  In this paper, we propose an ordered time series classification framework
that is robust against missing classes in the training data, i.e., during
testing we can prescribe classes that are missing during training. This
framework relies on two main components: (1) our newly proposed
ordinal-quadruplet loss, which forces the model to learn latent representation
while preserving the ordinal relation among labels, (2) testing procedure,
which utilizes the property of latent representation (order preservation). We
conduct experiments based on real world multivariate time series data and show
the significant improvement in the prediction of missing labels even with 40%
of the classes are missing from training. Compared with the well-known triplet
loss optimization augmented with interpolation for missing information, in some
cases, we nearly double the accuracy.
