A steady-state probabilities model for fuzzy time series forecasting

Shu Ching Kuo, Chih Chuan Chen, Hsuan Yu Chen, Sheng Tun Li, Hung Jen Wang

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

4 Citations (Scopus)

Abstract

In this time of big data, many people often using statistical or mathematical models to analyze historical data and use data analysis to predict the changes of future. In past studies, regression analysis, neural network and logistic regression models, etc., are often used to analyze. But the above methods are based on numerical data to accurately predict, for the fuzzy data, these methods cannot be used to analysis. Until 1965, Zadeh proposed the fuzzy theory, the linguistic variable finally can be analyzed and discussion. Currently, the fuzzy theory has been widely used in many fields, such as fuzzy time series, fuzzy regression, etc. For enhancing the prediction accuracy, there are still some issues in the study of fuzzy time series. In this study, we combine the fuzzy theory and the Markov theory, using Markov matrix instead of the traditional fuzzy relation matrix, consider the frequency of transfer status. Calculate the steady-state probabilities. Build a whole new predict model and enhance the prediction accuracy of the results. Finally, to justify the effeteness of the proposed forecasting model, we compare and analyze on prediction accuracy with real-world data sets.

Original languageEnglish
Title of host publicationProceedings - 2016 5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016
EditorsAyako Hiramatsu, Tokuro Matsuo, Akimitsu Kanzaki, Norihisa Komoda
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages615-619
Number of pages5
ISBN (Electronic)9781467389853
DOIs
Publication statusPublished - 2016 Aug 31
Event5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016 - Kumamoto, Japan
Duration: 2016 Jul 102016 Jul 14

Publication series

NameProceedings - 2016 5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016

Other

Other5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016
CountryJapan
CityKumamoto
Period16-07-1016-07-14

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Computer Networks and Communications
  • Computer Science Applications
  • Computer Vision and Pattern Recognition

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