Author

Yu-Sen Liu

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Conference Open access Sep 2026

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...

Yu-Sen Liu, Zhichen Lai, Hua Lu et al. · Proceedings of the Thirty-Fi... · 0 citations

Conference Open access Jul 2026

Causal Discovery with Inverted Self-attention for Multivariate Time Series

Causal discovery in multivariate time series data is challenging due to complex interactions, high dimensionality, and nonlinear dependencies among variables. Existing methods often struggle to capture these complexities, resulting in inaccurate causal structures. To address this issue, we propose a novel framework tha...

Yusen Liu, Yong Wang, Yifan Yin et al. · Pacific-Asia Conference on K... · 5 citations