GMRVVmSVR model for financial time series forecasting
The complex model GMRVVmSVR has been adopted to predict financial time series with such characteristics as small sample size, poor information, non-stationary, high noise and non-linearity. In order to construct GMRVVmSVR, the m-root grey model with revised verge value (GMRVVm) has been introduced and modified by support vector regression based on the calculation of the residual error sequence between predicted values and original data. Due to the recent data points providing more information than distant data points, more importance has been attached to the punishment parameter C of recent data points in support vector regression. Simultaneously, the parameter ? in ?-insensitive loss function has been determined according to smoothing overshooting. Pattern search (PS) algorithm has been carried out to tune free parameters. A real experimental result shows that the complex model can achieve comparative accurate prediction as well as smoothing overshooting in financial time series prediction.
m-root grey model support vector regression smoothing overshooting verge value conditions pattern search algorithm