Local Support Vector Regression for Financial Time Series Prediction

— We consider the regression problem for financial time series. Typically, financial time series are non-stationary and volatile in nature. Because of its good generalization power and the tractability of the problem, the Support Vector Regression (SVR) has been extensively applied in financial time series prediction. The standard SVR adopts the p-norm (p = 1 or 2) to model the functional complexity of the whole data set and employs a fixed -tube to tolerate noise. Although this approach has proved successful both theoretically and empirically, it considers data in a global fashion only. Therefore it may lack the flexibility to capture the local trend of data; this is a critical aspect of volatile data, especially financial time series data. Aiming to address this issue, we propose the Local Support Vector Regression (LSVR) model. This novel model is demonstrated to provide a systematic and automatic scheme to adapt the margin locally and flexibly; the margin is fixed globall...

• Type: Conference
• Year: 2006
• Where: IJCNN
• Authors: Kaizhu Huang, Haiqin Yang, Irwin King, Michael R. Lyu
• Keywords: IJCNN 2006, Standard Svr, Support Vector Regression, financial Time Series