LeNEPA: No-Augmentation Next-Latent Prediction for Time-Series Representation Learning
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Computer Science > Machine Learning
arXiv:2607.00958 (cs)
[Submitted on 1 Jul 2026]
Title:LeNEPA: No-Augmentation Next-Latent Prediction for Time-Series Representation Learning
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Abstract:Time series are central to modern data mining applications, from industrial telemetry and server metrics to finance and physiology, yet time-series self-supervised learning often depends on view and augmentation choices that encode domain-specific invariances. We study how an SSL recipe behaves when its method-specific configuration is reused unchanged after the pretraining signal family changes, framing this as a fixed-recipe stress test rather than a comparison against optimally tuned methods. We introduce Latent Euclidean Next-Embedding Prediction Architecture (LeNEPA), a no-augmentation next-latent-token objective with a causal backbone. LeNEPA replaces the stop-gradient/EMA stabilization used by vanilla NEPA with SIGReg-based isotropy regularization and computes the predictive loss in a lightweight projected space that is discarded for evaluation. We compare LeNEPA with an ECG-tuned JEPA recipe under a fixed-horizon frozen-probe protocol on PTB-XL and Diag, a synthetic diagnostic corpus generated with Aionoscope. Both methods are retrained independently on each dataset while keeping their method-specific recipes unchanged. In this protocol, the ECG-tuned JEPA recipe is strong in-domain on PTB-XL but weaker when reused unchanged on Diag, whereas LeNEPA preserves useful frozen-probe gains on both datasets. Learning curves suggest faster early representation acquisition: LeNEPA reaches 80% of its final AUROC/AUPRC gain after 2--5k updates, compared with 5--10k updates for the faster JEPA readout. As a separate external frozen-encoder check, a CauKer-pretrained LeNEPA variant reaches 77.65% mean UCR-128 Random-Forest accuracy in a single-seed, best-checkpoint run, within 1.16 points of Mantis and within 0.24 points of MOMENT (77.89%). Overall, the results support no-augmentation latent prediction as a useful candidate recipe for low-retuning time-series SSL.
| Comments: | 9 pages, 4 figures, 6 tables; accepted by the 12th Mining and Learning from Time Series (KDD MILETS 2026); source code and artifacts: this https URL |
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2607.00958 [cs.LG] |
| (or arXiv:2607.00958v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2607.00958
arXiv-issued DOI via DataCite (pending registration)
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View a PDF of the paper titled LeNEPA: No-Augmentation Next-Latent Prediction for Time-Series Representation Learning, by Alexander Chemeris and 2 other authors
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