A hybrid multi-order fuzzy time series for forecasting stock markets

This paper proposes a hybrid model based on multi-order fuzzy time series, which employs rough sets theory to mine fuzzy logical relationship from time series and an adaptive expectation model to adjust forecasting results, to improve forecasting accuracy. Two empirical stock markets (TAIEX and NASDAQ) are used as empirical databases to verify the forecasting performance of the proposed model, and two other methodologies, proposed earlier by Chen and Yu, are employed as comparison models. Besides, to compare with conventional statistic method, the partial autocorrelation function and autoregressive models are utilized to estimate the time lags periods within the databases. Based on comparison results, the proposed model can effectively improve the forecasting performance and outperforms the listing models. From the empirical study, the conventional statistic method and the proposed model both have revealed that the estimated time lags for the two empirical databases are one lagged period.

[1]  Vira Chankong,et al.  Rough set-based approach to rule generation and rule induction , 2002, Int. J. Gen. Syst..

[2]  Gwo-Hshiung Tzeng,et al.  Fuzzy ARIMA model for forecasting the foreign exchange market , 2001, Fuzzy Sets Syst..

[3]  A. Buse,et al.  Elements of econometrics , 1972 .

[4]  J. Kacprzyk,et al.  Fuzzy regression analysis , 1992 .

[5]  G. A. Miller.  The magical number seven plus or minus two: some limits on our capacity for processing information. , 1956, Psychological review.

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

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

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

[9]  Gwo-Hshiung Tzeng,et al.  A fuzzy seasonal ARIMA model for forecasting , 2002, Fuzzy Sets Syst..

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

[11]  Shyi-Ming Chen,et al.  Temperature prediction using fuzzy time series , 2000, IEEE Trans. Syst. Man Cybern. Part B.

[12]  Yuming Zhou,et al.  An improved accuracy measure for rough sets , 2005, J. Comput. Syst. Sci..

[13]  Kunhuang Huarng,et al.  Heuristic models of fuzzy time series for forecasting , 2001, Fuzzy Sets Syst..

[14]  H. Tanka.  Fuzzy data analysis by possibilistic linear models , 1987 .

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

[16]  Ching-Hsue Cheng,et al.  Trend-Weighted Fuzzy Time-Series Model for TAIEX Forecasting , 2006, ICONIP.

[17]  Ching-Hsue Cheng,et al.  Fuzzy time-series based on Fibonacci sequence for stock price forecasting , 2007 .

[18]  B. Chissom,et al.  Fuzzy time series and its models , 1993 .

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

[20]  Gwo-Hshiung Tzeng,et al.  Rough Set Theory in analyzing the attributes of combination values for the insurance market , 2007, Expert Syst. Appl..

[21]  M. Beynon,et al.  Variable precision rough set theory and data discretisation: an application to corporate failure prediction , 2001 .

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

[23]  Shyi-Ming Chen,et al.  A New Method to Forecast Enrollments Using Fuzzy Time Series , 2004 .

[24]  Janusz Zalewski,et al.  Rough sets: Theoretical aspects of reasoning about data , 1996 .

[25]  Maurice Kendall,et al.  Time Series , 2009, Encyclopedia of Biometrics.

[26]  Yian-Kui Liu,et al.  The completion of a fuzzy measure and its applications , 2001, Fuzzy Sets Syst..

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

[28]  Shyi-Ming Chen,et al.  FORECASTING ENROLLMENTS BASED ON HIGH-ORDER FUZZY TIME SERIES , 2002, Cybern. Syst..

[29]  K. Huarng,et al.  A Type 2 fuzzy time series model for stock index forecasting , 2005 .

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

[31]  Andrzej Skowron,et al.  Rudiments of rough sets , 2007, Inf. Sci..

[32]  G. A. Miller.  THE PSYCHOLOGICAL REVIEW THE MAGICAL NUMBER SEVEN, PLUS OR MINUS TWO: SOME LIMITS ON OUR CAPACITY FOR PROCESSING INFORMATION 1 , 1956 .

[33]  George J. Klir,et al.  Fuzzy Sets, Fuzzy Logic, and Fuzzy Systems - Selected Papers by Lotfi A Zadeh , 1996, Advances in Fuzzy Systems - Applications and Theory.

[34]  Yi-Shih Chung,et al.  Rough set approach for accident chains exploration. , 2007, Accident; analysis and prevention.

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

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

[37]  Hung T. Nguyen,et al.  Fuzzy time series analysis and forecasting , 2006 .

[38]  Z. Pawlak.  Rough set approach to knowledge-based decision support , 1997 .

[39]  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..

[40]  Jerzy W. Grzymala-Busse,et al.  A New Version of the Rule Induction System LERS , 1997, Fundam. Informaticae.

[41]  Frank Witlox,et al.  The application of rough sets analysis in activity-based modelling. Opportunities and constraints , 2004, Expert Syst. Appl..

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