Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Adaptive support vector regression (ASVR) applied to the forecast of complex time series is superior to the other traditional prediction methods. Hover, 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 forecasts. Interestingly, quantum-based minimization algorithm is in this study fo proposed r tuning the resulting ighted-average beten ASVR and NGARCH in such a way that the composite model ASVR/NGARCH can achieve the best accuracy of prediction. Quantum optimization here tackles so-called NP-completeness problem and outperforms real-coded genetic algorithm on the same problem due to the optimal or near-optimal ighted-values obtained over the search space.
['Bao Rong Chang', 'Hsiu Fen Tsai']
I am writing a research paper on a idea Financial Prediction Applications Using Quantum-Minimized Composite Model ASVR/NGARCH
[{"from": "human", "value": "I am writing a research paper on a idea Financial Prediction Applications Using Quantum-Minimized Composite Model ASVR/NGARCH"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Adaptive support vector regression (ASVR) applied to the forecast of complex time series is superior to the other traditional prediction methods. Hover, 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 forecasts. Interestingly, quantum-based minimization algorithm is in this study fo proposed r tuning the resulting ighted-average beten ASVR and NGARCH in such a way that the composite model ASVR/NGARCH can achieve the best accuracy of prediction. Quantum optimization here tackles so-called NP-completeness problem and outperforms real-coded genetic algorithm on the same problem due to the optimal or near-optimal ighted-values obtained over the search space."}]