Random Initialization as a Baseline for Time Series Foundation Models
Read the original on arXiv Machine Learning →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.
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