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

  • Authors:

  • Sheng-Tun Li;Yi-Chung Cheng

  • Affiliations:

  • Institute of Information Management, National Cheng Kung University, Taiwan, R.O.C. and Department of Industrial and Information Management, National Cheng Kung University, Taiwan, R.O.C.;Department of Industrial and Information Management, National Cheng Kung University, Taiwan, R.O.C. and Department of International Trade, Tainan Woman's College of Arts & Technology, Tainan, ...

  • Venue:
  • ICS'06 Proceedings of the 10th WSEAS international conference on Systems
  • Year:
  • 2006

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Abstract

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.