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This research introduces a static data-selection framework for time-series foundation models that uses forecasting loss to identify high-value training windows, demonstrating superior performance over random selection and even full-data pretraining. By linking normalized squared loss to optimization difficulty, the method enables efficient model scaling while preserving sample diversity across different architectures. This approach directly addresses the challenge of curating effective datasets for large-scale temporal prediction tasks without relying on computationally expensive active learning loops.
Loss-Guided Pretraining Data Selection for Time-Series Foundation Models
arXiv:2609.37255v1 Announce Type: new Abstract: Time series foundation models (TSFMs) are pretrained on heterogeneous collections containing billions of observations, yet their training windows are typically sampled without estimating whether they provide useful learning signal. We introduce a static data-selection framework that scores each window with a reference forecaster and retains an intermediate interval within every source dataset. Specifically, we connect forecasting loss to optimization difficulty by showing that normalized squared loss controls the per-sample gradient norm under a local Jacobian condition. We then define a reference loss score and apply dataset-stratified selection to preserve the diversity of samples. Across various TSFM architectures, our method outperforms random selection by an absolute margin and even improves both relative MASE and CRPS over full-data pretraining by retaining fewer candidate pretraining windows. Further analyses show strong cross-scale and cross-architecture score correlations, indicating that a small reference model can often select data for larger targets, provided that the reference and target share compatible difficulty orderings.
Abstract:Time series foundation models (TSFMs) are pretrained on heterogeneous collections containing billions of observations, yet their training windows are typically sampled without estimating whether they provide useful learning signal. We introduce a static data-selection framework that scores each window with a reference forecaster and retains an intermediate interval within every source dataset. Specifically, we connect forecasting loss to optimization difficulty by showing that normalized squared loss controls the per-sample gradient norm under a local Jacobian condition. We then define a reference loss score and apply dataset-stratified selection to preserve the diversity of samples. Across various TSFM architectures, our method outperforms random selection by an absolute margin and even improves both relative MASE and CRPS over full-data pretraining by retaining fewer candidate pretraining windows. Further analyses show strong cross-scale and cross-architecture score correlations, indicating that a small reference model can often select data for larger targets, provided that the reference and target share compatible difficulty orderings.
Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
arXiv:2609.37255 [cs.LG]
(or arXiv:2609.37255v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2609.37255 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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