Fuzzy time series forecasting method based on Gustafson-Kessel fuzzy clustering

Fuzzy time series approaches have being increasingly attracted researchers' attentions. The procedures on fuzzy time series actually consist of three stages; fuzzification, determination of fuzzy relations and defuzzification. Researches are generally concentrated on these stages and about improving them. In this study, we propose a new approach, which combines several techniques. In this approach, Gustafson-Kessel, which is a fuzzy clustering technique, is being used to fuzzification of time series. The proposed method is compared with the approaches in literature.

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

[2]  W. Woodall,et al.  A comparison of fuzzy forecasting and Markov modeling , 1994 .

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

[4]  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..

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

[6]  Uzay Kaymak,et al.  Improved covariance estimation for Gustafson-Kessel clustering , 2002, 2002 IEEE World Congress on Computational Intelligence. 2002 IEEE International Conference on Fuzzy Systems. FUZZ-IEEE'02. Proceedings (Cat. No.02CH37291).

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

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

[9]  Donald Gustafson,et al.  Fuzzy clustering with a fuzzy covariance matrix , 1978, 1978 IEEE Conference on Decision and Control including the 17th Symposium on Adaptive Processes.

[10]  Syed Muhammad Aqil Burney,et al.  A refined fuzzy time series model for stock market forecasting , 2008 .

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

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

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

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

[15]  Ching-Hsue Cheng,et al.  Multi-attribute fuzzy time series method based on fuzzy clustering , 2008, Expert Syst. Appl..

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

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

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

[19]  Erol Egrioglu,et al.  A new hybrid approach based on SARIMA and partial high order bivariate fuzzy time series forecasting model , 2009, Expert Syst. Appl..

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

[21]  Sheng-Tun Li,et al.  A FCM-based deterministic forecasting model for fuzzy time series , 2008, Comput. Math. Appl..