A fuzzy time series approach based on weights determined by the number of recurrences of fuzzy relations

Swarm and Evolutionary Computation - Tập 15 - Trang 19-26 - 2014
Vedide Rezan Uslu1, Eren Bas2, Ufuk Yolcu2, Erol Egrioglu1
1Department of Statistics, Ondokuz Mayis University, Samsun 55139, Turkey
2Department of Statistics, Giresun University, Giresun 28000, Turkey

Tài liệu tham khảo

Aladag, 2009, Forecasting in high order fuzzy time series by using neural networks to define fuzzy relations, Expert Syst. Appl., 36, 4228, 10.1016/j.eswa.2008.04.001 Aladag, 2010, A high order fuzzy time series forecasting model based on adaptive expectation and artificial neural networks, Math. Comput. Simul., 81, 875, 10.1016/j.matcom.2010.09.011 Aladag, 2012, A new time invariant fuzzy time series forecasting method based on particle swarm optimization, Appl. Soft Comput., 12, 3291, 10.1016/j.asoc.2012.05.002 Cao, 1999, Optimization of control parameters in genetic algorithms: a stochastic approach, Int. J. Syst. Sci., 30, 551, 10.1080/002077299292290 M.-Y. Chen, B.-T. Chens, Online fuzzy time Series analysis based on entropy discretization and a fast Fourier transform, Appl. Soft Comput. J., http://dx.doi.org/10.1016/j.asoc.2013.07.024, In Press. Chen, 1996, Forecasting enrollments based on fuzzy time-series, Fuzzy Sets Syst., 81, 311, 10.1016/0165-0114(95)00220-0 Chen, 2002, Forecasting enrollments based on high order fuzzy time series, Cybern. Syst., 33, 1, 10.1080/019697202753306479 Chen, 2006, Forecasting enrolments using high order fuzzy time series and genetic algorithms, Int. J. Intell. Syst., 21, 485, 10.1002/int.20145 Cheng, 2006, Entropy-based and trapezoid fuzzification-based fuzzy time series approach for forecasting IT project cost, Technol. Forecasting Soc. Change, 73, 524, 10.1016/j.techfore.2005.07.004 Cheng, 2008, Fuzzy time series based on adaptive expectation model for TAIEX forecasting, Expert Syst. Appl., 34, 1126, 10.1016/j.eswa.2006.12.021 Cheng, 2008, Multi-attribute fuzzy time series method based on fuzzy clustering, Expert Syst. Appl., 34, 1235, 10.1016/j.eswa.2006.12.013 V. Cicirello, S. Smith, Modeling GA performance for control parameter optimization, in: Proceedings of the Genetic and Evolutionary Computation Conference, 2000. S. Davari, M.H.F. Zarandi, I.B. Turksen, An Improved fuzzy time series forecasting model based on particle swarm intervalization, in: The 28th North American Fuzzy Information Processing Society Annual Conferences. NAFIPS 2009, Cincinnati, Ohio, USA, June 14–17, 2009. K.A. De Jong, Analysis of the Behaviour of a Class of Genetic Adaptive Systems, Ph.D. Dissertation, Department of Computer and Communication Science, University of Michigan, Ann Arbor, MI, 1975. Egrioglu, 2009, A new approach based on artificial neural networks for high order multivariate fuzzy time series, Expert Syst. Appl., 36, 10589, 10.1016/j.eswa.2009.02.057 Egrioglu, 2009, A new hybrid approach based on Sarima and partial high order bivariate fuzzy time series forecasting model, Expert Syst. Appl., 36, 7424, 10.1016/j.eswa.2008.09.040 Egrioglu, 2009, A new approach based on artificial neural networks for high order bivariate fuzzy time series, 265 Egrioglu, 2010, Finding an optimal interval length in high order fuzzy time series, Expert Syst. Appl., 37, 5052, 10.1016/j.eswa.2009.12.006 Egrioglu, 2011, A new approach based on the optimization of the length of intervals in fuzzy time series, J. Intell. Fuzzy Syst., 22, 15, 10.3233/IFS-2010-0470 Egrioglu, 2011, Fuzzy time series forecasting method based on Gustafson–Kessel fuzzy clustering, Expert Syst. Appl., 38, 10355, 10.1016/j.eswa.2011.02.052 E. Egrioglu, A New Time Invariant Fuzzy Time Series Forecasting Method Based On Genetic Algorithm, Advances in Fuzzy Systems Volume 2012, Article ID 785709, pp. 6. Egrioglu, 2013, Fuzzy time series forecasting with a novel hybrid approach combining fuzzy c-means and neural networks, Expert Syst. Appl., 40, 854, 10.1016/j.eswa.2012.05.040 F.P. Fu, K. Chi, W.G. Che, Q.J. Zhao, High-order difference heuristic model of fuzzy time series based on particle swarm optimization and information entropy for stock markets, in: International Conference on Computer Design and Applications, 2010. Goldberg, 1985 Grefenstette, 1986, Optimisation of control parameters for genetic algorithms, IEEE Trans. Syst. Man Cybern., SMC-16, 122, 10.1109/TSMC.1986.289288 Holland, 1975 Hsu, 2010, Temperature prediction and TAIFEX forecasting based on fuzzy relationships and MTPSO techniques, Expert Syst. Appl., 37, 2756, 10.1016/j.eswa.2009.09.015 Huang, 2011, An improved forecasting model based on the weighted fuzzy relationship matrix combined with a PSO adaptation for enrollments, Int. J. Innovative Comput. Inf. Control, 7, 4027 Huarng, 2001, Effective length of intervals to improve forecasting in fuzzy time-series, Fuzzy Sets Syst., 123, 387, 10.1016/S0165-0114(00)00057-9 Huarng, 2001, Heuristic models of fuzzy time series for forecasting, Fuzzy Sets Syst., 123, 369, 10.1016/S0165-0114(00)00093-2 Huarng, 2006, Ratio-based lengths of intervals to improve fuzzy time series forecasting, IEEE Trans. Syst. Man Cybern. Part B: Cybern., 36, 328, 10.1109/TSMCB.2005.857093 Huarng, 2006, The application of neural networks to forecast fuzzy time series, Physica A, 363, 481, 10.1016/j.physa.2005.08.014 Jilani, 2007, Multivariate high order fuzzy time series forecasting for car road accident, World Acad. Sci. Eng. Technol., 25, 288 Jilani, 2008, Multivariate stochastic fuzzy forecasting models, Expert Syst. Appl., 353, 691, 10.1016/j.eswa.2007.07.014 Kang, 2005, A fuzzy time series prediction method using the evolutionary algorithm, Adv. Intell. Comput. Lect. Notes Comput. Sci., 3654, 530, 10.1007/11538356_55 Kuo, 2009, An improved method for forecasting enrollments based on fuzzy time series and particle swarm optimization, Expert Syst. Appl., 36, 6108, 10.1016/j.eswa.2008.07.043 Kuo, 2010, Forecasting TAIFEX based on fuzzy time series and particle swarm optimization, Expert Syst. Appl., 37, 1494, 10.1016/j.eswa.2009.06.102 Lee, 2004, Fuzzy forecasting based on fuzzy time series, Int. J. Comput. Math., 817, 781, 10.1080/00207160410001712288 Lee, 2006, Handling forecasting problems based on two factor high-order fuzzy time series, IEEE Trans. Fuzzy Syst., 14, 468, 10.1109/TFUZZ.2006.876367 Lee, 2007, Temperature prediction and TAIFEX forecasting based on fuzzy logical relationships and genetic algorithms, Expert Syst. Appl., 33, 539, 10.1016/j.eswa.2006.05.015 Li, 2008, FCM-based deterministic forecasting model for fuzzy time series, Comput. Math. Appl., 56, 3052, 10.1016/j.camwa.2008.07.033 Park, 2010, TAIFEX and KOSPI 200 forecasting based on two factors high order fuzzy time series and particle swarm optimization, Expert Syst. Appl., 37, 959, 10.1016/j.eswa.2009.05.081 J.V. Ringwood, Optimization of fuzzy electricity forecasting models using genetic algorithms, in: Proceedings of the Fifth European Congress on Intelligent Techniques and Soft Computing Aachen Germany, vol. 3, 1997, pp. 2457–2464. J.D. Schaffer, R.A. Caruana, L.J. Eshelman, R. Das, A study of control parameters affecting on-line performance of genetic algorithms for function optimization, in: Proceedings of the Third International Conference on Genetic Algorithms and their Applications George Mason University, 1989, pp. 51–61. Singh, 2007, A simple method of forecasting based on fuzzy time series, Appl. Math. Comput., 186, 330, 10.1016/j.amc.2006.07.128 Singh, 2007, A robust method of forecasting based on fuzzy time series, Appl. Math. Comput., 188, 472, 10.1016/j.amc.2006.09.140 Song, 1993, Fuzzy time series and its models, Fuzzy Sets Syst., 54, 269, 10.1016/0165-0114(93)90372-O Song, 1993, Forecasting enrollments with fuzzy time series—Part I, Fuzzy Sets Syst., 54, 1, 10.1016/0165-0114(93)90355-L Song, 1994, Forecasting enrollments with fuzzy time series—Part II, Fuzzy Sets Syst., 62, 1, 10.1016/0165-0114(94)90067-1 Sullivan, 1994, A comparison of fuzzy forecasting and Markov modeling, Fuzzy Sets Syst., 64, 279, 10.1016/0165-0114(94)90152-X Tsaur, 2005, Fuzzy relation analysis in fuzzy time series model, Comput. Math. Appl., 49, 539, 10.1016/j.camwa.2004.07.014 Wang, 1997, Using genetic algorithms to optimize model parameters, Environ. Modell. Softw., 12, 27, 10.1016/S1364-8152(96)00030-8 Yolcu, 2009, A new approach for determining the length of intervals for fuzzy time series, Appl. Soft Comput., 9, 647, 10.1016/j.asoc.2008.09.002 Yolcu, 2013, Time series forecasting with a novel fuzzy time series approach: an example for İstanbul stock market, J. Comput. Stat. Simul., 83, 597, 10.1080/00949655.2011.630000 Yu, 2005, Weighted fuzzy time series models for TAIEX forecasting, Physica A, 349, 609, 10.1016/j.physa.2004.11.006 Zadeh, 1965, Fuzzy sets, Inf. Control, 8, 338, 10.1016/S0019-9958(65)90241-X