  Learning semantic-rich representations from raw unlabeled time series data is
critical for downstream tasks such as classification and forecasting.
Contrastive learning has recently shown its promising representation learning
capability in the absence of expert annotations. However, existing contrastive
approaches generally treat each instance independently, which leads to false
negative pairs that share the same semantics. To tackle this problem, we
propose MHCCL, a Masked Hierarchical Cluster-wise Contrastive Learning model,
which exploits semantic information obtained from the hierarchical structure
consisting of multiple latent partitions for multivariate time series.
Motivated by the observation that fine-grained clustering preserves higher
purity while coarse-grained one reflects higher-level semantics, we propose a
novel downward masking strategy to filter out fake negatives and supplement
positives by incorporating the multi-granularity information from the
clustering hierarchy. In addition, a novel upward masking strategy is designed
in MHCCL to remove outliers of clusters at each partition to refine prototypes,
which helps speed up the hierarchical clustering process and improves the
clustering quality. We conduct experimental evaluations on seven widely-used
multivariate time series datasets. The results demonstrate the superiority of
MHCCL over the state-of-the-art approaches for unsupervised time series
representation learning.
