  In this paper, we consider the problem of event classification with
multi-variate time series data consisting of heterogeneous (continuous and
categorical) variables. The complex temporal dependencies between the variables
combined with sparsity of the data makes the event classification problem
particularly challenging. Most state-of-art approaches address this either by
designing hand-engineered features or breaking up the problem over homogeneous
variates. In this work, we propose and compare three representation learning
algorithms over symbolized sequences which enables classification of
heterogeneous time-series data using a deep architecture. The proposed
representations are trained jointly along with the rest of the network
architecture in an end-to-end fashion that makes the learned features
discriminative for the given task. Experiments on three real-world datasets
demonstrate the effectiveness of the proposed approaches.
