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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.