An intuitionistic fuzzy time series model based on new data transformation method

Long Sheng Chen, Mu Yen Chen, Jing Rong Chang, Pei Yu Yu

研究成果: Article同行評審

6 引文 斯高帕斯(Scopus)


Traditional time series methods can predict seasonal problems, but not problems with transferred linguistic data. Thus, a forecasting method for such problems is required. However, existing intuitionistic fuzzy time series forecasting methods lack per-suasiveness in determining the degree of hesitation and the lengths of intervals. Hence, this research is mainly to explore how to decide the degree of hesitation for each interval for intuitionistic fuzzy time series. This paper proposes the weighted intuition-istic fuzzy time series model based on the Nth quantile discretization approach (NQDA). The proposed model can decide the appropriate number, interval length, degree of hesitation, and membership and nonmembership functions of linguistic values on the basis of the training data. In the experimental section, the forecasts of several data sets are made for model validation. Results indicate that the proposed model can be used to obtain forecasts for other time-related data sets.

頁(從 - 到)550-559
期刊International Journal of Computational Intelligence Systems
出版狀態Published - 2021

All Science Journal Classification (ASJC) codes

  • 一般電腦科學
  • 計算數學


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