Fuzzy forecasting with DNA computing

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

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

10 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationDNA Computing - 12th International Meeting on DNA Computing, DNA12, Revised Selected Papers
Number of pages13
Publication statusPublished - 2006
Event12th International Meeting on DNA Computing, DNA12 - Seoul, Korea, Republic of
Duration: Jun 5 2006Jun 9 2006

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4287 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other12th International Meeting on DNA Computing, DNA12
Country/TerritoryKorea, Republic of

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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