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)


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.


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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  1. 1.
    Adams, J.: On the Application of DNA Based Computation (1998),
  2. 2.
    Adleman, L.: Computing with DNA. Scientific American 279, 34–41 (1988)Google Scholar
  3. 3.
    Adleman, L.: Molecular computation of solutions to combinatorial problems. Science 226, 1021–1024 (1994)CrossRefGoogle Scholar
  4. 4.
    Boneh, D., Dunworth, C., Lipton, R., Sgall, J.: On the Computational Power of DNA. DAMATH: Discrete Applied Mathematics and Combinatorial Operations Research and Computer Science 71 (1996)Google Scholar
  5. 5.
    Chen, S.M., Hwang, J.R.: Temperature prediction using fuzzy time series. IEEE Transactions on Systems, Man, and Cybernetics 30, 263–275 (2000)CrossRefGoogle Scholar
  6. 6.
    Chiang, D., Chow, L., Wang, Y.: Mining time series data by a fuzzy linguistic summary system. Fuzzy Sets and Systems 112, 419–432 (2000)MATHCrossRefGoogle Scholar
  7. 7.
    Cho, A.: DNA Computing: Hairpins Trigger an Automatic Solution. Science 288, 1152–1153 (2000)CrossRefGoogle Scholar
  8. 8.
    Guarnieri, F., Fliss, M., Bancroft, C.: Making DNA Add. Science 273, 220–223 (1996)CrossRefGoogle Scholar
  9. 9.
    Huarng, K.: Heuristic models of fuzzy time series for forecasting. Fuzzy Sets and Systems 123, 369–386 (2001)MATHCrossRefMathSciNetGoogle Scholar
  10. 10.
    Johnson, R.C.: Time to Engineer DNA Computers. EE Times (2001),
  11. 11.
    Kari, L., Gloor, G., Yu, S.: Using DNA to solve the Bounded Post Correspondence Problem. Theoretical Computer Science 231, 192–203 (2000)CrossRefMathSciNetGoogle Scholar
  12. 12.
    Kumar, K., Wu, B.: Detection of change points in time series analysis with fuzzy statistics. International Journal of Systems Science 32, 1185–1192 (2001)MATHCrossRefMathSciNetGoogle Scholar
  13. 13.
    Lipton, R.: DNA Solution of Hard Computational Problems. Science 268, 542–545 (1995)CrossRefGoogle Scholar
  14. 14.
    Liu, Q., Wang, L., Frutos, A.G., Condon, A.E., Corn, R.M., Smith, L.M.: DNA computing on surface. Nature 403, 175–179 (2000)CrossRefGoogle Scholar
  15. 15.
    Miller, C.: Using DNA Algorithms to Solve NP-Complete Problems,
  16. 16.
    Normile, D.: Molecular Computing: DNA-Based Computer Takes Aim at Genes. Science 295, 951 (2002)CrossRefGoogle Scholar
  17. 17.
    Ouyang, Q., Kaplan, P.D., Liu, S., Libchaber, A.: DNA Solution of the Maximal Clique Problem. Science 278, 446–449 (1997)CrossRefGoogle Scholar
  18. 18.
    Owenson, G.G., Amos, M., Hodgson, D.A., Gibbsons, A.: DNA-based logic. Soft Computing 5, 102–105 (2001)MATHCrossRefGoogle Scholar
  19. 19.
    Parker, J.: Computing with DNA. European Molecular Biology Organization Reports 4, 7–10 (2003)Google Scholar
  20. 20.
    Păun, G.: Computing with Bio-Molecules: Theory and Experiments. Springer, Heidelberg (1998)MATHGoogle Scholar
  21. 21.
    Păun, G., Rozenberg, G., Salomaa, A.: DNA Computing - New Computing Paradigms. Springer, Heidelberg (1998)MATHGoogle Scholar
  22. 22.
    Reif, J.H., LaBean, T.H., Pirrug, M., Rana, V.S., Guo, B., Kingsford, C., Wickham, G.S.: Experimental construction of a very large scale DNA database with associatice search capability. In: The 7th International Workshop on DNA-Based Computers, pp. 241–250 (2001)Google Scholar
  23. 23.
    Tseng, F., Tzeng, G.: A fuzzy SARIMA model for forecasting. Fuzzy Sets and Systems 126, 367–376 (2002)MATHCrossRefMathSciNetGoogle Scholar
  24. 24.
    Tseng, F., Tzeng, G., Yu, H., Yuan, B.: Fuzzy ARIMA model for forecasting the foreign exchange market. Fuzzy Sets and Systems 118, 9–19 (2001)CrossRefMathSciNetGoogle Scholar
  25. 25.
    Winfree, E., Lin, F., Wenzler, L.A., Seeman, N.C.: Design and self-assembly of two-dimensional DNA crystals. Nature 394, 539–545 (1998)CrossRefGoogle Scholar
  26. 26.
    Wu, B., Hung, S.: A fuzzy identification procedure for nonlinear time series: with example on ARCH and bilinear models. Fuzzy Sets and Systems 108, 275–287 (1999)MATHCrossRefMathSciNetGoogle Scholar

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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