A new method for fuzzy forecasting based on two-factors high-order fuzzy-trend logical relationship groups and particle swarm optimization techniques

Author

Chen, Shyi-Ming ; Manalu, Gandhi Maruli Tua ; Shih, Shu-Chuan ; Sheu, Tian-Wei ; Liu, Hsiang-chuan

Author_Institution

Dept. of Comput. Sci. & Inf. Eng., Nat. Taiwan Univ. of Sci. & Technol., Taipei, Taiwan

fYear

2011

fDate

9-12 Oct. 2011

Firstpage

2301

Lastpage

2306

Abstract

This paper presents a new method for fuzzy forecasting based on two-factors high-order fuzzy-trend logical relationship groups and particle swarm optimization techniques. We fuzzify the historical training data of the main factor and the secondary factor, respectively, to form two-factors high-order fuzzy logical relationships. Then, we group the two-factors high-order fuzzy logical relationships into two-factors high-order fuzzy-trend logical relationship groups. Finally, we obtain the optimal weighting vectors for each fuzzy-trend logical relationship group by using particle swarm optimization techniques to perform the forecasting. The experimental results show that the proposed method gets higher average forecasting accuracy rates than the existing methods.

Keywords

fuzzy logic; fuzzy set theory; particle swarm optimisation; fuzzy forecasting; historical training data; main factor; optimal weighting vectors; particle swarm optimization techniques; secondary factor; two-factors high-order fuzzy-trend logical relationship groups; Forecasting; Fuzzy sets; Particle swarm optimization; Testing; Time series analysis; Training; Vectors; Fuzzy forecasting; Fuzzy time series; Particle swarm optimization techniques; Two-factors high-order fuzzy-trend logical relationship groups;

fLanguage

English

Publisher

ieee

Conference_Titel

Systems, Man, and Cybernetics (SMC), 2011 IEEE International Conference on

Conference_Location

Anchorage, AK

ISSN

1062-922X

Print_ISBN

978-1-4577-0652-3

Type

conf

DOI

10.1109/ICSMC.2011.6084021

Filename

6084021