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

Sheng Tun Li, Yi Chung Cheng

研究成果: Article同行評審

11 引文 斯高帕斯(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.

頁(從 - 到)1919-1924
期刊WSEAS Transactions on Systems
出版狀態Published - 2006 八月

All Science Journal Classification (ASJC) codes

  • 控制與系統工程
  • 電腦科學應用


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