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When, Not How Much: Evaluating Time-Series Foundation Models on Sparse Events

Schoess, Daniel et al.
arXiv (All) · Papers
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Pretrained time-series foundation models (TSFMs) are evaluated as forecasters of future values, yet for sparse series many decisions depend only on which future periods contain activity. Standard benchmarks do not assess this. On five sparse datasets, we rank positions within forecast windows that c
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