A HYBRID PSO-SA SCHEME FOR IMPROVING ACCURACY OF FUZZY TIME SERIES FORECASTING MODELS

Pham Dinh Phong, Nguyen Duc Du, Pham Hoang Hiep, Tran Xuan Thanh
Author affiliations

Authors

  • Pham Dinh Phong Faculty of Information Technology, University of Transport and Communications, Ha Noi, Viet Nam https://orcid.org/0000-0002-3736-5957
  • Nguyen Duc Du Faculty of Information Technology, University of Transport and Communications, Ha Noi, Viet Nam
  • Pham Hoang Hiep 12A3 Informatics - HUS High School For Gifted Students, VNU Ha Noi - University of Science, Viet Nam
  • Tran Xuan Thanh Faculty of Information Technology, East Asia University of Technology, Bac Ninh, Viet Nam

DOI:

https://doi.org/10.15625/1813-9663/38/3/17424

Keywords:

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.

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Published

22-09-2022

How to Cite

[1]
P. D. Phong, N. Duc Du, P. Hoang Hiep, and T. X. Thanh, “A HYBRID PSO-SA SCHEME FOR IMPROVING ACCURACY OF FUZZY TIME SERIES FORECASTING MODELS”, JCC, vol. 38, no. 3, p. 257–275, Sep. 2022.

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