Literature DB >> 18244753

Temperature prediction using fuzzy time series.

S M Chen1, J R Hwang.   

Abstract

A drawback of traditional forecasting methods is that they can not deal with forecasting problems in which the historical data are represented by linguistic values. Using fuzzy time series to deal with forecasting problems can overcome this drawback. In this paper, we propose a new fuzzy time series model called the two-factors time-variant fuzzy time series model to deal with forecasting problems. Based on the proposed model, we develop two algorithms for temperature prediction. Both algorithms have the advantage of obtaining good forecasting results.

Year:  2000        PMID: 18244753     DOI: 10.1109/3477.836375

Source DB:  PubMed          Journal:  IEEE Trans Syst Man Cybern B Cybern        ISSN: 1083-4419


  2 in total

1.  A novel hybrid fuzzy time series model for prediction of COVID-19 infected cases and deaths in India.

Authors:  Niteesh Kumar; Harendra Kumar
Journal:  ISA Trans       Date:  2021-07-06       Impact factor: 5.911

2.  Coercively adjusted auto regression model for forecasting in epilepsy EEG.

Authors:  Sun-Hee Kim; Christos Faloutsos; Hyung-Jeong Yang
Journal:  Comput Math Methods Med       Date:  2013-04-28       Impact factor: 2.238

  2 in total

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