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Impact of Regularization on the Model Space for Time Series Classification

Time series classification is an active research field and applicable in many domains, e.g. speech and gesture recognition. A recent approach to classify time series is based on modelling each time series by an Echo State Network and then to classify the time series in the readout weight or model space of these networks. In this paper, we investigate the effect of Echo State Network regularization on the model space. The results show that regularization has a strong impact on the model space structure and the separability of the time series in the model space.