An enhanced deterministic fuzzy time series forecasting model

Sheng Tun Li, Yi Chung Cheng

Research output: Contribution to journalArticlepeer-review

16 Citations (Scopus)


The study of fuzzy time series has attracted great interest and is expected to expand rapidly. Various forecasting models including high-order models have been proposed to improve forecasting accuracy or reducing computational cost. However, there exist two important issues, namely, rule redundancy and high-order redundancy that have not yet been investigated. This article proposes a novel forecasting model to tackle such issues. It overcomes the major hurdle of determining the k-order in high-order models and is enhanced to allow the handling of multi-factor forecasting problems by removing the overhead of deriving all fuzzy logic relationships beforehand. Two novel performance evaluation metrics are also formally derived for comparing performances of related forecasting models. Experimental results demonstrate that the proposed forecasting model outperforms the existing models in efficiency.

Original languageEnglish
Pages (from-to)211-235
Number of pages25
JournalCybernetics and Systems
Issue number3
Publication statusPublished - 2009 Apr

All Science Journal Classification (ASJC) codes

  • Software
  • Information Systems
  • Artificial Intelligence


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