Fuzzy forecasting with DNA computing

Don Jyh Fu Jeng, Junzo Watada, Berlin Wu, Jui Yu Wu

    研究成果: Conference contribution

    9 被引用数 (Scopus)

    抄録

    There are many forecasting techniques including: exponential smoothing, ARIMA model, GARCH model, neural networks and genetic algorithm, etc. Since financial time series may be influenced by many factors, conventional model based techniques and hard computing methods seem inadequate in the prediction. Those methods, however, have their drawbacks and advantages. In recent years, the innovation and improvement of forecasting techniques have caught more attention, and also provides indispensable information in decision-making process. In this paper, a new forecasting technique, named DNA forecasting, is developed. This may be of use to a nonlinear time series forecasting. The methods combined the mathematical, computational, and biological sciences. In the empirical study, we demonstrated a novel approach to forecast the exchange rates through DNA. The mean absolute forecasting accuracy method is defined and used in evaluating the performance of linguistic forecasting. The comparison with ARIMA model is also illustrated.

    本文言語English
    ホスト出版物のタイトルLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    ページ324-336
    ページ数13
    4287 LNCS
    DOI
    出版ステータスPublished - 2006
    イベント12th International Meeting on DNA Computing, DNA12 - Seoul
    継続期間: 2006 6 52006 6 9

    出版物シリーズ

    名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    4287 LNCS
    ISSN(印刷版)03029743
    ISSN(電子版)16113349

    Other

    Other12th International Meeting on DNA Computing, DNA12
    CitySeoul
    Period06/6/506/6/9

    ASJC Scopus subject areas

    • Computer Science(all)
    • Theoretical Computer Science

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