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 l_p-norm (p = 1 or 2) to model the functional complexity of the whole data set and employs a fixed e-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 globally in the standard SVR. Therefore, the LSVR can tolerate noise adaptively. We provide both theoretical justifications and empirical evaluations for this novel model. The experimental results on synthetic data and real financial data demonstrate its advantages over the standard SVR.