A hidden Markov model-based forecasting model for fuzzy time series

Sheng Tun Li, Yi Chung Cheng

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)


Vague and incomplete data represented as linguistic values massively exists in diverse real-word applications. The task of forecasting fuzzy time series under uncertain circumstances is thus of great important but difficult. The inherent uncertainty involving time evolution usually makes the transition of states in a system probabilistic. In this paper, we proposed a new forecasting model based on Hidden Markov Model for fuzzy time series to realize the probabilistic state transition. We conduct experiments of forecasting a real-world temperature application to validate the better accuracy of the proposed model achieved over traditional fuzzy time series models.

Original languageEnglish
Pages (from-to)1919-1924
Number of pages6
JournalWSEAS Transactions on Systems
Issue number8
Publication statusPublished - 2006 Aug

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Computer Science Applications


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