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Fuzzy Forecasting with DNA Computing

  • Don Jyh-Fu Jeng
  • Junzo Watada
  • Berlin Wu
  • Jui-Yu Wu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4287)

Abstract

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.

Keywords

Linguistic Variable Forecast Performance ARIMA Model Closing Price Forecast Technique 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Don Jyh-Fu Jeng
    • 1
  • Junzo Watada
    • 1
  • Berlin Wu
    • 2
  • Jui-Yu Wu
    • 3
  1. 1.Graduate School of Information, Production and SystemsWaseda UniversityFukuokaJapan
  2. 2.Department of Mathematical SciencesNational Chengchi UniversityTaipeiTaiwan
  3. 3.Department of Biochemistry, School of MedicineTaipei Medical UniversityTaipeiTaiwan

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