Xu Cheng

Frequency-Aware Augmentation and Alignment for Time Series Contrastive Learning

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 unpredictably disrupt the natu...