on PSO to estimate the parameters of the membership functions. The PSO method aims to achieve a global optimal solution with a rapid convergence rate. The three stock markets of Taiwan, Japan, and Germany were analyzed to illustrate the performance of the proposed method.