Practical methods of optimization; (2nd ed.)
Practical methods of optimization; (2nd ed.)
The nature of statistical learning theory
The nature of statistical learning theory
A fast quantum mechanical algorithm for database search
STOC '96 Proceedings of the twenty-eighth annual ACM symposium on Theory of computing
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Advanced Engineering Mathematics: Maple Computer Guide
Advanced Engineering Mathematics: Maple Computer Guide
Quantum computation and quantum information
Quantum computation and quantum information
Time Series Analysis: Forecasting and Control
Time Series Analysis: Forecasting and Control
Elements of Forecasting
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
The hybrid grey-based models for temperature prediction
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A New Intelligent System Methodology for Time Series Forecasting with Artificial Neural Networks
Neural Processing Letters
A Combined Forecast Method Integrating Contextual Knowledge
International Journal of Knowledge and Systems Science
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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.