Practical methods of optimization; (2nd ed.)
Practical methods of optimization; (2nd ed.)
Neural network fundamentals with graphs, algorithms, and applications
Neural network fundamentals with graphs, algorithms, and applications
A fast quantum mechanical algorithm for database search
STOC '96 Proceedings of the twenty-eighth annual ACM symposium on Theory of computing
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Digital and analog communication systems (5th ed.)
Digital and analog communication systems (5th ed.)
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
Quantum Computing and Communications: An Engineering Approach
Quantum Computing and Communications: An Engineering Approach
The hybrid grey-based models for temperature prediction
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Hi-index | 0.20 |
In this paper, an approach to resolving two crucial problems of the overshoot and volatility clustering effects in time-series forecast has been proposed. In time-series prediction, big residuals round the turning-point region of a data sequence due to the overshoot phenomenon. Volatility clustering effect suggests a time series with successive disturbances being serially dependent. Both effects degrade the efficiency and effectiveness of time-series prediction and give rise to large residual errors. To overcome the overshoot and volatility clustering problems, an adaptive neuro-fuzzy inference system (ANFIS) coupling a nonlinear generalized autoregressive conditional heteroscedasticity (NGARCH), which is adapted by quantum minimization (QM), is introduced to resolve the drawbacks of the predicted outputs with big residuals around the inflection points of a data sequence and time-varying conditional variance in residual errors. Besides, the trial of two distinct quantum amplitude amplification (QAA) techniques called by QM is also considered to show their different computational complexity. Two experiments on the financial time series were taken and a performance evaluation was made between the proposed one and several well-known alternative methods. Results show that our proposed method gains the best predictive accuracy to outperform the others. Goodness of fit of the proposed method was tested successfully by Ljung-Box Q-test. It followed that the proposed method in fact can reduce large residual errors significantly in time-series forecast because the overshoot and volatility clustering effects are simultaneously regulated to the trivial levels.