2026
Frequency-Aware Augmentation and Alignment for Time Series Contrastive Learning
IJCAI 2026
Contrastive learning has become a dominant paradigm for learning time series representations from large-scale unlabeled data. However, current methods are often adapted from computer vision and rely on random time-domain augmentations (e.g., jittering and cropping). Such augmentations can unpredicta