Localized support vector regression for time series prediction
Time series prediction, especially financial time series prediction, is a challenging task in machine learning. In this issue, the data are usually non-stationary and volatile in nature. Because of its good generalization power, the support vector regression (SVR) has been widely applied in this application. The standard SVR employs a fixed ? ? -tube to tolerate noise and adopts the ?p ? p -norm (p=1 p = 1 or 2) to model the functional complexity of the whole data set. One problem of the standard SVR is that 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 attack this issue, we propose the localized support vector regression (LSVR) model. This novel model is demonstrated to provide a systematic and automatic scheme to adapt the margin locally and flexibly; while the margin in the standard SVR is fixed globally. Therefore, the LSVR can tolerate noise adaptively. The proposed LSVR is promising in the sense that it not only captures the local information in data, but more importantly, it establishes connection with several models. More specifically: (1) it can be regarded as the regression extension of a recently proposed promising classification model, the Maxi-Min Margin Machine; (2) it incorporates the standard SVR as a special case under certain mild assumptions. 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.
support vector regression time series prediction second order conic programming