DocumentCode
517951
Title
Notice of Retraction
Second-order difference heuristic model of fuzzy time series based on particle swarm optimization
Author
Kai Chi ; Wen-Gang Che ; Fang-Ping Fu ; Ming-Kun Zhuo
Author_Institution
Sch. of Inf. Eng. & Autom., Kunming Univ. of Sci. & Technol., Kunming, China
Volume
2
fYear
2010
fDate
16-18 April 2010
Abstract
Notice of Retraction
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
Fuzzy time series (FTS) has become an effective method for forecasting some typical time series-enrollments, stock price and daily price of foreign exchange-due to its salient capacities for dealing with uncertainty, vagueness in historical observations. However, how to partition the universe of discourse and how to construct fuzzy logic relationships are two unsolved problems which may affect the forecasting accuracy. In order to supply an effective platform, relatively speaking, this paper proposed a novel heuristic model of fuzzy time series based on particle swarm optimization (PSO). The empirical results show that the presented model not only provides more effective fuzzy logic relationship matrix, but obtains higher forecasting accuracy rates than the existing models.
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
Fuzzy time series (FTS) has become an effective method for forecasting some typical time series-enrollments, stock price and daily price of foreign exchange-due to its salient capacities for dealing with uncertainty, vagueness in historical observations. However, how to partition the universe of discourse and how to construct fuzzy logic relationships are two unsolved problems which may affect the forecasting accuracy. In order to supply an effective platform, relatively speaking, this paper proposed a novel heuristic model of fuzzy time series based on particle swarm optimization (PSO). The empirical results show that the presented model not only provides more effective fuzzy logic relationship matrix, but obtains higher forecasting accuracy rates than the existing models.
Keywords
foreign exchange trading; fuzzy set theory; particle swarm optimisation; time series; foreign exchange price; fuzzy logic relationship matrix; fuzzy time series; particle swarm optimization; second order difference heuristic model; stock price; Automation; Channel hot electron injection; Economic forecasting; Fuzzy logic; Fuzzy set theory; Fuzzy sets; Particle swarm optimization; Partitioning algorithms; Predictive models; Weather forecasting; forecasting; fuzzy time series; heuristic model; paritcle swarm optimization; second-order difference;
fLanguage
English
Publisher
ieee
Conference_Titel
Computer Engineering and Technology (ICCET), 2010 2nd International Conference on
Conference_Location
Chengdu
Print_ISBN
978-1-4244-6347-3
Type
conf
DOI
10.1109/ICCET.2010.5485279
Filename
5485279
Link To Document