FreRA: A Frequency-Refined Augmentation for Contrastive Learning on Time Series Classification

A new method called Frequency Refined Augmentation (FreRA) enhances contrastive learning for time series classification by leveraging frequency domain analysis to preserve semantic information and improve data augmentation. Extensive experiments show that FreRA outperforms existing baselines across various datasets and tasks, demonstrating its effectiveness in contrastive representation learning and transfer learning scenarios.