Order can have multiple meanings depending on the context. Here are some common interpretations:

  1. Sequential Arrangement: Order can refer to the systematic or sequential arrangement of elements in a particular sequence or pattern.
  2. Organization: Order may imply a state of organization, structure, or arrangement that follows a logical or predetermined pattern.
  3. Command or Instruction: In a directive context, order can represent a command, directive, or authoritative instruction, instructing someone to do or not do something.
  4. Magnitude or Rank: Order can be used in a quantitative sense to denote the magnitude or rank of a quantity, often in a comparative manner.
  5. Classification: Order can refer to a classification or categorization, where items are arranged into specific groups or categories based on certain criteria.

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Ordinal Quadruplet: Retrieval of Missing Labels in Ordinal Time Series

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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.

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