MHCCL: Masked Hierarchical Cluster-Wise Contrastive Learning for Multivariate Time Series
MHCCL: Masked Hierarchical Cluster-Wise Contrastive Learning for Multivariate Time Series
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 …