DocumentCode :
3426370
Title :
High-order difference heuristic model of fuzzy time series based on particle swarm optimization and information entropy for stock markets
Author :
Fu, Fang-Ping ; Chi, Kai ; Che, Wen-Gang ; Zhao, Qing-Jiang
Author_Institution :
Fac. of Inf. Eng. & Autom., Kunming Univ. of Sci. & Technol., Kunming, China
Volume :
2
fYear :
2010
fDate :
25-27 June 2010
Abstract :
The forecasting problem of time series is an intriguing and pivotal research topic. Due to salient capabilities of tracking uncertainty and vagueness in observations, fuzzy time series has received more and more attention from not only researchers but investors. However, there exist two unsolved problems in the modeling of fuzzy time series, i.e., how to partition the universe of discourse and how to construct fuzzy logic relationships effectively. Here we introduced the technique of particle swarm optimization (PSO) to partition the universe of discourse, and combine information entropy to define the fuzzy sets. Based on these two algorithms, a novel model of fuzzy time series is proposed. To testify model´s validity, the authors forecasted the enrollments and Dow index. The empirical results demonstrate that the presented method has higher forecasting accuracy rates than the excising ones.
Keywords :
economic forecasting; fuzzy set theory; particle swarm optimisation; stock markets; time series; Dow index; enrollments; forecasting problem; fuzzy logic relationships; fuzzy sets; fuzzy time series; high-order difference heuristic model; information entropy; particle swarm optimization; stock markets; Economic forecasting; Fuzzy logic; Fuzzy sets; Information entropy; Particle swarm optimization; Partitioning algorithms; Predictive models; Stock markets; Testing; Uncertainty; forecasting; fuzzy time series; heuristic; information entropy; particle swarm optimization;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computer Design and Applications (ICCDA), 2010 International Conference on
Conference_Location :
Qinhuangdao
Print_ISBN :
978-1-4244-7164-5
Electronic_ISBN :
978-1-4244-7164-5
Type :
conf
DOI :
10.1109/ICCDA.2010.5541222
Filename :
5541222
Link To Document :
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