Method of time series forecasting for scientific data

Traditional methods have poor efficiency and effect to deal with the scientific data series forecasting. In this paper, a forecasting algorithm based on grey theory and self-organized map neural networks is proposed. Firstly, the scientific data time Series cluster in self-organized manner. Then the forecast model is established with grey theory. In clustering, a distance criterion is proposed to scale the difference between series. In grey theory, the whiten parameter is optimized. The experiments show that this algorithm surpasses those traditional forecasting methods in precision and time efficiency.