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Loss-Guided Pretraining Data Selection for Time-Series Foundation Models
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 wind
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