Forecasting Based on High-Order Fuzzy-Fluctuation Trends and Particle Swarm Optimization Machine Learning

Most existing fuzzy forecasting models partition historical training time series into fuzzy time series and build fuzzy-trend logical relationship groups to generate forecasting rules. The determination process of intervals is complex and uncertain. In this paper, we present a novel fuzzy forecasting model based on high-order fuzzy-fluctuation trends and the fuzzy-fluctuation logical relationships of the training time series. Firstly, we compare each piece of data with the data of theprevious day in a historical training time series to generate a new fluctuation trend time series (FTTS). Then, we fuzzify the FTTS into a fuzzy-fluctuation time series (FFTS) according to the up, equal, or down range and orientation of the fluctuations. Since the relationship between historical FFTS and the fluctuation trend of the future is nonlinear, a particle swarm optimization (PSO) algorithm is employed to estimate the proportions for the lagged variables of the fuzzy AR (n) model. Finally, we use the acquired parameters to forecast future fluctuations. In order to compare the performance of the proposed model with that of the other models, we apply the proposed method to forecast the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) time series datasets. The experimental results and the comparison results show that the proposed method can be successfully applied in stock market forecasting or similarkinds of time series. We also apply the proposed method to forecast Shanghai Stock Exchange Composite Index (SHSECI) and DAX30 index to verify its effectiveness and universality.

[1]  Mustafa Mat Deris,et al.  A new linguistic out-sample approach of fuzzy time series for daily forecasting of Malaysian electricity load demand , 2015, Appl. Soft Comput..

[2]  Hongjun Guan,et al.  A computational fuzzy time series forecasting model based on GEM-based discretization and hierarchical fuzzy logical rules , 2016, J. Intell. Fuzzy Syst..

[3]  Ching-Hsue Cheng,et al.  Fuzzy time-series based on adaptive expectation model for TAIEX forecasting , 2008, Expert Syst. Appl..

[4]  Ching-Hsue Cheng,et al.  OWA-based ANFIS model for TAIEX forecasting , 2013 .

[5]  Francisco Herrera,et al.  A model of consensus in group decision making under linguistic assessments , 1996, Fuzzy Sets Syst..

[6]  Mu-Yen Chen,et al.  A hybrid fuzzy time series model based on granular computing for stock price forecasting , 2015, Inf. Sci..

[7]  S. Askari,et al.  A high-order multi-variable Fuzzy Time Series forecasting algorithm based on fuzzy clustering , 2015, Expert Syst. Appl..

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

[9]  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.

[10]  B J Fregly,et al.  Parallel global optimization with the particle swarm algorithm , 2004, International journal for numerical methods in engineering.

[11]  Çagdas Hakan Aladag,et al.  A new approach based on artificial neural networks for high order multivariate fuzzy time series , 2009, Expert Syst. Appl..

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

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

[14]  Enriqueta Vercher,et al.  Improving stock index forecasts by using a new weighted fuzzy-trend time series method , 2017, Expert Syst. Appl..

[15]  Kunhuang Huarng,et al.  Ratio-Based Lengths of Intervals to Improve Fuzzy Time Series Forecasting , 2006, IEEE Trans. Syst. Man Cybern. Part B.

[16]  Ching-Hsue Cheng,et al.  A hybrid ANFIS model based on AR and volatility for TAIEX forecasting , 2011, Appl. Soft Comput..

[17]  Erol Egrioglu,et al.  A new hybrid method for time series forecasting: AR–ANFIS , 2018, Neural Computing and Applications.

[18]  Cem Kocak,et al.  A New High Order Fuzzy ARMA Time Series Forecasting Method by Using Neural Networks to Define Fuzzy Relations , 2015 .

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

[20]  T. Ouarda,et al.  Generalized autoregressive conditional heteroscedasticity modelling of hydrologic time series , 2012 .

[21]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[22]  Cem Kocak,et al.  ARMA(p, q) type high order fuzzy time series forecast method based on fuzzy logic relations , 2017, Appl. Soft Comput..

[23]  Primoz Jelusic,et al.  Discrete Optimization with Fuzzy Constraints , 2017, Symmetry.

[24]  Frederico G. Guimarães,et al.  Short-term load forecasting method based on fuzzy time series, seasonality and long memory process , 2017, Int. J. Approx. Reason..

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

[26]  Martin Stepnicka,et al.  Forecasting seasonal time series with computational intelligence: On recent methods and the potential of their combinations , 2013, Expert Syst. Appl..

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

[28]  Shyi-Ming Chen,et al.  TAIEX Forecasting Using Fuzzy Time Series and Automatically Generated Weights of Multiple Factors , 2012, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[29]  Çagdas Hakan Aladag,et al.  A new approach for determining the length of intervals for fuzzy time series , 2009, Appl. Soft Comput..

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

[31]  Erol Egrioglu,et al.  An ARMA Type Fuzzy Time Series Forecasting Method Based on Particle Swarm Optimization , 2013 .

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

[33]  Çagdas Hakan Aladag,et al.  Forecasting in high order fuzzy times series by using neural networks to define fuzzy relations , 2009, Expert Syst. Appl..

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

[35]  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.

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

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

[38]  Shyi-Ming Chen,et al.  Fuzzy forecasting based on two-factors second-order fuzzy-trend logical relationship groups, similarity measures and PSO techniques , 2017, Inf. Sci..

[39]  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..

[40]  A.J. Conejo,et al.  Day-ahead electricity price forecasting using the wavelet transform and ARIMA models , 2005, IEEE Transactions on Power Systems.

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

[42]  Çagdas Hakan Aladag,et al.  Finding an optimal interval length in high order fuzzy time series , 2010, Expert Syst. Appl..

[43]  Tahseen Ahmed Jilani,et al.  M-Factor High Order Fuzzy Time Series Forecasting for Road Accident Data , 2007, Analysis and Design of Intelligent Systems using Soft Computing Techniques.

[44]  Çagdas Hakan Aladag,et al.  A new approach based on the optimization of the length of intervals in fuzzy time series , 2011, J. Intell. Fuzzy Syst..

[45]  Witold Pedrycz,et al.  Effective intervals determined by information granules to improve forecasting in fuzzy time series , 2013, Expert Syst. Appl..

[46]  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..

[47]  Shiva Raj Singh,et al.  A computational method of forecasting based on high-order fuzzy time series , 2009, Expert Syst. Appl..

[48]  Sanjay Kumar,et al.  Partitions based computational method for high-order fuzzy time series forecasting , 2012, Expert Syst. Appl..