arXiv Machine Learning
Oct 23

Random Initialization as a Baseline for Time Series Foundation Models

The study evaluates how much self‑supervised pretraining contributes to time series foundation models (TSFMs) by comparing seven publicly released TSFMs with architecture‑matched randomly initialized versions on the UEA multivariate time series classification archive. Across 183 dataset‑level comparisons, pretraining yields modest accuracy gains (1.43–5.19 percentage points, average 2.58 points), with only one model (NuTime) remaining significant after Holm correction. The results highlight that strong non‑pretrained baselines such as ROCKET, Hydra, and Quant can compete without large‑scale pretraining, underscoring the need to separate architectural effects from pretrained weights in MTSC evaluations.

By Pinar Sungu Isiacik, Georgiana Ifrim
arXiv Machine Learning
Jul 2

LeNEPA: No-Augmentation Next-Latent Prediction for Time-Series Representation Learning

arXiv:2607. 00958v1 Announce Type: new 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.

By Alexander Chemeris, Ming Jin, Randall Balestriero