Summary:

Contrastive learning is effective for learning representations from time series; however, it often ignores inherent correlations. SoftCLT introduces a simple and effective soft contrastive learning strategy for time series by incorporating instance-wise and temporal contrastive loss with soft assignments. It enhances the quality of representations and demonstrates state-of-the-art performance in classification, transfer learning, and anomaly detection tasks.

• Proposes SoftCLT for contrastive learning in time series.
• Introduces instance-wise and temporal contrastive loss with soft assignments.
• Shows state-of-the-art performance in downstream tasks.
• Importance and Future Research: SoftCLT addresses the issue of learned representation quality in time series and opens avenues for further exploration in contrastive learning methodologies.