Ordinal-Quadruplet: Retrieval of Missing Classes in Ordinal Time Series
This addresses a practical issue in time series analysis for domains where data collection is incomplete, though it is incremental as it builds on existing ordinal and triplet loss frameworks.
The paper tackles the problem of classifying ordinal time series when some classes are missing from the training data, achieving significant improvement in predicting missing labels even with 40% of classes missing and nearly doubling accuracy compared to baseline methods in some cases.
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.