Hypothesis Testing is a statistical method used to make inferences about a population based on a sample of data. The process involves formulating a hypothesis, collecting and analyzing data, and drawing conclusions about the population based on the evidence from the sample. Hypothesis testing is commonly used in scientific research, quality control, and decision-making processes.

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Ordinal Quadruplet: Retrieval of Missing Labels in Ordinal Time Series

Ordinal Quadruplet: Retrieval of Missing Labels in Ordinal Time Series 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.