Analysis of switching dynamics with competing support vector machines
We present a framework for the unsupervised segmentation of time series using support vector regression. It is applied to non-stationary time series which alter in time. We follow the architecture of Pawelzik et al. (1996) which consists of competing predictors. In the above paper competing neural networks were used while here we exploit the use of support vector machines, a learning technique. Results indicate that the proposed approach is as good as that of that Pawelzik et al. Differences between the two approaches are also discussed.