A deterministic forecasting model for fuzzy time series

Sheng-Tun Li, Yi Chung Cheng

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

With the capability of dealing with vague and incomplete data, fuzzy time-series has recently received more attentions. There have been a number of works devote to improving forecasting accuracy or reduction of computation overhead, yet several essential issues needs to be addressed. We propose a deterministic forecasting model by revising Chen's high-order forecasting model for handling these issues. Experimental results on the enrollment data of the University of Alabama demonstrate that the resulting forecasting model outperforms the existing models in terms of accuracy.

Original languageEnglish
Title of host publicationProceedings of the IASTED International Conference on Computational Intelligence
Pages25-30
Number of pages6
Publication statusPublished - 2005 Dec 1
EventIASTED International Conference on Computational Intelligence - Calgary, AB, Canada
Duration: 2005 Jul 42005 Jul 6

Publication series

NameProceedings of the IASTED International Conference on Computational Intelligence
Volume2005

Other

OtherIASTED International Conference on Computational Intelligence
CountryCanada
CityCalgary, AB
Period05-07-0405-07-06

All Science Journal Classification (ASJC) codes

  • Engineering(all)

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