Two-factor high-order fuzzy-trend FTS model based on BSO-FCM and improved KA for TAIEX stock forecasting

Fuzzy time series has been an effective and attractive forecasting model for solving the problem of stock index forecasting. In particular, fuzzy-trend fuzzy time series models have been proposed recently to address complex cases and perform well in terms of forecasting accuracy. Nonetheless, they have just explored the two-factor second-order forecasting but cannot satisfy the complex stock system. In this study, we proposed a new two-factor high-order fuzzy-trend fuzzy time series model to explore the more complex situation on the TAIEX stock index forecasting. We presented the backtracking search optimization-fuzzy c-means method to obtain the optimal intervals of the data sets. In addition, an improved kidney-inspired algorithm is employed to integrate the high-order forecasting values. The proposed model shows outstanding forecasting accuracy than the benchmark methods on the TAIEX. Besides, we combined two other stock indexes (NASDAQ and the Dow Jones) as the secondary factors, respectively. It provides a useful method for two-factor high-order fuzzy-trend fuzzy time series stock index forecasting.

[1]  Aniruddha Bhattacharya,et al.  Backtracking search optimization based economic environmental power dispatch problems , 2015 .

[2]  Shyi-Ming Chen,et al.  Fuzzy Forecasting Based on Two-Factors Second-Order Fuzzy-Trend Logical Relationship Groups and the Probabilities of Trends of Fuzzy Logical Relationships , 2015, IEEE Transactions on Cybernetics.

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

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

[5]  Hussain Shareef,et al.  An application of backtracking search algorithm in designing power system stabilizers for large multi-machine system , 2017, Neurocomputing.

[6]  Pinar Çivicioglu,et al.  Backtracking Search Optimization Algorithm for numerical optimization problems , 2013, Appl. Math. Comput..

[7]  Shyi-Ming Chen,et al.  Fuzzy time series forecasting based on optimal partitions of intervals and optimal weighting vectors , 2017, Knowl. Based Syst..

[8]  Çagdas Hakan Aladag,et al.  A Novel Stochastic Seasonal Fuzzy Time Series Forecasting Model , 2017, International Journal of Fuzzy Systems.

[9]  Li Li,et al.  Application of SVR with backtracking search algorithm for long-term load forecasting , 2016, J. Intell. Fuzzy Syst..

[10]  Shyi-Ming Chen,et al.  Fuzzy time series forecasting based on fuzzy logical relationships and similarity measures , 2016, Inf. Sci..

[11]  Hamido Fujita,et al.  A novel forecasting method based on multi-order fuzzy time series and technical analysis , 2016, Inf. Sci..

[12]  James C. Bezdek,et al.  Pattern Recognition with Fuzzy Objective Function Algorithms , 1981, Advanced Applications in Pattern Recognition.

[13]  Mehdi Khashei,et al.  A least squares-based parallel hybridization of statistical and intelligent models for time series forecasting , 2018, Comput. Ind. Eng..

[14]  Shuai Zhang,et al.  A novel method for MCDM and evaluation of manufacturing services using collaborative filtering and IVIF theory , 2016 .

[15]  Adel M. Alimi,et al.  PSO-based analysis of Echo State Network parameters for time series forecasting , 2017, Appl. Soft Comput..

[16]  Pengjian Shang,et al.  Mutual-information matrix analysis for nonlinear interactions of multivariate time series , 2017 .

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

[18]  Meng Joo Er,et al.  Tracking-Error-Based Universal Adaptive Fuzzy Control for Output Tracking of Nonlinear Systems with Completely Unknown Dynamics , 2018, IEEE Transactions on Fuzzy Systems.

[19]  Yang Fu,et al.  Short-term wind power forecasts by a synthetical similar time series data mining method , 2018 .

[20]  Jianzhou Wang,et al.  Short-term load forecasting using a kernel-based support vector regression combination model , 2014 .

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

[22]  Stephen C. H. Leung,et al.  A new fuzzy time series forecasting model combined with ant colony optimization and auto-regression , 2015, Knowl. Based Syst..

[23]  Salwani Abdullah,et al.  Kidney-inspired algorithm for optimization problems , 2017, Commun. Nonlinear Sci. Numer. Simul..

[24]  Pengjian Shang,et al.  Weighted multifractal analysis of financial time series , 2017 .

[25]  Shuai Zhang,et al.  A multi-factor and high-order stock forecast model based on Type-2 FTS using cuckoo search and self-adaptive harmony search , 2017, Neurocomputing.

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

[27]  Yi Liang,et al.  Assessment Analysis and Forecasting for Security Early Warning of Energy Consumption Carbon Emissions in Hebei Province, China , 2017 .

[28]  Kalyani Mali,et al.  A novel data partitioning and rule selection technique for modeling high-order fuzzy time series , 2018, Appl. Soft Comput..

[29]  Shyi-Ming Chen,et al.  Multi-variable fuzzy forecasting based on fuzzy clustering and fuzzy rule interpolation techniques , 2010, Inf. Sci..

[30]  You-Shyang Chen,et al.  Modeling fitting-function-based fuzzy time series patterns for evolving stock index forecasting , 2014, Applied Intelligence.

[31]  Zhigang Zeng,et al.  A novel echo state network for multivariate and nonlinear time series prediction , 2018, Appl. Soft Comput..

[32]  Graham Kendall,et al.  Optimization of fed-batch fermentation processes using the Backtracking Search Algorithm , 2018, Expert Syst. Appl..

[33]  Shyi-Ming Chen,et al.  TAIEX forecasting based on fuzzy time series, particle swarm optimization techniques and support vector machines , 2013, Inf. Sci..

[34]  Taher Niknam,et al.  Probabilistic Load Forecasting Using an Improved Wavelet Neural Network Trained by Generalized Extreme Learning Machine , 2018, IEEE Transactions on Smart Grid.

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

[36]  Jeng-Shyang Pan,et al.  Fuzzy Forecasting Based on Two-Factors Second-Order Fuzzy-Trend Logical Relationship Groups and Particle Swarm Optimization Techniques , 2013, IEEE Transactions on Cybernetics.

[37]  Lotfi A. Zadeh,et al.  Fuzzy Sets , 1996, Inf. Control..

[38]  Salwani Abdullah,et al.  Optimization of neural network using kidney-inspired algorithm with control of filtration rate and chaotic map for real-world rainfall forecasting , 2018, Eng. Appl. Artif. Intell..

[39]  Elpiniki I. Papageorgiou,et al.  A two-stage model for time series prediction based on fuzzy cognitive maps and neural networks , 2017, Neurocomputing.