Biologically inspired fuzzy forecasting

A new forecasting methodology

Don Jyh Fu Jeng, Junzo Watada, Berlin Wu

    Research output: Contribution to journalArticle

    7 Citations (Scopus)

    Abstract

    There are many forecasting techniques including the ARIMA mnodel GARCH mnodel e.rponential smnoothing neural networks genetic algorithmn etc. Those mnethods however, have their drawbacks and advantages. Since financial time series may be influenced by many factors such as trading volumne business cycle oil pricey and seasonal factor conventional model based on prediction methodologies and hard computing mnethods seem inadequate. In recent years the innovation and improvement of forecasting methodologies have caught more attention and also provide indispensable information in the decision-making process, especially in the fields of financial economics and engineering management. In this paper a new forecasting methodology inspired by natural selection is developed. The new forecasting methodology may be of use to a nonlinear time series forecasting. The method combines mnathemnatical comnputational and biological sciences which includes fuzzy logic DNA encoding polymnerase chain reaction and DNA quantification. In the empirical study, currency exchange rate forecasting is demonstrated. The Mean Absolute Forecasting Accuracy method is defined for evaluating the performnance and the result comparing with the ARIMA method is illustrated.

    Original languageEnglish
    Pages (from-to)4835-4844
    Number of pages10
    JournalInternational Journal of Innovative Computing, Information and Control
    Volume5
    Issue number12
    Publication statusPublished - 2009 Dec

    Fingerprint

    Forecasting
    Methodology
    ARIMA
    Time series
    Nonlinear Time Series
    Time Series Forecasting
    Business Cycles
    DNA
    Natural Selection
    Financial Time Series
    Generalized Autoregressive Conditional Heteroscedasticity
    Currency
    Factor Models
    Exchange rate
    Empirical Study
    Quantification
    Fuzzy Logic
    Encoding
    Decision Making
    Economics

    Keywords

    • Bio-inspired computing
    • Forecasting
    • Fuzzy time series forecasting
    • Nonlinear time series analysis

    ASJC Scopus subject areas

    • Computational Theory and Mathematics
    • Information Systems
    • Software
    • Theoretical Computer Science

    Cite this

    Biologically inspired fuzzy forecasting : A new forecasting methodology. / Jeng, Don Jyh Fu; Watada, Junzo; Wu, Berlin.

    In: International Journal of Innovative Computing, Information and Control, Vol. 5, No. 12, 12.2009, p. 4835-4844.

    Research output: Contribution to journalArticle

    Jeng, Don Jyh Fu ; Watada, Junzo ; Wu, Berlin. / Biologically inspired fuzzy forecasting : A new forecasting methodology. In: International Journal of Innovative Computing, Information and Control. 2009 ; Vol. 5, No. 12. pp. 4835-4844.
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