Abstract
Volatility clustering degrades the efficiency and effectiveness of time series prediction and gives rise to large residual errors. This is because volatility clustering suggests a time series where successive disturbances, even if uncorrelated, are yet serially dependent. To overcome volatility clustering problems, an adaptive neuro-fuzzy inference system (ANFIS) is combined with a nonlinear generalized autoregressive conditional heteroscedasticity (NGARCH) model that is adapted by quantum minimization (QM) so as to tackle the problem of time-varying conditional variance in residual errors. The proposed method significantly reduces large residual errors in forecasts because volatility clustering effects are regulated to trivial levels. Two experiments using real financial data series compare the proposed method and a number of well-known alternative methods. Results show that forecasting performance by the proposed method produces superior results, with good speed of computation. Goodness of fit of the proposed method is tested by Ljung-Box Q-test. It is concluded that the ANFIS/NGARCH composite model adapted by QM performs very well for improved predictive accuracy of irregular non-periodic short-term time series forecast and will be of interest to the science of statistical prediction of time series.
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References
Chang, B.R.: Advanced Hybrid Prediction Algorithm for Non-Periodic Short-Term Forecasting. International Journal of Fuzzy System 5(3), 151–160 (2003)
Box, G.E.P., Jenkins, G.M., Reinsel, G.C.: Time Series Analysis: Forecasting & Control. Prentice-Hall, New Jersey (1994)
Chang, B.R.: Tuneable Free Parameters C and Epsilon-Tube in Support Vector Regression Grey Prediction Model -SVRGM(1,1|C,ε) Approach. In: Proc. IEEE International Conference on Systems, Man, and Cybernetics, pp. 2431–2437 (2004)
Castillo, O., Melin, P.: Simulation and Forecasting Complex Economic Time Series Using Neural Network and Fuzzy Logic. In: Proc. International Joint Conference on Neural Network, pp. 1805–1810 (2001)
Thomson, R., Hodgman, T.C., Yang, Z.R., Doyle, A.K.: Characterizing Proteolytic Cleavage Site Activity Using Bio-Basis Function Neural Networks. Bioinformatics 19(14), 1741–1747 (2003)
Chang, B.R.: Hybrid BPNN-Weighted Grey-CLMS Forecasting. Journal of Information Science and Engineering 21(1), 209–221 (2005)
Jang, J.-S.R.: ANFIS: Adaptive-Network-based Fuzzy Inference Systems. IEEE Transactions on Systems, Man, and Cybernetics 23(3), 665–685 (1993)
Wang, J.-S.: An Efficient Recurrent Neuro-Fuzzy System for Identification and Control of Dynamic Systems. In: Proc. IEEE International Conference on Systems, Man, and Cybernetics, tracking #: 146 (2003)
Neter, J., Wasserman, W., Kutner, M.H.: Applied Linear Statistical Models, 2nd edn. Irwin, Homewood, IL (1985)
Bellerslve, T.: Generalized Autoregressive Conditional Heteroscedasticity. Journal of Econometrics 31, 307–327 (1986)
Gourieroux, C.: ARCH Models and Financial Applications. Springer, New York (1997)
Durr, C., Hoyer, P.: A Quantum Algorithm for Finding the Minimum (1996), http://arxiv.org/abs/quant-ph/9607014
Hamilton, J.D.: Time Series Analysis. Princeton University Press, New Jersey (1994)
Pshenichnyj, B.N., Wilson, S.S.: The Linearization Method for Constrained Optimization. Springer, New York (1994)
Boyer, M., Brassard, G., Hoyer, P., Tapp, A.: Tight Bounds on Quantum Searching. Fortschritte Der Physik (1998)
Grover, L.K.: A Fast Quantum Mechanical Algorithm for Database Search. In: Proc. 28th Ann. ACM Symp. Theory of Comp., pp. 212–219. ACM Press, New York (1996)
Anguita, D., Ridella, S., Rivieccio, F., Zunino, R.: Training Support Vector Machines: a Quantum- Computing Perspective. In: Proc. IEEE IJCNN, pp. 1587–1592 (2003)
Diebold, F.X.: Elements of Forecasting. South-Western, Cincinnati (1998)
FIBV FOCUS MONTHLY STATISTICS, International Stock Price Index (2005)
Ljung, G.M., Box, G.E.P.: On a Measure of Lack of Fit in Time Series Models. Biometrika 65, 67–72 (1978)
London International Financial Futures and Options Exchange (LIFFE) (2002), http://www.liffe.com/
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Chang, B.R. (2006). Novel Prediction Approach – Quantum-Minimum Adaptation to ANFIS Outputs and Nonlinear Generalized Autoregressive Conditional Heteroscedasticity. In: Wang, L., Jiao, L., Shi, G., Li, X., Liu, J. (eds) Fuzzy Systems and Knowledge Discovery. FSKD 2006. Lecture Notes in Computer Science(), vol 4223. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11881599_113
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DOI: https://doi.org/10.1007/11881599_113
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