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Heuristic Solutions to Technical Issues Associated with Clustered Volatility Prediction using Support Vector Machines
2005 International Conference on Neural Networks and Brain
We outline technological issues and our findings for the problem of prediction of relative volatility bursts in dynamic time-series utilizing support vector classifiers (SVC). The core approach used for prediction has been applied successfully to detection of relative volatility clusters. In applying it to prediction, the main issue is the selection of the SVC training/testing set. We describe three selection schemes and experimentally compare their performances in order to propose a method fordoi:10.1109/icnnb.2005.1614948 fatcat:5gsvgejt5renzbyfvazutnlvxy