Bias Reduction for Time Series Models Based on Support Vector Regression

In the past few years a new learning method called Support Vector Machines (SVMs) has enjoyed increasing popularity. Based on statistical learning theory it shows very good generalization abilities. Though SVMs are mainly used for classification tasks, they are also applicable to regression problems and thus to modeling the dynamics of a time series. However when regression techniques are used to build dynamical models caution is advisable if the data are noisy. Due to correlations between data points, estimates of model parameters deviate systematically from the true values. An approach is presented to reduce such bias in SVM parameters.