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

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

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Pages (from-to)550-559
Number of pages10
JournalInternational Journal of Computational Intelligence Systems
Issue number1
Publication statusPublished - 2021

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Computational Mathematics


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