A hybrid of multiobjective Evolutionary Algorithm and HMM-Fuzzy model for time series prediction

Neurocomputing

Volumn 81, Issue , 2012, Pages 1-11

  Rafiul Hassan, Md ^a   Nath, Baikunth ^b   Kirley, Michael ^b   Kamruzzaman, Joarder ^c  

^a KING FAHD UNIVERSITY OF PETROLEUM AND MINERALS   (Saudi Arabia)

^b UNIVERSITY OF MELBOURNE   (Australia)

^c UNIVERSITY OF BALLARAT   (Australia)

Author keywords

Fuzzy logic; Hidden Markov model; Prediction methods; Time series

Indexed keywords

DATA DRIVEN; DATA PATTERNS; FINANCIAL TIME SERIES; FUZZY MODELS; HYBRID APPROACH; LOG LIKELIHOOD; MULTI OBJECTIVE; MULTI OBJECTIVE EVOLUTIONARY ALGORITHMS; NONLINEAR TIME SERIES; PREDICTION ACCURACY; PREDICTION METHODS; TIME SERIES PREDICTION; TRADEOFF SOLUTION;

EVOLUTIONARY ALGORITHMS; FINANCIAL DATA PROCESSING; FORECASTING; FUZZY RULES; HIDDEN MARKOV MODELS; TIME SERIES;

FUZZY LOGIC;

ACCURACY; ARTICLE; COMPUTER PREDICTION; CONTROLLED STUDY; EVOLUTIONARY ALGORITHM; FUZZY SYSTEM; HIDDEN MARKOV MODEL; HYBRID COMPUTER; MATHEMATICAL MODEL; NONLINEAR SYSTEM; PRIORITY JOURNAL; QUALITY CONTROL; SCORING SYSTEM; TIME SERIES ANALYSIS;

EID: 84856323880     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2011.09.012     Document Type: Article

References (48)

1. Zadeh L.A. Fuzzy logic, neural networks, and soft computing. Commun. ACM 1994, 37(3):77-84.
2. Gaweda A.E., Zurada J.M. Data-driven linguistic modeling using relational fuzzy rules. IEEE Trans. Fuzzy Syst. 2003, 11:121-134.
3. Sison L.G., Chong E. Fuzzy modelling by induction and pruning decision trees. Proceedings of IEEE International Symposium on Intelligent Control 1994, 16-18.
4. Kim C.H., Lee J.J. Adaptive network based fuzzy inference system with pruning. Proceedings of SICE Annual Conference in Fukui 2003, vol. 10:140-143.
5. Angelov P.P., Buswell R.A. Automatic generation of fuzzy rule-based models from data by genetic algorithms. Inf. Sci. 2003, 17-31.
6. Wang L.X., Mendel J. Generating fuzzy rules by learning from examples. IEEE Trans. Syst. Man Cybern. 1992, 22:1414-1427.
7. Similarity Search and Outlier Detection in Time Series, http://www.latest-science-articles.com/IT/Similarity-Search-and-Outlier-Detection-in-Time-Series-4480.html.
8. Rabiner L.R. A tutorial on hidden Markov models and selected applications in speech recognition. Proc. IEEE 1989, 77:257-286.
9. Hassan M.R. A combination of hidden Markov model and fuzzy model for stock market forecasting. Neurocomputing 2009, 72:3439-3446.
10. Hassan M.R., Hossain M.M., Begg R.K., Morsi Y., Kotagiri R. Breast-cancer identification using HMM-Fuzzy model. Comput. Biol. Med. 2010, 72:3439-3446.
11. Mamdani H., Assilian S. An experiment in linguistic synthesis with a fuzzy logic controller. Int. J. Man-Mach. Stud. 1975, 7:1-13.
12. Takagi T., Sugeno M. Fuzzy identification of systems and its application to modeling and control. IEEE Trans. Syst. Man Cybern. 1985, 1:116-132.
13. Jang J.S.R., Sun C.T., Mizutani E. Neuro-Fuzzy and Soft-Computing: A Computational Approach to Learning and Machine Intelligence 1997, Prentice-Hall, Upper Saddle River, NJ.
14. Zitzler E., Laumanns M., Bleuler S. A tutorial on evolutionary multiobjective optimization. Metaheuristics for Multiobjective Optimisation 2003, 3-38. Springer-Verlag.
15. Bezdek J.C. Pattern Recognition with Fuzzy Objective Function Algorithms 1981, Kluwer Academic Press.
16. Chiu S.L. An efficient method for extracting fuzzy classification rules from high dimensional data. J. Adv. Comput. Intell. 1997, 1:1-7.
17. J. Zurada, Optimal Data Driven Rule Extraction Using Adaptive Fuzzy-Neural Models, PhD Dissertation, University of Louisbille, 2002.
18. Zurada J., Lozowski A. Generating linguistic rules from data using neuro-fuzzy framework. Proceedings of the Fourth International Conference on Soft Computing 1996, 618-621.
19. Wang X.Z., Wang Y.D., Xu X.F., Ling W.D., Yeung D.S. A new approach to fuzzy rule generation: fuzzy extension matrix. Fuzzy Sets Syst. 2001, 291-306.
20. Chen S.M., Lee S.H. A new method for generating fuzzy rules from numerical data for handling classification problems. Appl. Artif. Intell. 2001, 645-664.
21. Ishibuchi H., Nakashima T., Murata T. Thee-objective genetics-based machine learning for linguistic rule extraction. Inf. Sci. 2001, 109-133.
22. Chang X., Lilly J.H. Evolutionary design of a fuzzy classifier from data. IEEE Trans. Syst. Man Cybern.-Part B: Cybern. 2004, 1894-1906.
23. Branson J.S., Lilly J.H. Obtaining fuzzy systems from data-the visit algorithm. Proceedings of Southeastern Symposium on System Theory, Auburn 1999.
24. Ishibuchi H., Yamamoto T. Fuzzy rule selection by multi-objective genetic local search algorithms and rule evaluation measures in data mining. Fuzzy Sets Syst. 2004, 59-88.
25. Abonyi J., Roubos J.A., Szeifert F. Data-driven generation of compact, accurate, and linguistically-sound fuzzy classifiers based on a decision tree initialization. Int. J. Approximate Reasoning 2003, 1-21.
26. Kasabov N.K., Song Q. Denfis: dynamic evolving neural-fuzzy inference system and its application for time series prediction. IEEE Trans. Fuzzy Syst. 2002, 10:144-154.
27. Kim E., Park M., Ji S., Park M. A new approach to fuzzy modeling. IEEE Trans. Fuzzy Syst. 1997, 5(3):328-337.
28. Wang L., Langari R. Building Sugeno-type models using fuzzy discretization and orthogonal parameter estimation techniques. IEEE Trans. Fuzzy Syst. 1995, 454-458.
29. Paul S., Kumar S. Subsethood-product fuzzy neural inference system (Supfunis). IEEE Trans. Neural Networks 2002, 13:578-599.
30. Vasko R.C., El-Jaroudi A., Boston J.R. An algorithm to determine hidden Markov model topology. Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing 1996, 3577-3580.
31. Makhoul J., Roucos S., Gish H. Vector quantization in speech coding. Proc. IEEE 1985, 73(11):1551-1585.
32. Huang X., Ariki Y., Jack M. Hidden Markov Models for Speech Recognition 1990, Edinburgh University Press.
33. Coello C.C., Veldhuizen D.V., Lamont G. EA for Solving Multi-Objective Problems 2002, Kluwer.
34. Deb K. Multi-Objective Optimization Using Evolutionary Algorithms 2001, John Wiley and Sons, Ltd., Chichester, England.
35. Mackey M., Glass L. Oscillation and chaos in physiological control systems. Science 1977, 197:287-289.
36. J.E.P. Box, G.M. Jenkins, Time Series Analysis: Forecasting and Control, Holden-Day, San Fransisco, CA, 1970, 1976 (third edition published in 1994).
37. Quinlan R. Combining instance-based and model-based learning. Proceedings on the Tenth International Conference of Machine Learning 1993, 236-243.
38. Hassan M.R., Nath B., Kirley M. HMM based fuzzy model for time series forecasting. Proceedings of World Congress on Computational Intelligence (WCCI2006) 2006, 9963-9968.
39. Hassan M.R., Nath B. Stock market forecasting using hidden Markov model: a new approach. Proceedings of the 5th International Conference on Intelligent Systems Design and Applications 2005, 192-196.
40. Kim E., Park M., Ji S., Park M. A transformed input-domain approach to fuzzy modeling. IEEE Trans. Fuzzy Syst. 1998, 546-604.
41. Onur H.F., Fatih K. Examining the link between carbon dioxide emissions and the share of industry in GDP: modeling and testing for the G-7 countries. Energy Policy 2011, 39(6):3612-3620.
42. Hocaoglua F.O., Ömer Nezih Gerekb, Kurban M. A novel wind speed modeling approach using atmospheric pressure observations and hidden Markov models. J. Wind Eng. Ind. Aerodyn. 2010, 98(8):472-481.
43. Baum L.E., Pitrie T., Souls G., Weiss N. A maximization technique occurring in the statistical analysis of probabilistic functions of Markov chains. Ann. Math. Stat. 1970, 41:164-171.
44. Jang J.R. Anfis: adaptive-network-based fuzzy inference system. IEEE Trans. Syst. Man Cybern. 1993, 23:51-63.
45. Yao X., Lin Y. A new evolutionary system for evolving artificial neural networks. IEEE Trans. Neural Networks 1997, 8:694-713.
46. Sugeno M., Tanaka M. Successive identification of a fuzzy model and its application to prediction of a complex system. Fuzzy Sets Syst. 1991, 315-334.
47. Sugeno M., Yasukawa T. A fuzzy logic based approach to qualitative modeling. IEEE Trans. Fuzzy Syst. 1993, 1(1):1-7.
48. Chiu S. Selecting input variables in fuzzy models. J. Intell. Fuzzy Syst. 1996, 4:243-256.