SaURL-TS: A self-adaptive framework for unsupervised time series representation learning
Yusen Liu, Zhichen Lai, Hua Cai Lu, Xu Cheng, Tianqing Zhu, Xiufeng Liu, Huan Huo · Pattern Recognition · 2026
Unsupervised time series representation learning, driven by recent advances in contrastive learning-based methods, has become a critical component for downstream tasks like forecasting and classification. However, time series data exhibit complex temporal dependencies and spectral patterns, posing challenges for existing approaches to adapt robustly. Moreover, existing contrastive learning-based approaches overlook frequency-domain information and struggle with selecting effective negative samples, further hindering model performance. To address these issues, we propose S a URL-TS, a novel self-adaptive framework for unsupervised time series representation learning. First, it dynamically learns dataset-specific augmentations to generate high-quality positive samples. Second, an adaptive self-supervised learning module with a multi-domain encoder captures both temporal and spectral patterns without relying on negative samples. Third, a representation-wise attention mechanism assigns dynamic weights to representations across domains. To the best of our knowledge, S a URL-TS is the first self-supervised learning framework to jointly model temporal and spectral patterns across both augmentation and learning stages. Extensive experiments confirm the superior performance of S a URL-TS over state-of-the-art models. Notably, its adaptive data augmentation module is plug-and-play and can be integrated into other contrastive learning frameworks, and its learning stage is capable of adapting to a wide range of time series patterns. Our codebase is available at https://github.com/YusenL/SAURL-TS .