Large margin AR model for time series classification

In this paper we propose a new method for time series pattern classification. It is based on the generative modeling using Autoregressive(AR) model and optimizing the boundaries between these models using the large margin concepts. The developed model captures the correlations in the time series data. Multi-class classification can be performed directly without performing binary classification. The optimization is performed using genetic algorithm for obtaining global optimal parameters. The developed method is applied on simulated and ECG data and found to perform better than the methods which utilize the AR coefficients as the features for the classification.