Developing a Fuzzy Time Series Forecasting Model Based on Hedge Algebras and Particle Swarm Optimization
DOI:
https://doi.org/10.48048/tis.2022.2157Keywords:
Fuzzy time series, Fuzzy relationship group, Hedge algebras, Particle swam optimization, EnrolmentsAbstract
In recent years, numerous fuzzy time series (FTS) forecasting models have been widely used. One of the important factors for obtaining high forecasting accuracy in fuzzy time series model is that the lengths of intervals in the universe of discourse. In this study, a hybrid forecasting model which uses hedge algebra (HA) and particle swarm optimization (PSO) is proposed to determine optimal lengths of intervals in FTS models. In that, HA is utilized as a tool to partition the universe of discourse into intervals with unequal-size corresponding to the semantic intervals calculated from the linguistic terms. After processing of generating the intervals, we define fuzzy sets based on the observation data of times series and use them to establish fuzzy relationship groups. Then, the proposed model is combined with the PSO technique to find the appropriate length of each interval with view to reaching the better forecasting accuracy rate. The performance of the proposed model is evaluated with the historical data of enrolments at the University of Alabama. The simulated results obtained indicate that the proposed model achieves higher forecasting accuracy compared other existing forecasting models and it can obtain better quality solutions for both the 1st-order and high-order FTS model.
HIGHLIGHTS
- In fuzzy time series forecasting model, the length of intervals and the order of fuzzy relationships are two critical factors for forecasting accuracy
- Hedge algebra and PSO are utilized as a tool to partition the universe of discourse into intervals with unequal - size corresponding to the semantic intervals calculated from the linguistic terms
- The defuzzification principles are used to calculate the forecasting results based on the fuzzy relationship groups
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LA Zadeh. Fuzzy sets. Inf. Control 1965, 8, 338-53.
Q Song and BS Chissom. Fuzzy time series and its models. Fuzzy Sets Syst. 1993; 54, 269-77.
Q Song and BS Chissom. Forecasting enrollments with fuzzy time series - Part I. Fuzzy Sets Syst. 1993; 54, 1-9.
SM Chen. Forecasting enrollments based on fuzzy time series. Fuzzy Sets Syst. 1996; 81, 311-9.
HK Yu. Weighted fuzzy time series models for TAIEX forecasting. Physica A 2005; 349, 609-24.
VR Uslu, E Bas, U Yolcu and E Egrioglu. A fuzzy time series approach based on weights determined by the number of recurrences of fuzzy relations. Swarm Evol. Comput. 2014; 15, 19-26.
SM Chen. Forecasting enrollments based on hight-order fuzzy time series. Cybern. Syst. 2002; 33, 1-16.
K Huarng. Effective lengths of intervals to improve forecasting in fuzzy time series. Fuzzy Sets Syst. 2001; 123, 387-94.
LW Lee, LH Wang, SM Chen and YH Leu. Handling forecasting problems based on two-factors high-order fuzzy time series. IEEE Trans. Fuzzy Syst. 2006; 14, 468-77.
SM Chen and K Tanuwijaya. Fuzzy forecasting based on high- order fuzzy logical relationships and automatic clustering techniques. Expert Syst. Appl. 2011; 38, 15425-37.
SM Chen and NY Chung. Forecasting enrollments of students by using fuzzy time series and genetic algorithms. Int. J. Inf. Manag. Sci. 2006; 17, 1-17.
SM Chen and NY Chung. Forecasting enrollments using high-order fuzzy time series and genetic algorithms. Int. Intell. Syst. 2006; 21, 485-501.
LW Lee, LH Wang and SM Chen. Temperature prediction and TAIFEX forecasting based on hight order fuzzy logical ralationship and genetic simulated annealing techniques. Expert Syst. Appl. 2008; 34, 328-36.
IH Kuo, SJ Horng, TW Kao, TL Lin, CL Lee and Y Pan. An improved method for forecasting enrollments based on fuzzy time series and particle swarm optimization. Expert Syst. Appl. 2009; 36, 6108-17.
YL Huang, SJ Horng, M He, P Fan, TW Kao, MK Khan, JL Lai and IH Kuo. A hybrid forecasting model for enrollments based on aggregated fuzzy time series and particle swarm optimization. Expert Syst. Appl. 2001; 38, 8014-23.
LY Hsu, SJ Horng, TW Kao, YH Chen, RS Run, RJ Chen, JL Lai and IH Kuo. Temperature prediction and TAIFEX forecasting based on fuzzy relationships and MTPSO techniques. Expert Syst. Appl. 2010; 37, 2756-70.
NC Dieu and NV 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, Can Tho, Vietnam. 2016, p. 125-33.
JI Park, DJ Lee, CK Song and MG Chun. TAIFEX and KOSPI 200 forecasting based on two-factors high-order fuzzy time series and particle swarm optimization. Expert Syst. Appl. 2010; 37, 959-67.
SM Chen and BHP Dang. Fuzzy time series forecasting based on optimal partitions of intervals and optimal weighting vectors. Knowl. Based Syst. 2016; 118, 204-16.
SM Chen and WS Jian. Fuzzy forecasting based on two-factors second-order fuzzy-trend logical relationship groups, similarity measures and PSO techniques. Inf. Sci. 2017; 391-392, 65-79.
M Bose and K Mali. A novel data partitioning and rule selection technique for modelling high-order fuzzy time series. Appl. Soft Comput. 2017; 63, 87-96.
Z Tian, P Wang and T He. Fuzzy time series based on K-means and particle swarm optimization algorithm. In: Proceedings of the International Conference on Man-Machine-Environment System Engineering, Xi'an, China. 2016, p. 181-9.
Z Zhang and Q Zhu. Fuzzy time series forecasting based on k-means clustering. Open J. Appl. Sci. 2012; 2, 100-3 .
NV Tinh and NC Dieu. Improving the forecasted accuracy of model based on fuzzy time series and k-means clustering. J. Sci. Technol. 2017; 3, 46-55.
E Bulut, O Duru and S Yoshida. A fuzzy time series forecasting model formulti-variate forecasting analysis with fuzzy c-means clustering. World Acad. Sci. Eng. Technol. 2012; 63, 765-71.
S Askari and N Montazerin. A high-order multi-variable Fuzzy Time Series forecasting algorithm based on fuzzy clustering. Expert Syst. Appl. 2015; 42, 2121-35.
NC Ho, NC Dieu and VN Lan. The application of hedge algebras in fuzzy time series forecasting. J. Sci. Technol. 2016; 54, 161-77.
NC Ho and W Wechler. Hedge algebra: An algebraic approach to structures of sets of linguistic truth values. Fuzzy Sets Syst. 1990; 35, 281-93.
H Tung, ND Thuan and VM Loc. The partitioning method based on hedge algebras for fuzzy time series forecasting. J. Sci. Technol. 2016; 54, 571-83.
NV Tinh. Enhanced forecasting accuracy of fuzzy time series model based on combined fuzzy C-mean clustering with particle swam optimization. Int. J. Comput. Intell. Appl. 2020; 19, 2050017.
J Kennedy and R Eberhart. Particle swarm optimization. In: Proceedings of the IEEE International Conference on Neural Network, Perth, WA, Australia. 1995, p. 1942-8.
NC Ho, TT Son and DT Long. Hedge algebras with limited number of hedges and applied to fuzzy classification problems. Vietnam J. Sci. Technol. 2012; 48, 13-22.
L Wang, XD Liu and W Pedrycz. Effective intervals determined by information granules to improve forecasting in fuzzy time series. Expert Syst. Appl. 2013; 40, 5673-79.
L Wang, X Liu, W Pedrycz and Y Shao. Determination of temporal information granules to improve forecasting in fuzzy time series. Expert Syst. Appl. 2014; 41, 3134-42.
W Lu, X Chen, W Pedrycz, X Liu and J Yang. Using interval information granules to improve forecasting in fuzzy time series. Int. J. Approx. Reason. 2015; 57, 1-18.
Y Wang, Y Lei, X Fan and Y Wang. Intuitionistic fuzzy time series forecasting model based on intuitionistic fuzzy reasoning. Math. Probl. Eng. 2016; 2016, 5035160.
K Khiabani and SR Aghabozorgi. Adaptive time-variant model optimization for fuzzy-time-series forecasting. IAENG Int. J. Comput. Sci. 2015; 42, 107-16.
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