When Does Self-Supervised Learning Transfer to Time-Series Tasks?

Excerpt

arXiv:2605.19462v2 Announce Type: replace Abstract: Self-supervised learning (SSL) assumes that solving pretext tasks on unlabeled data yields representations that transfer effectively across downstream applications via linear probing or fine-tuning. While this paradigm has driven major progress in vision and language, its benefits for time series remain under-investigated and often confounded by inconsistent experimental controls. To address this gap, we benchmark seven representative methods f