The nature of statistical learning theory
The nature of statistical learning theory
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
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Time Series Analysis: Forecasting and Control
Time Series Analysis: Forecasting and Control
The hybrid grey-based models for temperature prediction
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Simultaneous training of negatively correlated neural networks inan ensemble
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Learning to Trade with Incremental Support Vector Regression Experts
HAIS '08 Proceedings of the 3rd international workshop on Hybrid Artificial Intelligence Systems
Adaptive and high-precision grey forecasting model
Expert Systems with Applications: An International Journal
Grey system theory-based models in time series prediction
Expert Systems with Applications: An International Journal
GMRVVm-SVR model for financial time series forecasting
Expert Systems with Applications: An International Journal
Chaos-based support vector regressions for exchange rate forecasting
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Using neural network for forecasting TXO price under different volatility models
Expert Systems with Applications: An International Journal
An accurate signal estimator using a novel smart adaptive grey model SAGM(1,1)
Expert Systems with Applications: An International Journal
| Hi-index | 12.06 |
In order to reduce the volatility clustering effect that deteriorate the efficiency and effectiveness of time series prediction and gives rise to large residual errors, a composite method, which is SVRGM/GARCH model with neural network adaptation, is introduced to improve the predictive accuracy of the complex time series, e.g. stocks price index or futures trading index. A support vector regression (SVR) is employed to improve the control and environment parameters of grey model (GM) denoted by SVRGM. Thus, SVR learning functions to highly reduce the overshoot effect when GM is applied to time series prediction. Moreover, a generalized auto-regressive conditional heteroscedasticity (GARCH) is utilized to resolve the problem of volatility clustering in time series so as to best fit the model. Incorporating GARCH model into SVRGM prediction is scheming to effectively and efficiently tackle two crucial problems, the overshoot and volatility clustering effects, simultaneously. This composite system is adapted optimally by back-propagation neural network (BPNN).