DocumentCode
2285038
Title
Notice of Retraction
A Novel Forecasting Model of Fuzzy Time Series Based on K-means Clustering
Author
Chi Kai ; Fu Fang-Ping ; Che Wen-Gang
Author_Institution
Sch. of Inf. Eng. & Autom., Kunming Univ. of Sci. & Technol., Kunming, China
Volume
1
fYear
2010
fDate
6-7 March 2010
Firstpage
223
Lastpage
225
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) is an effective method in forecasting problems due to its salient capabilities of tracking uncertainty and vagueness in observation data. However, in FTS forecasting, it is required about 5-7 intervals in the universe of discourse, as a result, the method of partition intervals become a major consideration. Recently, some studies have demonstrated that the method using length-variant intervals is able to improve the forecasting accuracy at a large scale. In this paper, a K-means clustering technique is used while selecting the length of each interval, and then a novel model of FTS is introduced accordingly. The empirical results show that this model provides a more general platform for partitioning universe of discourse, and gets higher forecasting accuracy rates than those of the existing methods.
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) is an effective method in forecasting problems due to its salient capabilities of tracking uncertainty and vagueness in observation data. However, in FTS forecasting, it is required about 5-7 intervals in the universe of discourse, as a result, the method of partition intervals become a major consideration. Recently, some studies have demonstrated that the method using length-variant intervals is able to improve the forecasting accuracy at a large scale. In this paper, a K-means clustering technique is used while selecting the length of each interval, and then a novel model of FTS is introduced accordingly. The empirical results show that this model provides a more general platform for partitioning universe of discourse, and gets higher forecasting accuracy rates than those of the existing methods.
Keywords
forecasting theory; fuzzy set theory; pattern clustering; time series; K-means clustering; forecasting model; fuzzy time series; length-variant intervals; Clustering algorithms; Economic forecasting; Educational technology; Fuzzy logic; Fuzzy set theory; Fuzzy sets; Predictive models; Technology forecasting; Uncertainty; Weather forecasting; K-means; enrollments; forecasting; fuzzy time series; partition interval;
fLanguage
English
Publisher
ieee
Conference_Titel
Education Technology and Computer Science (ETCS), 2010 Second International Workshop on
Conference_Location
Wuhan
Print_ISBN
978-1-4244-6388-6
Type
conf
DOI
10.1109/ETCS.2010.249
Filename
5459007
Link To Document