Practical Bayesian support vector regression for financial time series prediction and market condition change detection

Support vector regression (SVR) has long been proven to be a successful tool to predict financial time series. The core idea of this study is to outline an automated framework for achieving a faster and easier parameter selection process, and at the same time, generating useful prediction uncertainty estimates in order to effectively tackle flexible real-world financial time series prediction problems. A Bayesian approach to SVR is discussed, and implemented. It is found that the direct implementation of the probabilistic framework of Gao et al. returns unsatisfactory results in our experiments. A novel enhancement is proposed by adding a new kernel scaling parameter to overcome the difficulties encountered. In addition, the multi-armed bandit Bayesian optimization technique is applied to automate the parameter selection process. Our framework is then tested on financial time series of various asset classes (ie equity …