Application of type-2 neuro-fuzzy modeling in stock price prediction

We present an application of type-2 neuro-fuzzy modeling to stock price prediction based on a given set of training data. Type-2 fuzzy rules can be generated automatically by a self-constructing clustering method and the obtained type-2 fuzzy rules cab be refined by a hybrid learning algorithm. The given training data set is partitioned into clusters through input-similarity and output-similarity tests, and a type-2 TSK rule is derived from each cluster to form a fuzzy rule base. Then the antecedent and consequent parameters associated with the rules are refined by particle swarm optimization and least squares estimation. Experimental results, obtained by running on several datasets taken from TAIEX and NASDAQ, demonstrate the effectiveness of the type-2 neuro-fuzzy modeling approach in stock price prediction.

[1]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[2]  C. H. Chen,et al.  An intelligent stock trading decision support system through integration of genetic algorithm based fuzzy neural network and artificial neural network , 2001, Fuzzy Sets Syst..

[3]  Johan A. K. Suykens,et al.  Financial time series prediction using least squares support vector machines within the evidence framework , 2001, IEEE Trans. Neural Networks.

[4]  Jerry M. Mendel,et al.  Enhanced Karnik--Mendel Algorithms , 2009, IEEE Transactions on Fuzzy Systems.

[5]  Guido Deboeck,et al.  Trading on the Edge: Neural, Genetic, and Fuzzy Systems for Chaotic Financial Markets , 1994 .

[6]  Mehdi Khashei,et al.  A novel hybridization of artificial neural networks and ARIMA models for time series forecasting , 2011, Appl. Soft Comput..

[7]  Richard Weber,et al.  A model updating strategy for predicting time series with seasonal patterns , 2010, Appl. Soft Comput..

[8]  Mohammad Hossein Fazel Zarandi,et al.  A Type-2 Fuzzy Model for Stock Market Analysis , 2007, 2007 IEEE International Fuzzy Systems Conference.

[9]  Lotfi A. Zadeh,et al.  The Concepts of a Linguistic Variable and its Application to Approximate Reasoning , 1975 .

[10]  Robert Ivor John,et al.  Neuro-fuzzy clustering of radiographic tibia image data using type 2 fuzzy sets , 2000, Inf. Sci..

[11]  Kun-Huang Huarng,et al.  A Multivariate Heuristic Model for Fuzzy Time-Series Forecasting , 2007, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[12]  Francis Eng Hock Tay,et al.  Support vector machine with adaptive parameters in financial time series forecasting , 2003, IEEE Trans. Neural Networks.

[13]  P. Young,et al.  Time series analysis, forecasting and control , 1972, IEEE Transactions on Automatic Control.

[14]  Tugrul U. Daim,et al.  Using artificial neural network models in stock market index prediction , 2011, Expert Syst. Appl..

[15]  V. Kvasnicka,et al.  Neural and Adaptive Systems: Fundamentals Through Simulations , 2001, IEEE Trans. Neural Networks.

[16]  Shie-Jue Lee,et al.  A multiple-kernel support vector regression approach for stock market price forecasting , 2011, Expert Syst. Appl..

[17]  C. Siriopoulos,et al.  Time series forecasting with a hybrid clustering scheme and pattern recognition , 2004, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

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

[19]  Mohammad Hossein Fazel Zarandi,et al.  A type-2 fuzzy rule-based expert system model for stock price analysis , 2009, Expert Syst. Appl..

[20]  Shie-Jue Lee,et al.  A neuro-fuzzy system modeling with self-constructing rule generationand hybrid SVD-based learning , 2003, IEEE Trans. Fuzzy Syst..

[21]  Nematollaah Shiri,et al.  Incremental Relational Fuzzy Subtractive Clustering for Dynamic Web Usage Profiling , 2005 .

[22]  Richard Weber,et al.  Improved supply chain management based on hybrid demand forecasts , 2007, Appl. Soft Comput..

[23]  Smriti Srivastava,et al.  Type-2 fuzzy wavelet networks (T2FWN) for system identification using fuzzy differential and Lyapunov stability algorithm , 2009, Appl. Soft Comput..

[24]  Gwilym M. Jenkins,et al.  Time series analysis, forecasting and control , 1971 .

[25]  D. G. Champernowne,et al.  Sampling Theory Applied to Autoregressive Sequences , 1948 .

[26]  Byung Ro Moon,et al.  A Hybrid Neurogenetic Approach for Stock Forecasting , 2007, IEEE Transactions on Neural Networks.

[27]  Oscar Castillo,et al.  Interval type-2 fuzzy logic and modular neural networks for face recognition applications , 2009, Appl. Soft Comput..

[28]  Y. Leung.  Fuzzy Set and Fuzzy Logic , 2009 .

[29]  Shie-Jue Lee,et al.  An Enhanced Type-Reduction Algorithm for Type-2 Fuzzy Sets , 2011, IEEE Transactions on Fuzzy Systems.

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

[31]  Lotfi A. Zadeh,et al.  The concept of a linguistic variable and its application to approximate reasoning-III , 1975, Inf. Sci..

[32]  Vikas Mehrotra,et al.  Competition and Market Structure of National Association of Securities Dealers Automated Quotations , 2007 .

[33]  Wei-Chang Yeh,et al.  Forecasting stock markets using wavelet transforms and recurrent neural networks: An integrated system based on artificial bee colony algorithm , 2011, Appl. Soft Comput..

[34]  Fangwen Zhai,et al.  Hybrid forecasting model research on stock data mining , 2010, 4th International Conference on New Trends in Information Science and Service Science.

[35]  Manoochehr Ghiassi,et al.  A dynamic architecture for artificial neural networks , 2005, Neurocomputing.

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

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

[38]  Shyi-Ming Chen,et al.  TAIEX Forecasting Based on Fuzzy Time Series and Fuzzy Variation Groups , 2011, IEEE Transactions on Fuzzy Systems.

[39]  Jose C. Principe,et al.  Neural and Adaptive Systems: Fundamentals through Simulations with CD-ROM , 1999 .

[40]  Nikolaos Kourentzes,et al.  Feature selection for time series prediction - A combined filter and wrapper approach for neural networks , 2010, Neurocomputing.

[41]  Youngohc Yoon,et al.  A Comparison of Discriminant Analysis versus Artificial Neural Networks , 1993 .

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

[43]  Mohammad Hossein Fazel Zarandi,et al.  Systematic image processing for diagnosing brain tumors: A Type-II fuzzy expert system approach , 2011, Appl. Soft Comput..

[44]  Hani Hagras,et al.  zSlices based general type-2 FLC for the control of autonomous mobile robots in real world environments , 2009, 2009 IEEE International Conference on Fuzzy Systems.

[45]  Dhanesh Ramachandram,et al.  Dynamic fuzzy clustering using Harmony Search with application to image segmentation , 2009, 2009 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT).

[46]  Kyung-shik Shin,et al.  A hybrid approach based on neural networks and genetic algorithms for detecting temporal patterns in stock markets , 2007, Appl. Soft Comput..

[47]  Valeriy V. Gavrishchaka,et al.  Support Vector Machine as an Efficient Framework for Stock Market Volatility Forecasting , 2006, Comput. Manag. Sci..

[48]  Pei-Chann Chang,et al.  A TSK type fuzzy rule based system for stock price prediction , 2008, Expert Syst. Appl..

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

[50]  Reza Monsefi,et al.  A New Hybrid Recommender System Using Dynamic Fuzzy Clustering , 2007 .

[51]  J. Mendel.  Uncertain Rule-Based Fuzzy Logic Systems: Introduction and New Directions , 2001 .

[52]  Feilong Liu,et al.  An efficient centroid type-reduction strategy for general type-2 fuzzy logic system , 2008, Inf. Sci..

[53]  Jerry M. Mendel,et al.  Centroid of a type-2 fuzzy set , 2001, Inf. Sci..