IMPROVING THE FORECASTED ACCURACY OF MODEL BASED ON FUZZY TIME SERIES AND K-MEANS CLUSTERING
IMPROVING THE FORECASTED ACCURACY OF MODEL BASED ON FTS AND K-MEANS CLUSTERING
Abstract
There are many approaches to improve the forecasted accuracy of model based on fuzzy time series such as: determining the optimal interval length, establishing fuzzy logic relationship groups, similarity measures, …wherein, the length of intervals is a factor that greatly affects forecasting results in fuzzy time series model. In this paper, a new forecasting model based on combining the fuzzy time series (FTS) and K-mean clustering algorithm with three computational methods, K-means clustering technique, the time - variant fuzzy logical relationship groups and defuzzification forecasting rules, is presented. Firstly, we apply the K-mean clustering algorithm to divide the historical data into clusters and tune them into intervals with proper lengths. Then, based on the new intervals obtained, the proposed method is used to fuzzify all the historical data and create the time -variant fuzzy logical relationship groups based on the new concept of time – variant fuzzy logical relationship group. Finally, Calculate the forecasted output value by the improved defuzzification technique in the stage of defuzzification. To evaluate performance of the proposed model, two numerical data sets are utilized to illustrate the proposed method and compare the forecasting accuracy with existing methods. The results show that the proposed model gets a higher average forecasting accuracy rate to forecast the Taiwan futures exchange (TAIFEX) and enrollments of the University of Alabama than the existing methods based on the first – order and high-order fuzzy time series.
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References
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[31] Hoang Tung, Nguyen Dinh Thuan, Vu Minh Loc. “The partitioning method based on hedge algebras for fuzzy time series forecasting”, Journal of Science and Technology 54 (5), 571-583, 2016.
[32] Vu Minh Loc, Nghia Huynh Pham Thanh. “Context-aware approach to improve result of forecasting enrollment in fuzzy time series, International Journal of Emerging Technologies in Engineering Research (IJETER), Volume 5, Issue 2, 28-33, 2017.
[2] Q. Song, B.S. Chissom. “Forecasting Enrollments with Fuzzy Time Series – Part II,” Fuzzy set and system, vol. 62, pp. 1-8, 1994.
[3] S.M. Chen. “Forecasting Enrollments based on Fuzzy Time Series,” Fuzzy set and system, vol. 81, pp. 311-319, 1996.
[4] S. M. Chen. “Forecasting enrollments based on high-order fuzzy time series”, Cybernetics and Systems: An International Journal, vol. 33, pp. 1-16, 2002.
[5] Singh, S. R. A simple method of forecasting based on fuzzytime series. Applied Mathematics and Computation, 186, 330–339, 2007a.
[6] Singh, S. R. A robust method of forecasting based on fuzzy time series. Applied Mathematics and Computation, 188, 472–484, 2007b
[7] H.K.. Yu. “Weighted fuzzy time series models for TAIEX forecasting ”, Physica A, 349 , pp. 609–624, 2005.
[8] Lee, L.-W., Wang, L.-H., & Chen, S.-M. Temperature prediction and TAIFEX forecasting based on fuzzy logical relationships and genetic algorithms. Expert Systems with Applications, 33, 539–550, 2007.
[9] Wang, N.-Y, & Chen, S.-M. Temperature prediction and TAIFEX forecasting based on automatic clustering techniques and two-factors high-order fuzzy time series. Expert Systems with Applications, 36, 2143–2154, 2009.
[10] Huarng, K, 2001b. Heuristic models of fuzzy time series for forecasting. Fuzzy Sets and Systems, 123, 369–386 .
[11] Chen, S.M., Chung, N.Y. Forecasting enrollments using high-order fuzzy time series and genetic algorithms. International of Intelligent Systems 21, 485–501, 2006b.
[12] H.K. Yu. A refined fuzzy time-series model for forecasting, Phys. A, Stat. Mech. Appl. 346, 657–681, 2004; http://dx.doi.org/10.1016/j.physa.07.024.
[13] Huarng, K.H., Yu, T.H.K. "Ratio-Based Lengths of Intervals to Improve Fuzzy Time Series Forecasting," IEEE Transactions on SMC – Part B: Cybernetics, Vol. 36, pp. 328–340, 2006.
[14] Kuo, I. H. et al. An improved method for forecasting enrollments based on fuzzy time series and particle swarm optimization. Expert Systems with applications, 36, 6108–6117, 2009
[15] I.H. Kuo et al. Forecasting TAIFEX based on fuzzy time series and particle swarm optimization, Expert Systems with Applications. 37, 1494–1502, 2010.
[16] Huang, Y. L. et al. A hybrid forecasting model for enrollments based on aggregated fuzzy time series and particle swarm optimization. Expert Systems with Applications, 38, 8014–8023, 2011
[17] Nguyen Cong Dieu, Nghiem Van Tinh, Fuzzy time series forecasting based on time-depending fuzzy relationship groups and particle swarm optimization, In :Proceedings of the 9th National conference on Fundamental and Applied Information Technology Research(FAIR’9), pp.125-133, 2016
[18] Nghiem Van Tinh, Nguyen Cong Dieu, An improved method for stock market forecasting combining high-order time-variant fuzzy logical relationship groups and particle swam optimization in : Proceedings of the International Conference, Advances in Information and Communication Technology, pp.153-166, 2016.
[19] S.-M. Chen, K. Tanuwijaya. “ Fuzzy forecasting based on high-order fuzzy logical relationships and automatic clustering techniques”, Expert Systems with Applications 38,15425–15437, 2011.
[20] Zhiqiang Zhang, Qiong Zhu. “Fuzzy time series forecasting based on k-means clustering”, Open Journal of Applied Sciences, 100-103, 2012.
[21] Bulut, E., Duru, O., & Yoshida, S. A fuzzy time series forecasting model formulti-variate forecasting analysis with fuzzy c-means clustering. WorldAcademy of Science, Engineering and Technology, 63, 765–771, 2012.
[22] S. Askari, N. Montazerin, A high-order multi-variable Fuzzy Time Series forecasting algorithm based on fuzzy clustering, Expert Systems with Applications ,42, 2121–2135, 2015.
[23] Chen, S.-M., Chung, N.-Y. Forecasting enrollments of students by using fuzzy time series and genetic algorithms. International Journal of Information and Management Sciences,17, 1–17, 2006a.
[24] Lee, L. W. et al. Handling forecasting problems based on two-factors high-order fuzzy time series. IEEE Transactions on Fuzzy Systems, 14, 468–477, 2006.
[25] Lee, L.-W. Wang, L.-H., & Chen, S.-M. “Temperature prediction and TAIFEX forecasting based on high order fuzzy logical relationship and genetic simulated annealing techniques”, Expert Systems with Applications, 34, 328–336, 2008b.
[26] Ling-Yuan Hsu et al. Temperature prediction and TAIFEX forecasting based on fuzzy relationships and MTPSO techniques, Expert Syst. Appl.37, 2756 –2770, 2010.
[27] Lizhu Wang, Xiaodong Liu, Witold Pedrycz, Yongyun Shao. “Determination of temporal information granules to improve forecasting in fuzzy time series”, Expert Systems with Applications ,41, 3134–3142, 2014.
[28] Wei Lu, XueyanChen, WitoldPedrycz, XiaodongLiua, JianhuaYang. “Using interval information granules to improve forecasting in fuzzy time series”, International Journal of Approximate Reasoning 57, 1–18, 2015.
[29] S.M. Chen , P.Y. Kao , “TAIEX forecasting based on fuzzy time series, particle swarm optimization techniques and support vector machines” , Inf. Sci. 247, 62–71, 2013.
[30] S.M. Chen , Bui Dang Ha Phuong, “Fuzzy time series forecasting based on optimal partitions of intervals and optimal weighting vectors”, Knowledge-Based Systems .118, 204 –216, 2017.
[31] Hoang Tung, Nguyen Dinh Thuan, Vu Minh Loc. “The partitioning method based on hedge algebras for fuzzy time series forecasting”, Journal of Science and Technology 54 (5), 571-583, 2016.
[32] Vu Minh Loc, Nghia Huynh Pham Thanh. “Context-aware approach to improve result of forecasting enrollment in fuzzy time series, International Journal of Emerging Technologies in Engineering Research (IJETER), Volume 5, Issue 2, 28-33, 2017.
Published
2017-12-31
How to Cite
TINH, Nghiem Van; CONG DIEU, Nguyen.
IMPROVING THE FORECASTED ACCURACY OF MODEL BASED ON FUZZY TIME SERIES AND K-MEANS CLUSTERING.
Journal of Science and Technology: Issue on Information and Communications Technology, [S.l.], v. 3, n. 2, p. 46-55, dec. 2017.
ISSN 1859-1531.
Available at: <http://ict.jst.udn.vn/index.php/jst/article/view/47>. Date accessed: 24 nov. 2024.
doi: https://doi.org/10.31130/jst.2017.47.
Section
Articles
