American Journal of Information Science and Computer Engineering
Articles Information
American Journal of Information Science and Computer Engineering, Vol.5, No.3, Sep. 2019, Pub. Date: Nov. 21, 2019
A Novel Forecasting Model Combining the High – Order Fuzzy Time Series with Particle Swam Optimization
Pages: 103-112 Views: 1181 Downloads: 326
Authors
[01] Nghiem Van Tinh, Faculty of Electronics, Thai Nguyen University of Technology-Thai Nguyen University, Thai Nguyen, Viet Nam.
[02] Nguyen Tien Duy, Faculty of Electronics, Thai Nguyen University of Technology-Thai Nguyen University, Thai Nguyen, Viet Nam.
Abstract
This paper proposes a novel fuzzy time series forecasting model for contributing to the stages of determining of interval lengths, establishing of fuzzy logical relationships (FLRs) and the stage of defuzzification. In fuzzy time series (FTS) models, lengths of intervals always affect the results of forecasting. Therefore, we use particle swarm optimization (PSO) technique to find the optimal length of intervals in the universe of discourse. Most of the existing forecasting models simply ignore the repeated FLRs without any proper justification or accept the number of recurrence of the FLRs without considering the appearance history of these fuzzy sets in the grouping fuzzy logical relationships process. Therefore, in this study, we consider the appearance history of the fuzzy sets on the right-hand side of the FLRs to establish the high – order fuzzy logical relationship groups, called the high-order time-variant fuzzy relationship groups (TV-FRGs) and then, a new forecasting computational technique in the stage of defuzzification is introduced with the intend to obtain the smallest forecasting error as possible. For verifying the suitability of proposed method, two numerical datasets about enrollments of the University of Alabama and Gasonline Price in Viet Nam are illustrated for forecasting process and comparing with other forecasting model. Experimental results show that the proposed model outperforms other baseline forecasting model based on the high-order FTS.
Keywords
Forecasting, Fuzzy Time Series, Fuzzy Logical Relationships, PSO, Enrollments
References
[01] Bose1, & Mali, K. A novel data partitioning and rule selection technique for modelling high – order fuzzy time series. Applied Soft Computing, 2017. https://doi.org/10.1016/j.asoc.2017.11.011.
[02] Chen, S. M., 1996. Forecasting Enrolments based on Fuzzy Time Series, Fuzzy set and systems, vol. 81, 311-319, 1996.
[03] Chen, S. M., 2002. Forecasting enrolments based on high-order fuzzy time series, Cybernetics and Systems, vol. 33, no. 1, 1-16, 2002.
[04] Chen, S. M., Bui Dang, H. P. Fuzzy time series forecasting based on optimal partitions of intervals and optimal weighting vectors. Knowledge-Based Systems 118, 204–216, 2017.
[05] Chen, S. M., Chung, N. Y., 2006b. Forecasting enrolments using high-order fuzzy time series and genetic algorithms. International Journal of Intelligent Systems, 21, 485–501, 2006b.
[06] Chen, S. M., Tanuwijaya, K. Fuzzy forecasting based on high-order fuzzy logical relationships and automatic clustering techniques, Expert Systems with Applications 38, 15425–15437, 2011.
[07] Chen, S. M., Jian, W. S. Fuzzy forecasting based on two-factors second-order fuzzy-trend logical relationship groups, similarity measures and PSO techniques. Information Sciences 391–392, 65–79, 2017.
[08] Cheng, S. H., Chen, S. M., Jian, W. S. Fuzzy time series forecasting based on fuzzy logical relationships and similarity measures. Information Sciences, 327, 272–287, 2016.
[09] Egrioglu, E., Aladag. C. H., Yolcu. Fuzzy time series forecasting with a novel hybrid approach combining fuzzy c-means and neural networks. Expert Systems with. Applications, 40 (3), 854–857, 2013.
[10] 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 7 (38), 8014–8023, 2011.
[11] Huarng, K. Effective lengths of intervals to improve forecasting in fuzzy time series, Fuzzy Sets and Systems, vol. 123, no. 3, 387-394, 2001.
[12] Hwang, J. R., Chen, S. M., & Lee, C. H. Handling forecasting problems using fuzzy time series. Fuzzy Sets and Systems, 100, 217–228, 1998.
[13] Kennedy, J., Eberhart, R. Particle swarm optimization, in: Proceedings of the 1995 IEEE International Conference on Neural Networks, 4, Perth, WA, Australia, 1942–1948, 1995.
[14] Kuo, I-H., et al. An improved method for forecasting enrolments based on fuzzy time series and particle swarm optimization, Expert systems with applications, 36, 6108–6117, 2009.
[15] Kuo, I-H., et al. Forecasting TAIFEX based on fuzzy time series and particle swarm optimization, Expert Systems with Applications 2 (37), 1494–1502, 2010.
[16] 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, 2008.
[17] Lee, L. W., Wang, L. H., Chen, S. M., Leu, Y. H. Handling forecasting problems based on two-factors high-order fuzzy time series. IEEE Transactions on Fuzzy Systems, 14, 468–477, 2006.
[18] Ling-Yuan Hsu et al,. Temperature prediction and TAIFEX forecasting based on fuzzy relationships and MTPSO techniques, Expert Systems with Applications. 37, 2756–2770, 2010.
[19] Nguyen Cong Dieu, Nghiem Van Tinh. Fuzzy time series forecasting based on time-depending fuzzy relationship groups and particle swarm optimization, Proceedings of the 9th National conference on Fundamental and Applied Information Technology Research (FAIR’9), pp. 125-133, 2016.
[20] Nghiem Van Tinh, Nguyen Cong Dieu. An improved method for stock market forecasting combining high-order timevariant fuzzy logical relationship groups and particle swam optimization in: Proceedings of the International Conference, ICTA, pp. 153-166, 2016.
[21] Park J. I., Lee, D. J., Song C. K., Chun M. G. TAIFEX and KOSPI 200 forecasting based on two-factors high-order fuzzy time series and particle swarm optimization Expert Systems with Applications 37, 959–967, 2010.
[22] Singh, S. R., A simple method of forecasting based on fuzzy time series. Applied Mathematics and Computation, 186, 330–339, 2007.
[23] Song, Q., Chissom, B. S., Forecasting enrolments with fuzzy time series-Part I, Fuzzy Sets and Systems, vol. 54, no. 1, 1-9, 1993a.
[24] Song, Q., Chissom, B. S. Fuzzy time series and its models, Fuzzy Sets and Systems, vol. 54, no. 3, 269-277, 1993b.
[25] Tian, Z. H., Wang, P., He, T. Y., Fuzzy time series based on K-means and particle swarm optimization algorithm. Man-Machine-Environement System Engineering. Lecture Note in Electrical Enginearing 406, 181-189, Springer, 2016.
[26] Tinh N. V., Dieu N. C. A novel forecasting model based on combining time-variant fuzzy logical relationship groups and K-means clustering technique. Proceedings of the 9th National Conference on Fundamental and Applied Information Technology Research (FAIR10), 2017, DOI: 10.15625/vap.2017.0002.
[27] Yu, H. K., A refined fuzzy time-series model for forecasting, Physical A: Statistical Mechanics and Its Applications, vol. 346, no. 3-4, 657-681, 2005.
[28] Yu, H. K., Weighted fuzzy time series models for TAIFEX forecasting, Phys. A, Stat. Mech. Appl. 349 (3–4), 609–624, 2005.
[29] Wang Hongxu, Guo Jianchun, Feng Hao, and Jin Hailong, “An improved forecasting model of fuzzy time series,” Applied Mechanics and Materials. vol. 678, pp. 64–69, [3rd International Conference on Mechatronics and Control Engineering (ICMCE 2014). 2014].
[30] Wang HongXu, JianChun Guo, Hao Feng, and HaiLong Jin, “A fuzzy time series forecasting model based on data differences,” 1 ICT IN EDUCATION. Frontiers in Computer Education. pp. 15–18, 2014 [2nd International Conference on Frontiers in Computer Education (ICFCE2014). Wuhan, China, 2014].
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