Grey relational grade in local support vector regression for financial time series prediction

Support vector regression (SVR) has often been applied in the prediction of financial time series with many characteristics. On account of much time consumption of global SVR, local machines are carried out to accelerate the computation. In this paper, we introduce local grey SVR (LG-SVR) integrated grey relational grade with local SVR for financial time series forecasting. Pattern search method and leave-one-out errors are adopted for model selection. Experimental results of three real financial time series prediction demonstrate that LG-SVR can speed up computing speed and improve prediction accuracy.