Classification of sparsely and irregularly sampled time series: A learning in model space approach

Classification of sparsely and irregularly sampled time series data is a challenging machine learning task. To tackle this problem, we present a learning in model space framework in which time-continuous dynamical system models are first inferred from individual time series and then the inferred models are used to represent these time series for the classification task. In contrast to the existing approaches using model point estimates to represent individual time series, we further employ posterior distributions over models, thus taking into account in a principled manner the uncertainty around the inferred model due to observation noise and data sparsity. Finally, we present a distributional classifier for classifying the posterior distributions. We evaluate the framework on a biological pathway model. In particular, we investigate the classification performance in the cases where model uncertainties in the training and test phases do not match.