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

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.