Financial time series prediction based on grey model integrated with support vector regression

In this paper the composite model GMRVV-SVR has been adopted to predict financial time series with such characteristics as poor information, small sample size, high noise, non-stationary, non-linearity, and varying associated risk. In construction of GMRVV-SVR, the common grey model with revised verge value (GMRVV) has been introduced and modified by support vector regression based on the calculation of the residual error sequence between predicted values and original data. Since the recent data points could provide 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 adopted to tune free parameters. A real experimental result shows that the composite model can achieve comparative accurate prediction as well as smoothing overshooting in financial time series prediction.