研究生: |
顏順健 Danio - Delano |
---|---|
論文名稱: |
A Hybrid Algorithm based on Reduced Space Searching Algorithm (RSSA) and Its Application in Forecasting Fuzzy Time Series A Hybrid Algorithm based on Reduced Space Searching Algorithm (RSSA) and Its Application in Forecasting Fuzzy Time Series |
指導教授: |
洪西進
Shi-Jinn Horng |
口試委員: |
郭奕宏
Yi-Hung Kuo 顏成安 Cheng-An Yen 林韋宏 Wei-Hung Lin 王獻 Hsien Wang 林琮烈 Tsung-Lieh Lin |
學位類別: |
碩士 Master |
系所名稱: |
電資學院 - 資訊工程系 Department of Computer Science and Information Engineering |
論文出版年: | 2010 |
畢業學年度: | 98 |
語文別: | 英文 |
論文頁數: | 51 |
中文關鍵詞: | Reduced Space Searching Algorithm (RSSA) 、Fuzzy Time Series 、Enrollments Forecasting 、TAIFEX Forecasting |
外文關鍵詞: | Reduced Space Searching Algorithm (RSSA), Fuzzy Time Series, Enrollments Forecasting, TAIFEX Forecasting |
相關次數: | 點閱:184 下載:0 |
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During the past decades, forecasting models based on the concept of fuzzy time series have been proposed. There are two main factors, which are the lengths of intervals and the content of forecast rules that will impact the forecasted accuracy of the models. How to find the proper content of the main factors to improve the forecasted accuracy has become an interesting research topic. Some forecasting models, which combined heuristic methods or evolutionary algorithms (such as genetic algorithms and particle swarm optimization) with the fuzzy time series, have been proposed. In this thesis, we use the reduced space searching algorithm (RSSA) to find the proper content of the main factors. A new hybrid forecasting model which combined RSSA with fuzzy time series is proposed to improve the forecasted accuracy. The experimental results of forecasting enrollments of students of the University of Alabama and TAIFEX (Taiwan Stock Index Futures) forecasting problems show that this new model is better than the existing models.
During the past decades, forecasting models based on the concept of fuzzy time series have been proposed. There are two main factors, which are the lengths of intervals and the content of forecast rules that will impact the forecasted accuracy of the models. How to find the proper content of the main factors to improve the forecasted accuracy has become an interesting research topic. Some forecasting models, which combined heuristic methods or evolutionary algorithms (such as genetic algorithms and particle swarm optimization) with the fuzzy time series, have been proposed. In this thesis, we use the reduced space searching algorithm (RSSA) to find the proper content of the main factors. A new hybrid forecasting model which combined RSSA with fuzzy time series is proposed to improve the forecasted accuracy. The experimental results of forecasting enrollments of students of the University of Alabama and TAIFEX (Taiwan Stock Index Futures) forecasting problems show that this new model is better than the existing models.
[1] Q. Zhang and M. Mahfouf, "A New Reduced Space Searching Algorithm (RSSA) and Its Application in Optimal Design of Alloy Steels," IEEE Congress on Evolutionary Computation, pp. 1815-1822, 2007.
[2] S.-M. Chen, "Forecasting enrollments based on fuzzy time series," Fuzzy Sets and Systems, vol. 81, pp. 311-319, 1996.
[3] S.-M. Chen, "Forecasting enrollments based on high-order fuzzy time series," Cybernetics and Systems, vol. 33, pp. 1-16, 2002.
[4] S.-M. Chen and N.-Y. Chung, "Forecasting enrollments using high-order fuzzy time series and genetic algorithms: Research Articles," International Journal of Intelligent Systems, vol. 21, pp. 485-501, 2006.
[5] S.-M. Chen and N.-Y. Chung, "Forecasting Enrollments of Students by Using Fuzzy Time Series and Genetic Algorithms," International Journal of Information and Management Sciences, vol. 17, pp. 1-17, 2006.
[6] I.-H. Kuo, S.-J. Horng, Y.-H. Chen, R.-S. Run, T.-W. Kao, R.-J. Chen, J.-L. Lai and T. L. Lin, "Forecasting TAIFEX based on fuzzy time series and particle swarm optimization," Expert Systems with Applications: An International Journal, vol. 37, pp. 1494-1502, 2010.
[7] C.-H. Cheng, T.-L. Chen, H. J. Teoh, and C.-H. Chiang, "Fuzzy time-series based on adaptive expectation model for TAIEX forecasting," Expert Systems with Applications, vol. 34, pp. 1126-1132, 2008.
[8] H.-M. Feng, C.-Y. Chen, and F. Ye, "Evolutionary fuzzy particle swarm optimization vector quantization learning scheme in image compression," Expert Systems with Applications, vol. 32, pp. 213-222, 2007.
[9] Q. He, L. Wang, and B. Liu, "Parameter estimation for chaotic systems by particle swarm optimization," Chaos, Solitons & Fractals, vol. 34, pp. 654-661, 2007.
[10] K. Huarng, "Heuristic models of fuzzy time series for forecasting," Fuzzy Sets and Systems, vol. 123, pp. 369-386, 2001.
[11] K. Huarng, "Effective lengths of intervals to improve forecasting in fuzzy time series," Fuzzy Sets and Systems, vol. 123, pp. 387-394, 2001.
[12] J.-R. Hwang, S.-M. Chen, and C.-H. Lee, "Handling forecasting problems using fuzzy time series," Fuzzy Sets and Systems, vol. 100, pp. 217-228, 1998.
[13] J. Kennedy and R. Eberhart, "Particle Swarm Optimization," Proceedings of IEEE international Conference on Neural Network, pp. 1942-1948, 1995.
[14] J. Kennedy, R. C. Eberhart, and Y. Shi, Swarm intelligence: Morgan Kaufmann Publishers, 2001.
[15] I.-H. Kuo, S.-J. Horng, T.-W. Kao, T.-L. Lin, and P. Fan, "An efficient flow-shop scheduling algorithm based on a hybrid particle swarm optimization model," Lecture Notes in Artificial Intelligence, vol. 4570, pp. 303-312, 2007.
[16] I. 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. In Press, Corrected Proof, doi:10.1016/j.eswa.2008.07.043.
[17] I. H. Kuo, S.-J. Horng, T.-W. Kao, T.-L. Lin, C.-L. Lee, T. Terano, and Y. Pan, "An efficient flow-shop scheduling algorithm based on a hybrid particle swarm optimization model," Expert Systems with Applications, vol. In Press, Corrected Proof, doi:10.1016/j.eswa.2008.08.054.
[18] L.-W. Lee, L.-H. Wang, and S.-M. Chen, "Temperature prediction and TAIFEX forecasting based on fuzzy logical relationships and genetic algorithms," Expert Systems with Applications, vol. 33, pp. 539-550, 2007.
[19] L.-W. Lee, L.-H. Wang, and S.-M. Chen, "Temperature prediction and TAIFEX forecasting based on high-order fuzzy logical relationships and genetic simulated annealing techniques," Expert Systems with Applications, vol. 34, pp. 328-336, 2008.
[20] L. W. Lee, L. H. Wang, S. M. Chen, and Y. H. Leu, "Handling forecasting problems based on two-factors high-order fuzzy time series," IEEE Transactions on Fuzzy Systems, vol. 14, pp. 468-477, 2006.
[21] L.-l. Li, L. Wang, and L.-h. Liu, "An effective hybrid PSOSA strategy for optimization and its application to parameter estimation," Applied Mathematics and Computation, vol. 179, pp. 135-146, 2006.
[22] S.-T. Li and Y.-C. Cheng, "Deterministic fuzzy time series model for forecasting enrollments," Computers & Mathematics with Applications, vol. 53, pp. 1904-1920, 2007.
[23] H.-T. Liu, "An improved fuzzy time series forecasting method using trapezoidal fuzzy numbers," Fuzzy Optimization and Decision Making, vol. 6, pp. 63-80, 2007.
[24] X. H. Shi, Y. C. Liang, H. P. Lee, C. Lu, and L. M. Wang, "An improved GA and a novel PSO-GA-based hybrid algorithm," Information Processing Letters, vol. 93, pp. 255-261, 2005.
[25] Y. Shi and R. C. Eberhart, "Empirical study of particle swarm optimization," Proceedings of the Congress on Evolutionary Computation, pp. 1945-1950, 1999.
[26] S. R. Singh, "A computational method of forecasting based on fuzzy time series," Mathematics and Computers in Simulation, vol. In Press, Corrected Proof, doi:10.1016/j.matcom.2008.02.026.
[27] S. R. Singh, "A robust method of forecasting based on fuzzy time series," Applied Mathematics and Computation, vol. 188, pp. 472-484, 2007.
[28] S. R. Singh, "A simple method of forecasting based on fuzzy time series," Applied Mathematics and Computation, vol. 186, pp. 330-339, 2007.
[29] Q. Song and B. S. Chissom, "Forecasting enrollments with fuzzy time series -- Part I," Fuzzy Sets and Systems, vol. 54, pp. 1-9, 1993.
[30] Q. Song and B. S. Chissom, "Fuzzy time series and its models," Fuzzy Sets and Systems, vol. 54, pp. 269-277, 1993.
[31] Q. Song and B. S. Chissom, "Forecasting enrollments with fuzzy time series -- part II," Fuzzy Sets and Systems, vol. 62, pp. 1-8, 1994.