Forecasting stock index price based on M-factors fuzzy time series and particle swarm optimization

In real time, one observation always relies on several observations. To improve the forecasting accuracy, all these observations can be incorporated in forecasting models. Therefore, in this study, we have intended to introduce a new Type-2 fuzzy time series model that can utilize more observations in forecasting. Later, this Type-2 model is enhanced by employing particle swarm optimization (PSO) technique. The main motive behind the utilization of the PSO with the Type-2 model is to adjust the lengths of intervals in the universe of discourse that are employed in forecasting, without increasing the number of intervals. The daily stock index price data set of SBI (State Bank of India) is used to evaluate the performance of the proposed model. The proposed model is also validated by forecasting the daily stock index price of Google. Our experimental results demonstrate the effectiveness and robustness of the proposed model in comparison with existing fuzzy time series models and conventional time series models.

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

[2]  Ting-Yu Chen.  A signed-distance-based approach to importance assessment and multi-criteria group decision analysis based on interval type-2 fuzzy set , 2012, Knowledge and Information Systems.

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

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

[5]  Arianna Mencattini,et al.  Short Term Local Meteorological Forecasting Using Type-2 Fuzzy Systems , 2005, WIRN/NAIS.

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

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

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

[9]  Ioan Cristian Trelea,et al.  The particle swarm optimization algorithm: convergence analysis and parameter selection , 2003, Inf. Process. Lett..

[10]  Vahid Majazi Dalfard,et al.  Efficiency appraisal and ranking of decision-making units using data envelopment analysis in fuzzy environment: a case study of Tehran stock exchange , 2012, Neural Computing and Applications.

[11]  Jui-Lin Lai,et al.  An efficient bi-objective personnel assignment algorithm based on a hybrid particle swarm optimization model , 2010, Expert Syst. Appl..

[12]  Bhogeswar Borah,et al.  Indian summer monsoon rainfall prediction using artificial neural network , 2013, Stochastic Environmental Research and Risk Assessment.

[13]  Robert John,et al.  Tuning of Type-2 Fuzzy Systems by Simulated A nnealing to Predict Time Series , 2011 .

[14]  Hao-Tien Liu,et al.  An improved fuzzy forecasting method for seasonal time series , 2010, Expert Syst. Appl..

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

[16]  Hsuan-Shih Lee,et al.  Fuzzy forecasting based on fuzzy time series , 2004, Int. J. Comput. Math..

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

[18]  Mohammad Hassan Moradi,et al.  A 2uFunction representation for non-uniform type-2 fuzzy sets: Theory and design , 2013, Int. J. Approx. Reason..

[19]  Yuh-Min Chen,et al.  Knowledge evolution course discovery in a professional virtual community , 2012, Knowl. Based Syst..

[20]  C. B. Tilanus,et al.  Applied Economic Forecasting , 1966 .

[21]  Xiaodong Liu,et al.  A generalized method for forecasting based on fuzzy time series , 2011, Expert Syst. Appl..

[22]  Shi-Jinn Horng,et al.  An efficient job-shop scheduling algorithm based on particle swarm optimization , 2010, Expert Syst. Appl..

[23]  Çagdas Hakan Aladag,et al.  Using multiplicative neuron model to establish fuzzy logic relationships , 2013, Expert Syst. Appl..

[24]  Pritpal Singh,et al.  High-order fuzzy-neuro expert system for time series forecasting , 2013, Knowl. Based Syst..

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

[26]  Engin Yesil,et al.  Exact inversion of decomposable interval type-2 fuzzy logic systems , 2013, Int. J. Approx. Reason..

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

[28]  Çagdas Hakan Aladag,et al.  A high order fuzzy time series forecasting model based on adaptive expectation and artificial neural networks , 2010, Math. Comput. Simul..

[29]  Wai Keung Wong,et al.  A heuristic time-invariant model for fuzzy time series forecasting , 2011, Expert Syst. Appl..

[30]  Shi-Jinn Horng,et al.  Temperature prediction and TAIFEX forecasting based on fuzzy relationships and MTPSO techniques , 2010, Expert Syst. Appl..

[31]  Wai Keung Wong,et al.  Adaptive Time-Variant Models for Fuzzy-Time-Series Forecasting , 2010, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

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

[33]  Myung-Geun Chun,et al.  TAIFEX and KOSPI 200 forecasting based on two-factors high-order fuzzy time series and particle swarm optimization , 2010, Expert Syst. Appl..

[34]  Jing-Wei Liu,et al.  Weighted Fuzzy Time Series Forecasting Model , 2010, ACIIDS.

[35]  Shi-Jinn Horng,et al.  Forecasting TAIFEX based on fuzzy time series and particle swarm optimization , 2010, Expert Syst. Appl..

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

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

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

[39]  T. Ross.  Fuzzy Logic with Engineering Applications , 1994 .

[40]  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).

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

[42]  Melike Sah,et al.  Forecasting Enrollment Model Based on First-Order Fuzzy Time Series , 2004, International Conference on Computational Intelligence.

[43]  Pritpal Singh,et al.  An effective neural network and fuzzy time series-based hybridized model to handle forecasting problems of two factors , 2013, Knowledge and Information Systems.

[44]  Ricardo Lüders,et al.  PSO with path relinking for resource allocation using simulation optimization , 2013, Comput. Ind. Eng..

[45]  Shyi-Ming Chen,et al.  Multivariate fuzzy forecasting based on fuzzy time series and automatic clustering techniques , 2011, Expert Syst. Appl..

[46]  Russell C. Eberhart,et al.  A new optimizer using particle swarm theory , 1995, MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science.

[47]  Adel Belouchrani,et al.  Identification and analysis of wind speed patterns extracted from multi-sensors measurements , 2012, Stochastic Environmental Research and Risk Assessment.

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

[49]  Chi Kai,et al.  Notice of Retraction A Novel Forecasting Model of Fuzzy Time Series Based on K-means Clustering , 2010 .

[50]  Pingzhi Fan,et al.  A hybrid forecasting model for enrollments based on aggregated fuzzy time series and particle swarm optimization , 2011, Expert Syst. Appl..

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

[52]  Jerry M. Mendel,et al.  Applications of Type-2 Fuzzy Logic Systems to Forecasting of Time-series , 1999, Inf. Sci..

[53]  R. John,et al.  Tuning fuzzy systems by simulated annealing to predict time series with added noise , 2010, 2010 UK Workshop on Computational Intelligence (UKCI).

[54]  Yi Pan,et al.  An improved method for forecasting enrollments based on fuzzy time series and particle swarm optimization , 2009, Expert Syst. Appl..

[55]  Vipul Jain,et al.  A grey approach for forecasting in a supply chain during intermittentdisruptions , 2013, Eng. Appl. Artif. Intell..

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

[57]  Liang-Ying Wei,et al.  A GA-weighted ANFIS model based on multiple stock market volatility causality for TAIEX forecasting , 2013, Appl. Soft Comput..

[58]  Shyi-Ming Chen,et al.  Handling forecasting problems using fuzzy time series , 1998, Fuzzy Sets Syst..

[59]  S. Bertazzoni,et al.  Local meteorological forecasting by type-2 fuzzy systems time series prediction , 2005, CIMSA. 2005 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications, 2005..

[60]  Francisco Chiclana,et al.  Accuracy and complexity evaluation of defuzzification strategies for the discretised interval type-2 fuzzy set , 2013, Int. J. Approx. Reason..

[61]  Witold Pedrycz,et al.  Granular fuzzy models: a study in knowledge management in fuzzy modeling , 2012, Int. J. Approx. Reason..

[62]  Yuhui Shi,et al.  Particle swarm optimization: developments, applications and resources , 2001, Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546).

[63]  Mansour Sheikhan,et al.  Time series prediction using PSO-optimized neural network and hybrid feature selection algorithm for IEEE load data , 2012, Neural Computing and Applications.