When Does Self-Supervised Learning Transfer to Time-Series Tasks?
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 from five key SSL paradigms across anomaly detection, classification, and forecasting under parameter- and data-matched budgets. We find that transfer efficacy depends heavily on the downstream task. SSL yields substantial gains in anomaly detection ...
| Authors | Noam Major, Kathy Razmadze, Yoli Shavit |
|---|---|
| Primary category | cs.LG |
| Published | 19 May 2026 |
| Last updated at source | 30 September 2026 |
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