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

Jing Zhao, Junzo Watada, Yoshiyuki Matsumoto

    研究成果: Conference 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.

    ホスト出版物のタイトル2015 10th Asian Control Conference: Emerging Control Techniques for a Sustainable World, ASCC 2015
    出版社Institute of Electrical and Electronics Engineers Inc.
    出版ステータスPublished - 2015 9月 8
    イベント10th Asian Control Conference, ASCC 2015 - Kota Kinabalu, Malaysia
    継続期間: 2015 5月 312015 6月 3


    Other10th Asian Control Conference, ASCC 2015
    CityKota Kinabalu

    ASJC Scopus subject areas

    • 制御およびシステム工学


    「A fuzzy time-series prediction by GA based rough sets model」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。