Skip to main content

Advertisement

SpringerLink
Log in

Composite of adaptive support vector regression and nonlinear conditional heteroscedasticity tuned by quantum minimization for forecasts

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

Adaptive support vector regression (ASVR) applied to the forecast of complex time series is superior to the other traditional prediction methods. However, the effect of volatility clustering occurred in time-series actually deteriorates ASVR prediction accuracy. Therefore, incorporating nonlinear generalized autoregressive conditional heteroscedasticity (NGARCH) model into ASVR is employed for dealing with the problem of volatility clustering to best fit the forecast’s system. Interestingly, quantum-based minimization algorithm is proposed in this study to tune the resulting coefficients between ASVR and NGARCH, in such a way that the ASVR/NGARCH composite model can achieve the best accuracy of prediction. Quantum optimization here tackles so-called NP-completeness problem and outperforms the real-coded genetic algorithm on the same problem because it accomplishes better approach to the optimal or near-optimal coefficient-found. It follows that the proposed method definitely obtains the satisfactory results because of highly balancing generalization and localization for composite model and thus improving forecast accuracy.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Box GEP, Jenkins GM, Reinsel GC (1994) Time series analysis: forecasting & control. Prentice-Hall, New Jersey

    Google Scholar 

  2. Deng JL (1982) Control problems of grey system. Syst Control Lett 1(5):288—294

    Article  Google Scholar 

  3. Chang BR (2003) A study of non-periodic short-term random walk forecasting based on RBFNN, ARMA, or SVR-GM(1,1,tau) approach. In: Proceedings of the IJCNN, pp 254—259

  4. Vapnik V (1995) The nature of statistical learning theory. Springer-Verlag, New York

    Google Scholar 

  5. Chang BR (2005) Compensation and regularization for improving the forecasting accuracy by adaptive support vector regression. Int J Fuzzy Syst 7(3):110—119

    MathSciNet  Google Scholar 

  6. Bellerslve T (1986) Generalized autoregressive conditional heteroscedasticity. J Econ 31:307—327

    Google Scholar 

  7. Gourieroux C (1997) ARCH models and financial applications. Springer-Verlag, New York

    Google Scholar 

  8. Nielsen MA, Chuang IL (2000) Quantum computation and quantum information. Cambridge University Press, London, ISBN 0 521 63503 9

  9. Anguita D, Ridella S, Rivieccio F, Zunino R (2003) Training support vector machines—a quantum-computing perspective. In: International joint conference on neural network IJCNN03, pp 1587—1592

  10. Durr C, Hoyer P (1996) A quantum algorithm for finding the minimum, http://arxiv.org/abs/quant-ph/9607014

  11. Fletcher R (1987) Practical methods of optimization, 2nd edn. Wiley and Sons, New York

    MATH  Google Scholar 

  12. Deutsch D, Jozsa R (1992) Rapid solution of problems by quantum computation. In: Proceedings of the Royal Society, London, A439, pp. 553—558

  13. Grover LK (1996) A fast quantum mechanical algorithm for database search. In: Proceedings of the 28th annual ACM symposium theory of comp. ACM Press, pp 212—219

  14. Boyer M, Brassard G, Hoyer P, Tapp A (1998) Tight bounds on quantum searching. Fortschritte Der Physik

  15. Cristianini N, Shawe-Taylor J (2000) An introduction to support vector machines. Cambridge University Press, London

  16. Kreyszig E (1999) Advanced engineering mathematics, 8th edn. Wiley, New York

  17. Chang CC, Lin CJ (2003) LIBSVM: a library for support vector machines, http://www.csie.ntu.edu.tw/~cjlin/papers/libsvm. pdf

  18. Murphey YL, Chen ZH, Putrus M, Feldkamp L (2003) SVM learning from large training data set. In: Proceedings of the IEEE IJCNN, pp 860—2865

  19. Hamilton JD (1994) Time series analysis. Princeton University Press, New Jersey

    Google Scholar 

  20. Hentschel L (1995) All in the family: Nesting symmetric and asymmetric GARCH models. J Finan Econ 39:71—104

    Article  MATH  Google Scholar 

  21. Huang YP, Yu TM (1997) The hybrid grey-based models for temperature prediction. IEEE Trans. SMC-Part B: Cybernetics 27(2):284—297

    Article  Google Scholar 

  22. Mori H, Kosemura N, Ishiguro K, Kondo T (2001) Short-term load forecasting with fuzzy regression tree in power systems. In: Proceedings of the IEEE international conference on SMC, pp 1948—1953

  23. Chang BR (2006) Applying nonlinear generalized autoregressive conditional heteroscedasticity to compensate ANFIS outputs tuned by adaptive support vector regression. Fuzzy Sets Syst 157(13):1832—1850

    Article  Google Scholar 

  24. Pshenichnyj BN, Wilson SS (1994) The linearization method for constrained optimization. Springer, New York

    Google Scholar 

  25. Ono I, Kobayashi S (1997) A real-coded genetic algorithm for function optimization using unimodal normal distribution crossover. In: Proceedings of the 7th international conference genetic Algorithms, pp 246—253

  26. Diebold FX (1998) Elements of forecasting. South-Western, Cincinnati.

  27. FIBV FOCUS MONTHLY STATISTICS (2005) International Stock Price Index

  28. Ljung GM, Box GEP (1978) On a measure of lack of fit in time series models. Biometrika 65:67—72

    Google Scholar 

  29. Central Weather Bureau, Typhoon Moving Trace Inquire, Taipei, Taiwan, 2005

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Information Engineering, National Taitung University, Taitung, Taiwan, 950

    Bao Rong Chang

  2. Department of International Business, Sue-Te University, Kaohsiung, County, Taiwan, 824

    Hsiu-Fen Tsai

Authors
  1. Bao Rong Chang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hsiu-Fen Tsai
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Bao Rong Chang.

Additional information

Bao Rong Chang is currently an Associate Professor in the Department of Computer Science and Information Engineering at National Taitung University in Taitung, Taiwan. He completed his BS degree from the Department of Electronic Engineering, Tam Kang University, Taiwan. In 1990, he earned his ME degree from the Department of Electrical Engineering, University of Missouri-Columbia, USA, and his Ph.D. in 1994 at the same University. His current research interests include Intelligent Computations, Applied Computer Network, and Financial Engineering.

Hsiu-Fen Tsai is currently a Senior Lecturer in the Department of International Business at Shu Te University in Kaohsiung, Taiwan. She completed her BA degree from the Department of International Business, National Taiwan University, Taiwan. In 1995, she earned her MBA degree from the Department of Business Administration, National Taiwan University, Taiwan. At present, she is a Ph. D. Candidate in Department of International Business since 2004 at the same University. Her current research interests include Intelligent Analysis of Business Models and Applications of Strategy Management.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Chang, B.R., Tsai, HF. Composite of adaptive support vector regression and nonlinear conditional heteroscedasticity tuned by quantum minimization for forecasts. Appl Intell 27, 277–289 (2007). https://doi.org/10.1007/s10489-006-0036-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-006-0036-9

Keywords

Search

Navigation