A fuzzy time-series prediction by GA based rough sets model

Jing Zhao, Junzo Watada, Yoshiyuki Matsumoto

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


    Fuzzy time-series (FTS) has been applied to handle non-linear problems, such as enrollment, weather and stock index forecasting. In the forecasting processes, fuzzy logical relation (FLR) plays a pivotal role in forecasting accuracy. Usually FTS uses an equal interval to obtain forecasting values. But in this paper, we use genetic algorithm (GA) to optimize the interval at first. Based on this, then rough set (RS) method is used to recalculate the values. In the empirical analysis, Japan stock index is used as experimental data sets and one fuzzy time-series method, as a comparison model. The experimental results showed that the proposed method is more efficient than the FTS method.

    Original languageEnglish
    Title of host publication2015 10th Asian Control Conference: Emerging Control Techniques for a Sustainable World, ASCC 2015
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    ISBN (Print)9781479978625
    Publication statusPublished - 2015 Sept 8
    Event10th Asian Control Conference, ASCC 2015 - Kota Kinabalu, Malaysia
    Duration: 2015 May 312015 Jun 3


    Other10th Asian Control Conference, ASCC 2015
    CityKota Kinabalu


    • Forecasting
    • Fuzzy time-series
    • Genetic algorithm
    • Rough set
    • Stock Index

    ASJC Scopus subject areas

    • Control and Systems Engineering


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