A hybrid PSO-SA scheme for improving accuracy of fuzzy time series forecasting models
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DOI:
https://doi.org/10.15625/1813-9663/38/3/17424Keywords:
Fuzzy time series, Particle Swarm Optimization, Simulated Annealing.Abstract
Forecasting methods based on fuzzy time series have been examined intensively during last years. Three main factors which affect the accuracy of those forecasting methods are length of intervals, the way of establishing fuzzy logical relationship groups, and defuzzification techniques. Many researches focus on optimizing length of intervals in order to improve forecasting accuracies by utilizing various optimization techniques. In the line of that research trend, in this paper, a hybrid particle swarm optimization combined with simulated annealing (PSO-SA) algorithm is proposed to optimize length of intervals to improve forecasting accuracies. The experimental results in comparison with the existing forecasting models show that the proposed forecasting model is an effective forecasting model.
References
Bose M., Mali K., A novel data partitioning and rule selection technique for modeling high-order fuzzy time series, Applied Soft Computing, 63 (2018) 87–96.
Bose M., Mali K., Designing fuzzy time series forecasting models: A survey, International Journal of Approximate Reasoning, 111 (2019) 78–99.
Box G. E. P., Jenkins G., Time Series Analysis, Forecasting and Control, Holden-Day, San Francisco, CA (1970).
Cai Q. et al., A new fuzzy time series forecasting model combined with ant colony optimization and auto-regression, Knowledge-Based Systems 74 (2015) 61–68.
Chen S. M., Forecasting Enrollments based on Fuzzy Time Series, Fuzzy set and systems, 81 (1996) 311-319.
Chen S. M., Forecasting Enrollments based on high-order Fuzzy Time Series, Int. Journal: Cybernetic and Systems, 33 (2022) 1-16.
Chen S. M., Chung N. Y., Forecasting enrolments of students by using fuzzy time series and genetic algorithms, International Journal of Intelligent Systems, 17 (2006a) 1–17.
Chen S. M., Chung N. Y., Forecasting enrollments using high-order fuzzy time series and genetic algorithms, International of Intelligent Systems 21 (2006b) 485501.
Chen S. M., Kao P. Y., TAIEX forecasting based on fuzzy time series, particle swarm optimization techniques and support vector machines, Information Sciences 247 (2013) 62–71.
Cheng C. H. et al., Multi-attribute fuzzy time series method based on fuzzy clustering, Expert Systems with Applications, 34 (2008) 1235–1242.
Chen S. M., Zou X. Y., Gunawan G. C., Fuzzy time series forecasting based on proportions of intervals and particle swarm optimization techniques, Information Sciences, 500 (2019) 127–139.
Cheng S. H., Chen S. M., Jian W. S., Fuzzy time series forecasting based on fuzzy logical relationships and similarity measures, Information Sciences 327 (2016) 272-287.
Eberhart R. C., Kennedy J., A new optimizer using particle swarm theory, Proceedings of the Sixth International Symposium on Micro Machine and Human Science, Nagoya, Japan, (1995) 39-43.
Kirkpatrick S., Gelatt C. D., Vecchi M. P., Optimization by simulated annealing, Science, 220 (4598) (1983) 671-680.
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 (2009) 6108–6117.
Kuo I. H. et al., Forecasting TAIFEX based on fuzzy time series and particle swarm optimization, Expert Systems with Applications, 37 (2010) 1494–1502.
Huarng K., Effective lengths of intervals to improve forecasting in fuzzy time series, Fuzzy Sets and Systems, 123 (3) (2001b) 387-394.
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 (2011) 8014-8023.
Kennedy J., Eberhart R. C., Particle Swarm Optimization, In Proceedings of the IEEE International Conference on Neural Networks, Piscataway, New Jersey. IEEE Service Center, (1995) 1942–1948.
Phong P. D., Thuy N. T., Thanh T. X., A Hybrid Multi-objective PSO-SA Algorithm for the Fuzzy Rule Based Classifier Design Problem with the Order Based Semantics of Linguistic Terms, VNU Journal of Science: Computer Science and Communication Engineering, 30 (4) (2014) 44-56.
Phong P. D., A time series forecasting model based on linguistic forecasting rules, Journal of Computer Science and Cybernetics, 37 (1) (2021) pp. 23-42.
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 (2007) 539-550.
Lee L. W., Wang L. H., Chen S. M., Temperature prediction and TAIFEX forecasting based on high-order fuzzy logical relationships and genetic simulated annealing techniques, Expert Systems with Applications 34 (2008) 328–336.
Lu W. et al., The modeling of time series based on fuzzy information granules, Expert Systems with Applications 41 (2014) 3799–3808.
Song Q., Chissom B. S., Fuzzy Time Series and its Model, Fuzzy set and systems, 54 (1993) 269-277.
Song Q., Chissom B. S., Forecasting Enrollments with Fuzzy Time Series – Part I, Fuzzy set and systems, 54 (1993) 1-9.
Song Q., Chissom B. S., Forecasting Enrollments with Fuzzy Time Series – Part II, Fuzzy set and systems, 62 (1994) 1-8.
Tinh N. V., Dieu N. C., A new hybrid fuzzy time series forecasting model based on combining fuzzy c-means clustering and particle swam optimization, Journal of Computer Science and Cybernetics, 35 (3) (2019) 267-292.
Uslu V. R., Bas E., Yolcu U., Egrioglu E., A fuzzy time series approach based on weights determined by the number of recurrences of fuzzy relations, Swarm and Evolutionary Computation, 15 (2014) 19–26.
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 (2009) 2143-2154.
Wang L. et al., Determination of temporal information granules to improve forecasting in fuzzy time series, Expert Systems with Applications, 41 (2014) 3134-3142.
Yu H. K., Weighted fuzzy time series models for TAIEX forecasting, Physica A: Statistical Mechanics and its Applications, 349 (3–4) (2005) 609–624.
Yu H.K., A refined fuzzy time-series model for forecasting, Physica A 346 (2005) 657–681.
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