When Does Self-Supervised Learning Transfer to Time-Series Tasks?
Quick summary
arXiv:2605.19462v2 Announce Type: replace-cross 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 from five key SSL paradigms across anomaly detection, classification, and forecasting under par
Key takeaways
- arXiv:2605.19462v2 Announce Type: replace-cross 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 from five key SSL paradigms across anomaly detection, classification, and forecasting under par
Why it matters
“When Does Self-Supervised Learning Transfer to Time-Series Tasks?” highlights the need for repeatable measurement rather than a single impressive demonstration. Independent validation across datasets and clearly stated limitations determine whether a result can guide product decisions.
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