A hybrid ANFIS model based on AR and volatility for TAIEX forecasting

[1]  Kun-Huang Huarng,et al.  A bivariate fuzzy time series model to forecast the TAIEX , 2008, Expert Syst. Appl..

[2]  Ching-Hsue Cheng,et al.  High-order fuzzy time-series based on multi-period adaptation model for forecasting stock markets , 2008 .

[3]  Engin Avci,et al.  Comparison of wavelet families for texture classification by using wavelet packet entropy adaptive network based fuzzy inference system , 2008, Appl. Soft Comput..

[4]  Chitralekha Mahanta,et al.  A novel approach for ANFIS modelling based on full factorial design , 2008, Appl. Soft Comput..

[5]  Mieko Tanaka-Yamawaki,et al.  Adaptive use of technical indicators for the prediction of intra-day stock prices , 2007 .

[6]  Elif Derya Übeyli,et al.  Adaptive neuro-fuzzy inference system to compute quasi-TEM characteristic parameters of microshield lines with practical cavity sidewall profiles , 2006, Neurocomputing.

[7]  Tae Hyup Roh,et al.  Forecasting the volatility of stock price index , 2006, Expert Syst. Appl..

[8]  Ingoo Han,et al.  An evolutionary approach to the combination of multiple classifiers to predict a stock price index , 2006, Expert Syst. Appl..

[9]  Ching-Hsue Cheng,et al.  Entropy-based and trapezoid fuzzification-based fuzzy time series approaches for forecasting IT project cost , 2006 .

[10]  Kun-Huang Huarng,et al.  The application of neural networks to forecast fuzzy time series , 2006 .

[11]  Shyi-Ming Chen,et al.  Forecasting enrollments using high-order fuzzy time series and genetic algorithms: Research Articles , 2006 .

[12]  Shyi-Ming Chen,et al.  Forecasting enrollments using high‐order fuzzy time series and genetic algorithms , 2006, Int. J. Intell. Syst..

[13]  Tao Xiong,et al.  A combined SVM and LDA approach for classification , 2005, Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005..

[14]  Hui-Kuang Yu.  Weighted fuzzy time series models for TAIEX forecasting , 2005 .

[15]  R. Shiller.  From Efficient Market Theory to Behavioral Finance , 2002 .

[16]  Kunhuang Huarng,et al.  Effective lengths of intervals to improve forecasting in fuzzy time series , 2001, Fuzzy Sets Syst..

[17]  Ingoo Han,et al.  Genetic algorithms approach to feature discretization in artificial neural networks for the prediction of stock price index , 2000 .

[18]  Shyi-Ming Chen,et al.  Forecasting enrollments based on fuzzy time series , 1996, Fuzzy Sets Syst..

[19]  Chris Nikolopoulos,et al.  A hybrid expert system for investment advising , 1994, Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94).

[20]  G. Soros.  The Alchemy of Finance , 1994 .

[21]  Stephen L. Chiu,et al.  Fuzzy Model Identification Based on Cluster Estimation , 1994, J. Intell. Fuzzy Syst..

[22]  Jyh-Shing Roger Jang,et al.  ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..

[23]  B. Chissom,et al.  Forecasting enrollments with fuzzy time series—part II , 1993 .

[24]  Kazuo Asakawa,et al.  Stock market prediction system with modular neural networks , 1990, 1990 IJCNN International Joint Conference on Neural Networks.

[25]  T. Bollerslev,et al.  Generalized autoregressive conditional heteroskedasticity , 1986 .

[26]  M. Sugeno,et al.  Derivation of Fuzzy Control Rules from Human Operator's Control Actions , 1983 .

[27]  R. Engle.  Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation , 1982 .

[28]  Michael D. Geurts,et al.  Time Series Analysis: Forecasting and Control , 1977 .