  Recently, various contrastive learning techniques have been developed to
categorize time series data and exhibit promising performance. A general
paradigm is to utilize appropriate augmentations and construct feasible
positive samples such that the encoder can yield robust and discriminative
representations by mapping similar data points closer together in the feature
space while pushing dissimilar data points farther apart. Despite its efficacy,
the fine-grained relative similarity (e.g., rank) information of positive
samples is largely ignored, especially when labeled samples are limited. To
this end, we present Rank Supervised Contrastive Learning (RankSCL) to perform
time series classification. Different from conventional contrastive learning
frameworks, RankSCL augments raw data in a targeted way in the embedding space
and adopts certain filtering rules to select more informative positive and
negative pairs of samples. Moreover, a novel rank loss is developed to assign
different weights for different levels of positive samples, enable the encoder
to extract the fine-grained information of the same class, and produce a clear
boundary among different classes. Thoroughly empirical studies on 128 UCR
datasets and 30 UEA datasets demonstrate that the proposed RankSCL can achieve
state-of-the-art performance compared to existing baseline methods.
