Heuristic Solutions to Technical Issues Associated with Clustered Volatility Prediction using Support Vector Machines
Hovsepian, K.
Anselmo, P.
Dept. of Comput. Sci., New Mexico Tech, Socorro, NM;
This paper appears in: Neural Networks and Brain, 2005. ICNN&B '05. International Conference on
Publication Date: 13-15 Oct. 2005
Volume: 3,
On page(s): 1656-1660
Location: Beijing,
ISBN: 0-7803-9422-4
INSPEC Accession Number: 9072730
Digital Object Identifier: 10.1109/ICNNB.2005.1614948
Current Version Published: 2006-04-10
Abstract
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 for training the SVC for the prediction problem. In addition to performing cross-validation experiments, we propose an improved variation to sliding window experiments utilizing the output from SVC's decision function. Together with these experiments, we show that accurate and robust prediction of volatile bursts can be achieved with our approach
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