Fuzzy time series forecasting models have been used to make prediction in many areas, such as to forecast the stock price, the university enrollments, the weather, etc. Many fuzzy forecasting methods have been presented to get better forecasting accuracy rates than the traditional models. If we can get better forecasting accuracy rates, then we can get more advantages.
In this thesis, we present two new fuzzy forecasting methods based on fuzzy time series and clustering techniques. In the first method, we present a new method to handle forecasting problems based on fuzzy time series and clustering techniques. The proposed clustering algorithm is used to partition the universe of discourse into different lengths of intervals. We apply the first proposed method to forecast the enrollments of the University of Alabama, to predict the daily average temperature, and to predict the Taiwan Future Exchange (TAIFEX). In the second method, we present a multivariate fuzzy forecasting method to forecast the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) based on fuzzy time series and clustering techniques. The proposed clustering algorithm is slightly different compared with the first method and is used to partition the universe of discourse into different lengths of intervals. The proposed method not only uses the training data, but also uses the already known testing data for dealing with the prediction to get a higher forecasting accuracy rate. The proposed two methods get higher forecasting accuracy rates than the existing methods.

H. Aladag, M. A. Basaran, E. Egrioglu, U. Yolcu, and V. R. Uslu, “Forecasting in high order fuzzy time series by using neural networks to define fuzzy relations,” Expert Systems with Applications, vol. 3, no. 1, pp. 4228-4231, 2008.
S. M. Chen, “Forecasting enrollments based on fuzzy time series,” Fuzzy Sets and Systems, vol. 81, no. 3, pp. 311-319, 1996.
S. M. Chen, “Forecasting enrollments based on high-order fuzzy time series,” Cybernetics and Systems, vol. 33, no. 1, pp. 1-16, 2002.
S. M. Chen and C. C. Hsu, “A new method to forecast enrollments using fuzzy time series,” International Journal of Applied Science and Engineering, vol. 2, no. 3, pp. 234-244, 2004.
S. M. Chen and N. Y. Chung, “Forecasting enrollments using high-order fuzzy time series and genetic algorithms,” International Journal of Intelligent Systems, vol. 21, no. 5, pp. 485-501, 2006.
S. M. Chen and H. R. Hsiao, “A new method to estimate null values in relational database systems based on automatic clustering techniques,” Information Sciences, vol. 169, no. 1-2, pp. 47-69, 2005.
S. M. Chen and J. R. Hwang, “Temperature prediction using fuzzy time series,” IEEE Transactions on Systems, Man, and Cybernetics-Part B: Cybernetics, vol. 30, no. 2, pp. 263-275, 2000.
C. H. Cheng, T. L. Chen, H. J. Teoh, and C. H. Chiang, “Fuzzy time-series based on adaptive model for TAIEX forecasting,” Expert Systems with Applications, vol. 34, no. 2, pp. 1126-1132, 2008.
C. H. Cheng, G. W. Cheng, and J. W. Wang, “Multi-attribute fuzzy time series method based on fuzzy clustering,” Expert Systems with Applications, vol. 34, no. 2, pp. 1235-1242, 2008.
C. H. Cheng, J. W. Wang, and C. H. Li, “Forecasting the number of outpatient visits using a new fuzzy time series based on weighted-transitional matrix,” Expert Systems with Applications, vol. 34, no. 4, pp. 2568-2575, 2008.
K. H. Huarng, “Effective lengths of intervals to improve forecasting fuzzy time series,” Fuzzy Sets and Systems, vol. 123, no. 3, pp. 387-394, 2001.
K. H. Huarng, “Heuristic models of fuzzy time series for forecasting,” Fuzzy Sets and Systems, vol. 123, no. 3, pp. 369-386, 2001.
K. H. Huarng and H. K. Yu, “A type 2 fuzzy time series model for stock index forecasting,” Physica A: Statistical Mechanics and its Applications, vol. 353, no. 1-4, pp. 445-462, 2005.
K. H. Huarng and Tiffany H. K. Yu, “Ratio-based lengths of intervals to improve fuzzy time series forecasting,” IEEE Transactions on Systems, Man, and Cybernetics-Part B: Cybernetics, vol. 32, no. 2, pp. 328-340, 2006.
K. H. Huarng and H. K. Yu, “The application of neural networks to forecast fuzzy time series,” Physica A: Statistical Mechanics and its Applications, vol. 363, no. 2, pp. 481-491, 2006.
K. H. Huarng, Tiffany H. K. Yu, and Y. W. Hsu, “A multivariate heuristic model for fuzzy time-series forecasting,” IEEE Transactions on Systems, Man, and Cybernetics-Part B: Cybernetics, vol. 37, no. 4, pp. 836-846, 2007.
J. R. Hwang, S. M. Chen, and C. H. Lee, “Handling forecasting problems using fuzzy time series,” Fuzzy Sets and Systems, vol. 100, no. 1-3, pp 217-228, 1998.
T. A. Jilani and S. M. A. Burney, “Multivariate stochastic fuzzy forecasting models,” Expert Systems with Applications, vol. 35, no. 3, pp. 691-700, 2008.
H. Kuo, S. J. Horng, T. W. Kao, T. L. Lin, C. L. Lee, and Y. Pan, “An improved method for forecasting enrollments based on fuzzy time series and particle swarm optimization,” Expert Systems with Applications, vol. 36, no. 3, pp. 6108-6117, 2009.
H. L. Lee, A. Liu, and W. S. Chen, “Pattern discovery of fuzzy time series for financial prediction,” IEEE Transactions on Knowledge and Data Engineering, vol. 18, no. 5, pp. 613-625, 2006.
L. W. Lee, L. H. Wang, S. M. Chen, Y. H. Leu, “Handling forecasting problems based on two-factors high-order fuzzy time series,” IEEE Transactions on Fuzzy Systems, vol. 14, no. 3, pp. 468-477, 2006.
Y. Leu, C. P. Lee, and Y. Z. Jou, “A distance-based fuzzy time series model for exchange rates forecasting,” Expert Systems with Applications, vol. 36, no. 4, pp. 8107-8114, 2009.
Q. Song and B. S. Chissom, “Fuzzy time series and its models,” Fuzzy Sets and Systems, vol. 54, no. 3, pp. 269-277, 1993.
Q. Song and B. S. Chissom, “Forecasting enrollments with fuzzy time series – Part I,” Fuzzy Sets and Systems, vol. 54, no. 1, pp 1-9, 1993.
Q. Song and B. S. Chissom, “Forecasting enrollments with fuzzy time series – part II,” Fuzzy Sets and Systems, vol. 62, no. 1, pp. 1-8, 1994.
J. Sullivan and W. H. Woodall, “A comparison of fuzzy forecasting and Markov modeling,” Fuzzy Sets and Systems, vol. 64, no. 3, pp. 279-293, 1994.
K. Tanuwijaya and S. M. Chen, “A new method to forecast enrollments using fuzzy time series and clustering techniques,“ Proceedings of the 2009 International Conference on Machine Learning and Cybernetics, Baoding, Hebei, China, July 2009.
K. Tanuwijaya and S. M. Chen, “TAIEX forecasting based on fuzzy time series and clustering techniques,” Proceedings of the 2009 International Conference on Machine Learning and Cybernetics, Baoding, Hebei, China, July 2009.
N. Y. Wang and S. M. Chen, “Temperature prediction and TAIFEX forecasting based on automatic clustering techniques and two-factors high-order fuzzy time series,” Expert Systems with Applications, vol. 36, no. 2, pp. 2143-2154, 2009.
H. K. Yu, “Weighted fuzzy time series models for TAIEX forecasting,” Physica A: Statistical Mechanics and its Applications, vol. 349, no. 3-4, pp. 609-624, 2005.
Tiffany H. K. Yu and K. H. Huarng, “A bivariate fuzzy time series model to forecast the TAIEX,” Expert Systems with Applications, vol. 34, no. 4, pp. 2945-2952, 2008.
L. A. Zadeh, “Fuzzy sets,” Information and Control, vol. 8, pp. 338-353, 1965.